Design and Optimization of Thermal Systems Episode 1 Part 8 doc

Modeling of Thermal Systems 147 a set of simultaneous ordinary differential equations arise For instance, the temperatures T1, T2, T3, , Tn of n components of a given system are governed by a system of equations of the form dT1 d F1 (T1 ,T2 ,T3 , , Tn , ) dT2 d F2 (T1 ,T2 ,T3 , , Tn , ) dTn d Fn (T1 , T2 , T3 , , Tn , ) (3.15) where the F’s are functions of the temperatures and thus couple the equations These equations can be solved numerically to yield the temperatures of the various components as functions of time (see Example 2.6) Partial differential equations are obtained for distributed models Thus, Equation (3.4) is the applicable energy equation for three-dimensional, steady conduction in a material with constant properties Similarly, one-dimensional transient conduction in a wall, which is large in the other two dimensions, is governed by the equation T C x k T x (3.16) if the material properties are taken as variable For constant properties, the equation becomes T T x2 (3.17) where k/ C is the thermal diffusivity Similarly, equations for two- and threedimensional cases may be written For convective transport, the energy equation is written for a two-dimensional, constant property, transient problem, with negligible viscous dissipation and pressure work, as Cp T u T x v T y T x2 k T y2 (3.18) where Cp is the specific heat of the fluid at constant pressure and u and v are the velocity components in the x and y directions, respectively Partial differential 148 Design and Optimization of Thermal Systems equations that govern most practical thermal systems are amenable to a solution by analytical methods in very few cases and numerical methods are generally necessary Finite-difference and finite-element methods are the most commonly employed techniques for partial differential equations Ordinary differential equations can often be solved analytically, particularly if the equation is linear The integral formulation is based on an integral statement of the conservation laws and may be applied to a small finite region, from which the finite-element and finite volume methods are derived, or to the entire domain For instance, conduction in a given region is governed by the integral equation Cp T dV k V S T dS n q dV (3.19) V where V is the volume of the region, A is its surface area, q is an energy source per unit volume in the region, and n is the outward drawn normal to the surface This integral equation states that the rate of net energy generated in the region plus the rate of net heat conducted in the region at the surfaces equals the rate of increase in stored thermal energy in the region Similar equations may be derived for convection in a given domain Radiative transport often leads to integral equations because energy is absorbed over the volume of a participating fluid or material In addition, the total radiative transport, in general, involves integrals over the area, wavelength, and solid angle Figure 3.13 shows a few examples of integral and differential formulations for the mathematical modeling of thermal systems Further Simplification of Governing Equations After the governing equations are assembled, along with the relevant boundary conditions, employing the various approximations and idealizations outlined here, further simplification can sometimes be obtained by a consideration of the various terms in the equations to determine if any of them are negligible This is generally based on a nondimensionalization of the governing equations and evaluation of the governing parameters, as given earlier in Equation (3.6) For instance, cooling of an infinite heated rod moving continuously at speed U along the axial direction x [Figure 1.10(d) and Figure 3.6(a)] is governed by the dimensionless equation Pe X (3.20) where is the Laplacian operator in cylindrical coordinates, nondimensionalized by the rod diameter D, and the Peclet number Pe is given by Pe UD/ The dimensionless temperature is defined as T/Tref, where the reference temperature Tref may be the temperature at x Also, dimensionless time is Modeling of Thermal Systems 149 T1 T2 T2 Streamlines T2 Isotherms (a) (b) A1 A2 V1 V2 2 ρ1V1A1 = ρ2V2 A2 (c) (d) FIGURE 3.13 Differential formulations (a) Flow in an enclosed region due to inflow and outflow of the fluid; and (b) temperature distribution due to conduction in a solid body Also shown are a few integral formulations: (c) flow in a pipe and (d) flow through a turbine defined here as /D2, where is physical time If Pe is very small, Pe 1, the second term from the left, which represents convective transport due to rod movement, may be neglected, reducing the given circumstance to a simple conduction problem Similarly, at low Reynolds number Re in a flow, Re 1, where Re UL/ , being the kinematic viscosity, the inertia or convection terms can be neglected in the momentum equation This is the creeping flow approximation, which is used in film lubrication modeling Several such approximations are well known and frequently employed in modeling, as discussed in standard textbooks Summary The preceding discussion gives a step-by-step approach that may be applied to the system or its parts in order to develop the appropriate mathematical model Generally, modeling is first applied to the various components and then these submodels are assembled to obtain an overall model for the system In many cases, rigorous proofs and appropriate estimates cannot be easily obtained to determine if a particular approximation is applicable In such cases, approximations and simplifications are made without adequate justification in order to derive relatively 150 Design and Optimization of Thermal Systems simple mathematical models The results from analysis and numerical solution of these models can then be used to verify if the approximations made are valid In addition, the approximations may be gradually relaxed to obtain models that are more accurate Thus, one may go from simple to increasingly complicated models, if needed Clearly, modeling requires a lot of experience, practice, understanding, and creativity The following simple examples illustrate the use of the approach given here to develop suitable mathematical models Example 3.1 Consider typical thermodynamic systems such as A power plant, shown in Figure 2.17, with the thermodynamic cycle in Figure 2.15(a) A vapor compression cooling system, shown in Figure 1.8, with the thermodynamic cycle in Figure 2.21 An internal combustion engine, with the thermodynamic cycle in Figure 2.15(b) Discuss the development of simple mathematical models for these in order to calculate the energy transport rates and the overall performance Solution In all of these commonly used systems, as well as in many others like them, the major focus is on the heat input or removal rate and on the work done Many of the details, such as the temperature and velocity distributions in the various components, are not critical Similarly, though the transients are important in controlling the system as well as at start-up and shutdown, the system performance under steady operation is of particular interest for system analysis and design Keeping the preceding considerations in mind, the two main assumptions that can be made for each component are: Steady-state conditions Lumped flow and temperature This implies that time dependence is neglected and uniform conditions are assumed to exist within each system component Energy loss to or gain from the environment may be neglected for idealized conditions, which will yield the best possible performance and can thus be used for calculating the efficiency Then, considering the vapor compression system of Figure 2.21, we obtain for a mass flow rate of m Heat rejected at the condenser m(h2 h3) Heat removed at the evaporator m(h1 h 4) Work done on the compressor m(h2 h1) yielding the coefficient of performance (COP), given in Equation (2.19), as (h1 h 4)/ (h2 h1) Similarly, for the power plant given by the cycle in Figure 2.15(a), the heat input in the boiler or condenser is m(hout hin) and work done by the turbine or the pump Modeling of Thermal Systems 151 is m(hin hout), where in and out refer to conditions at the inlet and outlet of the component Thus, boiler heat input is positive, condenser heat input is negative (heat rejected), work done by the turbine is positive, and work done by the pump is negative (work done on the pump) The internal combustion engine and other thermodynamic systems may be similarly analyzed to yield the net heat input and work done, allowing subsequent design and optimization of the system The design considerations are discussed in Chapter Example 3.2 For common heat exchangers, such as the parallel and counterflow heat exchangers shown in Figure 1.5, discuss the development of a simple mathematical model to analyze the system Solution In heat exchangers, the main physical aspect of interest is the overall heat transfer between the two fluids The velocity and temperature distributions at various crosssections of the heat exchanger are generally of little interest Similarly, transient aspects, although important in some cases, are usually not critical Thus, energy transfer under steady flow, as a function of the operating conditions and the heat exchanger design, is generally needed With this in mind as the major consideration, we can assume the following: The flow is lumped across the cross-sections of the channels or tubes The temperature is also uniform across these cross-sections Steady-state conditions exist With these assumptions, the temperature in, say, the inner tube or channel of the heat exchangers in Figures 1.5(a) and (b) varies only with distance in the axial direction The overall energy balance is mc Cp,c(Tc,out Tc,in) Q where Q is the rate of heat input to the colder fluid, indicated by the subscript c, over the entire length If energy loss to the ambient is neglected, we have for the hotter fluid, which is indicated by subscript h, mh Cp,h(Th,in Th,out) Q In addition, the total heat transfer Q may be written as Q qA q (2 DL) h A(Th Tc) where q is the heat flux per unit area due to the difference in the bulk temperatures, Th and Tc respectively of the hot and cold fluids, A is the contact area, being DL for a tube of diameter D and length L, and h is the convective heat transfer coefficient Further details on the analysis and design of such heat exchangers are discussed in Chapter 152 Design and Optimization of Thermal Systems Example 3.3 In the design of a hot water storage system, it is given that a steady flow of hot water at 75 C and a mass flow rate m of 113.1 kg/h enters a long circular pipe of diameter cm, with convective heat loss at the outer surface of the pipe to the ambient medium at 15 C with a heat transfer coefficient h of 100 W/m2K The density , specific heat at constant pressure Cp, and thermal conductivity k of water are given as 103 kg/m3, 4200 J/kgK, and 0.6 W/mK, respectively Develop a simple mathematical model for this process and calculate the water temperature after the flow has traversed 10 m of pipe Solution The problem can be simplified considerably by assuming steady-state conditions and lumped velocity and temperature conditions across any cross-section of the pipe This approximation applies for turbulent flow in a pipe of relatively small diameter In addition, interest lies in the average temperature at any given x, where x is the inlet and x is the distance along the pipe, as shown in Figure 3.14 The average velocity U in the flow is U m ( D / 4)3600 0.1 m/s where D is the pipe diameter The Reynolds number Re UD/ (0.1)(0.02)/(5.5 10 7) 3636 Turbulent flow arises in the pipe at this high value of the Reynolds number The Peclet number Pe UD/ (0.1)(0.02)/(1.5 10 7) 1.3 104 Therefore, convection dominates and axial diffusion effects may be neglected; see Equation (3.20) With the above approximations, the governing equation for the temperature T(x) is obtained from energy balance over a region of length x, as shown in Figure 3.14 The reduction in thermal energy transported in the pipe equals the convective loss to the ambient This gives the decrease in temperature T over an axial distance x as CpUA T hP x (T Ta) h, Ta U T Δx FIGURE 3.14 Assumption of uniform flow and temperature across the pipe cross-section in Example 3.3 Modeling of Thermal Systems Therefore, with x 153 0, we obtain the differential equation C pUA dT dx hP(T Ta ) where A is the cross-sectional area ( D 2/4) and P is the perimeter ( D) This gives the simple mathematical model for this problem The inlet temperature is given as 75 C and the ambient temperature Ta 15 C This equation may be solved analytically to give o exp hP x C pUA where T – Ta and o is the temperature difference at the inlet, i.e., 60 C Therefore, at x 10 m, we have 60 exp ( 0.0476x) 60 exp ( 0.476) 37.276 Therefore, the temperature at 10 m is 15 37.276 52.276 C Clearly, the temperature drops very slowly due to the high mass flow rate and relatively small heat loss rate This is a simple model and is easy to solve Models very similar to this one are frequently used for analysis of flow and heat transfer in pipes and channels, for example, in the design of heat exchangers The preceding three examples present relatively simple models of some commonly encountered thermal systems These included thermodynamic systems like heating/cooling systems and flows through channels as in heat exchangers Steady-state conditions could be assumed in these cases, along with lumping to further simplify the models The resulting models yielded algebraic equations and first-order ordinary differential equations, which could be easily solved analytically to yield the desired results However, many practical thermal systems are more involved than these and spatial and temporal variations have to be considered Then the resulting equations are partial differential equations, which generally require numerical methods for the solution In a few cases, these equations can be simplified or idealized to obtain ordinary differential equations, which may again be solved analytically The following two examples illustrate such problems that would generally need numerical methods for the solution and that may be idealized to obtain analytical results in some cases for validation of the numerical scheme Example 3.4 A large cylindrical gas furnace, m in diameter and m in height, is being simulated for design and optimization Its outer wall is made of refractory material, covered on the outside with insulation, as shown in Figure 3.15 The wall is 20 cm thick and the insulation is 10 cm thick The variations of the thermal conductivity k, 154 Design and Optimization of Thermal Systems Wall Insulation L D FIGURE 3.15 The cylindrical furnace, with the wall and insulation, considered in Example 3.4 specific heat at constant pressure Cp, and density of the wall material with temperature are represented by best fits to experimental data on properties as k Cp 2.2 (1 1.5 900 (1 10 2500 (1 10 T) T) 10 T) where T is the temperature difference from the reference temperature of 300 K and all the values are in S.I units The temperature difference across the wall is not expected to exceed 200 K The properties of the insulation may be taken as constant Develop a mathematical model for the time-dependent temperature distribution in the wall and in the insulation Solve the governing equations for the temperature distribution in the idealized steady-state circumstance, with the thermal conductivity of the insulation given as 1.0 W/mK, temperature (Tw)1 at the inner surface of the wall as 500 K, and temperature (Ti)2 at the outer surface of the insulation as 300 K Modeling of Thermal Systems 155 Solution The ratio of the wall thickness to the furnace diameter is 0.2/3.0, which gives 0.067 Similarly, the ratio of the insulation thickness to the furnace diameter is 0.1/3, or 0.033 Since both of these ratios are much less than 1.0, the curvature effects can be neglected, i.e., the wall and insulation may be treated as flat surfaces The ratio of the furnace height to the wall thickness is 5.0/0.2, or 25, and that to the insulation thickness is 50 In addition, the circumference is much larger than these thicknesses If there is good circulation of gases in the furnace, the thermal conditions on the inner surface of the wall can be assumed uniform Then, the wall, as well as the insulation, may be modeled as one-dimensional, with transient diffusion occurring across the thickness and uniform conditions in the other two directions The material properties are given as constant for the insulation However, these vary with temperature for the wall material Considering a maximum temperature difference of 200 K across the wall, the ratios k/ko, Cp /(Cp)o and /( )o may be are the differcalculated as 0.3, 0.02, and 0.012, respectively, where k, Cp, and ences in these quantities due to the temperature difference The reference values ko, (Cp)o, and o are used instead of the average values because the actual temperature levels are not known From these calculations, it is evident that the variations of Cp and with temperature may be neglected over the temperature range of interest However, the variation of k is important and must be included The governing equations for the wall and the insulation are thus obtained as, respectively, ( C p )w ( C p )i Tw x Ti k w (Tw ) Tw x Ti x2 ki where the corresponding temperatures and material properties are used, denoted by subscripts w and i for the wall and the insulation, respectively, and x is the coordinate distance normal to the surface, i.e., in the radial direction for the furnace; see Figure 3.16 Heat transfer conditions at the inner and outer surfaces of the wallinsulation assembly give the required boundary conditions for these equations In addition, at the interface between the wall and the insulation Tw Ti and k w Tw x ki Ti x Therefore, the governing equations for the wall and the insulation may be solved, with the appropriate boundary conditions, to yield the time-dependent temperature distributions in these two parts of the thermal system Because of the variation of k w with temperature, the two partial differential equations, which are coupled through the boundary conditions, are nonlinear Therefore, numerical modeling will generally be needed to solve these equations 156 Design and Optimization of Thermal Systems Wall Insulation (Tw)1 = 500 K (Ti)2 = 300 K x 10 cm 20 cm Temperature (a) 500 K 415.27 K 400 K 300 K Distance (b) FIGURE 3.16 Boundary conditions and analytical solution obtained for the steady-state circumstance in Example 3.4 The simpler steady-state problem, with temperatures specified at the inner and outer surfaces of the wall-insulation combination, is an idealized circumstance and may be solved analytically The equations that apply in the wall and the insulation for this case are dT d kw w dx dx and ki d 2Ti dx These equations may be solved analytically to yield 2.2(1 0.0015Tw ) dTw dx C1 and Ti or 2.2Tw 0.0033 Tw C1 x C4 C x C3 Modeling of Thermal Systems 157 where all the temperatures are taken as differences from the reference value of 300 K to simplify the analysis and the C’s are constants to be determined from the boundary conditions shown in Figure 3.16 At the interface, the heat flux and the temperature in the two regions match, as given previously The temperature distribution in the insulation is linear, with K at the outer surface, and that in the wall is nonlinear, with 200 K at the inner surface The temperature distributions are obtained as 2.2Tw 0.0033 Tw2 1152.7x 160.19 and Ti = 1152.7x which gives the interface excess temperature as 115.27 K Therefore, the actual temperature at the interface is 300 115.27 415.27 K The heat flux is obtained as 1152.7 W/m2 The calculated temperature distribution is sketched in Figure 3.16 Example 3.5 A hot-water storage system consists of a vertical cylindrical tank with its height L to diameter D ratio given as 8, the diameter being 40 cm The tank is made of 5-mmthick stainless steel Hot water from a solar energy collection system is discharged into the tank at the top and withdrawn at the bottom for recirculation through the collector system The tank loses energy to the ambient air at temperature Ta with a convective heat transfer coefficient h at the outer surface of the tank wall The temperature range in the system may be taken as 20 C to 90 C Develop a mathematical model for the storage tank to determine the temperature distribution in the water Also use nondimensionalization to obtain the governing parameters Then solve the steady-state problem Solution The temperature range being relatively small, the variation in material properties may be taken as negligible because parameters such as / avg, k/kavg, etc., where is the density and k is the thermal conductivity, are much less than 1.0 Because of the thinness of the stainless steel wall and its high thermal conductivity compared to water, the ratio being 23.59, the energy storage and temperature drop in the wall may be neglected compared to those in water This is justified from the ratio of the wall thickness, mm, to the tank diameter, 40 cm A substantial simplification of the problem is obtained by assuming that the temperature distribution across any horizontal cross-section in the tank is uniform This is based on axisymmetry, which reduces the original three-dimensional problem to two dimensions and the effect of buoyancy forces that tend to make the temperature distribution horizontally uniform Because hot water is discharged at the top, the water in the tank is stably stratified, with lighter fluid lying above denser fluid This curbs recirculating flow in the tank and promotes horizontal temperature uniformity Therefore, the temperature T in the water is taken as a function only of the vertical location z, i.e., T(z) The vertical velocity in the tank is also taken as uniform across each cross-section, by employing the average value This is obviously an approximation because the velocity at the walls is zero due to the noslip condition Therefore, the problem is substantially simplified because the flow 158 Design and Optimization of Thermal Systems field is taken as a uniform vertical downward velocity, which can easily be obtained from the flow rate Without this simplification, the coupled convective flow has to be determined, making the problem far more involved The governing energy equation for thermal transport in the water tank may be written with the above simplifications as T Cp A w T z T z2 kA hP(T Ta where is the density of the fluid, Cp is its specific heat at constant pressure, is the physical time, w is the average vertical velocity in the tank, k is the fluid thermal conductivity, A is the cross-sectional area, and P is the perimeter of the tank; see Figure 3.17 The problem is treated as transient because the time-dependent behavior can be important in such energy storage systems The initial and boundary conditions may be taken as At T z For > 0: 0: T (z ) Ta at z L and T To at z where To is the discharge temperature of hot water Therefore, a one-dimensional, transient, mathematical model is obtained for the hot water storage system The Flow of hot water z A h, Ta T(z) A P P h, Ta Storage tank w (a) (b) FIGURE 3.17 The hot-water storage system considered in Example 3.5, along with the simplified model obtained Modeling of Thermal Systems 159 various assumptions made, particularly that of uniformity across each crosssection, may be relaxed for more accurate simulation However, under the given conditions, this model is adequate for simulation and design of practical hot-water storage systems The governing equation and boundary conditions may be nondimensionalized by defining the dimensionless temperature , time , and vertical distance Z as T Ta To Ta Z L2 z L Then the dimensionless governing equation is obtained as W Z H Z2 where the two dimensionless parameters W and H are W wL hPL2 Ak H Here is the thermal diffusivity of water The initial and boundary conditions become At For > 0: Z 0: (Z ) at Z and at Z The governing equation is a parabolic partial differential equation, which may be solved numerically, as discussed in Chapter 4, to obtain the temperature distribution ( , Z) Let us now consider the idealized steady-state circumstance obtained at large time and see if an analytical solution is possible For steady-state conditions, the time dependence drops out and a second-order ordinary differential equation is obtained, which may be solved analytically or numerically to yield the temperature distribution (Z) The governing equation for this circumstance is W d dz d2 dz H with the boundary conditions at Z d dZ at Z 160 Design and Optimization of Thermal Systems This second order ordinary differential equation may be solved analytically to yield the solution a1 exp ( Z) a2 exp ( Z) where a1 exp( ) exp( ) exp( 2) 1exp( ) exp( ) exp( a2 ) with W W2 4H W W2 4H H and H If these values are substituted in the preceding If W 0, expressions, the standard solution for conduction in a fin with an adiabatic end is obtained (Incropera and Dewitt, 2001) The solution for this case is cosh[ H (1 Z )] cosh[ H ] Therefore, analytical solutions may be obtained in a few idealized circumstances Numerical solutions are needed for most realistic and practical situations However, these analytical results may be used for validating the numerical model 3.3.2 FINAL MODEL AND VALIDATION The mathematical modeling of a thermal system generally involves modeling of the various components and subsystems that constitute the system, followed by a coupling of all these models to obtain the final, combined model for the system The general procedure outlined in the preceding may be applied to a component and the governing equations derived based on various simplifications, approximations, and idealizations that may be appropriate for the circumstance under consideration The governing equations may be a combination of algebraic, differential, and integral equations The differential equations may themselves be ordinary or partial differential equations Though differential equations are the most common outcome of mathematical modeling, algebraic equations are obtained for lumped, steady-state systems and from curve fitting of experimental or material property data In the mathematical model of the overall system, one component may be modeled as lumped mass, another as one-dimensional transient, and still another as three-dimensional Thus, different levels of simplifications arise in different circumstances As an example, let us consider the furnace shown in Figure 3.18 The walls, insulation, heaters, inert gas environment, and material undergoing thermal processing may all be considered as components or Modeling of Thermal Systems 161 Heaters Wall Insulation Flow Inert gases Recirculating fan Opening Material FIGURE 3.18 An electric furnace for heat treatment of materials constituents of the furnace The general procedure for mathematical modeling may be applied to each of these components, using physical insight and estimates of the various transport mechanisms Such an approach may indicate that the wall and the insulation can be modeled as one-dimensional, the gases as fully mixed, and the heaters and the solid body undergoing heat treatment as lumped, with all temperatures taken as time-dependent Similarly, the importance of variable properties and of radiation versus convection in the interior of the furnace may be evaluated Once the final model of the thermal system is obtained, we proceed to obtain the solution of the mathematical equations and to study the behavior and characteristics of the system This is the process of simulation of the system under a variety of operating and design conditions However, before proceeding to simulation, we must validate the mathematical model and, if needed, improve it in order to represent the physical system more closely Several approaches may be applied for validating the mathematical model and for determining if it provides an accurate representation of the given thermal system The three commonly employed strategies for validation are: Physical behavior of the system In this approach, the operating, ambient, and other conditions are varied and the effect on the system is investigated It is ascertained that the behavior is physically reasonable For instance, if the energy input to the heater in the furnace of Figure 3.18 is increased, the temperature levels are expected to rise Similarly, if the wall or insulation thickness is increased, the temperatures within the furnace must increase An increase in the convective cooling at the 162 Design and Optimization of Thermal Systems outer surface of the furnace should lower the temperatures Thus, the results from the solution of the governing equations that constitute the mathematical model must indicate these trends if the model is a satisfactory representation of the system Comparison with results for simpler systems Usually experimental or numerical results are not available for the system under consideration However, the mathematical model may be applied to simpler systems that may be studied experimentally to provide the relevant data for comparison For instance, the model for a solar energy collection system may be applied to a simpler, scaled-down version, which could then be fabricated for experimentation Usually the geometrical complexities of a given system are avoided to obtain a simpler system for validation A fluid or material whose characteristics are well known may be substituted for the actual one For instance, a viscous Newtonian fluid such as corn syrup may be substituted for a more complicated non-Newtonian plastic material, whose viscosity varies with the shear rate in the flow, in order to simplify the model for validation A fewer number of components of the system may also be considered for a simpler arrangement The experimental study is then directed at validation and specific, well-controlled experiments are carried out to obtain the required data However, it must be borne in mind that such a simpler system or fluid may not have some of the important characteristics of the actual system, thus limiting the value of such a validation Comparison with data from full-scale systems Whenever possible, comparisons between the results from the model and experimental data from full-scale systems are made in order to determine the validity and accuracy of the model The system available may be an older version that is being improved through design and optimization or it may be a system similar to the one under consideration In addition, a prototype of the given system is often developed before going into production and this can be effectively used for validation of the model Generally, older versions and similar systems are used first and the prototype is used at the final stage to ensure that the model is valid and accurate In addition to the validation approaches given above, it must be remembered that the mathematical model is closely coupled with the numerical scheme, the system simulation, and the design evaluation and optimization Therefore, the model provides important inputs for the subsequent processes and obtains feedback from them This feedback indicates the accuracy of representation of the physical system and is used to improve and fine-tune the model Therefore, as we proceed with the simulation and design of the system, the mathematical model is also improved so that it very closely and accurately predicts the behavior of the given system Ultimately, a satisfactory mathematical model of the thermal system is obtained and this can be used for design, optimization, and control of the system, as well as for developing models for other similar systems in the future Modeling of Thermal Systems 163 The following example illustrates the main aspects for the development of a mathematical model for a thermal system Example 3.6 For the design of an electric heat treatment furnace, consider the system shown in Figure 3.18 For the walls and insulation, the thickness is much smaller than the corresponding height and width The flow of gases, which provides an environment of inert gases and nitrogen, is driven by buoyancy and a fan, giving rise to turbulent flow in the enclosed region The heat source is a thin metal strip with imbedded electric heaters The material being heat-treated is a metal block and is small compared to the dimensions of the furnace This thermal system is initially at room temperature Tr and the material is raised to a desired temperature level, followed by gradual cooling obtained by controlling the energy input to the heaters Discuss and develop a simple mathematical model for this system Solution The given thermal system consists of several parts or constituents that are linked to each other through energy transport These parts, with the subscripts used to represent them, are: Metal block, m Heater, h Gases, g Walls, w Insulation, i Let us first consider each of these components separately and obtain the corresponding governing equations and boundary conditions Clearly, the time-dependent variation of the temperature in the material being heat-treated is of particular interest, making it necessary to retain the transient effects Because this piece is made of metal and its size is given as small, the Biot number is expected to be small and it may be modeled as a lumped mass If additional information is given, the Biot number may be estimated to check the validity of this assumption Therefore, the governing equation for the metal block may be written as ( CV )m dTm d hm Am (Tg Tm m Am FmhTh4 FmwTw Tm where , C, V, A, and refer to the material density, specific heat, volume, surface area, and surface emissivity, respectively, and hm is the convective heat transfer coefficient at its surface Fmh and Fmw are geometrical view factors between the metal block and the heater and the wall, respectively, and enclosure radiation analysis is used The wall and the heater are taken as black and the energy reflected at the block surface is assumed to be negligible, otherwise the absorption factor or radiosity method may be used (Jaluria and Torrance, 2003) The gases, being nitrogen and inert gases, are taken as nonparticipating Only the initial condition is needed for this equation, this being written as Tm Tr at 0, where Tr is the room temperature 164 Design and Optimization of Thermal Systems Depending on the temperatures, various terms in the preceding energy balance equation may dominate or may be negligible For instance, the radiation from the wall and the material may be small compared to that from the heater Similarly, the heater may be treated as a lumped mass because it is a thin metal strip An equation similar to the one given previously for the metal block may be written as ( CV )h dTh d Q( ) hh Ah (Tg Th Ah Th4 FhwTw Fhm T m m where Q( ) is the heat input into the heater, hh is the convective heat transfer coefficient at the heater surface, and the F’s are the view factors Again, radiation from the heater may dominate over the other two radiation terms The initial condition is Th Tr at 0, when the heat input Q( ) is turned on The gases are driven by a fan and by buoyancy in an enclosed region As such, a well-mixed condition is expected to arise Therefore, if a uniform temperature is assumed in the gases, an energy balance gives ( CV ) g dTg d hh Ah (Th Tg hm Am (Tg Tm hw Aw (Tg Tw where the convective heat transfer occurs at the heater, the walls, and the metal block The gases gain energy at the heater and may gain or lose energy at the other two, depending on the temperatures The initial temperature is again Tr and the heat input is due to Q( ) Since the thickness of the walls is much smaller than the other two dimensions, the conduction transport in the walls may be approximated as one-dimensional, governed by the equation Tw ( C )w x kw Tw x where x is the coordinate distance normal to the wall surface, taken as positive toward the outside environment The thermal conductivity k can often be taken as a constant for typical materials and temperature levels If this is done, the term on the right-hand side becomes k w( 2Tw/ x2), further simplifying the model A similar governing equation applies for the temperature Ti in the insulation The boundary and initial conditions needed for these equations are at For kw 0: ki Ti x he (Ti Tw x Te ), at x 0, Tw = Ti = Tr hw (Tg d1 Tw ) d2 at x kw Tw Ti , at x d1 Tw T = ki i , at x x x d1 Modeling of Thermal Systems 165 where x is the inner surface, d1 is the wall thickness, d2 is the insulation thickness, he is the heat transfer coefficient at the outer surface of the furnace, and Te is the outside environmental temperature Continuity in temperature and/or heat flux at the interfaces is used to obtain these conditions The preceding system of equations, along with the corresponding boundary conditions, represents the mathematical model for the given thermal system Many simplifications have been made, particularly with respect to the dimensions needed, radiative transport, and variable properties These may be relaxed, if needed, for higher accuracy In addition, the various convective heat transfer coefficients are assumed to be known Heat transfer correlations available in the literature may be used for the purpose In actual practice, these should be obtained by solving the convective flow in the gases for the given geometrical configuration However, this is a far more involved problem and would require substantial effort with commercially available or personally developed computational software The mathematical model derived is relatively simple and provides the basis for a dynamic, or timedependent, numerical simulation of the system The equations can be solved to obtain the variation in the different components with time, as well as the temperature distribution in the wall and the insulation If all the components are taken as lumped, a system of ordinary differential equations is obtained instead of the partial differential equations derived here for a distributed model This additional approximation considerably simplifies the model Example 2.6 presents numerical results on a problem similar to this one when all the parts of the system are taken as lumped The variation of the temperature with time was obtained in that example for the different components, indicating the fast response of the heater and the relatively slow response of the walls 3.4 PHYSICAL MODELING AND DIMENSIONAL ANALYSIS A physical model refers to a model that is similar to the actual system in shape, geometry, and other physical characteristics Because experimentation on a fullsize prototype is often impossible or very expensive, scale models that are smaller than the full-size system are of particular interest in design The model may also be a simplified version of the actual system or may focus on particular aspects of the system Experiments are carried out on these models and the results obtained are employed to represent the behavior and characteristics of the given component or system Therefore, the information obtained from physical modeling provides inputs for the design process as well as data for the validation of the mathematical model Figure 3.19 shows a few scale models used for investigating the drag force and heat transfer from heated bodies of different shapes Physical modeling is of particular importance in the design of thermal systems because of the complexity of the transport processes that arise in typical practical systems In many cases, it is not possible or convenient to simplify the problem adequately through mathematical modeling and to obtain an accurate solution that closely represents the physical system In addition, the validity of some of the approximations may be questionable Experimental data are then needed for a check on accuracy and validity In some cases, the basic mechanisms are not easy to model For example, turbulent, separated, and unstable flows are often difficult 166 Design and Optimization of Thermal Systems U Ta Ts L D L Ts u Ta l l Ts d Ts FIGURE 3.19 Scale models with geometric similarity for flow and heat transfer to model mathematically Experimental inputs are then needed for a satisfactory representation of the problem However, experimental work is time-consuming and expensive Therefore, it is necessary to minimize the number of experiments needed for obtaining the desired information This is achieved through dimensional analysis by determining the dimensionless parameters that govern the given system This approach is widely used in fluid mechanics and heat and mass transfer (White, 1994; Incropera and Dewitt, 2001; Fox and McDonald, 2003) A brief discussion of dimensional analysis is given here for completeness and to bring out its relevance to the design process The following section may be skipped if the reader is already well versed in this material In addition, the references cited and other textbooks in these areas may be consulted for further details Scale-Up An important consideration that arises in physical modeling is the relationship between the results obtained from the scale model and the characteristics of the actual system Obviously, if the results from the model are to be useful with respect to the system, there must be known principles that link the two These are usually known as scaling laws and are of considerable interest to the industry because they allow the modeling of complicated systems in terms of simpler, scaled-down versions Using these laws, the results from the models can be scaled up to larger systems However, in many cases, different considerations lead to different scaling parameters and the appropriate model may not be uniquely defined In such cases, similarity is achieved only for the few dominant aspects between the model and the system (Wellstead, 1979; Doebelin, 1980) 3.4.1 DIMENSIONAL ANALYSIS Dimensional analysis refers to the approach to obtain the dimensionless governing parameters that determine the behavior of a given system This analysis is carried out largely to reduce the number of independent variables in the problem and to generalize the results so that they may be used over wide ranges of conditions A classic example of the value of dimensional analysis is provided by a Modeling of Thermal Systems 167 study of the drag force F exerted on a sphere of diameter D in a uniform fluid flow at velocity V If the fluid viscosity is denoted by and its density by , the drag force F may be given in terms of the variables of the problem as F f1(D, V, , where f1 represents the functional dependence The use of dimensional analysis reduces this equation for F to the form F V D2 f2 VD (3.21) where VD/ is the well-known dimensionless parameter known as the Reynolds number Re The functional dependence given by f has to be obtained experimentally But only a few experiments are needed to determine the functional relationship between the dimensionless drag force F/( V D2) and the Reynolds number Re Equation (3.21) also indicates the variation of the drag force F with the different physical variables in the problem such as D, V, and Clearly, considerable simplification and generalization has been obtained in representing this problem so that once the function f has been obtained through selected experiments, the results can be applied to spheres of different diameters, to different fluids, and to a range of fluid velocities V, as long as the flow characteristics remain unchanged, such as laminar or turbulent flow in this example Similarly, heat transfer data in forced convection are correlated in terms of the Nusselt number Nu hD/kf , kf being the fluid thermal conductivity The areas of fluid mechanics and heat transfer are replete with similar examples that demonstrate the importance and value of dimensional analysis There are two main approaches for deriving the dimensionless parameters in a given problem These are: Combinations of variables This method considers all the variables in the problem and the appropriate basic dimensions, such as length, mass, temperature, and time, associated with them Then the dimensionless parameters are obtained by forming combinations of these variables to yield dimensionless groups The Buckingham Pi theorem states that for n variables, (n – m) dimensionless ratios, or parameters, can be derived, where m is usually, but not always, equal to the minimum number of independent dimensions that arise in all the variables Thus, the number of important variables that characterize a given system must be obtained based on experience and physical interpretation of the problem The primary dimensions are determined and combinations of the variables are formed to obtain dimensionless groups If a particular group can be formed from others through multiplication, division, raising to a power, etc., it is not independent Thus, independent dimensionless groups may be obtained However, all the important variables must be included and the method does not yield the physical significance of the various dimensionless parameters 168 Design and Optimization of Thermal Systems Governing equations This approach is based on the nondimensionalization of the governing equations and boundary conditions, which are obtained by the mathematical modeling of the given system The governing equations are first written in terms of the physical variables, such as time, spatial coordinates, temperature, and velocity Characteristic quantities are then chosen based on experience and the physical nature of the system These are used to nondimensionalize the variables that arise in the equations The nondimensionalization is then applied to the governing equations, which are thus transformed into dimensionless equations, with all the dimensionless groups appearing as coefficients in the equation Similarly, the boundary conditions are nondimensionalized and these may yield additional dimensionless parameters For steady-state, three-dimensional conduction in the solid bar shown in Figure 3.9, Equation (3.6) gave the nondimensional governing equation, with L 2/H2 and L 2/W2, or simply L/H and L/W, as the two dimensionless parameters that characterize the temperature distribution in the solid For this problem, Equation (3.5) gave the transformation used for nondimensionalization, with L, H, and W as the characteristic length scales No additional parameters arise from the boundary conditions if the temperatures are specified as constant at the surfaces But if a convective boundary condition of the form k T x h(T (3.22) Ta ) is given at, say, the surface x L, this equation may be nondimensionalized, using Equation (3.5), to give T Ta Ts Ta hL , where k X (3.23) Here, Ts is a reference temperature and could be taken as the specified temperature at x Therefore, hL/k arises as an additional dimensionless parameter from the boundary conditions This parameter is generally referred to as the Biot number Bi, mentioned and defined earlier Similarly, the physical temperature T in an infinite rod moving at speed U along the x direction, which is taken along its axis, and losing energy by convection at the surface, as sketched in Figure 1.10(d) and Figure 3.6(a), is governed by the equation C where T is the Laplacian, given by U T x k / x2 T / y2 (3.24) / z Modeling of Thermal Systems 169 It can be easily confirmed that, using the nondimensional variables given earlier, the dimensionless equation obtained is Equation (3.20), where the Peclet number Pe was seen to arise as the only governing dimensionless parameter in the equation Other parameters such as the Biot number Bi may arise from the boundary conditions Similarly, other circumstances of interest in thermal systems may be considered to derive the governing parameters Both the approaches for dimensional analysis outlined here are useful for thermal processes and systems However, the nondimensionalization of equations does not require the listing of all the important variables in the problem and leads to a physical interpretation of the dimensionless groups, as discussed earlier for the two examples given previously Therefore, the nondimensionalization of the equations is the preferred approach However, characteristic quantities are needed and may be difficult to obtain if no simple scales are evident in the problem or if several choices are possible Therefore, both the approaches may be employed to derive the relevant parameters for a given thermal system Several different dimensionless groups have been used extensively in thermal sciences, with a set of these characterizing a given system or process Each group has a specific physical significance, often given in terms of the ratio of the orders of magnitude of two separate mechanisms or effects Some of the important dimensionless parameters that arise in fluid mechanics and in heat and mass transfer are listed in Table 3.1, along with the ratio of forces, transport mechanisms, etc., that they represent In this table, V, L, and T are the characteristic velocity, length, and temperature difference; g is the magnitude of gravitational acceleration; is the coefficient of thermal expansion; kf is the fluid thermal conductivity, as distinguished from the solid thermal conductivity k; DAB is the mass diffusivity; hm is the mass transfer coefficient; a is the speed of sound in the given medium; and is the surface tension The other symbols have been defined earlier There are many more dimensionless parameters that arise in the analysis and design of thermal processes and systems The ratio of effects just mentioned is largely qualitative and may be used for an interpretation of the physical significance of a given dimensionless group For further details on the derivation of these dimensionless parameters and their use, textbooks in heat transfer and in fluid mechanics may be consulted Example 3.7 The electronic system shown in Figure 3.20 is cooled by the forced flow of ambient air driven by a fan through openings near the top of the enclosure The dimension in the third direction is given as large and the problem may be treated as two-dimensional The various dimensions in the system and the locations of three electronic components are indicated in the figure The velocities are uniform over the inlet and outlet, with magnitudes vi and vo, respectively The temperature at the inlet is Ti and developed conditions, i.e., T/ y 0, may be assumed at the exit The outer wall of the system loses energy to ambient air at temperature Ti with a given convective heat transfer coefficient h Write down the governing equations and boundary 170 Design and Optimization of Thermal Systems TABLE 3.1 Commonly Used Dimensionless Groups in Fluid Mechanics and Heat and Mass Transfer Parameter Definition VL Ratio of Effects Inertia forces Viscous forces Reynolds number Re Froude number Fr V2 gL Inertia forces Gravitational forces Mach number M V a Flow speed Sound speed Weber number We Euler number Eu Prandtl number Pr Peclet number Pe Eckert number V 2L Inertia forces Surface tension forces p V2 Pressure forces Inertia forces Momentum diffusion Thermal diffusion VL Convective transport Conductive transport Ec V2 Cp T Kinetic energy Enthalpy difference Biot number Bi hL k Conductive resistance Convective resistance Fourier number Fo L2 Thermal diffusion Thermal energy storage Grashof number Gr Nusselt number Nu hL kf Convection Diffusion Sherwood number Sh hm L DAB Convective mass transfer Mass diffusion Schmidt number Sc DAB Momentum diffusion Mass diffusion Lewis number Le DAB Thermal diffusion Mass diffusion g TL3 Buoyancy forces Viscous forces Modeling of Thermal Systems 171 Wtot t3 vi Hi vo Ho t1 t2 H di qs Ls qs qs ds x Htot t4 y W FIGURE 3.20 The electronic system, with three electrical components as heat sources and forced air cooling, considered in Example 3.7 conditions for calculating the temperature distributions in the system Nondimensionalize these to obtain the governing dimensionless parameters in the problem Assume laminar flow and constant properties Solution This is clearly a fairly complicated problem and involves combined conduction and convection Because of spatial and temporal variations, partial differential equations will be obtained However, this example serves to indicate some of the major complexities that arise when dealing with thermal systems For the given two-dimensional laminar flow problem, the governing equations for convection in the enclosure may be written, in terms of the coordinate system shown, as (Burmeister, 1993) V V Cp T p g (T Ti ) V ( V) V ( T) k 2 V T where V is the velocity vector, p is the local pressure, is time, g is the gravitational acceleration, is the coefficient of volumetric thermal expansion, is density, Cp is the specific heat at constant pressure, k is the thermal conductivity, and T is the ... Modeling of Thermal Systems 14 9 T1 T2 T2 Streamlines T2 Isotherms (a) (b) A1 A2 V1 V2 2 ρ1V1A1 = ρ2V2 A2 (c) (d) FIGURE 3 .13 Differential formulations (a) Flow in an enclosed region due to inflow and. .. Further details on the analysis and design of such heat exchangers are discussed in Chapter 15 2 Design and Optimization of Thermal Systems Example 3.3 In the design of a hot water storage system,... equations and boundary 17 0 Design and Optimization of Thermal Systems TABLE 3 .1 Commonly Used Dimensionless Groups in Fluid Mechanics and Heat and Mass Transfer Parameter Definition VL Ratio of Effects