Abstract

Centrifugal compressors serve as critical components across many applications, yet their design and performance prediction present significant challenges. While computational fluid dynamics provides high-fidelity predictions of compressor behavior, its prohibitive computational cost renders it impractical for preliminary design phases where rapid exploration of the geometric and operating space is essential. Conversely, existing low-order models have enabled efficient performance estimation but suffer from limited accuracy and poor interpretability, particularly when applied to novel geometries or off-design operating conditions. This dissertation addresses these limitations through three complementary contributions that bridge the gap between high-fidelity CFD and preliminary design tools. The first contribution develops rigorous methodologies for extracting one dimensional model parameters directly from high-fidelity CFD results. An approach is presented for identifying the boundary between primary and secondary flow zones at the impeller outlet, enabling low-order model parameters to be determined from flow field data. This method is validated across multiple impeller geometries, demonstrating that the parameters vary with operating conditions in ways not captured by existing correlations. The second contribution presents a neural network-enhanced one dimensional model that integrates the governing physics equations with data-driven predictions. By linking the physical model with a neural network, superior performance predictions are achieved while maintaining physical interpretability and low computational cost. The third contribution develops and compares multiple reduced-order modeling techniques for predicting three-dimensional flow field distributions at an impeller outlet, demonstrating that linear decomposition methods should be preferred over more complicated nonlinear options. Collectively, these contributions provide practical frameworks for more accurate, efficient, and interpretable turbomachinery predictions for on- and off-design performance, enabling more sophisticated preliminary design and optimization workflows.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/