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sampling CAD models via an extended teaching-learning-based optimization technique

COMPUTER-AIDED design 2018年 100卷 52-67页

作者： Khan, Shahroz Gunpinar, Erkan Istanbul Tech Univ Istanbul Turkey

The Teaching-Learning-Based Optimization (TLBO) algorithm of Rao et al. has been presented in recent years, which is a population-based algorithm and operates on the principle of teaching and learning. This algorithm is based on the influence of a teacher on the quality of learners in a population. In this study, TLBO is extended for constrained and unconstrained CAD model sampling which is called sampling-TLBO (S-TLBO). sampling CAD models in the design space can be useful for both designers and customers during the design stage. A good sampling technique should generate CAD models uniformly distributed in the entire design space so that designers or customers can well understand possible design options. To sample N designs in a predefined design space, N sub-populations are first generated each of which consists of separate learners. Teaching and learning phases are applied for each sub-population one by one which are based on a cost (fitness) function. Iterations are performed until change in the cost values becomes negligibly small. Teachers of each sub-population are regarded as sampled designs after the application of S-TLBO. For unconstrained design sampling, the cost function favors the generation of space-filling and Latin Hypercube designs. Space-filling is achieved using the Audze and Eglais' technique. For constrained design sampling, a static constraint handling mechanism is utilized to penalize designs that do not satisfy the predefined design constraints. Four CAD models, a yacht hull, a wheel rim and two different wine glasses, are employed to validate the performance of the S-TLBO approach. sampling is first done for unconstrained design spaces, whereby the models obtained are shown to users in order to learn their preferences which are represented in the form of geometric constraints. Samples in constrained design spaces are then generated. According to the experiments in this study, S-TLBO outperforms state-of-the-art techniques particularly when

关键词： Teaching-learning-based optimization design sampling Computer-aided design Generative design

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A Generative design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics

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COMPUTER-AIDED design 2019年 111卷 65-79页

作者： Gunpinar, Erkan Coskun, Umut Can Ozsipahi, Mustafa Gunpinar, Serkan Istanbul Tech Univ Sch Mech Engn Inonu Caddesi 65 TR-34437 Istanbul Turkey

A design support system is developed in this work that can be integrated into the car side silhouette design tools and can estimate the drag coefficient of a given silhouette. This task is typically performed via two manners: namely wind tunnel testing and computational fluid dynamics (CFD) simulations. Due to the high computational cost for these two approaches, it is impractical to employ them during the silhouette conceptual design stage in a real time. Therefore, a mathematical model is obtained in this study for the drag coefficient estimation of a given silhouette. First, the desired number of silhouettes are generated via a generative design (silhouette sampling) technique so that the silhouettes are evenly distributed in the silhouette design space. Each silhouette is then tested via computational fluid dynamics simulations, and their corresponding drag coefficients (CDS) are obtained. A training dataset is formed with the silhouette geometries and CDS of the silhouettes, and a mathematical model that can estimate the drag coefficient (C-D) of a silhouette is finally obtained via principal component analysis (PCA) followed by regression/neural network methods. These three steps are repeated until a desired level of reliable mathematical model is obtained. Finally, three generative design test cases are illustrated based on the mathematical model obtained to predict C-D of a given silhouette. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Generative design Computational fluid dynamics design support system design sampling Machine learning

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