Global simulation performance predication method based on data digging

Abstract

The invention discloses a global simulation performance predication method based on data digging. The method includes the steps that1, in a historical model file database, concerned design parameters and performance parameters are extracted to serve as an original simulation data set; 2, the original simulation data set is pretreated and converted into a fixed format capable of being processed by a data digging algorithm; 3, targeted to the pretreatment result, a global simulation performance predication algorithm based on a nonlinear prediction model is utilized, and the nonlinear prediction model for representing the relation between key design parameters and global simulation performance parameters is established; 4, when the design parameters are changed, a global performance evaluation index is constructed for the obtained new design model, and the global simulation performance of a product is predicted through the nonlinear prediction model. By means of the global simulation performance predication method, on the premise of reducing actual simulation times, product performance is predicted rapidly, accordingly the design cost is saved, and design efficiency is improved.

Description

technical field [0001] The invention relates to the field of CAE (Computer Aided Engineering, computer aided engineering) and data mining technology, in particular to a global simulation performance prediction method based on data mining. Background technique [0002] The wide application of simulation technology shows significant advantages in reducing the cost of product development, shortening the cycle to market and improving product quality. Simulation technology has been integrated into the performance analysis and simulation process of complex products in multiple disciplines, such as finite element analysis (FEA), computational fluid dynamics (CFD), system dynamics analysis, etc. Simulation is gradually changing from design verification means and methods Becomes the enable of the driver design. [0003] However, product development is an iterative process of design and simulation. Each design of a product requires repeated revisions, and each revision requires simul...

AI Technical Summary

Problems solved by technology

[0007] (2) Interrelated coupling and influence of different design parameters

Complex products have many, even hundreds of design parameters, many of which may be irrelevant to the mining task, or redundant

If you directly select all the design parameters to train the model, it will not only lead to difficult modeling and complex models, but also reduce the generalization performance of the predictive model

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