Current and emerging deep-learning methods for the simulation of fluid dynamics
Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
PIGNN-CFD: A physics-informed graph neural network for rapid predicting urban wind field defined on unstructured mesh
Urban wind field plays an important role in quantitative assessment of urban environment.
Compared to field measurement and wind tunnel experiment, Computational Fluid …
Compared to field measurement and wind tunnel experiment, Computational Fluid …
[HTML][HTML] Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics
The simulation of fluid dynamics, typically by numerically solving partial differential
equations, is an essential tool in many areas of science and engineering. However, the high …
equations, is an essential tool in many areas of science and engineering. However, the high …
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
M Lazzara, M Chevalier, M Colombo, JG Garcia… - Aerospace Science and …, 2022 - Elsevier
Surrogate modelling can alleviate the computational burden of design activities as they rely
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
Accurate and efficient urban wind prediction at city-scale with memory-scalable graph neural network
The interaction between buildings and wind significantly impacts the comfort and safety of
pedestrians, thereby influencing the sustainability of cities. Computational fluid dynamics …
pedestrians, thereby influencing the sustainability of cities. Computational fluid dynamics …
[HTML][HTML] Inexpensive high fidelity melt pool models in additive manufacturing using generative deep diffusion
Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics
of the molten alloy near the laser, known as the melt pool. For instance, the melt pool can …
of the molten alloy near the laser, known as the melt pool. For instance, the melt pool can …
On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method
A graph convolutional network (GCN) is employed in the deep energy method (DEM) model
to solve the momentum balance equation in three‐dimensional space for the deformation of …
to solve the momentum balance equation in three‐dimensional space for the deformation of …
Grid adaptive reduced-order model of fluid flow based on graph convolutional neural network
In the interdisciplinary field of data-driven models and computational fluid mechanics, the
reduced-order model for flow field prediction is mainly constructed by a convolutional neural …
reduced-order model for flow field prediction is mainly constructed by a convolutional neural …
Forecasting three-dimensional unsteady multi-phase flow fields in the coal-supercritical water fluidized bed reactor via graph neural networks
Y Hao, X Xie, P Zhao, X Wang, J Ding, R Xie, H Liu - Energy, 2023 - Elsevier
The study of three-dimensional unsteady multi-phase flows inside the coal-supercritical
water fluidized bed (SCWFB) reactor coupled with fluid dynamics, heat transfer and …
water fluidized bed (SCWFB) reactor coupled with fluid dynamics, heat transfer and …
Self-supervised learning based on transformer for flow reconstruction and prediction
Machine learning has great potential for efficient reconstruction and prediction of flow fields.
However, existing datasets may have highly diversified labels for different flow scenarios …
However, existing datasets may have highly diversified labels for different flow scenarios …