A grid density optimization method based on flow field physical information

By optimizing the grid density based on flow field physical information, the displacement and distribution of grid points are adjusted, solving the efficiency and accuracy problems of grid generation and optimization in computational fluid dynamics, and realizing efficient and high-precision CFD numerical simulation.

CN116205153BActive Publication Date: 2026-06-16CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV OF POSTS & TELECOMM
Filing Date
2022-12-27
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

Existing technologies struggle to achieve both efficient and high-precision mesh generation and optimization in computational fluid dynamics simulations, resulting in excessively long computation times or large errors in the calculation results.

⚗Method used

By using a grid density optimization method based on flow field physical information, the gradient of characteristic physical quantities in the grid system is calculated, the displacement of grid points is adjusted, and the grid is iteratively optimized until the convergence condition is met, thereby optimizing the variance of the gradient of characteristic physical quantities in the grid.

🎯Benefits of technology

Without increasing computation time, it significantly improves the computational accuracy of numerical simulation of fluid dynamics problems, reduces mesh errors, and enhances the computational efficiency of CFD numerical simulation.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to a grid density optimization method based on flow field physical information, and belongs to the field of fluid mechanics simulation and simulation, and comprises the following steps: step one: generating an initial grid for a fluid in a fluid mechanics problem numerical simulation system, and giving the initial physical quantity distribution on a flow field; step two: calculating the characteristic physical quantity gradient of an internal edge in the grid system; step three: adjusting the grid based on the distribution of the characteristic physical quantity gradient, and calculating the displacement of each internal node; and step four: updating the grid according to the displacement vector of the internal node, and iteratively executing steps two to four until the characteristic physical quantity gradient variance of the grid meets a convergence condition, and outputting the optimized grid. The application can be simultaneously applied to two-dimensional and three-dimensional grids, and compared with the grid before optimization, the calculation precision can be improved under the condition that the calculation time length is basically unchanged; especially when the grid quantity is relatively small, the comparison result is more obvious.
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