Multiobjective Optimization of a Pin-Fin Heat Sink Using Evolutionary Algorithms

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Siwadol Kanyakam ◽  
Sujin Bureerat
Keyword(s):  
Evolutionary Algorithms ◽  
Heat Sink ◽  
Incremental Learning ◽  
Design Problem ◽  
Population Based ◽  
Design Approach ◽  
Mixed Integer ◽  
Pin Fin ◽  
Design Variables ◽  
Multiobjective Design

This paper presents the use of multiobjective evolutionary algorithms for the optimal geometrical design of a pin-fin heat sink. The multiobjective design problem is posed to minimize two conflicting objectives: the junction temperature and the fan pumping power of the heat sink. The design variables are mixed integer/continuous. The encoding/decoding process for this mixed integer/continuous design variables is detailed. The multiobjective optimizers employed to solve the design problem are population-based incremental learning, strength Pareto evolutionary algorithm, particles swarm optimization, and archived multiobjective simulated annealing. The approximate Pareto fronts obtained from using the various optimizers are compared based upon the hypervolume and generational distance indicators. From the results, population-based incremental learning (PBIL) outperforms the others. The new design approach is said to be superior to a classical design approach. It is also illustrated that the proposed multiobjective design process leads to better design compared to the current commercial pin-fin heat sinks.

scholarly journals Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
S. Sleesongsom ◽  
S. Bureerat
Keyword(s):  
Incremental Learning ◽  
Population Based ◽  
Population Diversity ◽  
Design Approach ◽  
Structural Safety ◽  
Morphing Wing ◽  
Design Problems ◽  
Design Variables ◽  
Multiple Resolutions ◽  
Multiobjective Design

This paper has twin aims. Firstly, a multigrid design approach for optimization of an unconventional morphing wing is proposed. The structural design problem is assigned to optimize wing mass, lift effectiveness, and buckling factor subject to structural safety requirements. Design variables consist of partial topology, nodal positions, and component sizes of a wing internal structure. Such a design process can be accomplished by using multiple resolutions of ground elements, which is called a multigrid approach. Secondly, an opposite-based multiobjective population-based incremental learning (OMPBIL) is proposed for comparison with the original multiobjective population-based incremental learning (MPBIL). Multiobjective design problems with single-grid and multigrid design variables are then posed and tackled by OMPBIL and MPBIL. The results show that using OMPBIL in combination with a multigrid design approach is the best design strategy. OMPBIL is superior to MPBIL since the former provides better population diversity. Aeroelastic trim for an elastic morphing wing is also presented.

scholarly journals Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Siwadol Kanyakam ◽  
Sujin Bureerat
Keyword(s):  
Process Model ◽  
Population Based ◽  
Heat Sinks ◽  
Junction Temperature ◽  
Mixed Integer ◽  
Surface Model ◽  
Comparative Performance ◽  
Geometrical Design ◽  
Pin Fin ◽  
Multiobjective Design

This paper presents the comparative performance of several surrogate-assisted multiobjective evolutionary algorithms (MOEAs) for geometrical design of a pin-fin heat sink (PFHS). The surrogate-assisted MOEAs are achieved by integrating multiobjective population-based incremental learning (PBIL) with a quadratic response surface model (QRS), a radial-basis function (RBF) interpolation technique, and a Kriging (KRG) or Gaussian process model. The mixed integer/continuous multiobjective design problem of PFHS with the objective to minimise junction temperature and fan pumping power simultaneously is posed. The optimum results obtained from using the original multiobjective PBIL and the three versions of hybrid PBIL are compared. It is shown that the hybrid PBIL using KRG is the best performer. The hybrid PBILs require less number of function evaluations to surpass the original PBIL.

Multiobjective Design of an Antisymmetric Angle-Ply Laminate by Nonlinear Programming

1983 ◽  
Vol 105 (2) ◽  
pp. 214-219 ◽  
Author(s):  
S. Adali
Keyword(s):  
Nonlinear Programming ◽  
Pareto Optimality ◽  
Performance Index ◽  
Fiber Orientation ◽  
Design Problem ◽  
Natural Frequencies ◽  
Interactive Process ◽  
Design Variables ◽  
Present Design ◽  
Multiobjective Design

An antisymmetrically laminated angle-ply plate is optimized with the objectives of minimizing the maximum dynamic deflection, maximizing the natural frequencies and/or maximizing the buckling load. The design variables are the fiber orientation and the thickness of individual layers and are computed by using the methods of nonlinear programming. The concept of Pareto optimality is used in formulating the design problem and in reducing the multiple objectives into a single performance index. Numerical results are presented in the form of optimal tradeoff curves which allow the designer to assess the various possibilities open to him before deciding on a certain design. In this sense, the present design is an interactive process.

scholarly journals Antioptimisation of Trusses Using Two-Level Population-Based Incremental Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Phinit Tontragunrat ◽  
Sujin Bureerat
Keyword(s):  
Optimum Design ◽  
Incremental Learning ◽  
Population Based ◽  
Numerical Results ◽  
Level Population ◽  
Case Scenario ◽  
Worst Case ◽  
Worst Case Scenario ◽  
Design Variables ◽  
Optimum Solution

Practical optimum design of structures often involves parameters with uncertainties. There have been several ways to deal with such optimisation problems, and one of the approaches is an antioptimisation process. The task is to find the optimum solution of common design variables while simultaneously searching for the worst case scenario of those parameters with uncertainties. This paper proposed a metaheuristic based on population-based incremental learning (PBIL) for solving antioptimisation of trusses. The new algorithm is called two-level PBIL which consists of outer and inner loops. Five antioptimisation problems are posed to test the optimiser performance. The numerical results show that the concept of using PBIL probability vectors for handling the antioptimisation of truss is powerful and effective. The two-level PBIL can be considered a powerful optimiser for antioptimisation of trusses.

Surrogate-Assisted Evolutionary Optimizers for Multiobjective Design of a Torque Arm Structure

2011 ◽  
Vol 101-102 ◽  
pp. 324-328 ◽  
Author(s):  
Nantiwat Pholdee ◽  
Sujin Bureerat
Keyword(s):  
Design Problem ◽  
Latin Hypercube Sampling ◽  
Population Based ◽  
Design Strategies ◽  
Element Approach ◽  
Design Case Study ◽  
Multiobjective Design ◽  
Structural Mass ◽  
Constrained Multiobjective Optimization

This paper presents two surrogate-assisted optimization strategies for structural constrained multiobjective optimization. The optimization strategies are based on hybridization of multiobjective population-based incremental learning (MOPBIL) and radial-basis function (RBF) interpolation. The first strategy uses MOPBIL for generating training points while the second strategy uses a Latin hypercube sampling (LHS) technique. The design case study is the shape and sizing design of a torque arm structure. A design problem is set to minimize structural mass and displacement while constraints include stresses due to three different load cases. Structural analysis is carried out by means of a finite element approach. The design problem is then tackled by the proposed surrogate-assisted design strategies. Numerical results show that the use of MOPBIL for generating training points is superior to the use of LHS based on a hypervolume indicator and root mean square error (RMSE).

scholarly journals Multi-objective Bayesian topology optimization of a lattice-structured heat sink in natural convection

2022 ◽  
Vol 65 (1) ◽  
Author(s):  
Koji Shimoyama ◽  
Atsuki Komiya
Keyword(s):  
Topology Optimization ◽  
Natural Convection ◽  
Thermal Performance ◽  
Heat Sink ◽  
Lattice Structure ◽  
Bayesian Optimization ◽  
Pin Fin ◽  
Structured Design ◽  
Material Cost ◽  
Design Variables

AbstractAdditive manufacturing (AM) has an affinity with topology optimization to think of various designs with complex structures. Hence, this paper aims to optimize the design of a lattice-structured heat sink, which can be manufactured by AM. The design objectives are to maximize the thermal performance of convective heat transfer in natural convection simulated by computational fluid dynamics (CFD) and to minimize the material cost required for AM process at the same time. The lattice structure is represented as a node/edge system via graph theory with a moderate number of design variables. Bayesian optimization, which employs the non-dominated sorting genetic algorithm II and the Kriging surrogate model, is conducted to search for better designs with the minimum CFD cost. The present topology optimization successfully finds better lattice-structured heat sink designs than a reference fin-structured design regarding thermal performance and material cost. Also, several optimized lattice-structured designs outperform reference pin-fin-structured designs regarding thermal performance though the pin-fin structure is still advantageous for a material cost-oriented design. This paper also discusses the flow mechanism observed in the heat sink to explain how the optimized heat sink structure satisfies the competing design objectives simultaneously.

Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted Multiobjective Evolutionary Algorithm

2011 ◽  
Vol 308-310 ◽  
pp. 1122-1128
Author(s):  
Siwadol Kanyakam ◽  
Sujin Bureerat
Keyword(s):  
Evolutionary Algorithm ◽  
Heat Sink ◽  
Work Performance ◽  
Performance Enhancement ◽  
Latin Hypercube Sampling ◽  
Population Based ◽  
Junction Temperature ◽  
Search Performance ◽  
Pin Fin ◽  
Multiobjective Evolutionary Algorithm

In this work, performance enhancement of a multiobjective evolutionary algorithm (MOEA) by integrating a surrogate model to the design process is presented. The MOEA used in this work is multiobjective population-based incremental learning (PBIL). The bi-objective design problem of a pin-fin heat sink (PFHS) is posed to minimize junction temperature and fan pumping power while meeting design constraints. A Kriging (KRG) model is used for improving the performance of PBIL. The training points for constructing a surrogate KRG model are sampled by means of a Latin hypercube sampling (LHS) technique. It is shown that hybridization of PBIL and KRG can enhance the search performance of PBIL.

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