Date of Award
Master of Science (MS)
Computer models of physical systems are widely used in lieu of, or in tandem with, experimental testing. It is critical to verify the accuracy of computer models through the process of calibration. Typical calibration methods are often computationally expensive and therefore cannot be performed in real time. This thesis presents a novel Bayesian calibration method using a Griddy Gibbs sampling algorithm to improve calibration speeds. This method was verified in two applications: a location-dependent dataset in the heat transfer analysis of an engine piston, and time-dependent tire forces in a drum test. The proposed method was directly compared to a traditional Bayesian calibration method in the engine piston application. It was found that the two methods were close in accuracy with large amounts of calibration data, and the Griddy Gibbs method was significantly less computationally expensive; it could calibrate in less than a minute, while the traditional method took several days.
Stewart, Hannah, "Creation and Validation of a Novel Bayesian Calibration Method with Griddy Gibbs Sampling" (2023). All Theses. 4177.