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NEURAL-NETWORK METAMODELLING FOR THE PREDICTION OF THE PRESSURE DROP OF A FLUID PASSING THROUGH METALLIC POROUS MEDIUM

卷 17, 册 5, 2014, pp. 431-438
DOI: 10.1615/JPorMedia.v17.i5.50
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摘要

The pressure drop across metallic porous mediums is a critical element in cooling aerospace engineering applications. This paper presents a metamodel based on artificial neural networks (ANNs) for estimating the pressure drop through metallic porous media. The ANN is developed using experimental data obtained from an experimental bench, developed at PRISME Laboratory, which ensures the monitoring of temperature, pressure, and mass flow rate in stationary and transient conditions. For each case the gas pressure which crosses the metallic porous material is measured as a function of inlet gas pressure, gas mass flow rate, and temperature. The optimal feedforward ANN architecture with error backpropagation (BPNN) was determined by the cross-validation method. The ANN architecture having 35 hidden neurons gives the best choice. Comparing the modelled values by ANN with the experimental data indicates that the neural-network model provides accurate results. The performance of the ANN model is compared with a metamodelling method using the multilinear regression approximation.

对本文的引用
  1. El Tabach Eddy, Chetehouna Khaled, Gascoin Nicolas, Gaschet Florian, Fau Guillaume, Modeling the spatio-temporal evolution of permeability during coking of porous material, 20th AIAA International Space Planes and Hypersonic Systems and Technologies Conference, 2015. Crossref

  2. El Tabach Eddy, Adishirinli Leyla, Gascoin Nicolas, Fau Guillaume, Prediction of transient chemistry effect during fuel pyrolysis on the pressure drop through porous material using artificial neural networks, Journal of Analytical and Applied Pyrolysis, 115, 2015. Crossref

  3. Maaoui Walaeddine , Lazhar Ramzi , Najjari Mustapha , SOIL MOISTURE RETRIEVAL MODEL BASED ON DIELECTRIC MEASUREMENTS AND ARTIFICIAL NEURAL NETWORK , Journal of Porous Media, 25, 8, 2022. Crossref

  4. Barroso-Maldonado J.M., Montañez-Barrera J.A., Belman-Flores J.M., Aceves S.M., ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling, Applied Thermal Engineering, 149, 2019. Crossref

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