An Explainable Deep Learning for Data-Driven Turbulence Model Feature Discovery

This work presents an explainable deep learning for turbulence modelling feature discovery in several flow configurations where conventional approaches perform poorly. The proposed neural network-based turbulence model predicts the Reynolds stress based on the mean flow quantities. Furthermore, the...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Muhammad Ridho Alhafiz, Lavi Rizki Zuhal, Duong Viet Dung, Pramudita Satria Palar
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2025-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Turbulence modeling neural network explainability Shapley additive explanations feature discovery
Online-Zugang:	https://ieeexplore.ieee.org/document/11136158/
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