Optimal thermal design of microchannel heat sinks with different geometric configurations

In this study, three-dimensional models of microchannel heat sinks (MCHSs) with different geometric configurations (such as single-layered- (SL), double-layered- (DL) or tapered-(T)-channels) are constructed by an optimization procedure. This procedure integrates a direct problem solver with a simplified conjugate-gradient method as the optimizer. The overall thermal resistance of an MCHS is the objective function to be minimized with respect to geometric parameters, such as the number of channels, channel width ratio, channel aspect ratio and tapered ratios, as the search variables. The optimal thermal resistance is found to decrease in the following order: the initial guess parallel channel (IGP channel), SL-, DL- and T-channel designs. In addition, the T-channel design has the minimum temperature difference and the most uniform temperature distribution, followed by the DL-, SL- and IGP-channel designs. Moreover, the optimal thermal resistance reduces with the pumping power for the various channel configuration designs, and the lowest thermal resistance corresponds to the T-channel design. The larger the pumping power, the larger the decrement in thermal resistance. Therefore, the optimal T-channel is the best MCHS design when considering thermal resistance and temperature distribution uniformity.     

Optimal design of baffles locations with interdigitated flow channels of a centimeter-scale proton exchange membrane fuel cell

In the present study, the simplified conjugate-gradient method (SCGM) is combined with commercial CFD code to build an optimizer for designing the baffles locations with interdigitated channels of a centimeter-scale proton exchange membrane fuel cell (PEMFC). Using the optimizer, the locations of the baffles are adjusted toward the maximization of the average current density of the flow field. The approach is developed by using the commercial CFD code as the direct problem solver, which is able to provide the numerical solutions for the three-dimensional mass, momentum and species transport equations as well as to predict the electron conduction and proton migration taking place in a PEMFC. Results show that the optimal design process of the locations of the baffles can be completed by using the present optimization approach in just a finite number of iterations. The optimization process may lead to an appreciable increase by 14% in the power output from the fuel cell.     

Shape design of millimeter-scale air channels for enhancing heat transfer and reducing pressure drop

The present study is aimed at shape design of a millimeter-scale air channel for increasing heat transfer to the air from the heated channel wall and reducing pressure drop between the inlet and the outlet. The approach is developed by combining a direct problem solver with an optimization method. A two-dimensional theoretical model is used to develop a direct problem solver, which provides the numerical predictions of the thermal and flow fields associated with the varying shape profile during the iterative optimization process. Meanwhile, the simplified conjugate-gradient method (SCGM) is used as the optimization method which continuously updates the shape until the objective function is minimized. In this paper, a method based on a point-by-point technique for constructing the shape profile is employed. This method is particularly suitable for determining the irregular profiles that cannot be approximated by the polynomial functions. The optimal shapes at different inlet velocities are obtained. It is found that the search process is robust and always leads to the same optimal solution regardless of the initial guess.     

Optimization of fractal-like branching microchannel heat sinks for single-phase flows

Fractal-like branching flow networks in disk-shaped heat sinks are numerically optimized to minimize pressure drop and flow power. Optimization was performed using a direct numerical search, gradient-based optimization, and genetic algorithm. A previously validated one-dimensional pressure drop and heat transfer model, with water as the working fluid, is employed as the objective function. Geometric constraints based on fabrication limitations are considered, and the optimization methodology is compared with results from a direct numerical search and a genetic algorithm.The geometric parameters that define an optimal flow network include the length scale ratio, width scale ratio, and terminal channel width. Along with disk radius, these parameters influence the number of branch levels and number of channels attached to the inlet plenum. The geometric characteristics of the optimized flow networks are studied as a function of disk radius, applied heat flux, and maximum allowable wall temperature. A maximum inlet plenum radius, minimum interior channel spacing, and ranges of terminal channel widths and periphery channel spacing are specified geometric constraints. In general, all geometric constraints and the heat flux have a significant influence on the design of an optimal flow network. Results from a purely geometrically derived network design are shown to perform within 15% of the direct search and gradient-based optimized configurations.     

Inverse analysis of forced convection in micro-channels with slip flow via integral transforms and Bayesian inference

The present work addresses the direct and inverse problems for convective heat transfer with incompressible laminar gas flow in micro-channels, within the range of validity of the slip-flow regime. The direct problem analysis combines the classical integral transform method and the generalized integral transform technique (GITT), by analytically solving the two-dimensional steady-state convection problem and finding a hybrid numerical-analytical solution for the required eigenvalue problem. The inverse problem analysis makes use of the accuracy and robustness of the direct problem solution and focus on the simultaneous identification of the momentum and thermal accommodation coefficients, related to gas flow and heat transfer within micro-channels, besides the usually unknown boundary condition parameters, here represented by the external Biot number. The inverse analysis is based on the availability solely of temperature measurements at the channel external wall, along its length, as obtained for instance via infrared camera thermography. A Bayesian inference approach is adopted in the solution of the identification problem based on the Monte Carlo Markov Chain method (MCMC) and the Metropolis-Hastings sampling algorithm. A typical example of slip flow in parallel-plates micro-channel is selected to illustrate both the direct and inverse problems solution approaches.     

Design optimization of rib/channel patterns in a PEMFC through performance heterogeneities modelling

In this paper, an approach coupling an along the channel and rib-channel models has been developed to perform a design optimization of PEM fuel cell bipolar plates rib/channel patterns. Overall performance mainly results from a competition between current collection and oxygen supply. The allows proposed to investigate the effect of geometry and operating parameters on the resulting equilibrium and optimum. Moreover, heterogeneities issued from the crushing effect by the rib on the GDL are accounted.The electrochemical parameters used in the model are fitted to experimental measurements before being used in the optimization process. Results at the channel/rib scale show the competition between the oxygen supply from the gas channel to the catalyst layer and the current collection by the ribs. This understanding is possible thanks to the access to local conditions (such as the oxygen concentration) given by the model which are difficult to reach with experimental measurements. In-plane and through plane heterogeneities of current density distribution in the catalyst layer are exhibited.Design optimization is performed on the channel width/total width ratio on the cathode side. The model suggests an optimal channel design by varying its width along the flow while the standard design considers a contant ratio. This optimal channel is shown to be mainly dependent on the stoichiometry ratio of oxygen.     

Modelling and optimization of reactant gas transport in a PEM fuel cell with a transverse pin fin insert in channel flow

A proton exchange membrane (PEM) fuel cell has many distinctive features which make it an attractive alternative clean energy source. Some of those features are low start-up, high power density, high efficiency and remote applications. In the present study, a numerical investigation was conducted to analyse the flow field and reactant gas distribution in a PEM fuel cell channel with transversely inserted pin fins in the channel flow aimed at improving reactant gas distribution. A fin configuration of small hydraulic diameter was employed to minimise the additional pressure drop. The influence of the pin fin parameters, the flow Reynolds number, the gas diffusion layer (GDL) porosity on the reactant gas transport and the pressure drop across the channel length were explored. The parameters examined were optimized using a mathematical optimization code integrated with a commercial computational fluid dynamics code. The results obtained indicate that a pin fin insert in the channel flow considerably improves fuel cell performance and that optimal pin fin geometries exist for minimized pressure drop along the fuel channel for the fuel cell model considered. The results obtained provide a novel approach for improving the design of fuel cells for optimal performance.     

An inverse problem methodology for design and optimization of an interior permanent magnetic BLDC motor

In the theory of inverse problem, the parameters identification by optimization is considered as one of its main applications. This paper presents an optimal design of a slotted permanent magnet Brushless DC (BLDC) motor with surface mounted magnets. The inverse problem method is applied by using a thriving solver afforded by the nonlinear optimization toolbox of Matlab ‘Fmincon’, this function is based on Active-Set and Sequential Quadratic Programming approaches with calculation of the Hessian from Quasi-Newton algorithm. The optimal magnetic field density considered as the main objective is obtained by picking several parameters and analyzing their effects. The proposed approach is highlighted by using the obtained parameters in the design of the motor. The Finite element method is applied on the motor for numerical analysis by using FEMM magnetic coupled with Matlab code. Effectiveness and robustness of the proposed approach are verified by a comparison between the initial and optimized design.     

Performance improvement of proton exchange membrane fuel cell by using annular shaped geometry

A complete three-dimensional and single phase CFD model for a different geometry of proton exchange membrane (PEM) fuel cell is used to investigate the effect of using different connections between bipolar plate and gas diffusion layer on the performances, current density and gas concentration. The proposed model is a full cell model, which includes all the parts of the PEM fuel cell, flow channels, gas diffusion electrodes, catalyst layers and the membrane. Coupled transport and electrochemical kinetics equations are solved in a single domain; therefore no interfacial boundary condition is required at the internal boundaries between cell components.This computational fluid dynamics code is used as the direct problem solver, which is used to simulate the three-dimensional mass, momentum and species transport phenomena as well as the electron- and proton-transfer process taking place in a PEMFC that cannot be investigated experimentally. The results show that the predicted polarization curves by using this model are in good agreement with the experimental results. Also the results show that by increasing the number of connection between GDL and bipolar plate the performance of the fuel cell enhances.     

Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.     

Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed.     

Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

This work presents a novel design and development of a fuzzy predictive supervisory controller, based on genetic algorithms (GA), for gas turbines of combined cycle units. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. A fuzzy model is considered in order to characterize the nonlinear behavior of the gas turbine, which is used in two supervisory control systems. The first fuzzy supervisory control design includes a fuzzy model, where its parameters are held constant for the successive predictions. For the second fuzzy supervisory control design, its parameters are updated in each prediction and its nonlinear optimization problem is solved using GAs. The proposed fuzzy supervisory controllers are compared against a supervisory controller based on linear models and a regulatory controller with constant optimal set-points. Results indicate that the fuzzy GA predictive supervisory controller captures adequately the nonlinearities of the process, which, in turn, provides a promising approach to improve the performance of the combined cycle unit.     

Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem-based optimization approach

Identifying accurate and precise photovoltaic models' parameters is the primary gate in providing a proper PV system design simulate its real behavior. Therefore, this article proposed a new approach based on a recent meta-heuristic algorithm of artificial ecosystem-based optimization (AEO) to identify the optimal parameters of PV cell and module models. Various PV models are considered in this work as single diode (SD), double diode (DD), and triple diode (TD)-based circuits. The analysis is performed on which are R.T.C. France silicon solar cell, FSM-25 PV module, and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel with the aid of experimental data under different operating conditions. Moreover, Lambert form is employed to validate the constructed model. Furthermore, comparative analysis with Harris hawks optimizer (HHO), gray wolf optimizer (GWO), and salp swarm algorithm (SSA) is performed. Additionally, statistical analysis using the Wilcoxon signed rank test is implemented across the three series of experiments for all employed optimizers. The obtained results confirmed the competence of the proposed approach in identifying the PV cell and modules equivalent circuits' parameters.     

Numerical analysis of effects of flow channel size on reactant transport in a proton exchange membrane fuel cell stack

Numerical simulation of transport phenomena in a six-cell PEM fuel cell stack is performed to adjust the gas channel size of individual cells to obtain evenly distributed cell voltages. Commercial software, CFD-ACE+, is used as the solution code, and the effects of various combinations of geometric parameters of the channels are investigated, including the channel width ratios (Λ) and the heights of the gas channels (h). Furthermore, a six-cell fuel cell stack was assembled for experiments, and the polarization curves of the stack were measured. With the help of the experimental data, the values of the physical and electrochemical parameters adopted in the computation model are determined, and the computation model is then used in numerical simulation. Under the assumption of no dry out in the cells, an even voltage distribution can be obtained if the distribution of reactant gas is uniform among the cells. In this study, better combinations of the channel width ratios and the heights of gas channels for individual cells are proposed to yield a uniform distribution of the reactant gases in the cells. An adjustment of the geometric parameters of gas channels that leads to evenly distributed voltages is attempted, and a 16.5% increase in the performance of the fuel cell stack is observed simply by adjusting the sizes of the gas channels.     

On preconditioned BiCGSTAB solver for MLPG method applied to heat conduction in complex geometry

Meshless local Petrov–Galerkin (MLPG) method is a promising meshfree method for continuum problems in complex domains, especially for large deformation, moving boundary and phase change problems. For large-scale problems, iterative methods for solving the discretized equations are more suitable than direct methods. Krylov subspace solvers of conjugate gradient type are the most preferred iterative solvers. The convergence rate of these methods depends on preconditioner used. Recently, proposed schedule relaxation Jacobi (SRJ) method can be used as a stand-alone solver and as a preconditioner. In the present work, the SRJ method is tested as a stand-alone solver and as a preconditioner for BiCGSTAB solver in the MLPG method, and its performance has been compared with successive overrelaxation (k) preconditioner. Two-dimensional linear steady-state heat conduction in complex shape geometry has been used as the model test problem.     

Simulation of gas-assisted injection mold-cooling process using line source model approach for gas channel

Whether it is feasible to perform an integrated simulation for process simulation based on a unified CAE model for gas-assisted injection molding (GAIM) is a great concern. In the present study, numerical algorithms based on the same CAE model used for process simulation regarding filling and packing stages were developed to simulate the cooling phase of GAIM using a cycle-averaged three-dimensional modified boundary element technique similar to that used for conventional injection molding. However, to use the current CAE model for analysis, gas channel was modeled by two-node elements using line source approach. It was found that this new modeling not only affects the mold wall temperature calculation very slightly but also reduces the computer time by 95% as compared with a full gas channel modeling required a lot of triangular elements on gas channel surface. This investigation indicates that it is feasible to achieve an integrated process simulation for GAIM under one CAE model resulting in great computational efficiency for industrial application.     

The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia

A current trend in electric power industries is the deregulation around the world. One of the questions arise during any deregulation process is: where will be the future generation expansion? In the present paper, the study is concentrated on the wheeling computational method as a part of mega watt (MW) linear programming-based optimal power flow (LP-based OPF) method. To observe the effects of power wheeling on the power system operations, the paper uses linear interactive & discrete optimizer (LINDO) optimizer software as a powerful tool for solving linear programming problems to evaluate the influence of the power wheeling. As well, the paper uses the optimization tool to solve the economic generation dispatch and transmission management problems. The transmission line flow was taken in consideration with some constraints discussed in this paper. The complete linear model of the MW LP-based OPF, which is used to know the future generation potential areas in any utility is proposed. The paper also explains the available economic load dispatch (ELD) as the basic optimization tool to dispatch the power system. It can be concluded in the present study that accuracy is expensive in terms of money and time and in the competitive market enough accuracy is needed without paying much.     

Inverse estimation of inlet parameters in diffusion-controlled turbulent combustion in an axisymmetric combustor

The major objective of the present study is to extend the application of an inverse analysis to a more realistic engineering problem with complex combustion process than traditional simple heat transfer problem. In order to do this, unknown inlet parameters for burner in an axisymmetric combustor, such as species mass fractions and inlet velocities of fuel/oxidizer, are estimated from measurement data using the inverse analysis. For efficient estimation in a practical diffusion-controlled turbulent combustion problem, the repulsive particle swarm optimization (RPSO) method is implemented as an inverse solver which belongs to the class of stochastic evolutionary global optimization methods. Based on the results of the present study, it is expected that useful information can be successfully predicted using the inverse analysis even in a design optimization of the real combustion systems.     

Numerical investigation of liquid water dynamics in wave‐like gas channels of PEMFCs

For the air feed in proton exchange membrane fuel cells (PEMFCs), the wave‐like gas channel (GC) shows obvious advantages over the straight GC because the former enhances collision of secondary flow and diffusion in the gas diffusion layer (GDL). However, it is prone to water flooding, which brings greater pressure drop, larger pressure oscillation, and blocking of reaction area. In the present study, numerical models of the water dynamic processes, including water droplets emerging from micropores on the GDL surface and removing through the GC, are established based on the volume of fluid (VOF) method. Water coverage ratio and pressure drop are calculated to evaluate the water flooding. The effects of the dimensional parameters of wave‐like GC and contact angle of channel walls on the water accumulation are studied. The emergence and removal of liquid water is a quasiperiodic and oscillating process. Multicycle simulations show that channel pressure drop increases linearly with greater growth rate than channel length. The equilibrium position of water droplet is strongly dependent on the relative wettability of the GDL and bipolar plate (BPP) surfaces. And the geometric parameters of GC have a significant impact on the pressure, water removal behavior and detachment time. Smaller bent angle brings bigger pressure drop, and larger cycle length is helpful for relieving the oscillation of pressure.     

Enhancement of thermal performance in double-layered microchannel heat sink with nanofluids

A three-dimensional analysis aimed at enhancing the thermal performance of a double-layered microchannel heat sink by using a nanofluid and varying the geometric parameters has been conducted. A system of fully elliptic equations that govern the flow and thermal fields are solved using the finite volume method. The analysis indicates that the dominant factors determining the thermal resistance of the channel include the type of nanofluid; particle volume fraction; geometric parameters of the channel, such as the channel number, channel width ratio, channel aspect ratio; and pumping power. The results indicate that the greatest enhancement in channel cooling can be expected when an Al₂O₃–water nanofluid is used. The thermal resistance of the channel can be minimized by properly adjusting the particle volume fraction under various pumping powers; the minimum thermal resistance depends on the geometric parameters. The study also reveals that the relationship between the thermal resistance and channel number, channel width ratio, or channel aspect ratio exhibits a decrease followed by an increase. The thermal performance of the channel can usually be improved by decreasing the channel number or channel aspect ratio, or increasing the channel width ratio. Finally, increasing the pumping power reduces the overall thermal resistance. An Al₂O₃ (1%)–water nanofluid shows an average improvement in thermal performance of 26% over that of pure water for a given pumping power. However, the design’s effectiveness declines significantly under high pumping power. In particular, the thermal resistance obtained by employing nanofluids was not necessarily lower than that of water under all pumping powers, but it can be reduced by properly adjusting the geometric parameters under optimal conditions.