**4. Discussion**

Disease severity was the first guide to govern systemic resistance in treated plants by cyanobacteria. Data presented in this study reported that *F. oxysporum* shows a highly destructive effect on pepper plants that caused typical wilt symptoms with DI 83.33%, similar to earlier studies on the same pathogenic fungus [40]. Using cyanobacterial extract to treat *Fusarium* wilt-infected pepper plants greatly reduced the symptoms of the disease, which is the primary criterion for assessing resistance in the pepper plant. Data showed that foliar spraying with cyanobacteria extracts was more effective than putting them in soil; foliar spraying with *A. platensis* showed high protection against *Fusarium* infection (95%), followed by *D. muscorum* (90%), then *A. oryzae* (69.9%), these results are consistent with [49], which stated that foliar spraying was more effective than soil application, where the nutrients in the foliar spray are absorbed up directly by the leaves of the plant.

Moreover, the results in this study revealed that the highest total phenolic and flavonoid contents were determined in *Arthrospira platensis,* which provided the pepper plant with the highest percentage of protection through foliar spraying. This result may recommend choosing cyanobacterial strains with elevated levels of phenolics and flavonoids to stimulate immune responses in pepper plants against *Fusarium* wilt. The results explained that spraying with cyanobacteria caused an improvement in pepper plant resistance against biotic stresses, including fungal pathogens [49]. Furthermore, antifungal activity of *A. platensis* [50], *D. muscorum* [51], and *A. oryzae* [18] against fungal pathogens were also reported. Reducing symptoms and severity of infection is one of the most important goals of using fungicides, whether chemical or biological [52]. Undoubtedly, applying natural or biological factors such as cyanobacteria is more environmentally friendly. Interestingly, the use of cyanobacteria understudy led to a reduction in symptoms, and a reduction in the severity of infection was reflected positively on the health of plant growth. These results are consistent with several scientific studies [25,53] that confirm that cyanobacteria contain many antioxidants, proteins, vitamins, hormones, and antimicrobials.

The results indicated that *F. oxysporum* hurt all vegetative pepper growth compared to healthy control. *F. oxysporum* minimized shoot length by 46.89%, root length by 48.45%, and several leaves by 45.07%, respectively. Therefore, the infection of the pepper plant with

*F. oxysporum* caused a significant inhibition of all growth parameters, where our findings align with those of a huge number of other researchers [3,54]. Regarding the effect of cyanobacteria, it was observed that infected plants treated with *D. muscorum*, *A. oryzae,* and *A. platensis* in soil and foliar spraying exhibited promising recovery. It was determined that *A. platensis* was the most effective cyanobacteria for recovering plant height by 70.64% and 69.53%, root lengths by 92.94%, 92.94%, and the number of leaves by 76.92%, and 58.97% through the soil and foliar application, respectively. These results agree with a previous study [21], which reported that the application of *A. platensis* improved plant growth through polysaccharides production. The use of cyanobacteria to enhance crop growth has been proposed as a potential executive performance in crop enhancement. [55]. These results align with [35], who found that treating plants with cyanobacteria greatly improved their vegetative growth. The increase in vegetative growth and crop yield with cyanobacteria could mainly be due to the release of plant nutrients like N, P, and K and the excretion of plant growth regulators (auxin, gibberellins), amino acids, and vitamins [56].

After applying cyanobacteria, photosynthetic pigments had become a clear positive indicator of sufficient treatments. By analyzing data from this investigation, it was obvious that chlorophylls a and b (Chl a and b) were severely inhibited in *Fusarium* infected pepper plants by 58.22% and 59.46%, respectively. This reduction in chlorophyll was induced by the production of reactive oxygen species (ROS) after the attack with *Fusarium*, which destroyed chlorophyll contents, preventing the plants from capturing sunlight and reducing photosynthesis [38,57]. Chlorophyll disruption, reduced chlorophyll synthesis, and thylakoid membrane strength are also diminished [58]. On the other hand, the present study showed that the level of carotenoids in pepper plants increased by 70% in response to *Fusarium* infection. These findings are consistent with those of other studies [3,6,39,59,60], which found that the content of carotenoids in plants increased dramatically in response to *Fusarium* infection. In terms of cyanobacteria's beneficial impacts, it was discovered that diseased plants improved after applying *D. muscorum*, *A. oryzae*, and *A. platensis* through various modalities. It was found that foliar and soil application of *A. platensis* was the most effective way to enhance infected plants' Chl a, b, and carotenoids levels. Increased chlorophyll contents in infected plants treated with cyanobacterial strains could be resulted from the higher amount of atmospheric nitrogen assimilation by cyanobacteria then transported to pepper plant tissues [59].

Moreover, cyanobacteria supply decreased ethylene production and chlorophyll, stimulated the synthesis of carotenoids which defend chlorophyll from oxidation, and increased chlorophyll content [60]. These results are in agreement with the results reported in this study. Photosynthetic protection may have been supplied by improved synthesis of carotenoids due to enhancing ROS scavenging [61].

In the current study, the results clearly showed *F. oxysporum* infection caused a major reduction in contents of soluble sugars by 38.50% and soluble protein by 57.13% over the healthy control. Still, it caused proline increment by 11.84% over the healthy control. These results agree with the previous studies [3,62]. The infected pepper plants treated with cyanobacteria showed amelioration in the contents of total carbohydrates, protein, and proline. These results may be explained by the potency of cyanobacteria to secrete complex heteropolymers, polysaccharides, and lipopolysaccharides to induce defense-related gene expression [63,64]. Cyanobacteria-treated plants were capable of fighting against *Fusarium* infection by accumulating more proline, which protects proteins from oxidation [65]. The osmolytes (soluble sugar, proline, and soluble protein) levels in the cyanobacteriatreated plants were significantly higher than those in the infected control plants. Also, oil organic carbon, nutrient absorption, and nitrogen fixation were all improved due to the cyanobacteria treatment [66]. As a result, our findings reveal that osmolyte content significantly increased when infected plants were treated with cyanobacteria, as previously explained [49,62]. This increase in soluble proteins can be explained by activating plant defense systems when pathogens are challenged.

According to the findings of this research, pepper *Fusarium* wilt disease resulted in a rise in phenolics over the healthy control plant. On the other hand, it was observed that infected plants treated with cyanobacteria exhibited a marked increase in phenolics over the infected control plants. The present data indicated that the highest growth in phenolic level was recorded by the infected plants treated with *A. platensis* in the soil treatment. The increased accumulation of phenolics by treatment with cyanobacteria resulted in stress tolerance of the pepper plant against *Fusarium* infection [66,67]. It was observed that cyanobacteria supplementation reduced the generation of H2O2 and lipid peroxidation (MDA) significantly over the infected control plants. Accumulation of H2O2 increased in infected control plants, causing an increase in lipid peroxidation (MDA) over the healthy control plants. Supplementation of infected plants with cyanobacteria reduced the generation of H2O2 over the infected control plants leading to a declined lipid peroxidation (MDA). The data revealed that the most effective cyanobacterial treatment was foliar spraying with *A. platensis*. The findings here are comparable to a previous study conducted by [26], which reported that the application of biological stimulators under stress conditions decreased MDA.

The activity of POD and PPO was assessed to identify enzymes involved in protecting the infected plant. *Fusarium* wilt disease increased the activities of POD and PPO enzymes in the infected control plants over the healthy control plants; however, the application of cyanobacteria through different modes to the infected plants significantly enhanced the PPO and POD enzyme activity over the infected control plants. The recorded data revealed that the maximal increase in the activities of PPO and POD enzymes was detected in the infected pepper plants treated with *A. platensis* and *A. oryzae* through the foliar spraying, respectively. Protective enzymes such as POD and PPO are the most significant in biotic stress response [68]. These enzymes are involved in the early stages of plant resistance to various stressors and the synthesis of phenolic compounds. According to our findings, the antioxidant enzymes in infected plants treated with cyanobacteria in multiple modes, such as soil mode and foliar spraying, significantly increased. The plant displayed distinct strategies for coping with stress by increasing the activity level of some antioxidant enzymes in the cell to maintain a low concentration of reactive oxygen species [26,69].

By detecting the antioxidant isozymes (POD and PPO) by native PAGE, the results showed that a significant part of the plant's response to various stresses is the isozyme substance, which also serves as an important metabolic regulator. Isozyme is a clear indication of the occurrence of resistance, as it plays an important role in mitigating or limiting free radicals that result from oxidative explosions. The findings of this research fully agree with the other scientific reports [70,71]. In the presence of a *Fusarium* wilt disease, cyanobacteria can induce gene expression in infected plants similarly to antioxidant enzymes. It can produce chemicals activating plant immunity, such as phenols and natural hormones [63].

In this study, results highlighted that *Fusarium* infection decreased IAA but increased SA and ABA contents in the infected control plants over the healthy control plants. However, applying cyanobacteria through different modes to the infected plants significantly increased the levels of IAA and SA hormones but decreased ABA concentrations over the infected control plants. Plant hormones are natural organic compounds that affect growth and metabolism, thereby affecting all external manifestations and chemical reactions. These act as chemical signals to activate or inhibit plant growth. When plants are exposed to infections, a considerable modulation in the biosynthesis of hormones occurs, affecting various growth processes [38]. Phytohormones produced by the cyanobacteria have an important function in controlling plant fungal diseases, activating several genes responsible for systemic resistance in plants [49]. The modulation of plant hormone contents is essential in plant defense reactions against *Fusarium* wilt [72]. The SA plays an important role as a plant hormone, as it stimulates growth and works to activate the chemical and synthetic resistance of the plants against any pathogen, increases the absorption of nutrients, and increases the process of photosynthesis [73,74]. Recently, SA has been used externally or

internally as inducers against various plant pathogens [75,76]. SA has been described as a key molecule in the signal transduction pathways of the biological stress response by using cyanobacteria to activate and increase SA, IAA as these hormones act as antimicrobial substances [76,77]. The significance of ABA in plant disease resistance is unclear when compared to that of the plant hormones jasmonic acid and salicylic acid, both of which play key roles in disease resistance [78]. Similarly to our findings, previous research has recorded the accumulation of ABA during the infection of sugar beets by fungi [79].

#### **5. Conclusions**

It is concluded that *Fusarium* wilt generated oxidative destruction and developed into reduced growth and dropped physiological performance. Applying cyanobacteria to *Fusarium* infected pepper plants through soil application or foliar spraying activated the immunity of infected pepper plants. The infected plants treated with cyanobacteria showed enhanced photosynthetic pigments and accumulation of osmoprotectants, phenols, and antioxidant systems that also act as a scavenging tool to remove the excess ROS under *Fusarium* infection. Therefore, it could be applied in agricultural fields through soil or foliar application. As far as we know, this is the first evidence to report that cyanobacteria metabolites influence the isozymes of pepper plants attacked with *Fusarium*, and supplementary molecular findings can provide information on the impact of cyanobacteria on the metabolism of the plant under biotic stress. This study supports the positive application of cyanobacteria in protecting pepper plants under fungal infection; however, further studies are required to unravel actual mechanisms.

**Author Contributions:** Conceptualization, M.S.A., A.M.A. and H.H.S. Methodology, A.M.A., W.A.A., H.H.S., D.A.R. and M.S.S. Software, D.A.R., A.M.A., M.S.S. and M.S.A.; Formal analysis, H.H.S., M.S.A., A.M.A., W.A.A. and M.S.S. Investigation, D.A.R., A.M.A. and M.S.A.; Resources, A.M.A., M.S.A. and H.H.S.; Data Curation, A.M.A. and M.S.A.; Writing—original draft preparation, M.S.A., A.M.A., W.A.A. and H.H.S.; Writing—Review and Editing, A.M.A., D.A.R., H.H.S., M.S.S. and W.A.A.; Supervision, M.S.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** The author would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. R-2022-231.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in the article.

**Acknowledgments:** The authors are grateful to Abd Almoneam Sharaf. Mohamed M. Ali and Amr S. Mohamed for their help during the study, critical reading of the manuscript, and discussions. The authors are also grateful to the Botany & Microbiology Department Faculty of Science, Al-Azhar University 11884 Nasr City, Cairo, Egypt & Botany, and Microbiology Department, Faculty of Science, Cairo University for the technical support offered during this work.

**Conflicts of Interest:** The authors declare no conflict of interest.


**2.** *S. fibuligera* **R64 secretes amylolytic enzyme**

*S. fibuligera* is a food-borne yeast that is widely used in the production of rice- or cassavabased fermented food, i.e. *Tape* in Indonesia and other Southeast Asia countries (1). The yeast, in combination with *Saccharomyces cerevisiae* or *Zymomonas mobilis*, has been employed in the production of ethanol using cassava starch as the starting material (2), where the starch is firstly degraded into simple sugars prior to (bio-) ethanol. Bioethanol has been promoted as a renewable energy replacing fossil fuels and at the moment is already used as an additive. As the demand for renewable energy grows, *S. fibuligera* emerges as an attractive workhorse for the bio-ethanol production.

© 2012 Ismaya et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Ismaya et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Degradation of starch into sugars is performed by amylolytic enzymes, such as α-amylase, glucoamylase, β-amylase, isoamylase, pullulanase, exo(1-4)-α-D-glucanase, α-D-glycosidase, and cyclomaltodextrin-D-glucotransferase (Fig. 1) (3). *S. fibuligera* secretes amylolytic enzymes, almost exclusively α-amylase and glucoamylase. α-Amylase acts as an endoenzyme, cleaving 1,4-α-glycosidic bond at random positions to result in liquefaction of starch. Glucoamylase, on the other hands, is an exo-enzyme that cleaves 1,4-α-glycosidic bond only at the non-reducing end to result in saccharification (4). Thereby upon the combined action of α-amylase and glucoamylase, starch is degraded into maltose, maltotriose, or dextrin, and subsequently hydrolyzed to glucose.

Chromatography as the Major Tool in the Identification and

the Structure-Function Relationship Study of Amylolytic Enzymes from Saccharomycopsis Fibuligera R64 273

amylase and pullulanase (14). Recently, amylolytic enzymes are employed in the production of lactic acid and ethanol by-product by lactic acid bacteria (LAB) (15), demonstrating that

Interestingly, numbers and characteristics of amylolytic enzymes secreted by *S. fibuligera* vary from one strain to another. For example, strain IFO 0111 secretes only glucoamylase whilst strain KZ secretes α-glucosidase along with α-amylase and glucoamylase (4). Further, glucoamylases from strain IFO 0111 and HUT 7212 demonstrate raw starch degradation whereas that from the strain KZ does not. Glucoamylase from strain KZ, however, demonstrates better thermal stability (4). Fascinating to find these variations in their properties despite of their near identical acid sequences (16). Strain KJ-1, an *S. fibuligera* strain from Indonesia, is also reported to secrete only glucoamylase and its partial amino acid sequence (residues 28-47) is identical to that of glucoamylase from strain KZ (1). This observation indicates differences in expression and secretion of amylolytic enzymes between *S. fibuligera* strains. Nevertheless, the practicality of *S. fibuligera* in producing

amylolytic enzymes applauds the proposal to sequence its whole genomic DNA (2).

Of 136 isolates screened from various places in Indonesia, *S. fibuligera* strain R64 was selected for demonstrating the highest amylolytic activities (α-amylase and glucoamylase). Strain R64 is able to degrade raw starch and its amylolytic enzymes demonstrate potent thermal stability. This finding makes the amylolytic enzymes from strain R64 attractive.

Extra-cellular amylolytic enzyme production by strain R64 is relatively simple, using a medium that contains of 1% sago starch and 1% yeast extract. The enzyme was harvested after 4-5 days of cultivation in a one-litre bioreactor, with constant aeration rate 1 vvm, volumetric oxygen transfer coefficient (*kLa*) 1.53 per hour, agitation speed 100 rpm, 30oC, and pH 7.0. Under this condition, enzyme activity observed on starch degradation was 1320 U/ml (Table 1). The production was also easily reproduced at a laboratory scale by means of Erlenmeyer flask (5). Thus, strain R64 offers a simple but convenient production scheme.

The amylolytic enzyme complex from strain R64, consisting of α-amylase (AMY) and glucoamylase (GLL1), is secreted into the production medium. Thus, the enzyme complex was harvested by simply cold-centrifugation (~4oC) at 6000 *g* for 30 minutes, to remove the yeast cells. The enzyme complex in the supernatant, which was designated as the crude extract, was subjected to a diafiltration system (Millipore Minitan II, Tangential Flow Filtration system, Merck Pte Ltd, Singapore) over a 10-kDa cut off membrane disc-plate, at a flow rate of 10-20 ml per minute, at room temperature. The enzyme complex was recovered in the retentate. Diafiltration step tremendously reduced the size of the sample, which is advantageous because the subsequent step to capture the amylolytic enzyme complex in the crude extract was precipitation with ammonium sulfate by 0-100%, on ice (~4oC). These sequential procedures demonstrate a straightforward way to isolate extra-cellular proteins that was accomplished within one-day operation, which is important for protein works due

the application of amylolytic enzymes continues to expand.

**3. Isolation of the amylolytic enzyme complex**

to, for example, possible degradation by proteases.

The use of amylolytic enzymes for the ethanol production in the course of renewable energy requires an ability to act on raw starch, allowing the use of biomass as the starting material. The ability of *S. fibuligera* α- and glucoamylase to degrade raw starch has been reported (4, 5). Interestingly, only 10% of amylolytic enzyme-secreting organisms are capable of producing raw starch degrading α-amylase (6, 7). Since starch degradation begins with αamylase action to produce simpler sugars, raw-starch acting α-amylase is highly desired. This situation strengthens the position of *S. fibuligera* for bioethanol production, being a raw starch degrading enzyme producer.

**Figure 1.** Starch degradation by amylolytic enzymes (3). The open and black coloured circles represent the reducing and non-reducing sugars, respectively. Note that the cleavage occurs at the reducing sugar.

α-Amylase is commonly used in food, beverage, paper industries (8), in textile industry and as additive in detergents (9, 10), for renewable energy (11, 12) and medical purpose (13). Glucoamylase has been the most important enzyme in food industry, mainly in the production of sugar or ethanol (14). Glucoamylase is normally employed in combination with amylolytic enzymes that are able to act on more complex polysaccharides, such as αamylase and pullulanase (14). Recently, amylolytic enzymes are employed in the production of lactic acid and ethanol by-product by lactic acid bacteria (LAB) (15), demonstrating that the application of amylolytic enzymes continues to expand.

Interestingly, numbers and characteristics of amylolytic enzymes secreted by *S. fibuligera* vary from one strain to another. For example, strain IFO 0111 secretes only glucoamylase whilst strain KZ secretes α-glucosidase along with α-amylase and glucoamylase (4). Further, glucoamylases from strain IFO 0111 and HUT 7212 demonstrate raw starch degradation whereas that from the strain KZ does not. Glucoamylase from strain KZ, however, demonstrates better thermal stability (4). Fascinating to find these variations in their properties despite of their near identical acid sequences (16). Strain KJ-1, an *S. fibuligera* strain from Indonesia, is also reported to secrete only glucoamylase and its partial amino acid sequence (residues 28-47) is identical to that of glucoamylase from strain KZ (1). This observation indicates differences in expression and secretion of amylolytic enzymes between *S. fibuligera* strains. Nevertheless, the practicality of *S. fibuligera* in producing amylolytic enzymes applauds the proposal to sequence its whole genomic DNA (2).

Of 136 isolates screened from various places in Indonesia, *S. fibuligera* strain R64 was selected for demonstrating the highest amylolytic activities (α-amylase and glucoamylase). Strain R64 is able to degrade raw starch and its amylolytic enzymes demonstrate potent thermal stability. This finding makes the amylolytic enzymes from strain R64 attractive.

Extra-cellular amylolytic enzyme production by strain R64 is relatively simple, using a medium that contains of 1% sago starch and 1% yeast extract. The enzyme was harvested after 4-5 days of cultivation in a one-litre bioreactor, with constant aeration rate 1 vvm, volumetric oxygen transfer coefficient (*kLa*) 1.53 per hour, agitation speed 100 rpm, 30oC, and pH 7.0. Under this condition, enzyme activity observed on starch degradation was 1320 U/ml (Table 1). The production was also easily reproduced at a laboratory scale by means of Erlenmeyer flask (5). Thus, strain R64 offers a simple but convenient production scheme.


**2.3. Mimicking different properties of nanofluids using simulation**

of viscosity to experimental findings.

104 Nanofluid Heat and Mass Transfer in Engineering Problems

using different ensembles.

**3. Methodology**

**3.1. Simulation strategy**

The simulation of nanoparticle dispersion is related to the MDS. For this, the software or the package that needs to be selected was based on the criteria of the conditions that were needed to be simulated, and the flexibility was a major concern for the applicability of different systems. The LAMMPS can be a best molecular dynamics package for simulating the nanofluidic system. This is the code generated by the Sandia Laboratories by Plimpton [50]. This molecular dynamics software has high viability over other available software like Montecarlo and Gromacs.

After selection of the MD package, to simulate a desired system with realistic features, it is highly vital to know and understand initial boundary conditions. These initial conditions for a dispersion of nanoparticles are related to charges within the system for interaction, molecular bonding, forces of attraction i.e. Vander Waal or electrostatic coulombs interactions, forcefields, pair potentials (i.e. molecular mechanics constants) and molecular weight. To perform MD simulation, initial boundary conditions are major and fundamental parameters to devise actual dynamics that exist in a real system. After setting the initial parameters, velocity of the system is equilibrated and ensembles are applied to mimic the real thermo-physical conditions.

In addition to above, after setting all the boundary conditions related to chemical and thermophysical parameters, the system is then equilibrated for certain time steps. Simulations are processed until converging results are obtained as that of the actual system. Over here, "time step" is the major dependent factor. This accounts for equilibrating the kinetics of the system that takes place; i.e. movement of system from an un-equilibrated state to equilibrium conditions. The above explained method has been compressed and illustrated using a flowchart for better understanding as shown in **Figure 2**.

After suggesting how to approach and initiate your work for simulation of nanofluids, it is also important to know the briefed-out details about the steps like force field, pair potentials, ensembles, etc.

After setting up the atoms in a coordinate system using a molecular modelling software, then force field is applied on the system (i.e. Universal, COMPASS, OPLS, etc.) by this atomic charges and bond configurations are setup. These force fields are interlinked with pair potentials (such as DPD, BD, Smoothed Particle Hydrodynamic, LJ, etc.), they are parameters which are used to describe vibrational and oscillation settings between two different atoms. Finally, ensembles are applied on the molecular dynamic system for equilibrating the actual thermal settings for example NVT, NPT, NPH, etc.

## *3.1.2. Techniques and tools*

As of now, it is known from the previous sections that to simulate and perform MDS it is necessary to know techniques and tools that can be beneficial for use and executing the work.

**Figure 2.** Flow chart of molecular dynamics simulation [26].

Today, there are several tools and ways to perform this; however, still researchers are unsure about "what are the clear steps for conducting nanofluid simulations using molecular dynamics?" Therefore, through this section, a brief and concise way is illustrated and conveyed for better and easier understanding for people working under the horizon of nanofluid simulations. These steps are as follow:

**a.** Firstly, for creating nanofluid simulation system, it is required to setup a nanoparticle and fluid, then combine them together, for which material studio is the best software for designing a nanoparticle. Now, the nanoparticles can be inserted and replicated in a box containing fluid particles, however, this may be tedious for bigger systems. Therefore, it is suggested to use Packmol after creating the Protein Data Bank (PDB) file from material studio and then create an input script for Packmol to replicate the system with as many particles and fluid molecules as per required. This software automatically packs up the overall molecular arrangement with in a confined imaginary box.

Finally, the data quantification, visualizing the effects and properties that can be analysed have been jotted below in different sections.

## *3.1.3. Data quantification*

 Now, the data obtained by using different compute commands can be quantified on MATLAB or Excel. MATLAB initially requires more time for developing its script for computing the mathematical problem or graphs. However, on a long run, it does save time. Whereas, excel is easy going but requires more time for plotting graph each time you feed the new data.

MATLAB scripting helps in formulating the work in a precise manner, and digitalise the work with high quality publishing of the data for journal publications. However, MATLAB requires good command over the MATLAB scripting and functions. By using MATLAB, it is easy to apply discrete as well as continuous algorithms and equations for refining and optimization of results. Furthermore, it helps in applying the regression on the noisy data for refinement.

In Excel, similar stuff is possible as in MATLAB, but in excel, it is quite complicated as you need to apply macros. These days the computation of MATLAB can be computed in parallel mode; again for excel, it is quite difficult. However, for graphical representation of data, excel is quite versatile.

Vice versa both tools have their own benefits over each other; it depends totally on a userfriendliness with certain software. In addition to excel, to compute or establish complex calculations, it will be required to interlink its macros with visual basic scripting, which is under a developer's tool library, mostly hidden from newbies.

## *3.1.4. Visualizing the effects*

Today, there are several tools and ways to perform this; however, still researchers are unsure about "what are the clear steps for conducting nanofluid simulations using molecular dynamics?" Therefore, through this section, a brief and concise way is illustrated and conveyed for better and easier understanding for people working under the horizon of nanofluid simula-

**a.** Firstly, for creating nanofluid simulation system, it is required to setup a nanoparticle and fluid, then combine them together, for which material studio is the best software for designing a nanoparticle. Now, the nanoparticles can be inserted and replicated in a box containing fluid particles, however, this may be tedious for bigger systems. Therefore, it is suggested to use Packmol after creating the Protein Data Bank (PDB) file from material studio and then create an input script for Packmol to replicate the system with as many particles and fluid molecules as per required. This software automatically packs up the

overall molecular arrangement with in a confined imaginary box.

tions. These steps are as follow:

**Figure 2.** Flow chart of molecular dynamics simulation [26].

106 Nanofluid Heat and Mass Transfer in Engineering Problems

After the successful execution of simulation, you will get dump files from LAMMPS, here a software that can read LAMMPS trajectories can be used for reading the file and visualizing it. For which Visual Molecular Dynamic (VMD) can be used. However, OVITO is also a good software for visualizing your trajectories.

The results generated by OVITO are represented as small spheres merged together to form a particular system representation, as shown in **Figure 3**, i.e. of a CuO-water nanofluid system.

In the similar way for showing how the VMD gives visual output is shown in **Figure 4**. It is similar to that of OVITO, however, VMD has capability of representing the trajectories in the form of molecular structure. This gives an extra possibility for researchers working in the area of Biochemistry, pharmacy, drug delivery and biomedical to represent and observe the chemical kinetics in real-time, i.e. how one atom reacts and interacts with another atom within a confined system.

## *3.1.5. Properties that can be analysed*

Some properties and parameters can directly be analysed using VMD using trajectories dump files. VMD has option for analysing the radial distribution function (RDF) and mean square displacement (MSD), they indicate about the agglomeration and dispersion rate, respectively.

**Figure 3.** Representation of OVITO output of molecular dynamics of CuO nanoparticles in water system [4].

**Figure 4.** Visual output showing two CuO nanoparticles in a water-based nanofluid.

In the similar way for showing how the VMD gives visual output is shown in **Figure 4**. It is similar to that of OVITO, however, VMD has capability of representing the trajectories in the form of molecular structure. This gives an extra possibility for researchers working in the area of Biochemistry, pharmacy, drug delivery and biomedical to represent and observe the chemical kinetics in real-time, i.e. how one atom reacts and interacts with another atom within

Some properties and parameters can directly be analysed using VMD using trajectories dump files. VMD has option for analysing the radial distribution function (RDF) and mean square displacement (MSD), they indicate about the agglomeration and dispersion rate, respectively.

**Figure 3.** Representation of OVITO output of molecular dynamics of CuO nanoparticles in water system [4].

a confined system.

*3.1.5. Properties that can be analysed*

108 Nanofluid Heat and Mass Transfer in Engineering Problems

When nanofluids are concerned the major parameters or properties researcher are interested to investigate are viscosity, thermal conductivity, specific heat capacity, thermal diffusivity, diffusion coefficient, total energy, heat loss, etc. To find these properties LAMMPS provide versatile options to compute what you require, using different algorithms or previously established techniques. Currently, main concerned variables out of above mentioned ones are viscosity, diffusion coefficient and thermal conductivity. Therefore, in the next section, we will discuss about how to validate and quantify your results obtained from the simulation.

## **4. Validation and quantification of results**

To validate the three major properties mentioned in Section 3.1.5, it is required to know initial experimental results, however, sometime it is hard to obtain those results as some simulation condition cannot be tested, either due to lack of experimental device or it is not possible to meet the boundary conditions as setup over the simulation platform.

Now, in this case, the best way is to analyse using autocorrelation function; which is a time series modelling of a function of a variable dependent on time fluctuation. Let us take the case of viscosity, as it is related with shearing stress, there are shear forces acting between the layers of molecular interaction causing pressure function to be induced. This pressure function is dependent on stress due to shearing force. If this stress is analysed using the function of time, this becomes stress tensor. This stress tensor is used for analysing stresses exiting between the molecular layers. Therefore, this is known as stress autocorrelation function (SACF). The SACF accounts for the stresses imparted on the system due to the diffusion of molecules and intermolecular kinetics; i.e. molecular stresses caused by attraction and repulsion of molecules. During the intermolecular kinetics drag is created between the molecular layers, this drag is due to the effect of shearing forces. Ultimately as the system is equilibrated, it shows unstable response of the SACF, however, as it approaches stability the SACF starts to converge to a monotonic level, which satisfies that the viscosity analysed is acceptable.

 In the similar manner, thermal conductivity is quantified, but here instead of stress and shear forces, heat is considered. Therefore, this is known as heat autocorrelation function (HACF), which quantifies or validates the thermal conductivity obtained is satisfactory.

In addition to HACF and SACF for thermal conductivity and viscosity, respectively, for diffusion coefficient, velocity autocorrelation function is used for its quantification. As diffusion coefficient is measured by taking the slope of the MSD. So to quantify and validate it, displacement with respect to time i.e. velocity can be used.

**Figure 5.** Autocorrelation output gained by running a molecular dynamics simulations [26].

The accuracy of results equilibrated for measuring the viscosity and thermal conductivity of a system can be justified in a better way with the estimation of heat autocorrelation function and stress autocorrelation function as show in **Figure 5**. The graphical result in **Figure 5** explains the process of the integration of non-equilibrated system to equilibration.

At step (a), the system starts with a thermodynamic equilibrium, but the system is not at equilibrium state. At step (b), the thermodynamic conditions are changed due to implementation of thermal ensemble so the system tends to go towards equilibrium. At step (c), the non-equilibrium system moves to equilibrated level of convergence at this level the system satisfies the convergences. This process is followed during the equilibration of the thermophysical quantities, the convergence time steps depend on the volume and quantity of the atoms in that system. For the larger system, large amount of computational power and time step will be required for convergence.

## **5. Discussion**

molecular layers, this drag is due to the effect of shearing forces. Ultimately as the system is equilibrated, it shows unstable response of the SACF, however, as it approaches stability the SACF starts to converge to a monotonic level, which satisfies that the viscosity analysed

 In the similar manner, thermal conductivity is quantified, but here instead of stress and shear forces, heat is considered. Therefore, this is known as heat autocorrelation function (HACF),

In addition to HACF and SACF for thermal conductivity and viscosity, respectively, for diffusion coefficient, velocity autocorrelation function is used for its quantification. As diffusion coefficient is measured by taking the slope of the MSD. So to quantify and validate it, dis-

The accuracy of results equilibrated for measuring the viscosity and thermal conductivity of a system can be justified in a better way with the estimation of heat autocorrelation function and stress autocorrelation function as show in **Figure 5**. The graphical result in **Figure 5**

At step (a), the system starts with a thermodynamic equilibrium, but the system is not at equilibrium state. At step (b), the thermodynamic conditions are changed due to implementation of thermal ensemble so the system tends to go towards equilibrium. At step (c), the

explains the process of the integration of non-equilibrated system to equilibration.

**Figure 5.** Autocorrelation output gained by running a molecular dynamics simulations [26].

which quantifies or validates the thermal conductivity obtained is satisfactory.

placement with respect to time i.e. velocity can be used.

110 Nanofluid Heat and Mass Transfer in Engineering Problems

is acceptable.

## **5.1. Problems faced for simulating nanofluids**

So far the topic has been conveying the techniques, approach and method for carrying out nanofluid simulations. Moreover, there has been no data available for the expertise to know what are the problems faced when these simulations are conducted, number of questions can arise, for example, (1) Till what level, computational power can support our simulations? (2) Is there any other way out rather than this? (3) How larger systems can be simulated? etc.

Therefore, to answer these questions, it is necessary to understand the material and knowledge given before, however, as the number of atoms are increased within a nanofluid system the molecular dynamics demonstrates sluggish performance due to less computational capabilities i.e. either central processing unit (CPU) power or graphic processing unit (GPU). Furthermore, it is not just simulation that need to be carried out but for the data quantification, the data that are gathered requires huge memory for storage. Thereby, requiring the random access memory (RAM) and hard disk drive (HDD) to be large enough to store the required data easily [51].

After hardware issues, the second set of problems faced by nanofluid simulation is the use of multiple software for designing, modelling, processing and visualization, which needs a lot of understanding of computer for a new geek. Furthermore, if this all is combined in one package, this can marvellously save time and money for purchasing different software for data acquisition. It is slightly known at the moment that there are few software in market for helping in simulating nanofluid; however, academia is not yet aware of it due to less versatility such as Medea and Scienomics MAPS.

One of the major problem is that, people of twenty-first century like working using graphical user interface (GUI), as it is easy and you can do everything by just clicks rather than using complicated commands, however, most of the molecular dynamics package are used on Linux operating system, moreover, commands are used for computing and feeding the data for computation.

In addition to high computing power, it should be known that before attempting to simulate large scale molecular dynamics (i.e. with more than 0.1 million atoms), it is required to have parallel processing enabled on the PC. For that high end, CPU or GPU is required with multi cores for processing the data in parallel mode. However, this processing has some drawbacks that are loop holes for simulations, one such kind is that sometimes the algorithm is not designed in a way to parallel the process efficiently, which in turn gives ambiguous simulation output and convergence. For avoiding this, it is necessary for the user to know the correct working of the algorithm. Moreover, the field programmable gate array (FPGA) is good outbreak technology that is being implemented for paralleling the process [52, 53], nevertheless, again this technology requires new stuff and bits coding to be learned before operating or using this module for rapidly solving the simulation.

## **6. Conclusion**

The chapter has brought about marvellous information and the literature for new geeks for conducting a nanofluid simulation. However, this chapter acts as a guide for a newbie for initialising the nanofluid simulation.

## **Nomenclature**

## **Author details**

## Adil Loyai

simulation output and convergence. For avoiding this, it is necessary for the user to know the correct working of the algorithm. Moreover, the field programmable gate array (FPGA) is good outbreak technology that is being implemented for paralleling the process [52, 53], nevertheless, again this technology requires new stuff and bits coding to be learned before

The chapter has brought about marvellous information and the literature for new geeks for conducting a nanofluid simulation. However, this chapter acts as a guide for a newbie for

Words **Abbreviation**

File format output from material studio CAR and.COR

Molecular dynamics simulation MDS Non-equilibrium molecular dynamics NEMD Equilibrium molecular dynamics EMD Embedded atom method EAM Condensed-phase optimized molecular potentials for atomistic simulation studies COMPASS Brownian dynamics BD Canonical **NVT** Grand canonical ΔPT Isobaric and isothermal NPT Micro canonical NVE Normal pressure hydrocephalus NPH Copper oxide CuO Titanium oxide TiO2 Cerium oxide CeO2 Poly ethylene oxide PEO Leonard Jones L-J Copper **Cu** Argon **Ar** Kelvin **K** Discrete particle dynamics DPD Large-scale atomic/molecular massively parallel simulator LAMMPS Optimized potential for liquid simulation sOPLS Protein Data Bank PDB

operating or using this module for rapidly solving the simulation.

**6. Conclusion**

**Nomenclature**

initialising the nanofluid simulation.

112 Nanofluid Heat and Mass Transfer in Engineering Problems

Address all correspondence to<EMAIL_ADDRESS>
PAF-Karachi Institute of Economics and Technology, Karachi, Pakistan

## **References**

[7] Karthik R, Harish Nagarajan R, Raja B, Damodharan P. Thermal conductivity of CuO– DI water nanofluids using 3-ω measurement technique in a suspended micro-wire.

[8] Sankar N, Mathew N, Sobhan C. Molecular dynamics modeling of thermal conductivity enhancement in metal nanoparticle suspensions. International Communications in Heat

[9] Sarkar S, Selvam RP. Molecular dynamics simulation of effective thermal conductivity and study of enhanced thermal transport mechanism in nanofluids. Journal of Applied

[10] Esmaeilzadeh P, Fakhroueian Z, Beigi M, Akbar A. Synthesis of biopolymeric α-lactalbumin protein nanoparticles and nanospheres as green nanofluids using in drug delivery and

[11] Rapoport L, Leshchinsky V, Lvovsky M, Lapsker I, Volovik Y, Feldman Y, et al. Superior tribological properties of powder materials with solid lubricant nanoparticles. Wear.

[12] Peng DX, Kang YA, Chen SK, Shu FC, Chang YP. Dispersion and tribological properties of liquid paraffin with added aluminum nanoparticles. Industrial Lubrication and

[13] Rajabpour A, Akizi FY, Heyhat MM, Gordiz K. Molecular dynamics simulation of the specific heat capacity of water-Cu nanofluids. International Nano Letters. 2013;3:1–6.

[14] Mohebbi A. Prediction of specific heat and thermal conductivity of nanofluids by a combined equilibrium and non-equilibrium molecular dynamics simulation. Journal of

[15] Li L, Zhang YW, Ma HB, Yang M. Molecular dynamics simulation of effect of liquid layering around the nanoparticle on the enhanced thermal conductivity of nanofluids.

[16] Lu W-Q, Fan Q-M. Study for the particle's scale effect on some thermophysical properties of nanofluids by a simplified molecular dynamics method. Engineering Analysis

[17] Mingxiang L, Lenore LD. Molecular dynamics simulations of surfactant and nanoparticle self-assembly at liquid–liquid interfaces. Journal of Physics: Condensed Matter.

[18] Rudyak VY, Krasnolutskii SL. Dependence of the viscosity of nanofluids on nanoparticle

food technology. Journal of Nano Research: Trans Tech Publ. 2011;16:89–96.

Experimental Thermal and Fluid Science. 2012;40:1–9.

and Mass Transfer. 2008;35:867–72.

114 Nanofluid Heat and Mass Transfer in Engineering Problems

Physics. 2007;102:074302.

2003;255:799–800.

2007;19:375109.

Tribology. 2010;62:341–48.

Molecular Liquids. 2012;175:51–8.

Journal of Nanoparticle Research. 2010;12:811–21.

size and material. Physics Letters A. 2014;378:1845–9.

with Boundary Elements. 2008;32:282–9.

[48] Bunte SW, Sun H. Molecular modeling of energetic materials: the parameterization and validation of nitrate esters in the COMPASS force field. Journal of Physical Chemistry B. 2000;104:2477–89.

[33] Li L, Zhang Y, Ma H, Yang M. An investigation of molecular layering at the liquidsolid interface in nanofluids by molecular dynamics simulation. Physics Letters A.

[34] Sun C, Lu W-Q, Liu J, Bai B. Molecular dynamics simulation of nanofluid's effective thermal conductivity in high-shear-rate Couette flow. International Journal of Heat and

[35] Lin YS, Hsiao PY, Chieng CC. Roles of nanolayer and particle size on thermophysical characteristics of ethylene glycol-based copper nanofluids. Applied Physics Letters.

[36] Kang H, Zhang Y, Yang M, Li L. Nonequilibrium molecular dynamics simulation of coupling between nanoparticles and base-fluid in a nanofluid. Physics Letters A. 2012;376:521–4.

[37] Loya A, Stair JL, Ren G. The study of simulating metaloxide nanoparticles in aqueous fluid. International Journal of Engineering Research & Technology. 2014;3:1954–60.

[38] Cooke DJ, Elliott JA. Atomistic simulations of calcite nanoparticles and their interaction

[40] Kowsari MH, Alavi S, Ashrafizaadeh M, Najafi B. Molecular dynamics simulation of imidazolium-based ionic liquids. I. Dynamics and diffusion coefficient. Journal of

[41] Spaeth JR, Kevrekidis IG, Panagiotopoulos AZ. A comparison of implicit- and explicitsolvent simulations of self-assembly in block copolymer and solute systems. Journal of

[42] Symeonidis V, Em Karniadakis G, Caswell B. Dissipative particle dynamics simulations of polymer chains: scaling laws and shearing response compared to DNA experiments.

[43] Gao L, Shillcock J, Lipowsky R. Improved dissipative particle dynamics simulations of

[44] Hoogerbrugge PJ, Koelman JMVA. Simulating microscopic hydrodynamic phenomena

[45] Koelman JMVA, Hoogerbrugge PJ. Dynamic simulations of hard-sphere suspensions

[46] Español P, Warren P. Statistical mechanics of dissipative particle dynamics. EPL

[47] Cheng S, Grest GS. Structure and diffusion of nanoparticle monolayers floating at liquid/ vapor interfaces: a molecular dynamics study. Journal of Chemical Physics. 2012;136:214702.

with dissipative particle dynamics. Europhysics Letters. 1992;19:155–60.

nanoparticles. Nano. 2007;02:301–3.

with water. Journal of Chemical Physics. 2007;127:104706–9.

lipid bilayers. Journal of Chemical Physics. 2007;126:015101–8.

under steady shear. Europhysics Letters. 1993;21(3):363–368.

2008;372:4541–4.

2011;98(15):153105.

Mass Transfer. 2011;54:2560–7.

116 Nanofluid Heat and Mass Transfer in Engineering Problems

[39] Hoang VV. Diffusion in simulated SiO2

Chemical Physics. 2008;129:224508–13.

Chemical Physics. 2011;134:164902–13.

Physical Review Letters. 2005;95:076001.

(Europhysics Letters). 1995;30:191.

**Application of Nanofluid for Combustion Engine**

## **Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids**

Wenzheng Cui, Zhaojie Shen, Jianguo Yang and Shaohua Wu Wenzheng Cui, Zhaojie Shen, Jianguo Yang and Shaohua Wu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/65554

#### **Abstract**

Nanofluids exhibit novel properties including significant heat transfer properties that make them potentially useful in internal combustion engine cooling. However, although there is a substantial number of mechanisms proposed, modeling works related to their enhanced thermal conductivity, systematic mechanisms, or models that are suitable for nanofluids are still lacked. With molecular dynamics simulations, thermal conductivities of nanofluids with various nanoparticles have been calculated. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, *Κ*, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, *E*, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of nanofluids. Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The prediction results of the present model are in good agreement with experimental results.

**Keywords:** nanofluids, internal combustion engine, heat transfer, mechanism

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **1. Introduction**

In recent years, nanofluids (NFs) have received much attention due to their strengthening heat transfer properties, which possess important application in heat transfer. The concept of nanofluidswasfirstproposedbyChoiin1995,whichindicates thefluids containingnanometer‐ sizedparticles, callednanoparticles (NPs)[1].Thesefluids are engineeredcolloidal suspensions of nanoparticles in a base fluid. Numerous experimental studies discovered that nanofluids exhibit thermal properties superior to those of base fluid or conventional solid‐liquid suspen‐ sions.Mostofthethermalpropertiesofnanofluidsmeasuredgreatlyexceedthevaluespredicted by classical macroscopic theories and models. Nanofluids possess significantly increased thermal conductivity and improved convective heat transfer coefficient. Therefore, they are potentiallyusefulinmanyenhancedheattransfer application,includingengine cooling,vehicle thermal management, and power battery. Researchers are working to explain the significant high thermal properties of nanofluids [2–4]. However, although there is a substantial number of mechanisms proposed, and modeling works related to their enhanced thermal conductivity, systematic mechanisms, or models that are suitable for nanofluids are still lacked.

Regarding the excellent thermal properties of nanofluids, researchers are interested in the application of nanofluids in internal combustion engine, and began the study of applying nanofluids in internal combustion engine. In 1999, Wambsganss in Argonne national labora‐ tory proposed the idea of applying nanofluids in car engine to improve the vehicle thermal management performance [5]. Choi indicates in a report that in Argonne national laboratory a research program of enhanced heat transfer by nanofluids is launched aiming at the cooling and heat transfer problems in the heavy‐duty engine [6]. The results show that due to the excellent heat transfer performance of nanofluids, the size and weight of the engine can be reduced by 10%. Choi pointed out that the application of nanofluids in engine is one of the best methods of improving heat transfer performance of the cooling system. Saripella et al. studied the heat transfer performance of nanocoolant (nanofluids) in Volvo truck engine, and the results indicate that with nanofluids the temperatures of combustion chamber components and coolant are lowered [7]. Lockwood et al. in Valvoline Company reported the application of nanofluids in the cooling for internal combustion engine [8]. The experiments found that adding 1% vol. carbon nanotube in engine oil could increase the thermal conductivity by 150%. Wallner et al. in Delphi Company found that applying nanofluids can efficiently improve the efficiency of internal combustion engine and decrease the size and weight of the cooling system [9]. Huminic et al. studied the performance of nanofluids in a car radiator with a numerical method and found that the convective heat transfer performance is distinctly better than that of single‐phase fluids [10]. Furthermore, the heat transfer properties of nanofluids are influenced by many factors, including the volume concentrations, temperatures, and fluid velocities. Vajjha et al. reported their research on the flow and heat transfer properties of Al2O3 and CuO nanofluids when applying them in the car radiator [11]. Their results reveal that nanofluids possess improved convective heat transfer properties and the increase rate is increased by increased volume concentrations. Leong et al. found that the heat transfer coefficient and heat transfer rate in the cooling system of internal combustion engine are improved by using nanofluids [12]. Peyghambarzadeh et al. experimentally verified that the application of nanofluids improves heat transfer efficiency of the car radiator by 45% when using Al2O3‐H2O nanofluids [13].

The authors have focused on the application of nanofluids in internal combustion engine (ICE) for heat transfer enhancement. In order to apply nanofluids in ICE, the mechanisms of heat transfer enhancement and the rules of enhanced heat transfer by nanofluids should be clarified first. The original cause of heat transfer enhancement is due to the adding of nanoscale particles. Therefore, we have attempted to use molecular dynamics (MD) simula‐ tions to study these microscopic mechanisms [14]. By using MD simulation, we have calcu‐ lated thermal conductivity of nanofluids via the Green‐Kubo equation and proposed an effective criterion for predicting the enhancement of apparent thermal conductivity. Fur‐ thermore, possible mechanisms of heat conduction increase in nanofluids are studied by MD simulation, including: (1) the micromotions of nanoparticles, (2) changed microstruc‐ ture of base fluid by adding nanoparticles, and (3) the influence of absorption layer of base fluid at the surface of nanoparticles. On the basis of the microscopic mechanisms found by MD simulations, we have also proposed a revised thermal conductivity model, which con‐ sidered both the static and dynamic mechanisms. The revised model is verified by experi‐ mental data, which has been proved to be quite accurate for predicting thermal conductivity of common types of nanofluids.

## **2. Influence rule and criterion for nanofluids' thermal conductivity**

## **2.1. Simulation results of thermal conductivity**

**1. Introduction**

122 Nanofluid Heat and Mass Transfer in Engineering Problems

In recent years, nanofluids (NFs) have received much attention due to their strengthening heat transfer properties, which possess important application in heat transfer. The concept of nanofluidswasfirstproposedbyChoiin1995,whichindicates thefluids containingnanometer‐ sizedparticles, callednanoparticles (NPs)[1].Thesefluids are engineeredcolloidal suspensions of nanoparticles in a base fluid. Numerous experimental studies discovered that nanofluids exhibit thermal properties superior to those of base fluid or conventional solid‐liquid suspen‐ sions.Mostofthethermalpropertiesofnanofluidsmeasuredgreatlyexceedthevaluespredicted by classical macroscopic theories and models. Nanofluids possess significantly increased thermal conductivity and improved convective heat transfer coefficient. Therefore, they are potentiallyusefulinmanyenhancedheattransfer application,includingengine cooling,vehicle thermal management, and power battery. Researchers are working to explain the significant high thermal properties of nanofluids [2–4]. However, although there is a substantial number of mechanisms proposed, and modeling works related to their enhanced thermal conductivity,

systematic mechanisms, or models that are suitable for nanofluids are still lacked.

Regarding the excellent thermal properties of nanofluids, researchers are interested in the application of nanofluids in internal combustion engine, and began the study of applying nanofluids in internal combustion engine. In 1999, Wambsganss in Argonne national labora‐ tory proposed the idea of applying nanofluids in car engine to improve the vehicle thermal management performance [5]. Choi indicates in a report that in Argonne national laboratory a research program of enhanced heat transfer by nanofluids is launched aiming at the cooling and heat transfer problems in the heavy‐duty engine [6]. The results show that due to the excellent heat transfer performance of nanofluids, the size and weight of the engine can be reduced by 10%. Choi pointed out that the application of nanofluids in engine is one of the best methods of improving heat transfer performance of the cooling system. Saripella et al. studied the heat transfer performance of nanocoolant (nanofluids) in Volvo truck engine, and the results indicate that with nanofluids the temperatures of combustion chamber components and coolant are lowered [7]. Lockwood et al. in Valvoline Company reported the application of nanofluids in the cooling for internal combustion engine [8]. The experiments found that adding 1% vol. carbon nanotube in engine oil could increase the thermal conductivity by 150%. Wallner et al. in Delphi Company found that applying nanofluids can efficiently improve the efficiency of internal combustion engine and decrease the size and weight of the cooling system [9]. Huminic et al. studied the performance of nanofluids in a car radiator with a numerical method and found that the convective heat transfer performance is distinctly better than that of single‐phase fluids [10]. Furthermore, the heat transfer properties of nanofluids are influenced by many factors, including the volume concentrations, temperatures, and fluid velocities. Vajjha et al. reported their research on the flow and heat transfer properties of Al2O3 and CuO nanofluids when applying them in the car radiator [11]. Their results reveal that nanofluids possess improved convective heat transfer properties and the increase rate is increased by increased volume concentrations. Leong et al. found that the heat transfer coefficient and heat transfer rate in the cooling system of internal combustion engine are improved by using nanofluids [12]. Peyghambarzadeh et al. experimentally verified that the

MD simulation is used to calculate thermal conductivity of nanofluids via the Green‐Kubo equation [14]. A series of influencing factors for the thermal conductivity of nanofluids have been considered, including: nanoparticles' volume concentrations, sizes, materials, and shapes [15]. In this work, inert liquid Ar is chosen as the base fluid because of the mature and credible potential function. The NP materials include Cu, Ag, Fe, and Au. We have chosen these types of nanoparticles because they are commonly reported in the litera‐ tures on nanofluids. The MD simulation results reveal that the thermal conductivity of nanofluids can be obviously increased by adding nanoparticles, as shown in **Table 1**. How‐ ever, the contributions of several influencing factors for thermal conductivity of nanofluids are different.

**Figure 1** shows the MD simulation results of thermal conductivities for nanofluids contain‐ ing spherical nanoparticles. In this case, the nanoparticle volume fractions, nanoparticle di‐ ameter, and thermal conductivity of nanoparticles are considered. For the influencing factors that have been considered in this work, the influencing rules are regular. The thermal conductivity of nanofluids is increased with increased volume fraction of NPs, decreased NP sizes, and higher thermal conductivity of NPs. For instance, the ratios of thermal con‐ ductivity enhancement for Ag, Cu, Fe, and Au nanofluids are 1.41, 1.15, 1.11, and 1.08 se‐ quentially when the other conditions are the same.

**Table 1.** Ratios of nanofluids' thermal conductivity enhancement with different influence factors.

**Figure 1.** MD simulation results for thermal conductivity of nanofluids.

To examine the influencing rules for thermal conductivity of nanofluids in depth, the ratio of thermal conductivity enhancement by nanoparticle volume fraction, *Κ*, is defined:

$$K = \frac{k \,/\, k\_f - 1}{V\_{\rm np} \,/\, V} \, \text{} \tag{1}$$

where *V*np and *V* are the volume of nanoparticles and nanofluids, respectively; *k* and *kf*represent the thermal conductivities of nanofluids and base fluid, respectively.

**Diameters (nm)**

**Volume fractions**

124 Nanofluid Heat and Mass Transfer in Engineering Problems

**Cu Ag Au Fe**

2 0.5 1.112 1.326 1.062 1.036

4 0.5 1.095 1.287 1.055 1.041

6 0.5 1.065 1.244 1.049 1.045

To examine the influencing rules for thermal conductivity of nanofluids in depth, the ratio of

*V V* (1)

thermal conductivity enhancement by nanoparticle volume fraction, *Κ*, is defined:

*K*

np / 1 , / - = *f k k*

**Table 1.** Ratios of nanofluids' thermal conductivity enhancement with different influence factors.

**Figure 1.** MD simulation results for thermal conductivity of nanofluids.

1 1.149 1.405 1.112 1.059 2 1.359 1.498 1.205 1.079 3 1.414 1.570 1.322 1.096

1 1.134 1.325 1.096 1.065 2 1.303 1.396 1.175 1.085 3 1.376 1.462 1.202 1.112

1 1.097 1.265 1.086 1.079 2 1.266 1.326 1.152 1.098 3 1.336 1.369 1.256 1.132

**(%)**

The physical significance of *K* is to evaluate the ratio of thermal conductivity increase and nanoparticle volume fraction. In other words, *K* could be used to evaluate the impact of nanoparticle properties for thermal conductivity. With *K*, the contributions from volume fraction and thermal conductivity of nanoparticles could be compared, as shown in **Figures 2** and **3**. From the figures i,t could be easily found that the contributions of nanoparticle materials are in the order of Ag, Cu, Au, and Fe. Furthermore, through comparing *K* values, it is also found that the influence of nanoparticle materials is weakened when the volume fraction or nanoparticle size is increased. With the help of *K* value the other influencing factors could be further evaluated, please refer to reference [15].

**Figure 2.** Comparison of *K* values against nanoparticle volume fraction for various nanofluids.

**Figure 3.** Comparison of *K* values against nanoparticle size for various nanofluids.

#### **2.2. Criterion for the increased thermal conductivity**

It is found that the nanoparticles containing higher atomic potential energy (energetic atoms) are better for thermal conductivity enhancement of nanofluids [15]. The ratio of energetic atoms in a nanoparticle *E* is proposed as a criterion for enhanced thermal conductivity of nanofluids, which is written as:

$$E = \frac{N\_E}{N} \,\prime \tag{2}$$

where *N* and *NE* are the quantity of atoms and energetic atoms in a nanoparticle, respectively.

If we set a standard for delimiting the energetic atoms in a nanoparticle, *E* can be calculated according to Eq. (2). **Figure 4** illustrates *E* of different types of nanoparticles. The ratio of energetic atoms in an Ag nanoparticle is the largest, and that of Fe nanoparticle is the lowest. The larger *E* value in a nanoparticle is better for thermal conductivity enhancement in nanofluids. **Figure 5** shows the influence of nanoparticle shapes (surface area to volume ratio S/V) for the *E* value. It is found that nanoparticle with larger S/V value possesses larger *E* value. **Figure 6** illustrates that the *E* value of spherical Ag nanoparticle is larger than that of Cu nanoparticle with larger S/V value. Therefore, the thermal conductivity of nanofluids with spherical Ag nanoparticles is higher than that of nanofluids with nonspherical Cu nanoparti‐ cles.

**Figure 4.** Atomic potential energy distributions of various nanoparticles.

Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids http://dx.doi.org/10.5772/65554 127

**2.2. Criterion for the increased thermal conductivity**

126 Nanofluid Heat and Mass Transfer in Engineering Problems

**Figure 4.** Atomic potential energy distributions of various nanoparticles.

nanofluids, which is written as:

cles.

It is found that the nanoparticles containing higher atomic potential energy (energetic atoms) are better for thermal conductivity enhancement of nanofluids [15]. The ratio of energetic atoms in a nanoparticle *E* is proposed as a criterion for enhanced thermal conductivity of

where *N* and *NE* are the quantity of atoms and energetic atoms in a nanoparticle, respectively. If we set a standard for delimiting the energetic atoms in a nanoparticle, *E* can be calculated according to Eq. (2). **Figure 4** illustrates *E* of different types of nanoparticles. The ratio of energetic atoms in an Ag nanoparticle is the largest, and that of Fe nanoparticle is the lowest. The larger *E* value in a nanoparticle is better for thermal conductivity enhancement in nanofluids. **Figure 5** shows the influence of nanoparticle shapes (surface area to volume ratio S/V) for the *E* value. It is found that nanoparticle with larger S/V value possesses larger *E* value. **Figure 6** illustrates that the *E* value of spherical Ag nanoparticle is larger than that of Cu nanoparticle with larger S/V value. Therefore, the thermal conductivity of nanofluids with spherical Ag nanoparticles is higher than that of nanofluids with nonspherical Cu nanoparti‐

*N* (2)

= , *NE E*

**Figure 5.** Comparison of atomic potential energy distributions for nanoparticles with different shapes.

**Figure 6.** Comparison of atomic potential energy distributions for nanoparticles with different materials.

## **3. Proposed mechanisms of heat conduction in nanofluids**

## **3.1. Altered microstructure of nanofluids**

In order to analyze the microscopic structure characteristics of nanofluids, number density distribution, radial distribution function (RDF), coordination number, and potentials of mean force (PMF) should be considered [16].

Number density distribution represents the distribution of liquid atoms around a centered nanoparticle. **Figure 7** illustrates the number density distributions of base fluid atoms in different types of nanofluids. It is found that at the positions of 0.25 and 0.5 nm all the curves show the first and second peak values. But for different types of nanoparticles, the first peak values of curves are different. The order of first peak values is of the same order of thermal conductivity of bulk materials of nanoparticles.

**Figure 7.** Statistical result of absorption layers around nanoparticles of different materials.

RDF represents the probability of finding an atom of a specified type near the central atom. Through RDF, the microscopic structure of fluid could be examined. **Figure 8** illustrates the RDF of Cu nanofluids with a 2 nm‐diameter nanoparticle. In the figure, the RDF curve of "Ar‐ Ar" represents the chance of finding an Ar atom near the central Ar atom. It could be found that the Ar‐Ar RDF shows typical characteristics of the liquid: "short‐range order and long‐ range disorder." In the figure, the "total" RDF represents the chance of finding an atom of any type near the central atom, which represents the microscopic structure of nanofluid. It is found that the first curvilinear peak in the RDF of nanofluids is larger than that of base fluid, which means the probability of finding an atom is higher than that in a single‐phase base fluid. It could also be found that there are several diminutive curvilinear peaks in the RDF of nano‐ fluids, which is due to the adding of nanoparticles in base fluid. In general, the microscopic structure of nanofluid exerts a mixed up structure characteristics of liquid and solid. Both the liquid characteristic of "short‐range order and long‐range disorder" and the solid character‐ istic of "long‐range order" have been found in the microscopic structure of nanofluids. Therefore, the microscopic structure of nanofluids is always ordered.

**3. Proposed mechanisms of heat conduction in nanofluids**

**Figure 7.** Statistical result of absorption layers around nanoparticles of different materials.

RDF represents the probability of finding an atom of a specified type near the central atom. Through RDF, the microscopic structure of fluid could be examined. **Figure 8** illustrates the RDF of Cu nanofluids with a 2 nm‐diameter nanoparticle. In the figure, the RDF curve of "Ar‐ Ar" represents the chance of finding an Ar atom near the central Ar atom. It could be found that the Ar‐Ar RDF shows typical characteristics of the liquid: "short‐range order and long‐ range disorder." In the figure, the "total" RDF represents the chance of finding an atom of any type near the central atom, which represents the microscopic structure of nanofluid. It is found that the first curvilinear peak in the RDF of nanofluids is larger than that of base fluid, which

In order to analyze the microscopic structure characteristics of nanofluids, number density distribution, radial distribution function (RDF), coordination number, and potentials of mean

Number density distribution represents the distribution of liquid atoms around a centered nanoparticle. **Figure 7** illustrates the number density distributions of base fluid atoms in different types of nanofluids. It is found that at the positions of 0.25 and 0.5 nm all the curves show the first and second peak values. But for different types of nanoparticles, the first peak values of curves are different. The order of first peak values is of the same order of thermal

**3.1. Altered microstructure of nanofluids**

128 Nanofluid Heat and Mass Transfer in Engineering Problems

force (PMF) should be considered [16].

conductivity of bulk materials of nanoparticles.

**Figure 8.** Radial distribution functions of nanofluids with spherical copper nanoparticle.

Coordinate number indicates the average adjacent atomic number for a certain atom within an interval of *r*. By comparing the coordinate number curve of Cu nanofluids with a 2 nm‐ diameter spherical nanoparticle, it is found that the coordinate number curves of Cu‐Ar and Ar‐Ar cross at 0.35 nm, which confirms that the local density of Ar atoms near a Cu atom is larger (**Figure 9**). Therefore, the existence of absorption layer is verified.

PMF reflects the combining capacity between particles in pairs. The value of PMF could be used to investigate the combining capacity between different particle pairs. **Figure 10** shows the PMF curve for Cu‐Ar nanofluids. The contact minimum (CM), separated minimum (SM), and the layer barrier (LB) could easily be found in the PMF curve of nanofluids. But the positions and values of CM, SM, and LB are different for disparate atom pairs. The cis‐trans direction of energy barrier between molecular layers of liquid Ar is different. When an atom is approaching the central particle, then it needs to conquer the energy barrier between the first and second molecular layers. But the atom is harder to leave the central particle. An atom of base fluid needs to conquer greater energy barrier to reenter the base fluid. The PMF of nanofluids is different from that of base fluid. At 0.3 nm, there is a huge energy barrier in the PMF of nanofluids, which indicates the surrounding atom needs to conquer two energy barriers to get close to the central atom. The cis‐trans direction of the first energy barrier is nearly the same, but the cis‐trans direction of the second energy barrier is obviously different. Once a base fluid atom enters the adjacent area of the central atom, then it is very hard to reenter the base fluid because of the large energy barrier.

**Figure 9.** The coordination number of nanofluids with spherical copper nanoparticle.

**Figure 10.** PMF of nanofluids with spherical copper nanoparticle.

### **3.2. Movements of nanoparticles in the base fluid**

barriers to get close to the central atom. The cis‐trans direction of the first energy barrier is nearly the same, but the cis‐trans direction of the second energy barrier is obviously different. Once a base fluid atom enters the adjacent area of the central atom, then it is very hard to

reenter the base fluid because of the large energy barrier.

130 Nanofluid Heat and Mass Transfer in Engineering Problems

**Figure 9.** The coordination number of nanofluids with spherical copper nanoparticle.

**Figure 10.** PMF of nanofluids with spherical copper nanoparticle.

Through MD simulation, the nanoparticles are observed to move chaotically at high speed in the base fluid. Through MD simulation, the instantaneous velocity and position coordinates of each atom could be obtained [17]. The translational and rotational velocity of nanoparticles could be acquired by defining a group for the Cu atoms within the nanoparticle. With commands provided by LAMMPS the time‐averaged translational and rotational velocity of the atom group could be calculated and output derived. For the case of imposed shearing velocity *v* = 50 m/s on the fluid, the translational velocity components of nanoparticles are statistically analyzed, as shown in **Figure 11**. Along *x*‐directions, the average translational velocity components are ‐2 m/s∼2 m/s, and the instantaneous peak value can reach 5 m/s. The translational velocity components of nanoparticles oscillate sharply, which demonstrate the chaotic movements of nanoparticles are mainly caused by their Brownian motion.

**Figure 11.** Translational velocity component of nanoparticle along *x*‐axis in shearing flow.

Rotation of nanoparticles is also statistically analyzed. For the case of imposed shearing velocity 50 m/s, the angular velocity component along the *x*‐axis of nanoparticle is shown in **Figure 12**. The peak angular velocity of nanoparticles can reach 6 × 109 rad/s, meanwhile the rotational directions of nanoparticles change randomly because of their nanoscale size. It is found that imposing shearing velocity affects little for rotation of nanoparticles. Imposed shearing velocity or not, the angular velocity components of nanoparticles are of the order of magnitude of 109 rad/s.

**Figure 12.** Angular velocity component of nanoparticle around *x*‐axis.

## **4. Modeling thermal conductivity of nanofluids**

Jeffrey applied Green's function method and relaxed the requirement of uniform configuration for particles. The formula, which is suitable for predicting suspensions with nonuniformly distributing nanoparticles and relatively large volume concentration, is written as [18],

$$\frac{k}{k\_f} = 1 + 3\beta \theta \phi\_p + 3\beta \left(\beta + \Sigma\right) \phi\_p^{-2},\tag{3}$$

where ∑ is a convergent series, which depends on the specific value of thermal conductivity of nanoparticle and base fluid /. *β* is a coefficient, which is determined by /:

$$
\beta = \frac{\alpha - 1}{\alpha + 2},
\tag{4}
$$

where *α* is the specific value of thermal conductivity of nanoparticle and base fluid /:

Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids http://dx.doi.org/10.5772/65554 133

$$\alpha = \frac{k\_m}{k\_f}.\tag{5}$$

Through considering the above‐mentioned mechanisms of thermal conductivity enhancement in nanofluids, a revised model for predicting thermal conductivity of nanofluids that takes into account both the static and dynamic mechanisms is proposed, which is written as[19]:

$$\frac{k\_{\eta'}}{k\_f} = \frac{k\_S}{k\_f} + \frac{k\_D}{k\_f} = 1 + 3\beta \left( 1 + \frac{2t\_{ab}}{d\_\rho} \right)^3 \phi\_\rho + c \left( 1 + \frac{2t\_{ab}}{d\_\rho} \right)^6 \phi\_\rho^{-2} + \frac{\phi\_\rho \left( \rho c\_\rho \right)\_\rho}{2k\_f} \sqrt{\frac{k\_g T}{3\pi r\_{cl}\mu\_f}} \tag{6}$$

In the equation, *k*nf is thermal conductivity of nanofluids; *kf* is thermal conductivity of base fluid; *kS* is the static part of thermal conductivity model for nanofluids, which takes into account the static mechanisms of heat conduction enhancement; *kD* is the static part of thermal conductivity model for nanofluids; = 1 / + 2 and = cl , where *kcl* is thermal conductivity of nanoparticle cluster; *t*ab is the thickness of absorption layer formed by absorbed liquid molecules which can be estimated by Langmuir monolayer equation; *dp* is the average diameter of nanoparticles; *Φp* is the initial volume concentration of nano‐ particles in nanofluids; = 32 + 3 is a coefficient in Jeffrey Model; *ρ* is the density of nanoparticle bulk material; *cp* is the specific heat of nanoparticle bulk material; *kB* is the Boltzmann constant and = 1.381 × 1023/; *T* is thermodynamic temperature; *rcl* is the radius of the nanoparticle cluster; and *μf* is the viscosity of base fluid.

**Figure 12.** Angular velocity component of nanoparticle around *x*‐axis.

132 Nanofluid Heat and Mass Transfer in Engineering Problems

**4. Modeling thermal conductivity of nanofluids**

*f k k*

Jeffrey applied Green's function method and relaxed the requirement of uniform configuration for particles. The formula, which is suitable for predicting suspensions with nonuniformly distributing nanoparticles and relatively large volume concentration, is written as [18],

( ) 2 = + + +å 13 3 , *p p*

of nanoparticle and base fluid /. *β* is a coefficient, which is determined by /:

a

 b b

where ∑ is a convergent series, which depends on the specific value of thermal conductivity

1 , 2 - = + a b

where *α* is the specific value of thermal conductivity of nanoparticle and base fluid /:

 f

(3)

(4)

bf

Compared with existing prediction models for thermal conductivity of nanofluids, the present model takes into account the static and dynamic mechanisms of strengthened heat conduction in nanofluids simultaneously and possesses more definite physics meaning. In addition, parameters used in the current model are more precise that ensures the veracity of prediction result. For instance, the thermal conductivity of nanoparticles *kp* is distinguished with that of bulk material of nanoparticles *km* and used in the prediction model as an independent param‐ eter. And the thermal conductivity of nanoparticles‐cluster *k*cl is introduced as an independent parameter to include the heat conduction of absorption layer, which further improves the prediction accuracy of the present model.

Through comparing the prediction results of the present model and existing experimental data, the present prediction model is proved to be quite effective for predicting thermal conductivity of common nanofluids, as shown in **Table 2**. For various types of nanofluids (with different materials including: metal, metallic oxide, and nonmetallic oxide, different volume fractions, or different nanoparticle diameters), the present model gives good pre‐ dictions.

**Table 2.** Comparison between the prediction results and experimental data.

## **5. Concluding remarks**

Thermal conductivities of nanofluids with various nanoparticles have been calculated through MD simulations. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, *Κ*, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, *E*, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of nanofluids.

Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Number density distribution, radial distribution function (RDF), coordination number, and potentials of mean force (PMF) are used to analyze the microscopic structure characteristics of nanofluids. Through MD simulation, the average translational and rotational velocities of nanoparticles are obtained.

Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The parameters in the model have definite physical meaning and are more precise. The prediction results of the present model are in good agreement with experimental results.

## **Acknowledgements**

**References NF NP size**

134 Nanofluid Heat and Mass Transfer in Engineering Problems

(60:40% EG/W)

**Table 2.** Comparison between the prediction results and experimental data.

Vajjha [24] Al2O3/

**5. Concluding remarks**

nanofluids.

**(nm)**

**Volume fraction (%)**

Masuda [20] Al2O3/Water 13 1.3–4.3 1.107–1.318 1.097–1.322 0.9% Teng et al. [21] Al2O3/Water 20 0.001–0.005 1.018–1.065 1.010–1.043 2.1% Chon et al. [22] Al2O3/Water 13 1 1.081 1.072 0.9% Xie et al. [23] Al2O3/EG 25 1.7–5 1.097–1.294 1.103–1.303 0.8%

Wang et al. [25] Al2O3/EG 28 5–8 1.246–1.404 1.276–1.445 2.9% Eastman et al. [26] CuO/EG 35 1–4 1.050–1.227 1.061–1.245 3.7% Lee et al. [27] CuO/EG 24 1–4 1.060–1.242 1.047–1.212 3.2% Xuan and Li [28] Cu/Water 100 1–5 1.078–1.434 1.090–1.459 3.1% Li et al. [29] Cu/Water 20 1–3 1.120–1.289 1.096–1.293 2.1% Eastman et al. [26] Cu/EG 10 0.33–0.55 1.041–1.101 1.061–1.122 2% Hwang et al. [30] CuO/Water 33 1 1.05 1.08 2.9% Zhang et al. [31] SiO2/Water 7 0.5–3 1.016–1.084 1.014–1.088 0.4% Hwang et al. [30] SiO2/Water 12 1 1.03 1.03 0

Thermal conductivities of nanofluids with various nanoparticles have been calculated through MD simulations. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, *Κ*, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, *E*, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of

Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Number density distribution, radial distribution function (RDF), coordination number, and potentials of mean force (PMF) are used to analyze the microscopic structure characteristics of nanofluids. Through MD simulation, the average translational and rotational velocities of nanoparticles are obtained.

Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The parameters in

**Thermal conductivity ratio (***k***nf/***kf* **)**

53 1–4 1.069–1.159 1.048–1.195 3.1%

**Present model Maximum error**

The authors have received funds from the National Natural Science Foundation of China (Grant Nos. 51506038, 51606052), Shandong Provincial Natural Science Foundation, China (Grant No. ZR2015EQ003), and China Postdoctoral Science Foundation (Grant Nos. 2016T90284, 2015M571411). We acknowledge the reviewers' comments and suggestions very much. Many thanks to INTECH Publishing and the editor and staffs who helped us publish this chapter.

## **Nomenclature**

## **Author details**

Wenzheng Cui1\*, Zhaojie Shen1 , Jianguo Yang1 and Shaohua Wu2

\*Address all correspondence to<EMAIL_ADDRESS>

## **References**

[10] Huminic G, Huminic A. The cooling performances evaluation of nanofluids in a compact heat exchanger. In: SAE Technical Paper; 2012. Paper number: 2012‐01‐1045. DOI: 10.4271/2012‐01‐1045

**Author details**

**References**

5978

2004.07.012

10.4271/2007‐01‐2141

01‐2141. DOI: 10.4271/2007‐01‐2141

Wenzheng Cui1\*, Zhaojie Shen1

136 Nanofluid Heat and Mass Transfer in Engineering Problems

, Jianguo Yang1

1 School of Automotive Engineering, Harbin Institute of Technology, Weihai, China

2 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China

[1] Lee S, Choi SUS, Li S, Eastman JA. Measuring thermal conductivity of fluids containing oxide nanoparticles. Journal of Heat Transfer. 1999;121(2):280–289. DOI: 10.1115/1.282

[2] Wen DS, Ding YL. Experimental investigation into convective heat transfer of nano‐ fluids at the entrance region under laminar flow conditions. International Journal of Heat and Mass Transfer. 2004;47(24):5181–‐5188. DOI: 10.1016/j.ijheatmasstransfer.

[3] Hussein AM, Sharma KV, Bakar RA, Kadirgama K. A review of forced convection heat transfer enhancement and hydrodynamic characteristics of a nanofluid. Renewable and

[4] Sundar LS, Singh MK. Convective heat transfer and friction factor correlations of nanofluid in a tube and with inserts: A review. Renewable and Sustainable Energy

[5] Wambsganss MW. Thermal management concepts for higher‐efficiency heavy vehicles. In: SAE Technical Paper ; 1999. Paper number: 1999‐01‐2240. DOI: 10.4271/1999‐01‐2240

[6] Choi SUS. Nanofluids for improved efficiency in cooling systems. In: Heavy Vehicle

[7] Saripella SK, Yu W, Routbort JL, France DM. Effects of nanofluid coolant in a class 8 truck engine. In: SAE Technical Paper; 2007. Paper number: 2007‐01‐2141. DOI:

[8] Lockwood FE, Zhang ZG, Forbus TR, Choi SUS, Yang Y, Grulke EA. The current development of nanofluid research. In: SAE Technical Paper; 2005. Paper number: 2007‐

[9] Wallner E, Sarma DHR, Myers B, Shah S, Ihms D, Chengalva S, Parker R, Eesley G, Dykstra C. Nanotechnology application in future automobiles. In: SAE Technical Paper;

2010. Paper number: 2010‐01‐1149. DOI: 10.4271/2010‐01‐1149

Sustainable Energy Reviews. 2014;29:734–743. DOI: 10.1016/j.rser.2013.08.014

Reviews. 2013;20:23–35. DOI: 10.1016/j.rser.2012.11.041

Systems Review; April 18–20,2006; Chicago. 2006.

\*Address all correspondence to<EMAIL_ADDRESS>
and Shaohua Wu2


**2. Applications of REE**

Rare earth elements are applied in many different fields (**Figure 2**). They are used in mature industries such as catalysts, glass production, and metallurgy. However, they play an increasingly important role in the applications of high technology, which include batteries, ceramics, and permanent magnets. In the case of mature markets, elements such as lanthanum and cerium are most often used, in contrast to high-technologies, where dysprosium, neodymium, and praseodymium are used [7]. Permanent magnets production is the largest and most important end use of REE, accounting for over 29% of the total consumption of these elements in 2020 [5]. The main uses of rare earth elements are presented in **Table 1**. Above mentioned applications, there are still areas that use rare earth elements in new ways, such as nanotechnology, which can be employed e.g. in medicine. This chapter focuses primarily on the advanced applications of these elements in nanotechnologies as well as in the most important application in the world today, which are permanent magnets.

#### **Table 1.**

*Applications of REEs (after Refs. [7–10]).*


We are IntechOpen, the world's leading publisher of Open Access books Built by scientists, for scientists

4,200+ 116,000+ 125M+

Open access books available International authors and editors Downloads

Our authors are among the

151 Top 1% 12.2%

Countries delivered to most cited scientists Contributors from top 500 universities

Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI)

### Interested in publishing with us? Contact<EMAIL_ADDRESS>
Numbers displayed above are based on latest data collected. For more information visit www.intechopen.com

## Meet the editor

Dr. Nar Singh Chauhan is currently a teaching faculty in the Department of Biochemistry, Maharishi Dayanand University, Rohtak, India. His doctor of philosophy degree, with thesis research on "Arsenic detoxification mechanisms in unculturable bacteria using function metagenomics" at the CSIR-Institute of Genomics and Integrative Biology, was granted by Savitribai Phule Pune University, Pune, India. His current research focus is

on the metagenomic characterization of diverse microbiome for their native community structure, physiological functions, survival strategies under abiotic stress, colonization factors, and host-microbial interactions. In this direction, he has established an association of the human microbiome with the onset of celiac disease and chronic obstructive pulmonary disease. Dr. Chauhan is the author of a number of peer-reviewed research publications in reputed international journals (*Genome Biology & Evolution*, *Scientific Reports*, *Frontiers in Microbiology*, etc.) and has also been awarded many research patents.

Contents

**Section 1**

**Section 2**

**Section 3**

*by Nar Singh Chauhan*

**Preface III**

Introduction **1**

**Chapter 1 3**

Gut Microbes **7**

**Chapter 2 9**

Microbes in Health and Diseases **35**

**Chapter 3 37**

**Chapter 4 55**

Introductory Chapter: Human and Microbes in Health and Diseases

The Therapeutic Potential of the "Yin-Yang" Garden in Our Gut

*by Shabarinath Srikumar and Séamus Fanning*

The Role of Leather Microbes in Human Health

Extra Pulmonary Tuberculosis: An Overview

*by Richard O. Oruko, John O. Odiyo and Joshua N. Edokpayi*

*by Onix J. Cantres-Fonseca, William Rodriguez-Cintrón, Francisco Del Olmo-Arroyo and Stella Baez-Corujo*

## Contents

Preface

The human body is colonized by a vast number of microorganisms that live on and in humans, which are collectively called the "human microbiome." The scientific expedition to define the human microbiome started with the efforts of Antonie van Leewenhoek to compare oral and fecal microbiota in 1680. Since then, several attempts have been made to identify the role of microbes in human health and in the onset of infectious diseases. A number of discoveries have been made to identify, classify, and characterize microbes responsible for host health and the onset of diseases, factors affecting their pathogenicity, microbial physiology during the onset of diseases, as well as the role of antibiotic resistance in survivability. Simultaneously, it was found that the number of microbes defining the human microbiome was almost tenfold higher than human cells, which collectively codes for approximately 3.3 million genes compared to ~22,000 host-encoded genes. These outcomes redefine the human body as a "supraorganism" harboring a vast collection of microbial and human cells working in close coordination for a healthy host physiology. This book is aimed at providing an overview of the role of microbes in human health and diseases, the functional role of microbes in the maintenance of human health, and various scientific discoveries made to answer questions such as "what are human commensals and pathogens?", "how can a disequilibrium in microbiome structure lead to the onset of diseases?", and "how can the human microbiome function and negotiate with pathogenic microbes to prevent diseases?" I hope this book will enrich the knowledge domain of readers and answer their questions on the human microbiome and its function in host health and diseases.

**Nar Singh Chauhan**

Rohtak, Haryana, India

Department of Biochemistry, Maharshi Dayanand University,


2.3.2. Cable Resistance

In addition to the requirement of low resistance, the load cables were also used as a shunt to determine the cell currents, which were calculated via the voltage drop at the cables. Since this drop correlates to the cable resistance, a trade off between the measurement accuracy and the influence on the current distribution arise. Therefore different cable resistances and their impacts on the current distribution were investigated by measurements with two parallel-connected cells. The cable resistances were examined by four terminal measurements using a multimeter (Keithley, DMM7510) and are given in Table 2.

**Table 2.** Investigated cable resistances.

**Figure 5.** Impacts of tab treatment, with the influences of surface cleaning and pressure (**a**,**b**) as well as the effects of air sealing (**c**) on the contact resistance at the cathode (aluminum) and anode (nickel-plated copper) tab.

In relation to the cell resistance, the cable resistances of the first type *R*Cab,Type1 range from 1.3% to 0.2% depending on the SoC and cell temperature and should have no significant influence on the current distribution. Type 3 *R*Cab,Type3 uses the complete measuring range of the measuring device, which should lead to the highest measurement accuracy. Type 2 *R*Cab,Type2 offers a trade off.

The current distribution of two parallel-connected cells with the discussed cables are presented in Figure 6. The temperature of the aluminum plates were kept constant at *T*Plate = 30 ◦C. The cells were discharged with a current load of *I* = −10.6 A, which corresponds to a C-rate of 1C. The discharge started with a SoC = 0.95 and lasted until the cell voltage reaches the voltage limit of *U*Cell < 2.8 V. After a break of 30 min, the cells were charged with a C-rate of 1C until the cell voltage reached the voltage limit of *U*Cell = 4.2 V. Thereafter the cells were relaxed for 30 min.

The current distribution is qualitatively the same for the three different cables with a shift to higher current differences within the parallel-connected cells with increasing cable resistance. However, this may also be due to the higher difference between the cable resistances. The differences are most obvious at the current rest and at the end of the discharge phase. This can be caused by the correlation of the time constant to the cell's resistance, as mathematically shown in previous work [8,9].

**Figure 6.** Influence of the cable resistances on the current distribution of two parallel-connected cells. With the current load (**a**), the cell and plate temperature (**b**) and the separated phases: Discharging (**c**), the first rest (**d**), charging (**e**) and the second rest (**f**). The relations of the cable resistances are *R*Cab,type3 ≈ 3 · *R*Cab,type2 ≈ 15 · *R*Cab,type1, with the exact values in Table 2.