Framework maps continuous-space inverse design problems for compression

Scilight ◽  
2021 ◽  
Vol 2021 (53) ◽  
pp. 531104
Author(s):  
Adam Liebendorfer
Keyword(s):  
Inverse Design ◽  
Design Problems ◽  
Continuous Space

Modeling and Inverse Design of Convective Heat Transfer in a Grooved Channel Using Proper Orthogonal Decomposition

2009 ◽  
Author(s):  
Hasan Gunes ◽  
Sertac Cadirci
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Reynolds Number ◽  
Proper Orthogonal Decomposition ◽  
Convective Heat Transfer ◽  
Orthogonal Decomposition ◽  
Inverse Design ◽  
Heat Sources ◽  
Design Problems ◽  
Proper Orthogonal

In this study we show that the POD can be used as a useful tool to solve inverse design problems in thermo-fluids. In this respect, we consider a forced convection problem of air flow in a grooved channel with periodically mounted constant heat-flux heat sources. It represents a cooling problem in electronic equipments where the coolant is air. The cooling of electronic equipments with constant periodic heat sources is an important problem in the industry such that the maximum operating temperature must be kept below a value specified by the manufacturer. Geometric design in conjunction with the improved convective heat transfer characteristics is important to achieve an effective cooling. We obtain a model based on the proper orthogonal decomposition for the convection optimization problem such that for a given channel geometry and heat flux on the chip surface, we search for the minimum Reynolds number (i.e., inlet flow speed) for a specified maximum surface temperature. For a given geometry (l = 3.0 cm and h = 2.3 cm), we obtain a proper orthogonal decomposition (POD) model for the flow and heat transfer for Reynolds number in the range 1 and 230. It is shown that the POD model can accurately predict the flow and temperature field for off-design conditions and can be used effectively for inverse design problems.

Double surrogate modeling usage in PSO

2017 ◽  
Vol 89 (6) ◽  
pp. 862-870 ◽  
Author(s):  
Volkan Yasin Pehlivanoglu
Keyword(s):  
Surrogate Modeling ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Surrogate Models ◽  
Inverse Design ◽  
Test Cases ◽  
Design Problems ◽  
Content Type ◽  
Individual Fitness ◽  
Response Surface Approximations

Purpose The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems. Design/methodology/approach The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison. Findings The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms. Research limitations/implications The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems. Practical implications The proposed algorithm can be applied to different inverse design problems. Originality/value The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.

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