Comparison of manifold learning algorithms used in FSI data interpolation of curved surfaces

Abstract

Purpose

The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.

Design/methodology/approach

Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms' efficiency and their influences on the accuracy of data transmission are investigated by three examples.

Findings

It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate.

Originality/value

The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.