Towards Improved Turbomachinery Measurements: A Comprehensive Analysis of Gaussian Process Modeling for a Data-Driven Bayesian Hybrid Measurement Technique

Abstract

A cost-effective solution to address the challenges posed by sensitive instrumentation in next-gen turbomachinery components is to reduce the number of measurement samples required to assess complex flows. This study investigates Gaussian Process (GP) modeling approaches within the framework of a data-driven hybrid measurement technique for turbomachinery applications. Three different modeling approaches—Baseline GP, CFD to Experiments GP, and Multi-Fidelity GP—are evaluated, and their performance in predicting mean flow characteristics and associated uncertainties on a low aspect ratio axial compressor stage, representative of the last stage of a high-pressure compressor, are focused on. The Baseline GP demonstrates robust accuracy, while the integration of CFD data in CFD into Experiments GP introduces complexities and more errors. The Multi-Fidelity GP, leveraging both CFD and experimental data, emerges as a promising solution, exhibiting enhanced accuracy in critical flow features. A sensitivity analysis underscores its stability and accuracy, even with reduced measurements. The Multi-Fidelity GP, therefore, stands as a reliable data fusion method for the proposed hybrid measurement technique, offering a potential reduction in instrumentation effort and testing times.

Keywords

• machine learning
• Gaussian process
• data fusion
• Bayesian inference
• uncertainty quantification
• measurement technique