DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

TL;DR

DeepCFD employs convolutional neural networks to rapidly approximate steady laminar flow solutions of the Navier-Stokes equations, significantly reducing computational time while maintaining low error rates, thus enabling faster aerodynamic and fluid dynamics analysis.

Contribution

This paper introduces DeepCFD, a CNN-based model that learns to predict fluid flow solutions directly from CFD data, offering a novel, efficient alternative to traditional CFD simulations.

Findings

Achieves up to 1000x speedup over traditional CFD methods.

Maintains low error rates in flow approximation.

Successfully predicts velocity and pressure fields from ground-truth data.

Abstract

Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling. However, the computational cost and memory demand required by CFD codes may become very high for flows of practical interest, such as in aerodynamic shape optimization. This expense is associated with the complexity of the fluid flow governing equations, which include non-linear partial derivative terms that are of difficult solution, leading to long computational times and limiting the number of hypotheses that can be tested during the process of iterative design. Therefore, we propose DeepCFD: a convolutional neural network (CNN) based model that efficiently approximates solutions for the problem of non-uniform steady laminar flows. The proposed model is able to learn complete solutions of the Navier-Stokes equations, for both velocity and pressure fields, directly from ground-truth data generated using a state-of-the-art CFD code. Using DeepCFD, we found a speedup of up to 3 orders of magnitude compared to the standard CFD approach at a cost of low error rates.