2. General thermal modification systems for housed livestock and poultry

vary greatly by region of the country. For all intensive purposes, HLPP buildings are considered

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Ventilation designs for HLPP systems range from complete natural ventilation (NV) to a combined hybrid natural/mechanical ventilation (NMV), to full mechanical ventilation (MV).

The NV building design (Figure 2) features controlled sidewall curtain and ridge vent openings. The sidewall and ridge vents are either manually or automatically controlled. The NV barn design has traditionally been used in broiler, turkey, beef, pig finishing, and dairy housing.

Orientation of building relative to historical summer winds is critical, as well as the percent calm periods during warm weather. For example, Figure 3a outlines the historical August wind rose pattern for Des Moines, Iowa, USA. For this region, the predominant summer winds are from the S-SE and a properly oriented NV building would have the ridge axis E-W or slightly tilted counter-clockwise to expose the sidewall curtains to the predominant summer winds. Deviation from predominant summer winds will significantly affect the potential freshair exchange rate in the building. Figure 3b outlines the predicted fresh air exchange rate (air changes per hour; ach) for a typical pig finishing facility designed to house 1000 pigs. The design maximum hot weather rate for this type of facility is 100–120 ach. For this example, both sidewall curtains are open 1.2 m. If the building is oriented completely E-W, with a

, 360o

, 270o

will sufficiently ventilate this building at and above design criteria. An orientation that deviates from the predominant wind direction significantly reduces ventilation potential. In theory,

although certainly some low-level exchange of fresh air will still take place. The NV building design is still a staple for many animal groups, especially dairy, beef, broilers, turkeys, and

Figure 2. Naturally ventilated (a) pig finishing building with (b) close-up of modern controlled ridge vents.

) wind, a 3 m s1 and above wind speed

) will not ventilate the building at all,

) or northern (0o

thermally light, responding quickly to outside weather influences.

2.2. Ventilation design basis for the USA HLPP building system

2.2.1. The naturally ventilated HLPP system

perpendicular southern (180o

swine finishers.

a lateral (along the ridge line) wind direction (90o

In the USA, the raising of food animals in controlled climate facilities has progressed rapidly since the 1980s. It was not uncommon for livestock and poultry producers to rear food animals in outdoor lots or partially contained facilities with minimal modification to the thermal environment. This small-scale production practice has now given way, for the most part, to intensive housing systems where thousands of food production livestock and poultry are raised in tightly controlled climates, with sophisticated thermal modification techniques. The following sections outline some of the systems in use today. For a more complete historical perspective on the development of HLPP systems, see [1].

### 2.1. Structural design basis for the USA HLPP building system

The majority of HLPP buildings in the USA are composed of wood-frame construction supplemented with concrete stub walls, polyethylene or equivalent curtain sidewall openings, and light-gauge steel roofing and siding (Figure 1a, b). Waste products are handled in repositories below a slotted flooring system (pigs, beef; Figure 1b), within the flooring material (e.g., sawdust) itself (broiler, turkey, hens; Figure 1c) or outside below- or above-grade earthen, concrete, or steel containment systems (pigs, beef, dairy, hens; Figure 1d). Insulation levels

Figure 1. (a) A common Midwestern USA swine finisher with curtain sidewalls and (b) metal flat interior ceiling with concrete slatted flooring, (c) common broiler housing [2], and (d) common above-ground metal manure storage system [3].

vary greatly by region of the country. For all intensive purposes, HLPP buildings are considered thermally light, responding quickly to outside weather influences.

#### 2.2. Ventilation design basis for the USA HLPP building system

Ventilation designs for HLPP systems range from complete natural ventilation (NV) to a combined hybrid natural/mechanical ventilation (NMV), to full mechanical ventilation (MV).

#### 2.2.1. The naturally ventilated HLPP system

2. General thermal modification systems for housed livestock and poultry

In the USA, the raising of food animals in controlled climate facilities has progressed rapidly since the 1980s. It was not uncommon for livestock and poultry producers to rear food animals in outdoor lots or partially contained facilities with minimal modification to the thermal environment. This small-scale production practice has now given way, for the most part, to intensive housing systems where thousands of food production livestock and poultry are raised in tightly controlled climates, with sophisticated thermal modification techniques. The following sections outline some of the systems in use today. For a more complete historical

The majority of HLPP buildings in the USA are composed of wood-frame construction supplemented with concrete stub walls, polyethylene or equivalent curtain sidewall openings, and light-gauge steel roofing and siding (Figure 1a, b). Waste products are handled in repositories below a slotted flooring system (pigs, beef; Figure 1b), within the flooring material (e.g., sawdust) itself (broiler, turkey, hens; Figure 1c) or outside below- or above-grade earthen, concrete, or steel containment systems (pigs, beef, dairy, hens; Figure 1d). Insulation levels

Figure 1. (a) A common Midwestern USA swine finisher with curtain sidewalls and (b) metal flat interior ceiling with concrete slatted flooring, (c) common broiler housing [2], and (d) common above-ground metal manure storage system [3].

perspective on the development of HLPP systems, see [1].

2 HVAC System

2.1. Structural design basis for the USA HLPP building system

The NV building design (Figure 2) features controlled sidewall curtain and ridge vent openings. The sidewall and ridge vents are either manually or automatically controlled. The NV barn design has traditionally been used in broiler, turkey, beef, pig finishing, and dairy housing.

Orientation of building relative to historical summer winds is critical, as well as the percent calm periods during warm weather. For example, Figure 3a outlines the historical August wind rose pattern for Des Moines, Iowa, USA. For this region, the predominant summer winds are from the S-SE and a properly oriented NV building would have the ridge axis E-W or slightly tilted counter-clockwise to expose the sidewall curtains to the predominant summer winds. Deviation from predominant summer winds will significantly affect the potential freshair exchange rate in the building. Figure 3b outlines the predicted fresh air exchange rate (air changes per hour; ach) for a typical pig finishing facility designed to house 1000 pigs. The design maximum hot weather rate for this type of facility is 100–120 ach. For this example, both sidewall curtains are open 1.2 m. If the building is oriented completely E-W, with a perpendicular southern (180o ) or northern (0o , 360o ) wind, a 3 m s1 and above wind speed will sufficiently ventilate this building at and above design criteria. An orientation that deviates from the predominant wind direction significantly reduces ventilation potential. In theory, a lateral (along the ridge line) wind direction (90o , 270o ) will not ventilate the building at all, although certainly some low-level exchange of fresh air will still take place. The NV building design is still a staple for many animal groups, especially dairy, beef, broilers, turkeys, and swine finishers.

Figure 2. Naturally ventilated (a) pig finishing building with (b) close-up of modern controlled ridge vents.

Figure 3. Typical (a) wind rose pattern evaluated when planning for a naturally ventilated building and (b) the influence of improper alignment relative to predominant summer winds.

3. Space and zone heating for housed livestock and poultry

as the ultimate diffuser opening control objective.

Space and/or zone heating are integral components of modern HLPP systems. Space heating has been traditionally accomplished with unvented forced air furnaces. Heated air distribution is handled primarily with a single diffuser attached to the heater outlet, providing minimal distribution and thus uniformity. Ducting of heated air to targeted locations is traditionally not employed in the HLPP systems. Zone heating has traditionally been handled with radiant spot (Figure 5a), radiant tube, or heat lamps (Figure 5b). In some of the colder regions of the USA (e.g., Minnesota), additional microclimate enclosures with heat lamps are provided for immature animals in the coldest weather (Figure 5b). Cold climate ventilation rates are designed to control moisture, gases, and temperature, with moisture control almost always governing the cold weather ventilation rates. Many HLPP ventilation control platforms available today allow the producer to control for building temperature, with relative humidity sensing as a backup for assessing moisture control. Gases such as ammonia, carbon dioxide, hydrogen sulfide, and methane can be an issue in many HLPP systems. In most cold weather situations, and during

Figure 5. (a) Radiant spot heater with shielded radiant sensor for feedback control and (b) microclimate with heat lamps.

Figure 4. Example ceiling fresh-air diffuser commonly used in HLPP systems. The cable shown passing through the diffuser is used to automatically control baffle opening in response to ventilation rate changes with static pressure control

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#### 2.2.2. The hybrid natural/mechanical HLPP system

The NV method of HLPP building ventilation, although once the predominant method of ventilating HLPP systems, has given way in many cases to NMV or MV approaches where tighter control of the thermal environment is desired. The NMV method uses exhaust fan ventilation in cold-to-mild conditions with sidewall curtains and wind potential handling warm weather ventilation (see Figure 1a). The NMV method was developed to replace the NV action during cold weather in an attempt to better control the thermal environment. Warm and hot weather ventilation is handled with sidewall curtain opening action and wind, requiring the same basic orientation requirements of an NV building. The NMV method does not require cold weather ridge vents and therefore in the NMV method a flat interior ceiling is often used (see Figure 1b) with a heavily insulated attic space.

#### 2.2.3. The mechanically ventilated HLPP system

The vast majority of modern intensive HLPP systems use a negative pressure MV system with exhaust fans connected in parallel and fresh inlet air drawn in through ceiling diffusers (Figure 4) in cold-to-mild conditions and sidewall and/or endwall curtains (Figure 1a, b) in warm-to-hot conditions. In modern HLPP systems, the ceiling diffusers are cable controlled to stage inlet action with fan staging, using static pressure differential as feedback. Typical operating static pressures range from 10 to 30 Pascals (Poutside-Pinside). The MV system will typically incorporate at least one sidewall 'drop' curtain opening for emergency power loss events. The negative pressure MV arrangement has become a popular choice, preventing moisture and gas-laden air from exfiltrating through uncontrolled locations where condensation and building deterioration could be an issue. Specialty-designed positive pressure systems are becoming more popular, which are discussed in a later section.

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Figure 4. Example ceiling fresh-air diffuser commonly used in HLPP systems. The cable shown passing through the diffuser is used to automatically control baffle opening in response to ventilation rate changes with static pressure control as the ultimate diffuser opening control objective.


**1. Introduction**

Papaya (*Carica papaya* L) is a tall herbaceous plant native to the Americas, specifically Mexico, Central America, and tropical areas of South America regions [1, 2]. Indigenous people have known of papaya and managed its cultivation since pre-Columbus times [3]. Papaya fruit, leaves, seeds, and sap have all been utilized widely as food, food additives including papain, and packaging for cosmetics. Although there are no remnants of papaya tissue in the archaeological record, analysis of 497 indigenous plant species databases confirm that papaya was one of the food sources for Mayans [4]. Following Spanish contact with central and south America, papaya was gradually introduced to Africa, South Pacific Islands, and the rest of the world as fruit [5, 6]. Presently, it is found in tropical and sub-tropical regions around the world. Green papaya fruit, young leaves, and shoots are used in many traditional Asian dishes, including those in India, Malaysia, Indonesia, and Thailand, and are consumed either fresh or cooked [7–9].

#### *Tropical Plant Species and Technological Interventions for Improvement*

#### **Table 1.**

*Top ten papaya production countries in 2020 [10].*

#### **Table 2.**

*Top ten papaya importers in 2020 by value [10].*

In 2020, papaya was ranked the third most-produced tropical fruit crop in the world [10]. The major producing countries include India, the Dominican Republic, Brazil, Mexico, and Indonesia (**Table 1**). Papaya is also a highly traded fruit on international markets in fresh and processed form with the major importers being the USA, Germany, and Portugal (**Table 2**) [10].


3.2.3. High Level

At the highest level, we define the task management module. A task gets completely defined by a specific combination of modules. Therefore the task manager is in charge of deciding which modules of the lower layers will be executed. This module can also serve as a high-level interface with the agent (the user) or other robots, allowing to develop collaborative robotics applications, which is a relevant topic [38]. This last feature covers one of the limitations of the navigation stack described in [26].


**1. Introduction**

Axial fans typically work in very turbulent flow conditions, e.g., because of their installations in pipelines, behind radiators, etc. This results in very unstable aerodynamic forces on the impeller blades, which in turn cause excessive sound radiation. Noise from flow machines consists of tonal noise, as a result of the interactions among the turbine blades and stationary housing components or guide vanes and broadband noise resulting from the acoustic signal generated by strong turbulent structures occurring in the flow. The most modern aeroacoustic computational methods enable increasingly reliable predictions of the generated noise. They usually require specific information about the transient flow field, obtained by simulation using computational fluid dynamics methods.

The most accurate of these methods, a direct numerical simulation (DNS), could solve the Navier–Stokes equation with no simplifications and could predict the unsteady flow and the associated acoustic field. Unfortunately, DNS is not feasible for a complex geometry, such as a fan, due to the enormous computational costs. Finding a non-stationary flow field with less effort requires modelling of at least part of the of the turbulent fluctuation [1]. Two different ways are currently used to reduce computational costs. The first is time averaging, which is known as the unsteady Reynolds-averaged Navier–Stokes (URANS) simulation (URANS), the second is a spatial filtering of the full Navier–Stokes equations, called large eddy simulation (LES).

In the case of URANS, the reduction of calculation costs is enormous but the cost is a large level of approximation. All random turbulent fluctuations are modeled, so only tonal sources of rotating machine sounds can be predicted. LES solves large turbulent structures, and only small eddies are modeled, but the computational costs are still high. Since this paper contains a very large number of numerical calculations that involve long-term calculations, the authors decided to use the URANS method in the simulations

**Citation:** Romik, D.; Czajka, I. Numerical Investigation of the Sensitivity of the Acoustic Power Level to Changes in Selected Design Parameters of an Axial Fan. *Energies* **2022**, *15*, 1357. https://doi.org/ 10.3390/en15041357

Academic Editors: Marcin Kami ´nski and Davide Astolfi

Received: 30 November 2021 Accepted: 7 February 2022 Published: 14 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

carried out. The relevance of this decision is confirmed by Kissner et al. [2], who show that this way of modelling allows for satisfying accuracy to be achieved.

Solving aeroacoustics problems requires even more computational efforts. In addition to determining the sound sources, it is important to determine how the sound wave propagates. Assuming that the energy difference of the flow and the acoustic wave is sufficiently large, one can focus on the one-way coupling between the fluid flow and the acoustic signal generated. This line of reasoning led Lighthill to develop the aeroacoustic analogy named after him.

Lighthill [3–5] first made a formulation of the acoustic analogy for jet noise in 1952, which demonstrated that the flow mechanisms that were responsible for sound radiation could be expressed in terms of the quadrupole source. Curle [6] extended Lighthill's analogy to the fluid–structure interaction and implemented an extra acoustic source produced from the reaction force that was exerted on the fluids surrounding the body that did not move. FW-H [7,8] generalized Curle's analogy and extended the analogy to a moving structure. The FW-H equation splits up the aeroacoustic source into three different kinds of source: monopoles, dipoles, and quadrupoles.

The general theory of Lighthill introduced by FW-H, which takes into account the motion of a body as a potential source, has been used in many noise studies of rotating machines, i.e., turbines, fans, helicopters, etc. The FW-H analogy also takes surface sources into account, which makes it possible to determine noise from sources other than the quadrupole sources proposed by Lighthill. Schmitz and Yu [9] proved that, at a low Mach number, the volume integral makes no significant contribution to the noise generated by a hovering helicopter rotor. For a given range of speeds, the rotor is a surface of monopole and dipole sources and their contributions depend on factors, i.e., geometry, speed, and forces acting on the surface.

Brentner and Farassat [10], present a comprehensive review of the mathematical basis of the FW-H equation, comparing integral formulas and sufficiently powerful numerical methods applied to helicopter noise. They found that the contribution of the volume integral is small for subsonic flows, but gives a larger result for supersonic and transonic flows. In addition, they found that, by applying a permeable FW-H surface, instead of to the body surface, this would allow quadrupole sources to be included.

Konstantinov et al. [11] showed results of URANS, Delayed Detached Eddy Simulation (DDES), and LES flow and noise distributions in the test cabin segment. Compared to the FW-H mathematical model and the hybrid approach of solving the wave equation, including non-reflecting boundary conditions, a small influence on the sound pressure level from the imperfect boundary condition in the LES was shown.

Sundström et al. [12] used the LES method to investigate which acoustic sources predominated in low mass flux flows. They found that blade forces resulting from varying wall pressures are the main sources of generated noise at low mass flux flows. It turned out that sound sources coming from forces on blades (dipoles), were much larger than quadrupole sources, especially in subsonic flows. Comparing these two sound sources, one can see the relation *Wd* : *Wq* ∼ 1 : *M*2 can be observed. Moreover, it can be concluded that monopole sources have a greater influence on the generated noise in sonic flows.

In a study by Al-Am et al. [13], the LES approach was used to numerically calculate the influence of selected parameters on the noise generated around a flat plate. Noise generated at the trailing edge and noise of turbulent nature was investigated. The flow character and geometry were chosen to correspond to the Amiet model. It is shown that the adopted model gives very good noise calculations in agreement with the analytical model and DNS calculations. Moreover, regarding the ACAT1 fan noise test [2], RANS-based analytical methods are commonly used to predict broadband fan noise. The accuracy of the aerodynamic noise results obtained in post-processing calculations depends not only on the choice of acoustic model itself, but to a large extent on the turbulent model adopted, which has a significant influence on the nature of the flow and which in turn affects the fan broadband noise. In continuation of this work, [14] focused on the importance of acoustic

models. Twelve different models were investigated and several different solvers were used to solve them. Both models—-based on acoustic analogies and those using direct methods—were compared. The methods used are distinguished by the turbulence models, the applied boundary conditions related to the propagation of the acoustic wave and the noise from the rotor blade phenomena. It turns out that at low frequencies, the differences in the generated noises are quite large, while at higher frequencies, the sound power level is within ±3 dB. Furthermore, it is proven that by increasing the rotational speed, the generated acoustic power is similar for different models.

Biedermann et al. in their study [15], provided detailed information on the broadband noise reduction possibilities of a low pressure axial fan with serrations on the leading edges. For the area of instability under partial load conditions, it is proposed that the dominant noise reduction mechanisms are dependent on aerodynamic effects related to the serrated geometry at the leading edge, which results in a reduction of the dominant low and medium frequency noise levels. It was also shown [16] that under the same operating conditions, the sound pressure levels at the two measurement points of the radial fan increases by approximately 5.8% and 2.8%, when the ambient pressure increases from 50 kPa to 100 kPa. As the ambient pressure increases, the fan sound pressure level shows an approximate logarithmic increase trend. It is worth mentioning that researchers [17] have attempted various techniques to identify the source of the sound, i.e., isocontours of the dilatation field, which revealed sources of acoustic scale disturbance, and may be the cause of the noise, a dynamic mode decomposition for the pressure upstream and downstream of the fan blade, which shows several strong fashions around the first three blade frequencies, and finally the acoustic analogy of FW-H, which showed a difference of about 5 dB between the blade tip and the lower parts of the blade in a specific frequency range. These results are consistent with the expectations that higher flow velocities would yield higher acoustic pressures.

The effect of the blade curvature on the generated noise was also investigated [18,19], in relation to the classic Amiet formulation. It has been proven that a curved blade causes a reduction in noise; this effect is particularly noticeable at the blade tip. Significant differences were also observed regarding noise generation at the leading and trailing edges of the blade, where the former is globally dominant but takes on values close to the latter at around 3.75 kHz and higher [20,21]. Similarly, other researchers confirm this relationship that the pressure fluctuation of the radial fan was smallest when the blade outlet angle was smaller, and it was also shown that a corresponding increase in the blade outlet angle reduced the amplitude of the pressure fluctuation in the blade pass frequency and its harmonics, which is conducive to reducing rotor noise [22]. In [23], it was shown that, on the upstream side on the blade walls, the sound pressure level was higher than on the downstream side. This was due to the separation of the boundary layer at the leading edge with increasing radial velocity near the ring, resulting in a low frequency noise. The leading edge therefore turned out to be the dominant dipole source generating tonal noise in contrast to the other rotor elements. The issues of stream separation at too low of a rotational speed of the rotor are also discussed [24], where under rotating stall conditions the fluctuation of the sound pressure amplitude becomes much greater than under other conditions, and the fluctuation of the sound pressure level is greater at a low frequency under stall conditions than under normal operating conditions.

The sensitivity analysis consists of examining how a given model depends on the parameters entering into it. By testing the sensitivity, you can determine which input variables have the greatest impact on a given output, and in this way, areas that require more attention may be identified [25]. Sensitivity analysis is a concept used in various engineering fields, i.e., acoustics, material science, environmental protection, etc. [26–28].

In the above works, different types of fans have been studied, both in terms of increasing their efficiency and reducing noise emissions, which proves the relevance of the issue. Researchers are still looking for a compromise to design these machines in such a way that they achieve the greatest efficiency with the least possible noise emissions. The authors of this paper, in previous studies [29–31], also discussed the influence of design parameters on the noise generated and the efficiency of different types of fans.

The research carried out in this work focuses on the sensitivity of the generated noise to changes in the design parameters of the fan rather than on the determination of the exact value of the acoustic power level.

### **2. Numerical Simulations**

#### *2.1. Research Object*

The numerical study was carried out on an axial fan (see Figure 1) with a diameter of 220 mm and a rotational speed of 3000 revolutions per minute, installed in a pipe of a circular cross-section with a diameter of 230 mm. The rotor has six blades on a hub with a diameter of 100 mm and a length of 200 mm at an angle of 20◦. For such an assumed rotational speed, the fan achieves a volume flow rate of 750 m3·h−1.

**Figure 1.** Geometrical model of the fan: (**a**) rotor, (**b**) pipeline.

The numerical model was a computational grid consisting of 2,120,050 cells, in which the Navier–Stokes equations were solved using the finite volume method. The main fan parameters are shown in Table 1.

In the model, two characteristic zones can be identified: a rotating zone in which the rotor is located and a stationary zone representing a curved pipeline. The geometric model does not include the fan fixing elements inside the pipeline. To simplify the model, steering systems and airflow straightening elements were also omitted. Marked in Figure 2, distances *Lp* and *Lt* are equal to each other and are 70.16 mm. Inlet straight sections *Li* and outlet *Lo* of the pipeline are equal and amount to 100 mm.

**Figure 2.** Diagram of built-in fan.

The geometric model of the rotor was made in SolidWorks and then adapted to the ICEM CFD environment. The ICEM CFD program was used to create a orthogonal computational mesh based on a multi-block structure grids topology. In the areas close to the leading and trailing edges, vortices were created due to the separation of the boundary layer, which is why the computational grid was appropriately compacted at these points in order to solve the flow with greater precision. In addition, a wall layer was modeled to provide a parameter *y*+ < 5. In order to analyze the validity of the model performed, an analysis of the independence of the results from the calculation grid was performed. The mesh used (see Figure 3) allowed for results of sufficient accuracy to be obtained in a reasonable time.

**Figure 3.** Fan computational grid: (**a**) rotor mesh, (**b**) pipeline mesh.

#### *2.2. Mathematical Model*

The first and very important step in the simulation is to locate the sources that can be used to calculate the generated noise. This can be achieved using appropriate computational fluid dynamics techniques. In this field, the basic equations are based on the Navier–Stokes equations derived from the conservation of mass (continuity equation), conservation of momentum, and conservation of energy.

The continuity equation can be written for fluids as:

$$
\rho \frac{\partial u\_i}{\partial x\_i} = 0 \tag{1}
$$

where *ρ* is the density of the fluid, *t* is the time, components of the velocity vector **u** in the coordinate system, and *xi* coordinates in the Cartesian system. The momentum equation is written as:

$$
\rho \frac{\partial u\_i}{\partial t} + \rho \frac{\partial u\_i u\_j}{\partial x\_j} = -\frac{\partial p}{\partial x\_i} + \frac{\partial v\_{ij}}{\partial x\_j} \tag{2}
$$

where *p* is the pressure and *τij* are viscous stresses. Equation (2) is derived by applying Newton's second law of dynamics, which relates the forces acting on a fluid volume to its acceleration.

Turbulence model *k* − *ω* is one of the most popular models, which shows the phenomena of turbulent flow. It belongs to a family of models in which all turbulence effects are modeled. This is a two equation model. This means that the transport equations are solved to include phenomena, such as convection and turbulent energy dissipation. The variables considered in the equation are the turbulent kinetic energy *k*, representing the turbulence energy, and the specific turbulent dispersion coefficient *omega*, denoting the dispersion rate of the turbulence kinetic energy. Variable *ω* is also known as the turbulence scale. The standard *k* − *omega* model works well for low Reynolds number flows where the boundary layer is appropriately sized and the viscous sublayer is well separated. Thus, the standard model *k* − *ω* is best suited for modelling the boundary layer. Other advantages include excellent performance in complex near-wall flows with adverse pressure gradients and separation, e.g., in rotating machinery. The model also predicts excessive and early vortex separations.

Model *k* − *ω* SST is a model that offers the strengths of the *k* − proposed by Launder and Spalding [32] and model *k* − *ω* proposed by Wilcox [33], and provides an additional component to limit the overproduction of turbulent kinetic energy in areas of high pressure gradients (stagnation points, areas of separation vortex near wall layer). Menter [34] examining models *k* − and *k* − *ω*, and observed that the first handles turbulence well in free and shear layers and shows negligible sensitivity to inlet boundary conditions for quantities describing turbulent flow. This is a desirable quality because these quantities are often not exactly known in practical calculations. However, the *k* − *ω* model better models turbulent flow in the boundary layer but is more sensitive in free flow.

The sound pressure level (SPL) was determined using the FW-H analogy. This model is based on the Lighthill analogy and allows noise to be determined by equivalent acoustic sources. Ansys Fluent uses these equations to determine the sound pressure at a given distance from a sound source by an integral over the surface containing those sources. The FW-H equation is a non-homogeneous wave equation [7,35], which can be derived by combining the continuity and Navier–Stokes equations. It can be written as

$$\begin{split} \frac{1}{\alpha\_0^2} \frac{\partial^2 p'}{\partial t^2} - \nabla^2 p' &= \frac{\partial^2}{\partial x\_i \partial x\_j} \{ T\_{ij} H(f) \} \\ &- \frac{\partial}{\partial x\_i} \left\{ \left| P\_{ij} n\_j + \rho u\_i (u\_n - v\_n) \right| \delta(f) \right\} \\ &+ \frac{\partial}{\partial t} \left\{ \left| \rho\_0 v\_n + \rho (u\_n - v\_n) \right| \delta(f) \right\} \end{split} \tag{3}$$

where *ui*—air velocity in the direction of *xi*, *vi*—surface velocity in the direction of *xi*, *un*—air velocity normal to the surface *f* = 0, *δ*(*f*)—Dirac delta, *vn*—velocity of the surface normal to the surface, *H*(*f*)—Heaviside function, *p* —sound pressure in the far field (*p* − *p*0), *ni*—normal vector pointing to the external area (*f* > 0), *a*0—speed of sound in the far field, *Pij*—compressive stress tensor, *Tij*—Lighthill stress tensor.

To solve Equation (3), the Green's function must be used to the open area. The complete solution involves the calculation of surface and volume integrals, the first representing monopole, dipole, and partially quadrupole acoustic sources, and the second representing quadrupole sources in the area outside of the source surface. The volume integral becomes negligible when the Mach number value of the flow is small and the source area covers the source area. In Ansys Fluent, choosing a source on a solid surface-like rotor, the volume integrals are neglected, then the equation takes the following form:

$$p' = (\vec{\mathbf{x}}, t) = p'\_T(\vec{\mathbf{x}}, t) p'\_L(\vec{\mathbf{x}}, t) \tag{4}$$

$$\begin{split} 4\pi p\_T'(\vec{x}, t) &= \int\_{f=0} \left[ \frac{\rho\_0 (\mathcal{U}\_n + \mathcal{U}\_h)}{r (1 - M\_r)^2} \right] dS \\ &+ \int\_{f=0} \left[ \frac{\rho\_0 \mathcal{U}\_h \left\{ r \mathcal{M}\_r + a\_0 \left( \mathcal{M}\_r - \mathcal{M}^2 \right) \right\}}{r^2 (1 - M\_r)^3} \right] dS \end{split} \tag{5}$$

$$\begin{split} 4\pi p\_L'(\vec{x}, t) &= \frac{1}{a\_0} \int\_{f=0} \left[ \frac{L\_r}{r (1 - M\_r)^2} \right] dS \\ &+ \int\_{f=0} \left[ \frac{L\_r - L\_M}{r^2 (1 - M\_r)^2} \right] dS \\ &+ \frac{1}{a\_0} \int\_{f=0} \left[ \frac{L\_r \left\{ r \mathcal{M}\_r + a\_0 (M\_r - M^2) \right\}}{r^2 (1 - M\_r)^3} \right] dS \end{split} \tag{6}$$

where

$$\mathcal{U}\_i = v\_i + \frac{\rho}{\rho\_0} (u\_i - v\_i) \tag{7}$$

$$L\_i = P\_{i\bar{j}}\hat{n}\_{\bar{j}} + \rho u\_i (u\_{\bar{n}} - v\_{\bar{n}}) \tag{8}$$

The contribution of quadrupole terms (volume integrals) in the FW-H analogy is proportional to the square of the Mach number (*M*2). In the analyzed system, the Mach number reaches values below 0.1, which means that the volume integrals can be omitted. Considering the time *t* and a distance to the observer *r*, the integral equation takes into account the delay due to the distance from the source to the receiver, according to the following formula:

$$
\pi = t - \frac{r}{a\_0} \tag{9}
$$

$$\begin{array}{ll} M\_r = M\_i r\_i & \dot{M}\_r = \frac{\partial M\_i}{\partial \tau} r\_i \\ Q\_{\text{il}} = Q\_i \dot{\boldsymbol{n}}\_i & \dot{Q}\_{\text{\textell}} = \frac{\partial Q\_i}{\partial \tau} \dot{\boldsymbol{n}}\_i & Q\_{\text{\textell}} = Q\_i \frac{\partial \boldsymbol{n}\_i}{\partial \tau} \\ L\_i = L\_i \dot{\boldsymbol{n}}\_i & L\_r = \frac{\partial L\_i}{\partial \tau} \dot{\boldsymbol{r}}\_i & L\_r = L\_i \boldsymbol{\mathfrak{f}}\_i & L\_M = L\_i M\_i \end{array} \tag{10}$$

where *n*,*r*—unit vectors of radiation and normal to the wall, *M*—Mach number of the surface source velocity component along the direction of the radiation vector.


**2.3 Data analysis**

Data cleaning was conducted using R. JASP was used to examine demographic information and descriptives of the sample. It was also used to conduct Chi-Square tests regarding early adopter status, perceived safety of AVs, and delayed licensing for teenage drivers. The Chi-Square statistic is used to analyze whether there is a statistically significant difference between the expected frequencies and the observed frequencies in one or more categories between groups in a contingency table. If significant, this test suggests that the distributions across categories and between groups are truly and significantly different [32].


**3. Materials and Methods**

Figure 1 shows the general workflow of our study. First, we collected web map data, including geometrical information and Point of Interests (POI) in Nanjing, which were downloaded with the open source AutoNavi API. We filtered the collected data into a geometrical dataset and additional social information (distribution of related infrastructure) based on functionality with the ArcGIS software. We exported images for the case slices in terms of plots.

Second, the morphological features were automatically extracted through a deep convolutional neural network with inception-v3 modules into high dimensional feature vectors (HDFV). Third, the cluster analysis was visualized based on the t-SNE algorithm in a two-dimensional plane. The Euclidean distance was applied to calculate the similarity between the cases. Finally, the performance of the HDFV on the similarity analysis was verified by a comparison case retrieval study.

**Figure 1.** The general workflow of quantitative urban morphological analysis for case-based design via deep learning.


**3. Conclusions**

A charge pump circuit system was presented for energy harvesting based on a flexibletype thermoelectric generator with high-output impedance. Even though the charge pump was operated with a highly resistive TEG, the input voltage could be controlled at a voltage higher than *VDDMIN* by modulating the input impedance of the CP using two-phase operation with low- and high-input impedance modes. The average input impedance could be matched with the output impedance of TEG. The design methodology was proposed to determine the *N* and *C* of the main charge pump when *VOC*, *RTEG*, *VPP*, *f*, and *VDD* MIN were given. The system was fabricated in 65 nm CMOS to demonstrate the functionality of the system with the TEG. Using an equivalent circuit for the TEG, the system was validated with a *VOC* of 0.8 V and an *RTEG* of 400 Ω. *VPP* regulation was successfully observed. The circuit system was also measured with a flexible-type TEG and a thermal source. The system converted thermal energy into power to 30 µW at 2.5 V. By adding a full-bridge rectifier between the energy transducer and the proposed converter, the control circuit would be able to work even with other energy transducers such as piezoelectric or electrostatic vibration energy transducers with an AC equivalent voltage source and high-output impedance.

**Author Contributions:** Conceptualization, T.T.; methodology, K.K. and T.T.; software, K.K.; validation, K.K. and T.T.; formal analysis, K.K. and T.T.; investigation, K.K. and T.T.; writing—original draft preparation, K.K.; writing—review and editing, T.T.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by Zeon Corp. and Micron Foundation.

**Acknowledgments:** This work was supported by Zeon Corp., VDEC, Synopsys, Inc., Cadence Design Systems, Inc. Rohm Corp., and Micron Foundation. The authors wish to thank M. Futagawa and H. Hirano and S. Ota for technical discussions.

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


suitable for dynamic systems, which is well known among the control community as it offers excellent performance qualities for designing regulation algorithms. It makes the tool an ideal candidate for process and control engineers, working with the same software package. The environment provided by Matlab software facilitates a general overview of all computations step by step, that can be used for a wide range of applications including simulation, data acquisition, data analysis, and visualization as well as scientific and engineering graphics.

For the dynamic modeling purpose, Simulink provides a graphical user interface (GUI) for building models as block diagrams, using click-and-drag mouse operations. Because Matlab and Simulink are integrated, one can simulate, analyze, and revise models in either environment at any point. Application of Matlab language code and its integration with the Simulink environment, in order to create the desired models of components considered in the analysis of energy systems, depend on the equations used, the methodology applied, and the input/

AMESim is an advanced modeling environment for performing simulations of engineering

• performance evaluation of intelligent systems from the early stage of development until

• the optimization of interactions between subsystems of a multi-mechanism system like

• overall product quality and performance improvement through exploration of critical

• enhancing the product development process using novel design features and applying

The software has the typical feature of an icon-based program and engineering systems can be built by adding symbols or icons to a drawing area. AMESim therefore enables the users to build complex multi-domain system models through an interactive graphical interface. The resulting sketch is easy to understand and offers a logical representation of the system model under investigation. When the sketch is complete, simulation of the system proceeds in the

• Graphical representation of the components is associated with the mathematical models.

• Graphical representation of the results is prepared and post-processing is performed to

electric/electronic, mechanical, hydraulic, pneumatic, and thermal systems;

output parameters, which will be discussed in the next sections [18].

systems. The main features and applications of this tool are:

on-site application of the system;

118 Organic Rankine Cycle Technology for Heat Recovery

functions and design update;

• The component parameters are defined.

customize the results' visualization.

proper algorithms.

following stages:

• The system is simulated.

*3.5.2. AMESim*

Modelica language is designed basically for the modeling of multi-component systems to provide an environment in which a virtual system can be built, similar to what is implemented by engineers in real system development. As an example, standard components like compressor, turbine, and heat exchanger can be selected based on the manufacturers' catalogs with related technical interfaces and specifications [27]. For this purpose, some manufacturer's catalogs are proposed to be used with Modelica standard libraries. If a particular subsystem or component doesn't exist in the software library, it can be built by the user based on software standardized interfaces.

Mathematical component models are generally described by differential, algebraic, and discrete equations in Modelica. Hence, the solution method of the describing equations doesn't need to be specified by the users. The software decides the algorithm, the computational methodology, or strategy to be used to solve the equations of a particular system and its components.

In [28], the results of development of a Modelica library for CO2 refrigeration systems based on the free Modelica library ThermoFluid are presented. The library development is carried out in an Airbus Deutschland research project and is focused on obtaining a library for detailed numerical investigation of refrigeration systems with carbon dioxide (CO2 ) as the refrigerant. Using the developed component library a CO2 refrigeration cycle has been analyzed (**Figure 5**). The developed model can be used for optimization of specific heat exchangers and evaluation of optimum system configuration as well as for optimization of system control layout.

#### *3.5.4. Customized tools for simulation of energy systems*

The literature survey concerning non-commercial models of inter-connectable components of energy system networks resulted in very few examples, which has led to the conclusion that this field still lacks a complete and exhaustive review work. The models proposed are mainly developed for specific applications and often are limited concerning scalability, flexibility, or generality. Examples of custom libraries of components for dynamic modeling and analysis of complex energy systems can be found in [29–31].

In [29, 30] a modular code for dynamic simulation of a single-shaft gas turbine is presented. The code, called CAMEL, is a modular object-oriented process simulator, for energy

significant modeling techniques are applied in order to generate components applicable to

Dynamic Modeling of ORC Power Plants http://dx.doi.org/10.5772/intechopen.78390 121

Dynamic models are necessary for the better understanding of the ORC plants' behavior during transient operation, such as start-up, shutdown, and during rapid load changes. The estimation of plant operating parameters during transient operation is crucial for monitoring and control of the plant so that the system state variables do not exceed the pre-defined operating ranges. Using dynamic models enables plant operators to predict changes in power output as a function of the plant's boundary conditions such as temperature of the heat source and ambient conditions, so that they can respond to the expected heat and power demand

Key elements of an ORC dynamic model are firstly the proper transient configuration of the component's sub-models and secondly the assembly of individual sub-models to build the whole system model. Thermal and mass inertia of the components (component body and

Arrangement of the input/output variables and the system constraints play an important role in receiving reliable results. The fixed input state variables and constraints of the model have

For the dynamic modeling of an ORC system the initial conditions have to be defined and applied appropriately to the model for simulation initialization. All system variables have to be pre-defined as an array at the starting time based on the assumed initial state conditions. Obviously, the initial conditions have to be set up based on the purpose of the dynamic analysis: start-up, shut-down, system response to an input, or any other operating modes. For many cases, the initial conditions can be generated as a result of a steady-state simulation

For transient modeling of ORC plants, one can either use suitable commercial software or open source codes for formulation and parameter selection flexibility. Some ready-made data libraries for system components as well as material properties have been developed and cus-

In sum, the following general steps are defined and proposed based on ORC power plants'

**1.** specifying the modeling assumptions for every single component as well as the whole

**2.** validation of the component models, to guarantee validity and accuracy for both steady-

containing fluid) are two decisive factors for the system's response time.

to be coincident with the real plant fixed and controlled variables.

with the initial operating condition setup of the system.

dynamic modeling framework:

state and transient conditions;

tomized for the transient modeling of thermal power systems.

system according to the purpose of the analysis and application;

**3.** parameterizing the ORC components and the boundary sources and sinks;

any kind of micro-turbine-based energy conversion system.

**4. Conclusion**

accordingly.

**Figure 5.** Modelica object diagram of a CO2 cooling cycle [28].

conversion system (with specific reference to micro-turbines), and the modeling method is a black-box approach. Each component of the system under analysis can be assembled in an engineering process scheme where the connections between two elements represent either mechanical power coupling or a working fluid stream. The code is devoted to energy system design and is based on C++. For each separate component, a proper set of first-order (linear/ non-linear) algebraic or differential equations are cast and all the block equations pertaining to the different blocks are assembled together and solved in matrix form, once assigned proper boundary conditions and parameters. Equations are integrated in time according to a fourth-order Runge-Kutta method. The code is used for both steady-state off-design and dynamic operating conditions.

In another original code [31] called TRANSEO, transient and dynamic simulation of microgas turbine-based systems is presented. The code is a model-based software developed in the Simulink platform The library features some advanced components such as heat exchangers, which are modeled according to finite volume approach. Many of the blocks have been validated with reference to a commercial recuperated micro-turbine. From the models realized, some advance-cycle configurations have been proposed and analyzed: for example, externally fired micro-turbine cycles, closed Brayton cycles for space applications, and hybrid micro-turbine fuel cell systems. The library indeed provides some advanced components, and significant modeling techniques are applied in order to generate components applicable to any kind of micro-turbine-based energy conversion system.

## **4. Conclusion**

conversion system (with specific reference to micro-turbines), and the modeling method is a black-box approach. Each component of the system under analysis can be assembled in an engineering process scheme where the connections between two elements represent either mechanical power coupling or a working fluid stream. The code is devoted to energy system design and is based on C++. For each separate component, a proper set of first-order (linear/ non-linear) algebraic or differential equations are cast and all the block equations pertaining to the different blocks are assembled together and solved in matrix form, once assigned proper boundary conditions and parameters. Equations are integrated in time according to a fourth-order Runge-Kutta method. The code is used for both steady-state off-design and

cooling cycle [28].

In another original code [31] called TRANSEO, transient and dynamic simulation of microgas turbine-based systems is presented. The code is a model-based software developed in the Simulink platform The library features some advanced components such as heat exchangers, which are modeled according to finite volume approach. Many of the blocks have been validated with reference to a commercial recuperated micro-turbine. From the models realized, some advance-cycle configurations have been proposed and analyzed: for example, externally fired micro-turbine cycles, closed Brayton cycles for space applications, and hybrid micro-turbine fuel cell systems. The library indeed provides some advanced components, and

dynamic operating conditions.

**Figure 5.** Modelica object diagram of a CO2

120 Organic Rankine Cycle Technology for Heat Recovery

Dynamic models are necessary for the better understanding of the ORC plants' behavior during transient operation, such as start-up, shutdown, and during rapid load changes. The estimation of plant operating parameters during transient operation is crucial for monitoring and control of the plant so that the system state variables do not exceed the pre-defined operating ranges. Using dynamic models enables plant operators to predict changes in power output as a function of the plant's boundary conditions such as temperature of the heat source and ambient conditions, so that they can respond to the expected heat and power demand accordingly.

Key elements of an ORC dynamic model are firstly the proper transient configuration of the component's sub-models and secondly the assembly of individual sub-models to build the whole system model. Thermal and mass inertia of the components (component body and containing fluid) are two decisive factors for the system's response time.

Arrangement of the input/output variables and the system constraints play an important role in receiving reliable results. The fixed input state variables and constraints of the model have to be coincident with the real plant fixed and controlled variables.

For the dynamic modeling of an ORC system the initial conditions have to be defined and applied appropriately to the model for simulation initialization. All system variables have to be pre-defined as an array at the starting time based on the assumed initial state conditions. Obviously, the initial conditions have to be set up based on the purpose of the dynamic analysis: start-up, shut-down, system response to an input, or any other operating modes. For many cases, the initial conditions can be generated as a result of a steady-state simulation with the initial operating condition setup of the system.

For transient modeling of ORC plants, one can either use suitable commercial software or open source codes for formulation and parameter selection flexibility. Some ready-made data libraries for system components as well as material properties have been developed and customized for the transient modeling of thermal power systems.

In sum, the following general steps are defined and proposed based on ORC power plants' dynamic modeling framework:

**4.** defining the operating ranges and limitations for all components and introducing them to individual component models;

[5] Twomey BL. Dynamic simulation and experimental validation of an organic Rankine cycle model [PhD thesis]. University of Queensland, School of Mechanical and Mining

Dynamic Modeling of ORC Power Plants http://dx.doi.org/10.5772/intechopen.78390 123

[6] Sohel MI, Krumdieck S, Sellier M, Brackney LJ. Dynamic modeling and simulation of an organic Rankine cycle unit of a geothermal power plant. In: Proceedings World

[7] Colonna Paliano P, van Putten H. Dynamic modeling of steam power cycles. Part I— Modeling paradigm and validation. Applied Thermal Engineering. 2007;**27**(2-3):467-480

[8] van Putten H, Colonna Paliano P. Dynamic modeling of steam power cycles: Part II—Simulation of a small simple Rankine cycle system. Applied Thermal Engineering.

[9] Wei D, Lu X, Lu Z, Gu J. Dynamic modeling and simulation of an organic Rankine cycle (ORC) system for waste heat recovery. Applied Thermal Engineering. 2008;**28**:1216-1224

[10] Casella F, Mathijssen T, Colonna di Paliano P, Buijtenen J. Dynamic modeling of organic Rankine cycle power systems. Journal of Engineering for Gas Turbines and Power.

[11] Triogen. Triogen High-speed Turbo Generator [Online]. 2013. Available from: http:// www.triogen.nl/technology/triogen-high-speed-turbo-generator [Accessed: July 24, 2013]

[12] Desideri A, Gusev MV, Lecompte S, Lemort V, Quoilin S.Experimental study and dynamic modeling of a WHR ORC power system with screw expander. In: 2nd International

[13] Ziviani D, Beyene A, Venturini M. Advances and challenges in ORC systems modeling

[14] Grelet V, Lemort V, Nadri M, Dufour P, Reiche T.Waste heat recovery Rankine cycle based system modeling for heavy duty trucks fuel saving assessment. In: 3rd International

[15] Grelet V, Reiche T, Lemort V, Nadri M, Dufour P. Transient performance evaluation of waste heat recovery Rankine cycle based system for heavy duty trucks. Applied Energy

[16] Marchionni M, Bianchi G, Kontakiotis AK, Pesiridis A, Tassou SA. Dynamic modeling and optimization of an ORC unit equipped with plate heat exchangers and turboma-

[17] Xu B, Rathod D, Kulkarni S, Yebi A, Filipi Z, Onori S, Hoffman M. Transient dynamic modeling and validation of an organic Rankine cycle waste heat recovery system for

heavy duty diesel engine applications. Applied Energy Journal. 2017;**205**:260-279 [18] Vaja I. Definition of an object oriented library for the dynamic simulation of advanced energy systems [PhD thesis]. Department of Industrial Engineering, University of Parma

Seminar on ORC Power Systems; Rotterdam, Netherlands; October 2013

for low grade thermal energy recovery. Applied Energy. 2014;**121**:79-95

Seminar on ORC Power Systems; Brussels, Belgium; October 12-14, 2015

chines. Journal of Energy Procedia. 2017;**129**:224-231

Geothermal Congress 2010; Bali, Indonesia; April 25-29, 2010

Engineering; 2015

2007;**27**(14-15):2566-2582

2013;**135**:042310-1-12

Journal. 2016;**165**:878-892

Studies; 2009

## **Author details**

Mohsen Assadi1 \* and Yaser Mollaei Barzi2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Energy and Petroleum Engineering, University of Stavanger, Stavanger, Norway

2 Department of Mechanical Engineering, Islamic Azad University, Kaeshan Branch, Kashan, Iran

## **References**

[5] Twomey BL. Dynamic simulation and experimental validation of an organic Rankine cycle model [PhD thesis]. University of Queensland, School of Mechanical and Mining Engineering; 2015

**4.** defining the operating ranges and limitations for all components and introducing them to

**5.** defining appropriate known and unknown (input/output) variables for the system, based on the design and analysis requirements and strategies. This may lead to omitting unnecessary fixed variables of the individual component models or adding new fixed common

**7.** compiling, executing and diagnosis of the system model, first for steady-state conditions;

1 Department of Energy and Petroleum Engineering, University of Stavanger, Stavanger,

[1] William J. Baumol, Economic Dynamics: An Introduction, 3rd edition, London: Collier-

[2] North GR. Lessons from energy balance models, In: Schlesinger ME. (Eds.), Physically-Based Modeling and Simulation of Climate and Climatic Change, NATO ASI Series (Series C: Mathematical and Physical Sciences), Vol. 243. Springer, Dordrecht, pp. 627-651,

[3] Mollayi Barzi Y, Ghassemi M, Hamedi MH. A 2D transient numerical model combining heat/mass transport effects in a tubular solid oxide fuel cell. Journal of Power Sources.

[4] Franco A, Villani M. Optimal design of binary cycle power plants for water-dominated,

medium-temperature geothermal fields. Geothermics. 2009;**38**(4):379-391

2 Department of Mechanical Engineering, Islamic Azad University, Kaeshan Branch,

**6.** interconnecting the ORC components properly to build the whole system model;

**10.** customizing the dynamic model for a particular purpose or application.

individual component models;

122 Organic Rankine Cycle Technology for Heat Recovery

**8.** initial condition definition and data preparation;

**9.** running the transient model, diagnosis and validation;

\* and Yaser Mollaei Barzi2

\*Address all correspondence to<EMAIL_ADDRESS>
Macmillan. pp. 160, 1970. ISBN: 0-02-306660-1

1988. ISBN 90-277-2789-9

2009;**192**:200-207

state variables;

**Author details**

Mohsen Assadi1

Norway

Kashan, Iran

**References**

[19] Bestrin R, Vermeulen AG. Mathematical modeling and analysis of vapour compression system. International Report. Eindhoven, Netherlands: University of Technology; August 2003

**Section 3**

**Applications**

**Section 3**


*2.2. Details of Specimen*

The steel fiber was hook-end shaped with a length of 29.8 mm, a diameter of 0.5 mm and a tensile strength *f* sf of 1150 MPa.

A total of sixty dog-bone shape specimens were used in the study. Three series with fifteen group of pullout test were designed; each group had four specimens—refer to the pullout specimen design in the specification of China code CECS 13 [34]. Figure 1a presents the geometric details of specimen for Series IA with two pairs of inclined steel fibers. The specimens, marked as IA0, IA1, IA2, IA3 and IA4, respectively corresponded to the inclination angle of 0◦, 15◦, 30◦, 45◦ and 60◦. Figure 1b presents the geometric details of specimen for Series HIA with two pairs of steel fibers. One pair of steel fiber was arranged with inclination angle of 0◦, 15◦, 30◦, 45◦ and 60◦, respectively, while other pair aligned horizontally along the pullout direction. The specimens were respectively marked as HIA0, HIA1, HIA2, HIA3 and HIA4. Figure 1c presents the geometric details of specimen for Series NA with different number of aligned steel fibers. The area of 16 mm × 16 mm in the center of the cross section is designed as the area for fiber placement. The fiber number are designed as 1, 2, 9, 16 and 25, corresponding to the fiber center-to-center spacing *L*sf of infinite, 21.2 mm, 7.5 mm, 5 mm and 3.5 mm. The specimens are marked as NA0, NA1,

NA2, NA3 and NA4, successively. Meanwhile, the specimens of Series IA and Series HIA with inclination angle of 0◦ were used as the ones with 4 aligned steel fibers in spacing of 15 mm.

**Figure 1.** The geometric details of specimen for the pullout test. (**a**) Series IA (**b**) Series HIA (**c**) Series NA.

The pull-out test of steel fibers in mortar was realized by different bond length embedded in the two parts of specimen. One part was used to fix steel fiber with longer length in the mortar, while the steel fibers with shorter length in other part would be pulled out of the mortar. The embedded length and fixed length of steel fibers for Series IA, HIA and NA are given in Table 2.

**Table 2.** Details of the specimen designed for the pullout test.


**4. Challenges and perspectives**

#### **4.1. Electrode designs**

The current energy storage devices are usually too heavy, rigid and bulky to match the requirements of flexible electronics [1]. Therefore, there is keen interest in the development of the light, elastic and mechanical properties with shape conformability in the next generation of energy storages. However, the bottleneck issues faced by the current MSCs are the lesser surface area of the electrode material and the more substantial mean ionic path of the electrolyte ions due to the diffraction-limited spatial resolution of the laser beam used for the preparation of electrode materials [24].

The first generation of MSCs is mainly involved in the 2D planar designs. However, these planar 2D electrodes are limited in the amount of energy storage capacities; on the other hand, increasing the thickness of the electrodes results in the low transportation of the electrolyte ions, which limits the rate transfer capabilities. The issue is initially addressed by Gao et al. where a variety of onchip designs are tested to obtain an optimized design concept for the self-powering on-chip applications, and the interdigital electrode designs seem to be out-performing the other designs [6].

However, the fast growth of technologies like the flexible MEMS or self-reliant buildings demands the high-performance on-chip energy storages.

#### *4.1.1. Fractal designs*

*Cdevice*= *i*/{−*dV*/*dt*} (1)

where *i* is the applied current (in amps, A) and *dV/dt* is the slope of the discharge curve (in

*Cvol* = *Cdevice*/*V* (2)

) and volume (cm3

The power density of the device is calculated from galvanostatic curves at different charge/

*P* = (ΔE)2 /4 *RESR V* (3)

*E* = *Cv*∗ (Δ*E*)2 /(2∗ 3600) (4)

The current energy storage devices are usually too heavy, rigid and bulky to match the requirements of flexible electronics [1]. Therefore, there is keen interest in the development of the light, elastic and mechanical properties with shape conformability in the next generation of energy storages. However, the bottleneck issues faced by the current MSCs are the lesser surface area of the electrode material and the more substantial mean ionic path of the electrolyte ions due to the diffraction-limited spatial resolution of the laser beam used for the preparation

The first generation of MSCs is mainly involved in the 2D planar designs. However, these planar 2D electrodes are limited in the amount of energy storage capacities; on the other hand, increasing the thickness of the electrodes results in the low transportation of the electrolyte ions, which limits the rate transfer capabilities. The issue is initially addressed by Gao et al. where a variety of onchip designs are tested to obtain an optimized design concept for the self-powering on-chip applications, and the interdigital electrode designs seem to be out-performing the other designs [6].

), respectively.

, ΔE is the operating voltage window and *RESR* is the internal

, *Cv* is the volumetric capacitance and Δ*E* is the oper-

volts per second, V/s).

Volumetric capacitance was given by

108 Supercapacitors - Theoretical and Practical Solutions

where A and V refer to the area (cm2

where P is the power in *W/cm3*

ating voltage window, V.

**4.1. Electrode designs**

of electrode materials [24].

where E is the energy density in *Wh/cm3*

**4. Challenges and perspectives**

discharge densities and given by the formula:

resistance of the device and can be given by the formula:

The energy density of the device can be calculated by the formula:

New electrode designs can be a possible solution to improve the energy density of MSCs, and an example is the origami designs utilized in the flexible energy storages and electronics [25]. In the case of integrable energy storages using DLW, a better choice can be biomimetic designs [26]. The *Fern Leafs* is an efficient platform for energy storage in biological processes such as photosynthesis enabled by water transport on its vein density [27] as well as information compression [28]. The biomimetic structures inspired by the internal structure of *American Fern* (*Polystichum munitum*), which is also known as the *Barnsley fractals* [29], are a possible design for the enhancement of the active electrode material loading per unit area using DLW [30]. American fern leafs with self-repeating patterns of internal structure resemble the fractal design family known as *space-filling curves* [31].

From the comparison between the various designs of the space-filling fractal family, it is found that the Hilbert space-filling designs have the highest active surface area comparing to the other designs. The resulting MSCs have an energy density of 10−1 Whcm−3 without compensating the rate of charge transfer (power density) and have a flexibility up to 60° [30]. The fractal designs offer a further chance to improve the performance of MSCs and still need a detailed investigation of various fractal families (**Figure 5**).

#### *4.1.2. Three-dimensional micro-supercapacitors*

Printing of high-performance MSCs in lesser footprints is possible with the development of three-dimensional (3D) MSCs. In 2015, Tour's group demonstrated the concept of the layer by layer stacking of individual laser-induced graphene (LIG) MSCs obtained from the PET sheets, which result in an areal capacitance of >9 mF/cm2 as shown in **Figure 6** [32] and can be considered for the ultra-portable and flexible application levels. In addition, reports of using multilayer structures made of rGO/Au particles [33] and LIG from polyamides using femtosecond laser reduction with improved spatial resolution are promising directions for the 3D energy storages [34] (**Table 1**).

**Figure 5.** Fractal MSC [30]. Copyright 2017, Nature Publishing Group.

1–2 V and ionic liquids (ILs) with a voltage range of 3.5–4.0 V. The solid or quasi-solid state electrolytes can be classified as organic and inorganic electrolytes with a voltage range of 2.5–2.7 V. Among different electrolytes, aqueous ones possess high conductivity and capacitance but are limited by low cell voltage window, whereas organic and IL electrolytes can operate at higher cell voltage windows [36]. ILs are widely used in the energy storages owing to their attractive properties like the non-flammability, low vapor pressure and large operating potential window. Solid state electrolytes are devoid of leakage issues but are limited by the low conductivity.

Direct Laser Writing of Supercapacitors http://dx.doi.org/10.5772/intechopen.73000 111

The considerable advantage of the DLW-MSC is the possibility of large-scale production with the industrial grade. However, the scale-up process might introduce the difficulties of heat, enhancement of effective series resistance (ESR) and overcharge [1]. In addition, the cost of the process needs to be compatible to the existing battery technology. DLW technique due to its straight single-step process, which combines with the efficient tuning of nanomaterials, can provide an efficient solution to the issue in long term. The successful generation of high-performance MSCs will enable a step closer to the biocompatible light-weight portable and wearable devices as well

The self-powered autonomous systems will be the future direction with impact in large areas of technologies by the inclusion of additional features like portability, flexibility and stretchability. One of the integrated DLW-MSC with existing renewable technology is discussed below.

Self-powered electronics and buildings, which utilize the renewable energy resources like solar energy, provide a green platform for the next-generation technology and find applications in skyscrapers, flexible, wearable, consumable and portable devices [37]. The primary issue faced by the renewable energies to be considered as the major electricity source such as the intermittent nature which limits the use of those energies during certain climatic conditions or in the remote areas which are isolated from the grid line electricity [38]. The current solar modules used to be accompanied by the energy storages in the commercial market are the traditional batteries, which intake almost 30% of the total cost of the solar module. In addition, the protective storage space for the energy storages becomes a significant issue when it comes to large-scale applications.

An integrated on-chip solar energy storages, which can be simultaneously charged using the solar electricity, can be a possible solution for the problem and energy stored can be used during the required times irrespective of the intermittent solar energy. However, the primary challenge for the integrated solar energy storage is to design the cell structure by incorporating both photoelectric and storage functions. The initial efforts are oriented around co-operating

**6. Scale-up process of micro-supercapacitors**

**7. Integration with applications**

**7.1. Solar energy storages**

as the replacement of large area spaces required for the energy storages.

**Figure 6.** Stackable laser-scribed supercapacitors obtained using the direct laser writing in PET substrate [32]. Copyright 2015, ACS Publishing Group.

**Table 1.** Summary of the graphene supercapacitors fabricated using direct laser writing.

### **5. Electrolytes**

The electrolyte has a significant role in determining other essential properties such as the energy density, power density, internal resistance, rate performance, operating temperature range, cycling lifetime, self-discharge, non-volatile nature and toxicity of the energy storage. The electrochemical range of an electrolyte decides the cell voltage window of the energy storages like the batteries and supercapacitors [9] as shown in the equation,

$$\mathbf{E} = \mathbf{1}/2\,\mathbf{C}\mathbf{V} \tag{5}$$

where E is the energy density, C is the specific capacitance and V is the cell voltage.

So far, the electrolytes used in an energy storage can be classified as liquid electrolytes and solid/quasi-solid state electrolytes [35]. Liquid electrolytes can be further grouped as aqueous electrolytes with a voltage range of 1.0–1.3 V, organic electrolytes within the voltage range of 1–2 V and ionic liquids (ILs) with a voltage range of 3.5–4.0 V. The solid or quasi-solid state electrolytes can be classified as organic and inorganic electrolytes with a voltage range of 2.5–2.7 V.

Among different electrolytes, aqueous ones possess high conductivity and capacitance but are limited by low cell voltage window, whereas organic and IL electrolytes can operate at higher cell voltage windows [36]. ILs are widely used in the energy storages owing to their attractive properties like the non-flammability, low vapor pressure and large operating potential window. Solid state electrolytes are devoid of leakage issues but are limited by the low conductivity.