Grid adaptive method for high-precision flow field analysis

Abstract

The embodiment of the invention discloses a grid adaptive method for high-precision flow field analysis, relates to the field of flow field numerical simulation of fluid mechanics, and can improve the efficiency of a whole flow field analysis system and also improve the utilization rate of computing resources. The method comprises the following steps: establishing a flow field equation and calculating an initial flow field according to flow field information set by a client; constructing training data to train an AdaBoost classifier to classify grid nodes, and training a neural network for flow field solution prediction; constructing a measurement tensor by using the initial flow field solution and the classification result, and obtaining a new grid by constructing a grid equation; and predicting a flow field solution on the new grid by using a neural network for moving grid nodes in the next round, and returning a finally obtained result to a customer service end. The method is suitable for industrial design related to flow field numerical simulation.

Description

technical field [0001] The invention relates to the technical field of flow field numerical simulation of fluid mechanics, in particular to a grid adaptive method for high-precision flow field analysis. Background technique [0002] Computational fluid dynamics (CFD) is a subject that closely combines physical calculation models with mathematical analysis, and it is an important part of numerical simulation of flow fields. High-precision and high-efficiency computational simulation of the flow field has been the goal pursued by technicians in the field of Computational Fluid Dynamics (CFD). No matter from engineering technology or theoretical analysis, the accuracy of flow field numerical simulation is very important, and among them, the grid quality is closely related to the accuracy of flow field calculation. [0003] At present, most of the grid adaptive analysis schemes in the field of CFD mostly increase the grid nodes in the calculation, such as adding a large number ...

AI Technical Summary

Problems solved by technology

However, due to the need to add a large number of grid nodes, the number of nodes in the adaptive grid may reach more than ten times that of the initial grid, resulting in a significant drop in analysis efficiency

In addition, because the adaptive grid destroys the topological structure of the initial grid, it is necessary to reconstruct the topological connection relationship of the grid, these factors will eventually lead to a decrease in computational efficiency

This will lead to a surge in the amount of calculations in the experiment, and the experimental equipment needs to run for a long time to get the calculation data

The result in the actual scientific research work is that the loss of equipment is increased, and the calculation cost is out of control.

When encountering larger-scale flow field calculations, it is often necessary to rent a supercomputer for calculation, which is very costly and often requires queuing up for a supercomputing quota, causing great inconvenience

However, every year in China, the computing resources of some supercomputing centers are difficult to be effectively utilized, and the final result is increased equipment loss and increased computing costs.

Images