High-efficiency Latin hypercube experimental design method

Abstract

The invention relates to a high-efficiency Latin hypercube experimental design method, belonging to the technical field of engineering optimization designs. For realizing the purpose that users extract m experimental design points from an n-dimensional design space in actual engineering, the method comprises the following steps of: presetting the first experimental design point by establishing a space hypercube; determining the second experimental design point in a second unit hypercube by utilizing the first experimental design point; determining the sth experimental design point in the sth unit hypercube by utilizing the obtained s-1 (s is equal to 3, 4, 5,......, m-1) experimental design points until the (m-1) experimental design points are determined; and uniquely determining the mth point meeting the design condition in the mth unit hypercube. The design method can obtain the experimental design points meeting the characteristic requirements of space uniformity and projection uniformity without an optimization process, thereby having high sampling efficiency; and the obtained experimental design points have good properties and are suitable for the optimization fields of any engineering designs and the like with huge operation.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this new method described by the inventor relates to improving testing accuracy while reducing time required during data collection compared to previous methods. This means it becomes faster than previously possible due to its ability to achieve better space distribution evenly throughout different parts or areas within complex structures.

Problems solved by technology

Technological Problem: Current Computational Fluid Dynamics Analysis ("CFDA") methods cannot accurately analyze engine designs due to their complexity. To solve this problem, researchers developed simulations called High Density Particle Hydrocodes ("HDP"), allowing scientists to study how different aspects affect mechanical behavior during flight operations. However, current CDFA techniques only allow for single stage testing while ignoring correlations among them, leading to overfitting data sets and slow down computation times. Therefore there needs a new approach to efficiently perform fluid dynamic analyses without compromising quality results.

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