Turbomachinery Blade Surrogate Modeling Using Deep Learning

Shirui Luo, Jiahuan Cui, Vignesh Sella, Jian Liu, Seid Koric, Volodymyr Kindratenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recent work has shown that deep learning provides an alternative solution as an efficient function approximation technique for airfoil surrogate modeling. In this paper we present the feasibility of convolutional neural network (CNN) techniques for aerodynamic performance evaluation. CNN approach will enable designer to fully utilize the ability of computers and statistics to interrogate and interpolate the nonlinear relationship between shapes and flow quantities, and rapidly perform a thorough optimization of the wide design space. The principal idea behind the current effort is to uncover the latent constructs and underlying cross-sectional relationships among the shape parameters, categories of flow field features, and quantities of interest in turbo-machinery blade design. The proposed CNN method is proved to automatically detect essential features and effectively estimate the pressure loss and deviation much faster than CFD solver.

Original languageEnglish (US)
Title of host publicationHigh Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers
EditorsHeike Jagode, Hartwig Anzt, Hatem Ltaief, Piotr Luszczek
Number of pages13
ISBN (Print)9783030905385
StatePublished - 2021
EventInternational Conference on High Performance Computing, ISC High Performance 2021 - Virtual, Online
Duration: Jun 24 2021Jul 2 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12761 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on High Performance Computing, ISC High Performance 2021
CityVirtual, Online


  • Deep learning
  • Shape parameterization
  • Turbomachinery blade

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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