Multi-output calibration of a honeycomb seal via on-site surrogates

TL;DR

This paper introduces an advanced multi-output calibration method using on-site surrogates with principal components to improve accuracy and efficiency in modeling honeycomb seals, addressing high-dimensional and nonstationary challenges.

Contribution

It develops a principal-components based multi-output OSS approach for large-scale industrial model calibration, enhancing parameter estimation and prediction accuracy.

Findings

Reduced uncertainty in calibrated parameters

Improved prediction accuracy over univariate methods

Efficient handling of high-dimensional inputs

Abstract

We consider large-scale industrial computer model calibration, combining multi-output simulation with limited physical observation, involved in the development of a honeycomb seal. Toward that end, we adopt a localized sampling and emulation strategy called "on-site surrogates (OSSs)", designed to cope with the amalgamated challenges of high-dimensional inputs, large-scale simulation campaigns, and nonstationary response surfaces. In previous applications, OSSs were one-at-a-time affairs for multiple outputs leading to dissonance in calibration efforts for a common parameter set across outputs for the honeycomb. We demonstrate that a principal-components representation, adapted from ordinary Gaussian process surrogate modeling to the OSS setting, can resolve this tension. With a two-pronged - optimization and fully Bayesian - approach, we show how pooled information across outputs can reduce uncertainty and enhance efficiency in calibrated parameters and prediction for the honeycomb relative to the previous, "data-poor" univariate analog.