DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state thermal convection fields based on deep learning technology. The proposed model aims to overcome limitations in traditional frameworks caused by network types, such as the requirement for extensive training data, accuracy loss due to pixelated preprocessing of original data, and inability to predict information near the boundaries with precision. We propose a new framework that consists primarily of a physical-informed neural network (PINN) and a graph convolutional neural network (GCN). The GCN serves as the prediction module and predicts thermal convection in the two-dimensional computational domain by considering the mutual influence between unstructured nodes and their neighbors. On the other hand, the PINN acts as the physical constraint module of the framework by embedding the control equation of thermal convection into the loss function of the neural network, ensuring that the inference and prediction results of the GCN comply with the constraints of the control equation. The advantages of this framework lie in two aspects. First, the computation mechanism of the GCN is more in line with the actual evolution of temperature fields. Second, the PINN enhances the cognitive ability of the surrogate model toward the convection field information. It accurately describes the changes of temperature gradient information at the boundary position and reduces the model's demand for training data. To validate the advantages of the proposed model, we gradually analyzed the model's geometric adaptability and predictive accuracy from the single cylinder case to the double cylinder case. We also investigated the impact of the number of sampling points on model training and compared the model's prediction results with those of a purely data-driven model. The results show that the proposed model exhibits good geometric adaptability and stability. With only 20 training data, the mean error of the proposed model in predicting the velocity and temperature field is less than 1% and 0.6% for the single cylinder, and less than 2% and 1% for the double cylinder case, while the mean error of the purely data-driven GCN model in predicting the velocity and temperature field is 9.4% and 6.4% for the double cylinder case. These findings demonstrate the effectiveness of the proposed physics-informed graph convolutional neural network, allowing for more accurate prediction of fluid flow and heat convection using surrogate model.

Physics-informed graph convolutional neural network for modeling fluid flow and heat convection

Peng

Hua

et al. 2023

A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines

Li,

Ge,

et al. 2024

Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake control and load assessment in wind farms. This paper proposes a real-time dynamic wake prediction model for wind turbines based on a physics-guided neural network. The model can predict the instantaneous dynamic wake field under various operating conditions using only the inflow wind speed as input. The model utilizes Taylor's frozen-flow hypothesis and a steady-state wake model to convert instantaneous inflow wind speed and turbine parameters into neural network input features. A deep convolutional neural network then maps these features to desired wake field snapshots, enabling dynamic wake predictions for wind turbines. To train the model, we generated approximately 255 000 instantaneous flow field snapshots of single-turbine wakes using the large eddy simulation, covering different thrust coefficients and yaw angles. The model was trained using the supervised learning method and verified on the test set. The results indicate that the model can effectively predict the dynamic wake characteristics, including the dynamic wake meandering and the wake deflection of the yawed turbines. The model can also assess both the instantaneous wake velocity and the instantaneous wake center of a wind turbine. At a thrust coefficient of 0.75, the root mean square error for the predicted instantaneous wake velocity is around 6.53%, while the Pearson correlation coefficient for the predicted instantaneous wake center can reach 0.624. Furthermore, once the model is trained, its prediction accuracy does not decrease with the increase in the time span.

An improved physical information network for forecasting the motion response of ice floes under waves

Peng,

Wang,

Xia

et al. 2024

Physics-informed neural networks (PINNs) have increasingly become a key intelligent technology for solving partial differential equations. Nevertheless, for simulating the dynamic response of ice floes to waves, researchers often still resort to traditional numerical methods and empirical formulas. The limitations of these methods include extended computational durations and challenges in precisely conforming to physical principles. To effectively overcome these challenges and achieve efficient and accurate prediction of sea ice motion response, this study proposes an improved PINN method for the longitudinal motion response of sea ice under regular wave action. The approach features two principal innovations: first, a neural network loss function module tailored to the ice motion response equations, and second, an attention mechanism focused on temporal sequences and wave data. Through case studies of sea ice motion under three different wave conditions, this research validates the effectiveness of the improved PINN method. A comparison between the network's training and testing outcomes with experimental figures reveals significant consistency, affirming the method's robustness and accuracy. The application of this study demonstrates the potential for accurately predicting the dynamic response of sea ice in complex sea conditions, providing significant technical support and new research directions for future studies.