Fast Neural Network Predictions from Constrained Aerodynamics Datasets
TLDR
This work presents a novel model-free approach in which it reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions.Abstract:
Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches.read more
Citations
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Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review
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Model-free short-term fluid dynamics estimator with a deep 3D-convolutional neural network
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Aerodynamic Data Predictions Based on Multi-task Learning.
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A data-driven model based on modal decomposition: application to the turbulent channel flow over an anisotropic porous wall
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