Dimension Reduction via Gaussian Ridge Functions

Seshadri, Pranay; Yuchi, Shaowu; Parks, Geoffrey T.

doi:10.48550/arxiv.1802.00515

Cited by 1 publication

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“…We point the interested reader to Sect. 2.2 of [27] for further details on these differences and to the work of Glaws et al [9,10], who provide a theoretical and computational framework for utilizing two well-known SDR techniques with computer models.…”

Section: Statistical Sufficient Dimension Reductionmentioning

confidence: 99%

Supporting Multi-point Fan Design with Dimension Reduction

Seshadri¹,

Yuchi²,

Shahpar³

et al. 2019

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Motivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these subspaces exist across different blades-under the same parametrization-largely independent of their Mach number or Reynolds number. This is demonstrated via a numerical study on three different blades. Then, in an attempt to reduce the computational cost of identifying a suitable dimension reducing subspace, we examine statistical sufficient dimension reduction methods, including sliced inverse regression, sliced average variance estimation, principal Hessian directions and contour regression. Unsatisfied by these results, we evaluate a new idea based on polynomial variable projection-a non-linear least squares problem. Our results using polynomial variable projection clearly demonstrate that one can accurately identify dimension reducing subspaces for turbomachinery functionals at a fraction of the cost associated with prior methods. We apply these subspaces to the problem of comparing design configurations across different flight points on a working line of a fan blade. We demonstrate how designs that offer a healthy compromise between performance at cruise and sea-level conditions can be easily found by visually inspecting their subspaces.

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Section: Statistical Sufficient Dimension Reductionmentioning

confidence: 99%