Session
2025 Session 1
Location
Brigham Young University Engineering Building, Provo, UT
Start Date
5-5-2025 9:40 AM
Description
Radial pumps and compressors are used in various engineering applications, including rocket turbopumps, automotive turbochargers, refrigeration systems, etc. Several different physical effects, including viscous losses, flow separation, compressibility, and rotational dynamics, dominate the flow in a radial impeller. These effects make predicting the flow field in a radial impeller very difficult, requiring computationally expensive CFD. However, a majority of an individual flow field calculated using CFD is often not pertinent to a designer, who only requires information at certain positions within the flow field. This often results in most of the simulation data generated being unused. Accurately predicting the flow field at the impeller exit is often a key part of impeller design. Recent advances in reduced-order modeling and machine learning have shown promise in their ability to allow a priori prediction of flow fields. In this study, a reduced-order model is created to predict the exit flow field of radial flow impellers in real time. The reduced order model uses the impeller geometry, including the angle distribution of the blade, the thickness distribution of the blade, the number of blades, and the contours that define the end walls as inputs. Other inputs are the inlet and outlet boundary conditions. The model is trained using over 1600 flow fields generated by high-fidelity CFD simulations. The simulated flow fields were chosen to represent a large design space for radial flow compressors. Key variables chosen for the design space are the geometric parameters of the machine, the Reynolds number, the flow coefficient, and the head coefficient. The model predictions show a velocity profile that is consistent with the CFD results. The model also accurately predicts the location and size of important flow features, such as the tip vortex and other secondary flows.
A Comparison of Surrogate Modeling Techniques for Predicting Radial Turbomachinery Flow Fields
Brigham Young University Engineering Building, Provo, UT
Radial pumps and compressors are used in various engineering applications, including rocket turbopumps, automotive turbochargers, refrigeration systems, etc. Several different physical effects, including viscous losses, flow separation, compressibility, and rotational dynamics, dominate the flow in a radial impeller. These effects make predicting the flow field in a radial impeller very difficult, requiring computationally expensive CFD. However, a majority of an individual flow field calculated using CFD is often not pertinent to a designer, who only requires information at certain positions within the flow field. This often results in most of the simulation data generated being unused. Accurately predicting the flow field at the impeller exit is often a key part of impeller design. Recent advances in reduced-order modeling and machine learning have shown promise in their ability to allow a priori prediction of flow fields. In this study, a reduced-order model is created to predict the exit flow field of radial flow impellers in real time. The reduced order model uses the impeller geometry, including the angle distribution of the blade, the thickness distribution of the blade, the number of blades, and the contours that define the end walls as inputs. Other inputs are the inlet and outlet boundary conditions. The model is trained using over 1600 flow fields generated by high-fidelity CFD simulations. The simulated flow fields were chosen to represent a large design space for radial flow compressors. Key variables chosen for the design space are the geometric parameters of the machine, the Reynolds number, the flow coefficient, and the head coefficient. The model predictions show a velocity profile that is consistent with the CFD results. The model also accurately predicts the location and size of important flow features, such as the tip vortex and other secondary flows.