Study of Data-Driven Prediction of Roughness Skin Friction

Jiasheng Yang (), Alexander Stroh and Pourya Forooghi
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Jiasheng Yang: Institute of Fluid Mechanics (ISTM), Karlsruhe Institute of Technology (KIT)
Alexander Stroh: Institute of Fluid Mechanics (ISTM), Karlsruhe Institute of Technology (KIT)
Pourya Forooghi: Aarhus university, Department of Mechanical and Production Engineering

A chapter in High Performance Computing in Science and Engineering '22, 2024, pp 151-165 from Springer

Abstract: Abstract The potential of developing a machine learning predictive model for the roughness equivalent sand-grain size $$k_s$$ k s is assessed in the present work. The training dataset for the machine learning model is obtained by carrying out Direct Numerical Simulations (DNS) on the artificially generated roughness at friction Reynolds number Re $$_\tau =800$$ τ = 800 . The generation of the roughness is based on a mathematical algorithm in which the roughness power spectrum (PS) as well as the height probability density function (PDF) can be prescribed. A roughness repository that contains 4000 artificial roughness is constructed with the generation algorithm. 50 roughness out of the repository are used for training the model. The selection of the training roughness samples is made following the active learning (AL) framework, where the AL framework selects roughness samples based on the model uncertainty. The ensemble neural network (ENN) model is employed to quantify the model uncertainty. Excellent performance of the ENN model is observed in the present work. Eventually an averaged prediction error of 8.5% is achieved for the ENN model tested on 4 realistic surfaces.

Date: 2024
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DOI: 10.1007/978-3-031-46870-4_11

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