首页|Data from Karlsruhe Institute of Technology (KIT) Advance Knowledge in Machine Learning (Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer)
Data from Karlsruhe Institute of Technology (KIT) Advance Knowledge in Machine Learning (Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Karlsruhe, Germany, by NewsRx editors, research stated, “Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems.” Financial supporters for this research include Deutsche Forschungsgemeinschaft; Ministerium Fur Wissenschaft, Forschung Und Kunst Baden-wurttemberg; Bundesministerium Fur Bildung Und Forschung. Our news journalists obtained a quote from the research from Karlsruhe Institute of Technology (KIT): “The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics.”
Karlsruhe Institute of Technology (KIT)KarlsruheGermanyEuropeCyborgsEmerging TechnologiesMachine LearningMathematicsNumerical Modeling
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