A method for automatically identifying a grid type of a data center CFD simulation

By using time-averaging processing and normal flux determination of multi-frame time-series data, combined with the geometric characteristics of data centers, the problem of automatic identification of mesh boundary types in data center CFD simulation was solved. This achieved accurate differentiation of boundary types and full-process automation, improving the accuracy and efficiency of simulation analysis.

CN122452171APending Publication Date: 2026-07-24XINJIANG UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XINJIANG UNIVERSITY
Filing Date
2026-06-05
Publication Date
2026-07-24

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Abstract

The application discloses a kind of data center CFD simulation grid type automatic identification method, belong to industrial simulation post-processing technical field, to solve the problems such as existing technology transient fluctuation interference, flow direction determination is fuzzy, boundary label loss, low degree of automation etc..The present application is by obtaining multiple time sequence grid data and carries out time averaging processing, eliminates turbulent pulsation and numerical noise;Iterate grid unit and identify physical boundary surface, accurately calculate the unit normal vector pointing to the outside;Based on normal flux and adaptive threshold, determine the boundary type as air inlet, air outlet or wall;Combined with the geometric characteristics of data center, the ceiling area is forced to correct as wall, improve the accuracy of identification.The method has clear physical meaning and strong anti-interference ability, and is compatible with VTU format data exported by simulation software such as 6SigmaET, which can realize the full-process automation from grid reading to boundary identification and visual output without manual marking, greatly improving the efficiency and accuracy of data center simulation post-processing, suitable for large-scale complex grid batch intelligent analysis.
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