  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.
