Enhanced Multi-Objective Optimization of a Microchannel Heat Sink Using Multiple Surrogates Modeling

2009 ◽  
Author(s):  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Heat Sink ◽  
Design Space ◽  
Surrogate Models ◽  
Pareto Optimal ◽  
Microchannel Heat Sink ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Design Variables

A liquid flow microchannel heat sink has been studied and optimized with the help of three-dimensional numerical analysis and multiple surrogate methods. Two objective functions, thermal resistance and pumping power have been selected to assess the performance of the microchannel heat sink. The design variables related to the microchannel top and bottom widths, depth and fin width, which contribute to objective functions, have been identified and design space has been explored through some preliminary calculations. Design of experiments was performed and a three-level full factorial design was selected to exploit the design space. The numerical solutions obtained at these design points were utilized to construct surrogate models namely Response Surface Approximations and Kriging. A hybrid multi-objective evolutionary algorithm coupled with surrogate models and a gradient-based search algorithm is applied to find global Pareto-optimal solutions. Since, the surrogate models are highly problem-dependent, the accuracy of the two surrogate models has been discussed in view of their predictions at on- and off-Pareto-optimal front. The trade-off analysis was performed in view of the two competing objectives. The Pareto-optimal sensitivity (change in value along the Pareto-optimal front) of the design variables has been found out to economically compromise with the design variables contributing relatively less to the objective functions. The application of the multiple surrogate methods not only improves quality of multi-objective optimization but also gives the feedback of the fidelity of the model near the optimum region.

Multiobjective Optimization of a Microchannel Heat Sink Using Evolutionary Algorithm

2008 ◽  
Vol 130 (11) ◽  
Author(s):  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Performance Optimization ◽  
Heat Sink ◽  
Algorithm Design ◽  
Pareto Optimal ◽  
Microchannel Heat Sink ◽  
Objective Functions ◽  
Design Variables ◽  
Polynomial Response Surface

A multiobjective performance optimization of microchannel heat sink is carried out numerically applying surrogate analysis and evolutionary algorithm. Design variables related to microchannel width, depth, and fin width are selected, and two objective functions, thermal resistance and pumping power, are employed. With the help of finite volume solver, Navier–Stokes analyses are performed at the design sites obtained from full factorial design of sampling methods. Using the numerically evaluated objective function values, polynomial response surface is constructed for each objective functions, and multiobjective optimization is performed to obtain global Pareto optimal solutions. Analysis of optimum solutions is simplified by carrying out trade-off with design variables and objective functions. Objective functions exhibit changing sensitivity to design variables along the Pareto optimal front.

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

Shape Optimization of Microchannels Using Surrogate Modelling

2018 ◽  
Author(s):  
Muhammad Ansab Ali ◽  
Tariq S. Khan ◽  
Saqib Salam ◽  
Ebrahim Al Hajri
Keyword(s):  
Shape Optimization ◽  
Heat Sink ◽  
Pareto Front ◽  
Sampling Method ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Optimum Shape ◽  
Microchannel Heat Sink ◽  
Objective Functions ◽  
Computational Resources

To minimize the computational and optimization time, a numerical simulation of 3D microchannel heat sink was performed using surrogate model to achieve the optimum shape. Latin hypercube sampling method was used to explore the design space and to construct the model. The accuracy of the model was evaluated using statistical methods like coefficient of multiple determinations and root mean square error. Thermal resistance and pressure drop being conflicting objective functions were selected to optimize the geometric parameters of the microchannel. Multi objective shape optimization of design was conducted using genetic algorithm and the optimum design solutions are presented in the Pareto front. The application of the surrogate methods has predicted the performance of the heat sink with the sufficient accuracy employing significantly lower computational resources.

scholarly journals The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2271 ◽  
Author(s):  
Stephen Ntiri Asomani ◽  
Jianping Yuan ◽  
Longyan Wang ◽  
Desmond Appiah ◽  
Kofi Asamoah Adu-Poku
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Operating Conditions ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Operating Range ◽  
Design Variables ◽  
Pareto Optimal Points

Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables (b2, β2, β1, and z) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.

Robustness Against Large Variations in Multi-Objective Optimization Problems

2013 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis
Keyword(s):  
Optimization Problems ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Environment ◽  
Multi Objective ◽  
Uncertainty Sources ◽  
Design Variables ◽  
Robustness Index ◽  
Environment Parameters

In the presence of multiple optimal solutions in multi-modal optimization problems and in multi-objective optimization problems, the designer may be interested in the robustness of those solutions to make a decision. Here, the robustness is related to the sensitivity of the performance functions to uncertainties. The uncertainty sources include the uncertainties in the design variables, in the design environment parameters, in the model of objective functions and in the designer’s preference. There exist many robustness indices in the literature that deal with small variations in the design variables and design environment parameters, but few robustness indices consider large variations. In this paper, a new robustness index is introduced to deal with large variations in the design environment parameters. The proposed index is bounded between zero and one, and measures the probability of a solution to be optimal with respect to the values of the design environment parameters. The larger the robustness index, the more robust the solution with regard to large variations in the design environment parameters. Finally, two illustrative examples are given to highlight the contributions of this paper.

Pareto-optimal front for multi-objective optimization of the suspension of a full-vehicle model in the frequency domain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Frequency Domain ◽  
Computational Time ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
Content Type ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Suspension Systems ◽  
Road Profile

PurposeThis study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.Design/methodology/approachUnlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.FindingsThe results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.Originality/valueDespite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

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