Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

DOI10.1016/j.compfluid.2021.104899OpenAlexW3134940680MaRDI QIDQ2245306

Stéphane Aubert, Franck Mastrippolito, F. Ducros

Publication date: 15 November 2021

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compfluid.2021.104899

zbMATH Keywords

krigingmulti-objective optimizationheat exchangeradaptive samplingself-organizing mapsmulti-scale modellingCFD

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Related Items (1)

Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters

Uses Software

• Code_Saturne
• EGO
• DiceKriging
• DiceOptim
• Scikit
• rgenoud
• DiceDesign
• Python
• KrigInv
• GitHub
• DiceEval
• GPareto
• SOMPY
• PRMLT

Cites Work

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• Efficient global optimization of expensive black-box functions
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• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• \textit{KrigInv}: an efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging
• Efficient optimisation procedure for design problems in fluid mechanics
• Global optimization of stochastic black-box systems via sequential kriging meta-models
• Aggregate objective functions and Pareto frontiers: required relationships and practical implications
• Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction

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