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\(\lambda \)-DNNs and their implementation in conjugate heat transfer shape optimization

  • Special issue on Advances of Neural Computing phasing challenges in the era of 4th industrial revolution
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Abstract

A data-driven two-branch deep neural network (DNN), to be referred to as \(\lambda \)-DNN, used to predict scalar fields is presented. The network architecture consists of two separate branches (input layers) connected to the main one towards its output. In multi-disciplinary shape optimization problems, such as those this paper is dealing with, the input to the \(\lambda \)-DNN contains data relevant to the geometrical shape and the case itself. Herein, the \(\lambda \)-DNN is used in conjugate heat transfer (CHT) analysis and shape optimization problems, synergistically with codes simulating flows over the fluid domain and solving the heat conduction equations over the solid one. It is used to optimize a duct and an internally cooled turbine blade-airfoil surrounded by hot gas. The \(\lambda \)-DNNs, after being trained on fields computed using the CHT solver, are used as surrogates for either the heat conduction equation solver of the solid domain, replicating either one out of the two disciplines of the problem or the coupled CHT solver.

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Abbreviations

CFD:

Computational fluid dynamics

CHT:

Conjugate heat transfer

CNN:

Convolutional neural network

DNN:

Deep neural network

EA:

Evolutionary algorithm

FSI:

Fluid solid interface

MAEA:

Metamodel-assisted evolutionary algorithm

MLP:

Multilayer perceptron

NURBS:

Non-uniform rational B-splines

PDE:

Partial differential equation

PSM:

Problem specific model

RANS:

Reynolds-averaged Navier Stokes

RBF:

Radial basis function

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Acknowledgements

The authors are thankful to the Greek Research and Technology Network (GRNET) High Performance Computing Services for providing the computational means to conduct this research, through the CGT-DNN Project.

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Authors and Affiliations

  1. Parallel CFD and Optimization Unit, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece

    Marina Kontou, Dimitrios Kapsoulis, Ioannis Baklagis, Xenofon Trompoukis & Kyriakos Giannakoglou

Authors
  1. Marina Kontou
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  2. Dimitrios Kapsoulis
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  3. Ioannis Baklagis
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  4. Xenofon Trompoukis
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  5. Kyriakos Giannakoglou
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Correspondence to Marina Kontou.

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Kontou, M., Kapsoulis, D., Baklagis, I. et al. \(\lambda \)-DNNs and their implementation in conjugate heat transfer shape optimization. Neural Comput & Applic 34, 843–854 (2022). https://doi.org/10.1007/s00521-021-05858-2

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