  This paper presents an advancement to an approach for model-independent
surrogate-based optimization with adaptive batch sampling, known as Adaptive
Model Refinement (AMR). While the original AMR method provides unique decisions
with regards to "when" to sample and "how many" samples to add (to preserve the
credibility of the optimization search process), it did not provide specific
direction towards "where" to sample in the design variable space. This paper
thus introduces the capability to identify optimum location to add new samples.
The location of the infill points is decided by integrating a Gaussian
Process-based criteria ("q-EI"), adopted from Bayesian optimization. The
consideration of a penalization term to mitigate interaction among samples (in
a batch) is crucial to effective integration of the q-EI criteria into AMR. The
new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is
tested on benchmark functions, demonstrating better performance compared to
Bayesian EGO. In addition, it is successfully applied to design surface riblets
for bio-inspired passive flow control (where high-fidelity samples are given by
costly RANS CFD simulations), leading to a 10% drag reduction over the
corresponding baseline (i.e., riblet-free aerodynamic surface).
