The present study investigates the thermal conductivity and coefficient of thermal expansion of bimodal SiCp reinforced Aluminum matrix composites formed via powder metallurgy method. The after-effects of proportion of particulate reinforcement as size distribution and sintering parameters on the thermal properties have been explored. The Box-Behnken design for response surface methodology was adopted to recognize the significance of chosen variables on the thermal conductivity and coefficient of thermal expansion of the composite. It is witnessed that the thermal conductivity and coefficient of thermal expansion enhanced due to increase in fine SiC particulates volume fraction. It has been exhibited that the fine SiC particulates (37μm) doped Al-matrix occupied interstitial positions and developed continuous SiC-matrix network. SEMs were conducted to evaluate the microstructure architecture for MMCs.

Heat removal capacity, coolant pumping pressure drop and surface temperature non-uniformity are three major challenges facing single-phase flow microchannel compact heat exchangers. In this paper multi-objective optimization has been performed to increase heat removal capacity, and decrease pressure drop and temperature non-uniformity in single-flow microchannels. Three-dimensional (3D) 4-floor branching networks have been applied to increase heat removal capacity of a microchannel from silicon substrate (15×15×2 mm). Each floor has four different branching sub-networks with opposite flow direction with respect to the next one. Each branching network has four inlets and one outlet. However, branching patterns of each of these sub-networks could be different from the others. Conjugate heat transfer analysis has been performed by developing a software package which uses quasi-1D thermo-fluid analysis and a 3D steady heat conduction analysis. These two solvers are coupled through their common boundaries representing surfaces of the cooling microchannels. Using quasi-1D solver significantly decreases computing time and its results are in good agreement with 3D Navier-Stokes equations solver for these types of application. The analysis package is capable of generating 3D branching networks with random topologies. 1341 random cooling networks were simulated using this analysis package. Multi-objective optimization using modeFrontier software was performed using response surface approximation and genetic algorithm. Diameters and branching pattern of each sub-network and coolant flow direction on each floor were design variables of multi-objective optimization. Maximizing heat removal capacity, minimizing pressure drop and temperature non-uniformity on the hot surface were three simultaneous objectives of the optimization. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm 2 can be managed with 3D 4-floor microchannel cooling networks.

In recent years, there has been interest in employing atomistic computations to inform macroscale thermal transport analyses. In heat conduction simulations in semiconductors and dielectrics, for example, classical molecular dynamics (MD) is used to compute phonon relaxation times, from which material thermal conductivity may be inferred and used at the macroscale. A drawback of this method is the noise associated with MD simulation, which is generated due to the possibility of multiple initial configurations corresponding to the same system temperature; for phonon relaxation times, the spread may be as high as 20%. In this work we propose a method to quantify the uncertainty in thermal conductivity computations due to MD noise, and its effect on the computation of the temperature distribution in heat conduction simulations. Bayesian inference is used to construct a probabilistic surrogate model for thermal conductivity as a function of temperature, accounting for the statistical spread in MD relaxation times. The surrogate model is used in probabilistic computations of the temperature field in macroscale Fourier conduction simulations. These simulations yield probability density functions of the spatial temperature distribution. To allay the cost of probabilistic computations, a stochastic collocation technique based on generalized polynomial chaos (gPC) is used to construct a response surface for the variation of temperature (at each physical location in the domain) as a function of the random variables in the thermal conductivity model. Results are presented for the spatial variation of the probability density function of temperature as a function of spatial location in a typical heat conduction problem to establish the viability of the method.

In the present study, the robust thermal design of a power device package was accomplished using thermal conduction calculation, design of experiment, response surface method and Monte Carlo simulation. Initially, the effects of the design parameters on the solder strain were examined in terms of the thermal expansion difference as a result of unsteady thermal conduction simulation. From the factorial effects of design parameters, the design proposals were screened. Then, robustness of the thermal resistance was evaluated for the three design proposals obtained. The thermal resistances were calculated by solving the steady thermal conduction equation under the design of experiment conditions. The solder thickness, the substrate thickness, and the cooling fin performance were considered as the fluctuation factors, assuming the error associated with manufacturing process. Using a response surface method, the values of thermal resistance were expressed as a function of the design variables. The variances of the thermal resistance were examined based on Monte Carlo simulations. Related to the cooling fin design, the Pareto line showing the trade-off relation between the fin dimension and the fan velocity was obtained. By repeating the Monte Carlo simulations, the Pareto solution was calculated so that the thermal resistances satisfy the criteria in the position of 95 percrntile of the thermal resistance variation. Under the same flow velocity conditions, the fin dimensions become about 10% higher compared to the case where the manufacturing error was not taken into account. By carrying out the thermal design following this Pareto line, even if the manufacturing error was taken into consideration, the thermal resistance could satisfy the desired value.

This study is focused on improving cooling performance of the housing fin for Total Enclosed Fan Cooled (TEFC) motors. We conducted a sensitivity study on the motor housing fin to determine key design parameters and developed an optimization procedure. The goal is to use the optimizer to achieve an efficient design process for optimal fin design under specified operating conditions. Response Surface Methodology (RSM) was constructed out of the numerical data with multi-quadratics (MQ) as basis functions to predict the response. The RSM, in conjunction with generic optimization methods, was used to find the optimal fin design in the parametric design space. The parameter database was non-dimensionalized so that the optimizer can be applied to various motor frame sizes. Compared with the original fin design, in some cases the optimal fin configuration reduces thermal resistance to heat convection from the fin surface by more than 50%.

The heat transfer and fluid flow characteristics of airside plate fins of the condenser coil of a gravity-assisted heat pipe are numerically predicted. Based on these CFD computations, the optimization of the airside structure of the heat pipe is carried out by studying the influence of different geometry design variables on a combined heat exchanger evaluation function K , which can be changed by an engineer according to his application experience. In this work, one hundred of DOE’s points are sampled across the design space of the heat pipe based on the Latin Hypercube method. The Least Squares Fitting method is used to fit these sampled points to the RSM of the design space. The Mixed Integer Optimization Algorithm is used to explore the maximum specially-defined K on the Response Surface; and the corresponding optimal size and shape of the heat pipe are finally obtained. The optimization procedure is performed automatically by employing the optimizer-Optimus, grid generator-Gambit, and CFD solver-Fluent.

A critical aspect of the design of systems or experiments is a sensitivity analysis to determine the effects of the different variables. This is usually done by representing the response by a Taylor series and evaluating the first order derivatives at a nominal operating point. When there is uncertainty about the operating point, the common approach is the construction of a response surface and Monte Carlo sampling based on the probability distribution of these uncertain variables. When there are many variables it is important to restrict the analysis to those variables to which the response is most sensitive. This can be conveniently done using Global sensitivity , that both defines the most critical variables and also quantifies the effects of interacting variables. This can be a computationally expensive process and for complex models is generally prohibitively expensive. A solution is the use of Gaussian processes that allows one to create a response surface using easy-to-evaluate functions. This paper describes the use of these ideas for a heat transfer problem.

This paper presents the response surface optimization method using three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved blades centrifugal fan. For numerical analysis, Reynolds-averaged Navier-Stokes equations with k-ε turbulence model are discretized with finite volume approximations. In order to reduce huge computing time due to a large number of blades in forward-curved blades centrifugal fan, the flow inside of the fan is regarded as steady flow by introducing the impeller force models. Three geometric variables, i.e., location of cut off, radius of cut off, and width of impeller, and one operating variable, i.e., flow rate, were selected as design variables. As a main result of the optimization, the efficiency was successfully improved. And, optimum design flow rate was found by using flow rate as one of design variables. It was found that the optimization process provides reliable design of this kind of fans with reasonable computing time.

Developments of volume averaging theory (VAT) used to describe transport phenomena in heterogeneous media are applied to optimization of heat dissipation from a heterogeneous media. The media is a porous media representation of a pin fin heat sink (a heterogeneous layer) and the optimization process is accomplished with rigor using the idea of scaled energy transport. The problem is addressed in four steps: 1) determine the parameters needed for optimization from the two temperature VAT equations, 2) use statistical design of experiments (simulating the problem) for the many optimization parameters, 3) perform numerical simulation of the cases that are suggested through the statistical analysis of the optimization parameters, and 4) statistically analyze the numerical results to obtain an optimization response surface. The two applications are enhancement of heat transfer dissipation from a heterogeneous media while minimizing the frictional resistance and minimization of the thermal resistance (a problem of importance to all designers of heat exchangers).

This work presents the experimental results of the effect of porous heating surfaces, and the Marangoni effect on the convective heat transfer coefficient for pool boiling, h. The porous heating surfaces fabricated for these experiments, and the interfacial tension gradients in the binary mixtures reduced the bubbles’ size and their coalescence in the proximity of the heating surface. The convective heat transfer coefficient was calculated for the boiling of pure water and three aqueous mixtures with 12, 16, and 20% weight of ethanol on five different porous coverings on the heating element. Some combinations of these variables were studied in a 3 2 factorial design, and represented by the response surface calculated. The maximum h for boiling of pure water on the bare surface of the heating element was 50 kW/m 2 °C. Using the porous coverings, the maximum h value was 180 kW/m 2 °C. For boiling the binary mixtures on the smooth heating element surface the maximum h value was 65 kW/m 2 °C, while on the porous coverings the values of h attained a maximum of 220 kW/m 2 °C. The maximum values of h correspond to the composition of 16% ethanol, and a porous covering with the smallest porous diameter.