A Bayesian hybrid experimental methodology for accurate and robust flow field assessment with optimal sensor placement using stochastic variational Gaussian processes

This work presents a complete hybrid testing methodology for flow assessment that combines computational fluid dynamics (CFD) simulations, stochastic variational Gaussian processes (SVGP), and multi-fidelity Gaussian processes (MFGP) to achieve accurate assessments with fewer measurements. Focused on minimizing energy consumption and testing times, the methodology utilizes SVGP to identify informative measurement locations based on CFD data, enabling a data-driven design of experiments (DoE) approach. This optimizes sensor placement and minimizes redundant measurements. Acquired experimental data are then fused with CFD data using a MFGP model, providing a comprehensive flow field reconstruction with associated uncertainty estimates within a Bayesian framework comparable to traditional methodologies. Two test cases demonstrate the methodology's effectiveness: the H25 axial compressor, used as a development test case due to availability of preexisting experimental data, and a state-of-the-art ECL5 ultra-high bypass ratio fan, for which the methodology was applied in a blind validation scenario. Results show a significant reduction in required measurements (around 70% for the H25 and 50% for the ECL5) without sacrificing accuracy, leading to a substantial decrease in experimental testing time. The SVGP DoE framework outperforms random and Latin hypercube sampling, effectively capturing relevant flow features with fewer measurements. The methodology also adapts to varying flow conditions, accurately predicting flow fields and quantifying uncertainties across different Reynolds numbers. This work introduces a shift in experimental campaigns, strategically optimizing measurement strategies during the planning phase, emphasizing machine learning trends in shaping new research paths toward more efficient, cost-effective, and sustainable experimental practices for fluid dynamics.