Data-Driven Surrogate Modeling of Microturbine Combustors Burning Hydrogen Blends

Abstract

Abstract Hydrogen is a good candidate as an alternative fuel for power generation, due to the absence of carbon. Retrofitting available combustion systems to run hydrogen or its blends with natural gas is an effective option to decarbonize this sector. This demands a reassessment of the system response for the entire range of operation. Experimentation can be expensive and prohibitive from safety standpoints, and though computational models can provide data to fill the gaps, they come with their own sets of challenges. Data-driven surrogates use machine learning methods to learn from the sparse available data and can fill this knowledge gap by learning statistical correlations that describe the system. In this paper, a data-driven surrogate model is developed for a Capstone C65 microturbine combustor that is modified to burn pure natural gas and blends with up to 60% hydrogen for various power demands. Gaussian process (GP) regression modeling is used to learn from this dataset to emulate the system characteristics. Active learning is also invoked to learn a good model using as few data points as possible. Additional data is, then, generated using Reynolds-averaged Navier-Stokes (RANS) simulations of the same combustor geometry across a wider range of operating conditions i.e., 0–100% hydrogen for 0–65kW power loads. This provides low-fidelity information that can be included in a multi-fidelity learning setting which shows distinct improvements over the single-fidelity model. The novel multi-input-multi-output multi-fidelity GP surrogate modeling framework used in this work, is developed in-house using GPyTorch, which runs on a PyTorch backend, and is capable of superior scaling compared to traditional GP approaches.