Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models

DOI10.1007/s11081-007-9031-1zbMath1300.76025OpenAlexW2054928060MaRDI QIDQ1039913

Temesgen Mengistu, Wahid Ghaly

Publication date: 23 November 2009

Published in: Optimization and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11081-007-9031-1

zbMATH Keywords

genetic algorithmartificial neural networkNURBSresponse surface approximation

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Flow control and optimization for compressible fluids and gas dynamics (76N25)

Related Items

U-Net-Based Surrogate Model for Evaluation of Microfluidic Channels ⋮ Wind turbine design through evolutionary algorithms based on surrogate CFD methods ⋮ Rotorcraft low-noise trajectories design: black-box optimization using surrogates ⋮ Discrete adjoint gradient evaluations for linear stress and vibration analysis ⋮ Multi-fidelity shape optimization of hydraulic turbine runner blades using a multi-objective mesh adaptive direct search algorithm

Cites Work

• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Unnamed Item
• Unnamed Item

This page was built for publication: Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models