*Numerical and Experimental Studies on Combustion Engines and Vehicles*

#### **1.1 Tyres system**

The tyres system (tyres and rigid suspension) maintains contact with the ground and filters the disturbances imposed by road imperfections [3]. This system allows two motions of the vehicle: displacement in the *z*-direction and a roll rotation around the *x*-axis [4], as shown in **Figure 2**.

### *1.1.1 Kinematic chain for tyres system*

Mechanical systems can be represented by kinematic chains composed of links and joints, which facilitate their modelling and analysis [5–7].

• While tyres 2 and 3 have a lateral deformation and may slide laterally,

contact was modelled as a prismatic joint *P* in the *y*-direction.

*(a) Kinematic chain of the tyres system. (b) Tyres system including actuators.*

by prismatic joints *P*, [11, 12].

*Movement constraints in Tyre-road contact.*

*Stability Analysis of Long Combination Vehicles Using Davies Method*

*DOI: http://dx.doi.org/10.5772/intechopen.92874*

time.

**Figure 5.**

**133**

**Figure 3.**

**Figure 4.**

*Vehicle on a curved path.*

model of the tyres system.

producing a track width change of their respective axles. As a consequence, tyres 2 and 3 have only a constraint on the *z*-direction. Therefore, tyre-ground

• Tyres are assumed as flexible mechanical components and can be represented

• In vehicles with rigid suspension, tyres remain perpendicular to the axle all the

Applying these constraints, **Figure 5a** shows the proposed kinematic chain

The kinematic chain is composed of five links identified by letters A (road), B (outer tyre in the turn), C and D (inner tyre in the turn), and E (vehicle axle); and the five joints are identified by numbers as follows: two revolute joints *R* (tyre-road

The kinematic chain of the tyres system in **Figure 2** has 2-DoF (M *= 2*), the workspace is planar (λ = 3), and the number of independent loops is one (*ν* = 1). Based on the mobility equation, the kinematic chain of tyres system should be composed of five links (*n=5*) and five joints (*j=5*) [7].

To model this system, the following considerations were taken into account:

**Figure 2.** *Tyres system.* *Stability Analysis of Long Combination Vehicles Using Davies Method DOI: http://dx.doi.org/10.5772/intechopen.92874*

#### **Figure 3.** *Movement constraints in Tyre-road contact.*

#### **Figure 4.**

**1.1 Tyres system**

*Simplified trailer model.*

**Figure 1.**

**Figure 2.** *Tyres system.*

**132**

around the *x*-axis [4], as shown in **Figure 2**.

and joints, which facilitate their modelling and analysis [5–7].

*Numerical and Experimental Studies on Combustion Engines and Vehicles*

composed of five links (*n=5*) and five joints (*j=5*) [7].

*1.1.1 Kinematic chain for tyres system*

The tyres system (tyres and rigid suspension) maintains contact with the ground and filters the disturbances imposed by road imperfections [3]. This system allows two motions of the vehicle: displacement in the *z*-direction and a roll rotation

Mechanical systems can be represented by kinematic chains composed of links

The kinematic chain of the tyres system in **Figure 2** has 2-DoF (M *= 2*), the workspace is planar (λ = 3), and the number of independent loops is one (*ν* = 1). Based on the mobility equation, the kinematic chain of tyres system should be

To model this system, the following considerations were taken into account:

or brake force, *Fyi* is the lateral force, and *Fzi* is the normal force;

ground contact was modelled as a pure revolute joint *R* along *x*-axis.

• There are up to three different components of forces acting on the tyre-ground contact *i* of the vehicle [8–10], as shown in **Figure 3**, where *Fxi* is the traction

• However, at rollover threshold, tyres 1 and 4 (outer tyres in the turn, **Figure 4**) receive greater normal force than tyres 2 and 3 (inner tyre in the turn, **Figure 4**), and thus tyres 1 and 4 are not prone to slide laterally. We consider that tyres 1 and 4 only allow vehicle rotation along the *x*-axis. Therefore, tyre*Vehicle on a curved path.*

Applying these constraints, **Figure 5a** shows the proposed kinematic chain model of the tyres system.

The kinematic chain is composed of five links identified by letters A (road), B (outer tyre in the turn), C and D (inner tyre in the turn), and E (vehicle axle); and the five joints are identified by numbers as follows: two revolute joints *R* (tyre-road

**Figure 5.** *(a) Kinematic chain of the tyres system. (b) Tyres system including actuators.*

contact of joints 1 and 4) and three prismatic joints *P*, two that represent tyres of the system (2 and 5), and one the lateral slide of tyre 2 (3).

The mechanism of **Figure 5a** has 2-DoF, and it requires two actuators to control its movement. The mechanism has a passive actuator in each prismatic joint of tyres (2 and 5—axial deformation); these actuators control the movement along the *x*and *z*-axes, as shown in **Figure 5b**.

In this model, the revolute joint (3) and the prismatic joint (4) can be changed by a spherical slider joint (*Sd*), with constraint in the *z*-axis, as shown in **Figure 6**.

#### *1.1.2 Kinematics of tyre system*

The movement of this system is orientated by the forces acting on the mechanism (trailer weight (*W*) and the inertial force (*may*)) [13]. These forces affect the passive actuators of the mechanism, as shown in **Figure 7**.

Eqs. (1)–(5) define the kinematics of the tyres system.

$$l\_i = \delta\_T + l\_r = \frac{\Im \Delta F}{k\_t + a\_c} + l\_r \approx \frac{-F\_{Ti} + F\_{xi}^{start}}{k\_T} + l\_r \tag{1}$$

$$\beta\_i = \Re 0 - \arcsin\left(\frac{l\_{i+1}}{\sqrt{t\_{i+1}^2 + l\_{i+1}^2}}\right) \tag{2}$$

*ti+1* is the axle width, and *θi;j* are the rotation angles of the revolute joints *i* and *j*

*Stability Analysis of Long Combination Vehicles Using Davies Method*

*DOI: http://dx.doi.org/10.5772/intechopen.92874*

assumed that the vehicle has this suspension on the front and rear axles.

vehicle's body under the action of lateral forces: displacement in the *z-* and *y*direction and a roll rotation about the *x*-axis [1, 8], as shown in **Figure 9a** and **b**.

This system comprises the linkage between the sprung and unsprung masses of a vehicle, which reduces the movement of the sprung mass, allowing tyres to maintain contact with the ground, and filtering disturbances imposed by the ground [3]. In heavy vehicles, the suspension system most used is the leaf spring suspension or rigid suspension [15], as shown in **Figure 8**. For developing this model (trailer), it is

The rigid suspension is a mechanism that allows the following movements of the

The system of **Figure 10a** has 3-DoF (M *= 3*), the workspace is planar (*λ = 3*) and the number of independent loops is one (*ν* = 1). From the mobility equation, the kinematic chain of suspension system should be composed of six links (*n=6*) and

To model this system the following consideration is considered: leaf springs are assumed as flexible mechanical components with an axial deformation and a small shear deformation, and can be represented by prismatic joints *P* supported in

To allow the rotation of the body in the z-axis, the link between the body and the leaf spring is made with revolute joint. Applying these concepts to the system, a

model with the configuration shown in **Figure 10b** is proposed.

*Solid axle with leaf spring suspension. Source: Adapted from Rill et al. [15].*

respectively.

six joints (*j=6*).

**Figure 8.**

**Figure 9.**

**135**

*(a) Body motion. (b) Suspension system.*

revolute joints *R* [16].

**1.2 Suspension system**

*1.2.1 Kinematic chain of the suspension system*

$$t\_i = \sqrt{t\_{i+1}^2 + l\_{i+1}^2 + l\_i^2 - 2\left(\sqrt{t\_{i+1}^2 + l\_{i+1}^2}\right)} l\_i \cos\left(\beta\_i\right) \tag{3}$$

$$\delta\_{\dot{i}} = \arcsin\left(l\_{\dot{i}}\sin\left(\beta\_{\dot{i}}\right)/\mathfrak{t}\_{\dot{i}}\right) \tag{4}$$

$$
\theta\_i = \theta\_j = \mathbf{9} \mathbf{0} - \delta\_i - \beta\_i \tag{5}
$$

where *δT* is the normal deformation of the tyre [14], Δ*F* is the algebraic change in the initial load, *kt* is the vertical stiffness of the tyre, *ac* is the regression coefficient, *FTi* is the instantaneous tyre normal load, *li* is the instantaneous dynamic rolling radius of the tyre *i*, *Fstart zi* is the initial normal load *i*, *kT* is the equivalent tyre vertical stiffness, *lr* is the initial dynamic rolling radius of tyre *i*, *ti* is the track width,

**Figure 6.** *Tyres system model.*

**Figure 7.** *Movement of tyres system.*

*ti+1* is the axle width, and *θi;j* are the rotation angles of the revolute joints *i* and *j* respectively.


**Author details**

*Healthcare Access - Regional Overviews*

sense of perceived control.

including SUDs [11].

ous and confounding effects.

**5. Summary**

the core components is 'belief in a higher power.'

counseling. Research has shown [35] that involvement in spiritual/religious activities led to decreased depression among some other adults. In the U.S., the 12-Step model of addiction recovery has principles that guide the processes [36] and one of

Reminiscence or life review therapy has yielded mixed results [33]. This potentially preventive approach involves literally reviewing one's life experiences as far back as one can remember and examining each life stage in terms of one's choices and the consequences of those choices for self and others. Given that the consequences of such experiences could be either positive or negative, perhaps it is the attitude toward such consequences that is critical to the effectiveness of this approach for prevention. If one believes that life is an opportunity to learn, then the results of the life review may prove to be positive and instructive for further learning and avoiding the same mistakes, thereby allowing for self-correcting and a

Psychodynamic therapy has also been evaluated empirically continue to have popularity as an approach within a managed care environment that reimburses only empirically supported treatments for older adults [33]. With homeless older adults, such interventions have been facilitated in substance abuse recovery program and in other shelter-based program that address mental health conditions,

Pharmacotherapy may also be an effective prevention strategy with older adults and there are a number of recommendation for addressing risk and side effects of antidepressant medication when prescribed to older adults, and especially those that are in addiction recovery [37]. One precaution is that approximately 20% of older adults have serious health problems that can be aggravated by antidepressant medications, including increased risk of physical injuries and hypertension disorders [33]. Moreover, research suggests that anti-anxiety and other sedative medications worsen conditions of depression and perhaps anxiety, among older people with substance use disorders [38–42]. Noteworthy is caveat methodological flaws and limitations may contribute to these negative findings in the research. Variations in interventions techniques, provider characteristic and sociocultural diversity of the participants are not clearly explained and such omissions can result in ambigu-

As a moral imperative, fundamental in the profession of social work, the core value and belief that every human being deserves a home, food, clothing and access to health care compel us to make this call to action. Older adults should not be excluded from the inalienable rights and dignities, worldwide [19] to have their basic needs met. As the risk of homelessness among older adults increases, this becomes a public health and human right concern for healthcare systems and providers. This call to action for expedient problem-solving to prevent older adults from living in poverty and becoming homeless. Solutions are within our local, regional, national and global reach, such as expanding and strengthening the existing safety net of health care and minimum income supports, such as Temporary Assistance for Needy Families (TANF), Child Support Enforcement, Medicare and Medicaid, as well as, including a livable wage for those whom continue to be employed. An increase in the supply and accessibility of affordable housing for the aging population and low-cost or no-cost community based mental health and substance disorder prevention and intervention services are a few of the recom-

**158**

mended solutions [42].

Ramona Bullock-Johnson and Karen Bullock\* North Carolina State University, Raleigh, NC, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. 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.


**2. Materials and Methods**

Before discussing the experiment, thermoelectric generation phenomena will be briefly explained. The solid-state direct energy conversion is based on the thermoelectric phenomena which cover the Seebeck, Peltier, and Thompson effects. For the thermoelectric generation case, the dominating thermoelectric phenomenon is the Seebeck effect of which the main principle is as follows: When the junctions of two dissimilar semiconductors, as shown in Figure 1, are maintained at different temperatures, the current flows through the closed-loop. The temperature difference between the two junctions and the electromotive force has the following linear relation where is the Seebeck coefficient.

$$E = \alpha\_{AB} \Delta T \tag{1}$$

where: *E*—open circuit voltage (V); α—Seebeck coefficient (V/K); ∆*T*—hot/cold side temperature difference [17]. The figure of merit ZT (figure of merit) determines the generation performance of a specific thermoelectric material and is characterized by the thermal/electrical conductivity, Seebeck coefficient, and the hot/cold side average temperature as described by Equation (2).

$$\text{ZT} = \frac{\alpha^2 \sigma}{\kappa} \overline{T} \tag{2}$$

where: ZT—figure of merit (-); κ—thermal conductivity (W/m2 ·K); σ—electrical conductivity (Ω)**;** *T*—mean temperature (K) [17].

ZT is a measure that determines the performance of thermoelectric material itself. Higher ZT is desirable in general. Looking at Equation (2), we can find why higher ZT is desirable. Low thermal conductivity between the hot and cold side of the module ensures ∆*T* is relatively well preserved so that electricity is well-produced according to Equation (1). High electrical conductivity and the Seebeck coefficient implies the material has more capability to produce electricity. Higher mean operating temperature implies the material is capable of operating at high temperature, and consequently higher ∆*T*.

Seebeck coefficient.

**2. Materials and Methods**

where: *E—*open circuit voltage (V); *α—*Seebeck coefficient (V/K); Δ*T—*hot/cold side temperature difference [17]. The figure of merit ZT (figure of merit) determines the generation performance of a

coefficient, and the hot/cold side average temperature as described by Equation (2).

generators for automobile exhaust waste heat recovery [17]. Four main factors that control the thermal efficiency of the thermoelectric generator were presented: heat exchanger geometry, heat exchanger materials, the installation site of the thermoelectric generator within the car, and the

Regarding the above works, the present study tried to hold focus on the other aspects of optimized conditions. As an example, thermoelectric generation performance was evaluated depending on the compressive force applied to the module. The compressive force is, in specific, a crucial means not only to lock TEG device to the heat source, but also to minimize the heat resistance, so TEG can perform maximum power. However, some optimal force exists where, putting more compressive force can reduce the performance because of heat transfer convergence to its maximum level, and putting more stress on the module can reduce the power because of the material's deformation. Moreover, putting more stress may result in mechanical failure. Additionally, the effect of the thermoelectric module surface temperature uniformity on the overall power generation performance was evaluated quantitatively. On the other hand, the experiment on the thermoelectric module power performance depending on the heat sink was conducted in a similar but partially different way from what Esarte et al. did [16]. The main difference was that in the present study, various types of heat sinks with different geometry (fin height, fin array) were applied whereas in Esarte's work, heat sinks were almost similar to one other with the same size except that fin thicknesses and numbers were varied from one another. After all, the main purpose of the present study was to simulate the waste heat recovery TEG system in automobiles and small-sized industrial facilities and thus, find the design factor that enhances the overall performance of the TEG system.

Before discussing the experiment, thermoelectric generation phenomena will be briefly explained. The solid-state direct energy conversion is based on the thermoelectric phenomena which cover the Seebeck, Peltier, and Thompson effects. For the thermoelectric generation case, the dominating thermoelectric phenomenon is the Seebeck effect of which the main principle is as follows: When the junctions of two dissimilar semiconductors, as shown in Figure 1, are maintained at different temperatures, the current flows through the closed-loop. The temperature difference between the two junctions and the electromotive force has the following linear relation where is the

coolant system of the thermoelectric generator.

**Figure 1.** Schematic of the basic thermoelectric generation circuit. A temperature difference between two dissimilar semiconductors produces a voltage difference between the two substances, which can be applied to thermal-to-electrical energy conversion. **Figure 1.** Schematic of the basic thermoelectric generation circuit. A temperature difference between two dissimilar semiconductors produces a voltage difference between the two substances, which can be applied to thermal-to-electrical energy conversion.

The conversion efficiency, described by Equation (3) could be derived by using energy balance and is shown as a function of ZT and the hot/cold side temperature in the thermoelectric material.

$$\varepsilon = \frac{T\_H - T\_C}{T\_H} \frac{\sqrt{\text{ZT} + 1} - 1}{\sqrt{\text{ZT} + 1} + \frac{T\_C}{T\_H}} \tag{3}$$

=∆ (1)

where: *TH*—hot side temperature (K); *TC*—cold side temperature (K) [17].

As seen in Equation (3), *TH* and *TC* are fixed with varying ZT. We can see conversion efficiency increases with higher ZT.

Current commercial thermoelectric materials usually have ZT of 0.8–1 and conversion efficiency of 4–5%. However, thermoelectric material Bi2Te3, GeTe, PbTe are being developed continuously with each material being doped with Bi and In, and due to the recent developments, it has been reported ZT as high as 2.4 and that conversion efficiency as high as 15% can be achieved [18–20].

Now, looking at the thermoelectric generation system for exhaust gas waste heat recovery, the overall system efficiency cannot exactly match the thermoelectric conversion efficiency due to heat loss and thermal contact resistance which occurs at the interfaces. Considering such irreversible effects, the overall system efficiency of the TEG can be analyzed as follows:

$$
\eta\_{\text{ov}} = \varepsilon \times \eta\_{\text{HX}} \times \rho\_{\prime} \tag{4}
$$

where: ε—conversion efficiency (-); η*HX*—heat exchanger efficiency; ρ—heat flux efficiency [16].

η*HX* and ρ indicates the heat exchanger efficiency and heat flux efficiency, respectively. Here, heat exchanger efficiency, η*HX*, is defied the ratio of ∆*T*co-ci to ∆*T*hi-ci, which depends on fin geometry, convection type, and flow condition. Heat flux efficiency can be defined as a ratio of actual heat flux to maximum theoretical heat flux. Thermal resistance indeed exists within the interface region of the TEG system such as heat source to a thermoelectric module, and heat sink. Since the conversion efficiency, ε is an uncontrollable value at this moment, the focus should be on improving the other two values, by either designing a highly efficient heat exchanger or reducing thermal contact resistance.

In the present study, three different experiments were conducted, independently. In the first experiment, the thermoelectric module generation performance was evaluated using a high-performance thermoelectric module with heat source and sink block. The general specifications of the thermoelectric module are specified in Table 1. In the second experiment, the TEG system for exhaust gas waste heat recovery was constructed and the power performance characteristics were compared to those of the first experiment. In the last experiment, various types of heat sinks were applied to the thermoelectric module to determine the power generation performance. For the measurement devices, a K-Type thermocouple was used to measure the hot/cold side temperature of the thermoelectric module as well as the inlet/outlet gas temperature inside the TEG system. The voltage was measured by extending the lead wire of the thermoelectric module to the multimeter. The airflow rate in the TEG system was measured using the TESTO portable sensing probe. Overall, such data were converted into digitalized formats by using the Agilent 34970A data logger. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 5 of 12

**Table 1.** Specifications for thermoelectric (TE) modules.

#### *2.1. Module Performance Experimental Setup 2.1. Module Performance Experimental Setup*

As described in Table 1, the thermoelectric module is originally produced from Hi-Z Inc., USA, and was applied to the experimental device to evaluate its performance as seen in Figure 2. The HZ20 model was specified as having a maximum power performance of 19 W. A copper plate with four cartridge heaters inserted inside the plate was used as a heating device to ensure the temperature uniformity of the hot side surface of the thermoelectric module. An aluminum water cooling jacket with a thickness of 2 mm, was attached on the cold side surface of the thermoelectric module. Using a PID temperature controller, the module hot side temperature was controlled from 40–230 ◦C for the HZ20 model. On the other hand, the cooling jacket was constantly supplied with water of 8LPM and an approximate temperature of 17–18 ◦C. The hot side temperature was increased by 10 ◦C for each step, and the open-circuit voltage was measured. When each module achieved its maximum operating temperature, the electric power was measured with variable load resistance which ranges from 0.1–100 Ω. The reason for such measurement was to find the matching load in which the maximum power could be achieved. Additionally, by putting the load cell above the heat sink, the whole pile was compressed by the C-clamp. After so, the module generation performance depending on the compressive force was measured as well. As described in Table 1, the thermoelectric module is originally produced from Hi-Z Inc., USA, and was applied to the experimental device to evaluate its performance as seen in Figure 2. The HZ20 model was specified as having a maximum power performance of 19 W. A copper plate with four cartridge heaters inserted inside the plate was used as a heating device to ensure the temperature uniformity of the hot side surface of the thermoelectric module. An aluminum water cooling jacket with a thickness of 2 mm, was attached on the cold side surface of the thermoelectric module. Using a PID temperature controller, the module hot side temperature was controlled from 40–230 °C for the HZ20 model. On the other hand, the cooling jacket was constantly supplied with water of 8LPM and an approximate temperature of 17–18 °C. The hot side temperature was increased by 10 °C for each step, and the open-circuit voltage was measured. When each module achieved its maximum operating temperature, the electric power was measured with variable load resistance which ranges from 0.1–100 Ω. The reason for such measurement was to find the matching load in which the maximum power could be achieved. Additionally, by putting the load cell above the heat sink, the whole pile was compressed by the C-clamp. After so, the module generation performance depending on the compressive force was measured as well.

**Figure 2.** Experiment setup for measuring module performance. **Figure 2.** Experiment setup for measuring module performance.

### *2.2. TEG Setup and Power Performance Testing 2.2. TEG Setup and Power Performance Testing*

measured.

A waste-heat recovering TEG system was constructed by the assembly of a duct, a heat exchanger, a water cooling jacket, and a thermoelectric module. The details of such assembly are described in Figure 3. The exhaust gas was simulated by a hot air blower which was capable of discharging air at a temperature ranging from 25–250 °C. Making indirect contact with hot air, the module was attached to the upper wall of the duct. Unlike the previous experiments, the module hot side temperature was controlled by using a hot air blower which was attached to the duct inlet. Figure 4 shows the schematic of the TEG system experimental setup. Strictly, it was the center of the module hot side temperature which was measured, but it will be abbreviated as just module hot side temperature as a convenience from next on. Additionally, the hot air inlet/outlet temperature and air flowrate at the normal temperature were measured. The module hot side temperature was controlled from 40–230 °C for the HZ20 model. As the whole system stabilized, the power performance was A waste-heat recovering TEG system was constructed by the assembly of a duct, a heat exchanger, a water cooling jacket, and a thermoelectric module. The details of such assembly are described in Figure 3. The exhaust gas was simulated by a hot air blower which was capable of discharging air at a temperature ranging from 25–250 ◦C. Making indirect contact with hot air, the module was attached to the upper wall of the duct. Unlike the previous experiments, the module hot side temperature was controlled by using a hot air blower which was attached to the duct inlet. Figure 4 shows the schematic of the TEG system experimental setup. Strictly, it was the center of the module hot side temperature which was measured, but it will be abbreviated as just module hot side temperature as a convenience from next on. Additionally, the hot air inlet/outlet temperature and air flowrate at the normal temperature were measured. The module hot side temperature was controlled from 40–230 ◦C for the HZ20 model. As the whole system stabilized, the power performance was measured.

*Symmetry* **2020**, *12*, x FOR PEER REVIEW 6 of 12

**Figure 3.** Assembly of waste heat recovering thermoelectric generator (TEG) system. pipe system, a commercial thermoelectric module is attached.

**Figure 4.** Schematic of a TEG experimental setup. The experimental setup is divided into two parts: (1) hardware for the experiment—hot air blower, exhaust pipe, a thermoelectric system with heat exchanger, water chamber to supply cold water; (2) data acquisition system: desktop computer, **Figure 4.** Schematic of a TEG experimental setup. The experimental setup is divided into two parts: (1) hardware for the experiment—hot air blower, exhaust pipe, a thermoelectric system with heat exchanger, water chamber to supply cold water; (2) data acquisition system: desktop computer, sensors to measure electricity, temperature, data logger.

**Figure 4.** Schematic of a TEG experimental setup. The experimental setup is divided into two parts: *2.3. Power Generation Performance of TEM with Various Heat sink Types*  For the present experiment, a total of six types of heat sinks were applied to the module, and each heat sink had its belonging case as shown in Figure 5. The experiment was conducted in two Imitating exhaust pipe systems capable of waste heat recovery, the porous copper heat exchanger is stored inside the pipe system to maximize the heat transfer. The water jacket attached to the other side of the thermoelectric module was designed and aluminum is selected due to anti-corrosion characteristics and high heat conductivity. Between the water cooling jacket and exhaust pipe system, a commercial thermoelectric module is attached.

#### (1) hardware for the experiment—hot air blower, exhaust pipe, a thermoelectric system with heat exchanger, water chamber to supply cold water; (2) data acquisition system: desktop computer, steps. For natural convection cooling, only the heat sink was attached to the cold side of the module in the first step. In the second step, a cooling fan was attached to the heat sink, for forced convection *2.3. Power Generation Performance of TEM with Various Heat sink Types*

sensors to measure electricity, temperature, data logger.

temperature of the module stabilized, the power was soon measured.

sensors to measure electricity, temperature, data logger. *2.3. Power Generation Performance of TEM with Various Heat sink Types*  For the present experiment, a total of six types of heat sinks were applied to the module, and each heat sink had its belonging case as shown in Figure 5. The experiment was conducted in two steps. For natural convection cooling, only the heat sink was attached to the cold side of the module in the first step. In the second step, a cooling fan was attached to the heat sink, for forced convection cooling. The cooling fan had a maximum flowrate of 10 CFM and consumed 5 W of electrical power. cooling. The cooling fan had a maximum flowrate of 10 CFM and consumed 5 W of electrical power. Additionally, it was attached to the heat sink in a way so that the air could flow in a parallel direction to the fins. As the module performance experiment, the module hot side temperature was controlled by the copper heating plate from 50–230 ° C and was increased by 30 °C for each step. To decrease the thermal contact, the thermal compound (silicone grease) was injected at the interface and the compressive force was applied on the whole pile by using the C-clamp. As the hot/cold side temperature of the module stabilized, the power was soon measured. For the present experiment, a total of six types of heat sinks were applied to the module, and each heat sink had its belonging case as shown in Figure 5. The experiment was conducted in two steps. For natural convection cooling, only the heat sink was attached to the cold side of the module in the first step. In the second step, a cooling fan was attached to the heat sink, for forced convection cooling. The cooling fan had a maximum flowrate of 10 CFM and consumed 5 W of electrical power. Additionally, it was attached to the heat sink in a way so that the air could flow in a parallel direction to the fins. As the module performance experiment, the module hot side temperature was controlled by the copper heating plate from 50–230 ◦ C and was increased by 30 ◦C for each step. To decrease the thermal contact, the thermal compound (silicone grease) was injected at the interface and the

the thermal contact, the thermal compound (silicone grease) was injected at the interface and the compressive force was applied on the whole pile by using the C-clamp. As the hot/cold side

Additionally, it was attached to the heat sink in a way so that the air could flow in a parallel direction

compressive force was applied on the whole pile by using the C-clamp. As the hot/cold side temperature of the module stabilized, the power was soon measured. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 7 of 12

**Figure 5.** Heat sink specifications. Basic specification of the six types of heat sinks used in the **Figure 5.** Heat sink specifications. Basic specification of the six types of heat sinks used in the thermoelectric experiment (size, fin numbers, and thickness of fin/pitch).


*5.3.1 Physical activation*

The physical activation method involves heating carbonized biomass material to a predetermined activation temperature, typically ranging from 700 to 1100°C [40]. The activation agents used in this method include air, nitrogen, steam, or CO2 [40]. The use of activation gas can prevent CO2 formation and generate a wide range of pore sizes in the activated carbon during the activation process [43]. Carbon dioxide is the most commonly used activation agent in the physical activation process due to its ease of handling [44], which leads to higher carbon yield and the development of uniform pores [45]. However, the steam activation process results in lower carbon yield, which could be attributed to the rapid degradation of biomass materials at high temperatures [46]. Furthermore, physical activation with CO2 produces microporous carbons, whereas activation with steam at identical conditions results in mesoporous and macroporous carbons [15]. Activation reactions of O2, CO2, and steam are given below [15]:

The main parameters that affect the physical activation process are activation temperature, activation time, heating rate, and activator flow rate.

#### *5.3.2 Chemical activation*

The chemical activation process involves the use of a chemical activator to enhance the surface properties of biochar such as porosity, pore size distribution, and surface area. Various chemicals can be used as activation agents, including H3PO4, H2SO4, KOH, K2CO3, and ZnCl2 [47]. It is crucial to ensure complete mixing between the biomass material and the activation agent before the activation step to achieve the desired quality of the produced activated carbon. Chemical activators work by degrading and dehydrating organic carbon molecules of the starting biomass materials to increase the porosity and surface area of the produced BAC [40]. Chemical activation can be carried out in a single step or in two steps. In the single-step method, the biomass material is impregnated with the activator and then carbonized, while in the two-step method, the biomass material is initially carbonized in the first step and then impregnated with the activation agent and subjected to thermal activation in the second step. **Figure 4** shows the steps of the main methods for the preparation of biomass-based activated carbons.

#### *5.3.3 Activation agents*

Chemical activation has proven to be a successful method for producing activated carbons using a range of chemical reagents such as KOH, NaOH, ZnCl2, H2SO4, and H3PO4.


*4.1.5 Hypercholesterolaemia*

Among 11,320 newly diagnosed patients with hypercholesterolemia initiated with statins, late statin initiation increased the risk of CVD events compared with early statin initiation (HR 1.24, 95% CI 1.02–2.51). Among early initiators, statin discontinuation was associated with increased risk for CVD (HR 1.71, 95%CI 1.10–2.67), but statin reinitiation was associated with decreased risk (HR 1.34, 95%CI 0.79–2.30) [46]. Another study in China with 99,655 adult patients indicated a 37% reduced risk of MACEs in those with ≥50% adherence with a statin (aHR0.63, 95% CI, 0.41–0.98). Unlike primary prevention, no relationship between secondary prevention and statin adherence (PDC ≥ 50%) was detected in this study [47]. Previous studies, however, found statin adherence benefits in reducing the risk of adverse health outcomes for secondary prevention [48–51]. These discrepancies might be due to different baseline patient characteristics (CVD and its severity) and PDC cutoff points (50% in the Chinese study vs. 80% in others' studies). Secondary prevention seems to require ≥80% adherence to reduce cardiovascular risk.

In the elderly, a study on 29,047 patients aged ≥65 receiving polypharmacy found that those who discontinued statins while maintaining other medications had an increased risk of hospital admissions for any cardiovascular outcome (HR 1.14, 95% CI 1.03–1.26), HF (HR 1.24, 95% CI, 1.07–1.43), all-cause mortality (HR 1.15, 95% CI 1.02–1.30), and emergency admissions (HR 1.12, 95% CI 1.05–1.19) (all *P* < 0.05) [52]. In diabetic patients aged ≥65 with comorbidities, those adhering optimally to statins (PDC ≥ 80) had a decreased LDLc (<100 mg/dl) across all age groups (e.g., ≥85: RR 1.13, 95% CI 1.09–1.16, *P* < 0.05) and in all comorbid levels (e.g. ≥4: RR 1.13, 95% CI 1.12–1.15, *P* < 0.05) [41]. The LDLc target of <100 mg/dl was associated with a lower risk of adverse cardiac outcomes [53].


**4.3 w-GA algorithm**

The framework of the w-GA algorithm is similar to the n-GA algorithm. We focus on the crossover and mutation operations. Assume that we selected a parent such as in Figure 7(a), the following steps will be taken.

**Step 1:** Determine the critical path of each solution. The procedure to determine this is described in Algorithm 4. In each solution of our example, the sub-jobs joined with the critical 14 Will-be-set-by-IN-TECH

for this is located in we do the crossover and mutation operations. In particular, we do not carefully consider the critical path of the workflow. The runtime of the workflow depends mainly on the execution time of the critical path. With a defined solution and timetable, the

5: *prev*\_*subjob* is determined as the sub-job having latest finished data output transfer to

We start with the last sub-job determined. The next sub-job of the critical path will have the latest finish data transfer to the previously determined sub-job. The process continues until

The purpose of the crossover operation in the n-GA algorithm is creating new solutions in the hope that they are superior to the old one. In the crossover phase of the GA algorithm, when the sub-jobs of the critical path are not moved to other RMSs, the old critical path will have very low probability of being shortened. Thus, the overall makespan of the workflow has a

The primary purpose of the mutation operation is to maintain genetic diversity from one generation of a population of chromosomes to the next. In particular, it allows the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other. When the sub-jobs of the critical path are not moved to other RMSs, the old critical path will have very low probability of being changed. Thus, the mutation operation

With the standard GA algorithm, it is always possible that the above situation happens and thus creates a long convergent process. We can see an example scenario in Figure 7. Assume that we select a parent as presented in Figure 7a. Using the procedure in Algorithm 4, we know the critical path of each solution which is marked by colour boxes. After the crossover and mutation operation as described in Figure 7b, 7c, the old critical path remains the same.

The framework of the w-GA algorithm is similar to the n-GA algorithm. We focus on the crossover and mutation operations. Assume that we selected a parent such as in Figure 7(a),

**Step 1:** Determine the critical path of each solution. The procedure to determine this is described in Algorithm 4. In each solution of our example, the sub-jobs joined with the critical

To overcome this elimination, we propose an algorithm called the w-GA algorithm.

critical path of a workflow is defined with the algorithm as described in Algorithm 4.

**Algorithm 4** Determining critical path algorithm

1: Let C is the set of sub-jobs in the critical path

the next sub-job becomes the first sub-job.

does not have as good effect as it should have.

low probability of improvement.

**4.3 w-GA algorithm**

the following steps will be taken.

2: Put last sub-job into C 3: *next*\_*subjob*=last sub-job

*next*\_*subjob* 6: Put *prev*\_*subjob* into C 7: *next*\_*sj* = *prev*\_*subjob* 8: **until** *prev*\_*sj*= first sub-job

4: **repeat**

path are marked with color. The sub-jobs joined with the critical path in solution 1 include 0, 2, 4, 7. The sub-jobs joined the critical path in solution 2 include 0, 3, 4, 7.

**Step 2:** Form the critical set. The critical set includes sub-jobs have appeared in both critical paths. With our example, the critical set includes sub-jobs 0, 2, 3, 4, 7.

**Step 3:** Create derived configurations. The derived configuration is extracted from the old one by getting only sub-jobs which have appeared in the critical set. After this step, the two new configurations of the example are presented in Figure 8.

Fig. 8. The new derived configurations

**Step 4:** Exchange assignment if there is improvement signal. A couple (*si* : *rj*|*si* ∈ *S*,*rj* ∈ *Ki*) is called an assignment. Assume that (*s*1*i* : *rj*) is an assignment of the derived configuration 1, and (*s*2*i* : *rk*) is an assignment of the derived configuration 2. If we change (*s*1*i* : *rj*) to (*s*1*i* : *rk*) and the finished time of the data transfer from the sub-job *s*1*i* to the next sub-job in the critical path is decreased, we say that the improvement signal appears. Without the improvement signal, the critical path cannot be shortened and the *makespan* cannot be improved. The algorithm for doing the exchange assignment is presented in Algorithm 5.

**Algorithm 5** Exchange assignment algorithm

```
1: imp_signal ← 0
2: for each sub-job si in the critical set do
3: (s1i : rj) change to (s1i : rk)
4: if has improving signal then
5: imp_signal ← 1
6: else
7: (s1i : rk) change to (s1i : rj)
8: end if
9: (s2i : rk) change to (s2i : rj)
10: if has improving signal then
11: imp_signal ← 1
12: else
13: (s1i : rj) change to (s1i : rk)
14: end if
15: end for
```
With each sub-job, we exchange the RMS between two configurations. If the exchange indicates an improvement signal, we keep the change. Otherwise, we return to the old assignment.

2 3 3 4 5 5 3 6

19 w-TG: A Combined Algorithm to Optimize the Runtime

(1)

(2)

7 to 32.

have *k* = 0.

DangMinh/desc\_expe2.txt.

Fig. 11. Newly created configurations

of the Grid-Based Workflow Within an SLA Context

• Have different amounts of data transfer.

sub-job has the same CPU performance requirement.

2 2 3 2 2 3 4 6

• Have a different number of sub-jobs with the number of sub-jobs being in the range from

• Have different sub-job specifications. Without loss of generality, we assume that each

The runtime of each sub-job in each type of RMS is assigned by using Formula 4.

*rtj* = *rti*

*pki*+(*pkj*−*pki*)∗*k pki*

With *pki*, *pkj* is the performance of a CPU in RMS *ri*, *rj* respectively and *rti* is the estimated runtime of the sub-job with the resource configuration of RMS *ri*. *k* is the speed up control factor. Within the performance experiment, in each workflow, 60% of the number of sub-jobs have *k* = 0.5, 30% of the number of sub-jobs have *k* = 0.25, and 10% of the number of sub-jobs

The complexity of the workflow depends on the number of sub-job in the workflow. In the experiment, we stop at 32 sub-jobs for a workflow because it is much greater than the size of the recognized workflows. As far as we know, with our model of parallel task sub-job, most existing scientific workflows as described by Ludtke et al. Ludtke et al. (1999), Berriman et al. Berriman et al. (2003) and Lovas et al. Lovas et al. (2004) include just 10 to 20 sub-jobs.

As the difference in the static factors of an RMS such as OS, CPU speed and so on can be easily filtered by SQL query, we use 20 RMSs with the resource configuration equal to or even better than the requirement of sub-jobs. Those RMSs have already had some initial workload in their resource reservation and bandwidth reservation profiles. In the experiment, 30% of the number of RMS have CPU performance equals to the requirement, 60% of the number of RMS have CPU performance which is 100% more powerful than requirement, 10% of the number

We created 20 RMSs in the experiment because it closely parallels the real situation in Grid Computing. In theory, the number of sites joining a Grid can be very large. However, in reality, this number is not so great. The number of sites providing commercial service is even smaller. For example, the Distributed European Infrastructure for Supercomputing Applications (DEISA) has only 11 sites. More details about the description of resource configurations and workload configurations can be seen at the address: http://it.i-u.de/schools/altmann/

of RMS have CPU performance which is 200% more powerful than requirement.

(4)

If there are some changes in either of the two configurations, we move to step 5. If there is no change, we move to step 4. In our example, the possible changes could be presented in Figure 9.

Fig. 9. Derived configurations after exchanging

**Step 5:** Do crossover. When there is no change with step 4 we do a normal crossover with the two derived configurations. This procedure is presented in Figure 10.

Fig. 10. Normal crossover operations

**Step 6:** Do mutation. The mutation is done on the derived configuration with no successful change. With each selected configuration, the mutation point is chosen randomly. At the mutation point, *rj* of *si* is replaced by another RMS in the candidate RMS set. Like the normal GA algorithm, the probability to do mutation with a configuration is small. We choose a random selection because the main purpose of mutation is to maintain genetic diversity from one generation of a population of chromosomes to the next. If we also use mutation to improve the quality of the configuration, the operation mutation needs a lot of time. Our initial experiment shows that the algorithm cannot find a good solution within the allowable period.

**Step 7:** Reform the configuration. We return the derived configurations to the original configurations to have the new configurations. With our example, assume that step 4 is successful so we have two new derived configurations as in Figure 9. The new configurations are presented in Figure 11.


Abstract

This chapter has been prepared with the hope that its readers will become interested in lightweight concrete (LWC). Therefore, after a brief background of lightweight concrete, different types of LWC will be introduced and then LWC made of lightweight aggregates (LWA) will be specifically discussed. Compressive strength and density of LWC are the main points of interest in this chapter. In addition to conventional compression test, a nondestructive test (NDT) method will be used to assess the compressive strength of a variety of lightweight concrete mixes. A case study has been designed and conducted including an experimental program on the LWC made of expanded glass aggregate. The experimental program includes about 150 specimens, incorporating different unit weight for the entire specimens. In the end, it can be observed that the properties of LWC depend on the properties of the used LWA, and therefore for each specific type of lightweight aggregate, a brand new equation will be required for prediction of concrete compressive strength. The author hopes that the present chapter and the discussed case study on LWC would attract the attention of researchers to the importance of LWC in the future of construction industry.

Keywords: lightweight aggregate, lightweight concrete, nondestructive testing, compressive strength, expanded glass aggregate