Ventilated cavity flows behind a backward facing step with a combination computational fluid dynamics and Error Back Propagation algorithm

He, Zanyang; Sun, Tiezhi; Zou, Li; Jiang, Yichen; Duan, Lei

doi:10.1016/j.oceaneng.2022.111741

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In this context, cavity flow is commonly applied under various conditions and for different purposes. In [41], the authors proposed the combined solution of CFD with Back Propagation for modeling a cavity with a backward-facing step, and as can be seen in our work, the technique is promising and deserves investigation.…”

Section: Discussionmentioning

confidence: 82%

Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case

et al. 2023

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In this work we present the development, testing and comparison of three different physics-informed deep learning paradigms, namely the ConvLSTM, CNN-LSTM and a novel Fourier Neural Operator (FNO), for solving the partial differential equations of the RANS turbulence model. The 2D lid-driven cavity flow was chosen as our system of interest, and a dataset was generated using OpenFOAM. For this task, the models underwent hyperparameter optimization, prior to testing the effects of embedding physical information on performance. We used the mass conservation of the model solution, embedded as a term in our loss penalty, as our physical information. This approach has been shown to give physical coherence to the model results. Based on the performance, the ConvLSTM and FNO models were assessed in forecasting the flow for various combinations of input and output timestep sizes. The FNO model trained to forecast one timestep from one input timestep performed the best, with an RMSE for the overall x and y velocity components of 0.0060743 m·s−1.

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Section: Discussionmentioning

confidence: 82%

Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case

et al. 2023

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Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

Zhang,

Xu,

Sun

et al. 2024

Physics of Fluids

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A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π.

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Ventilated cavity flows behind a backward facing step with a combination computational fluid dynamics and Error Back Propagation algorithm

Cited by 2 publications

References 26 publications

Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case

Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case

Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

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