تقرير
Deep learning-based predictive modelling of transonic flow over an aerofoil
| العنوان: | Deep learning-based predictive modelling of transonic flow over an aerofoil |
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| المؤلفون: | Chen, Li-Wei, Thuerey, Nils |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Fluid Dynamics, Computer Science - Computational Engineering, Finance, and Science |
| الوصف: | Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network representations, our approach provides a framework for assessing fundamental physical properties via global instability analysis. This integration bridges deep neural network models and traditional modal analysis, offering valuable insights into transonic flow dynamics and enhancing the interpretability of neural network models in flowfield diagnostics. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.17131 |
| رقم الأكسشن: | edsarx.2403.17131 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |