Estimating thermofluid system parameters using a Markov chain Monte Carlo method, with an example of oscillating heat pipes

When simulating complex thermal and fluid systems with coupled physical models, there are often some model parameters that are not known a priori but substantially influence the observable results. An oscillating heat pipe, or OHP, exemplifies this challenge. This thermal management device consists of a large, dynamic number of liquid slugs interspersed with vapor bubbles inside of a serpentine tube embedded into a metallic plate. In an OHP the characteristics of nucleate boiling and the liquid film on the tube wall adjacent to the bubbles each affect the temperature measured at fixed locations. Data assimilation, and particularly, Bayesian inference, can be used to estimate the unknown parameters and their uncertainties from sufficiently detailed spatio-temporal experimental observations. In this study, we present a data-assimilation framework for an oscillating heat pipe using the Markov chain Monte Carlo (MCMC) method. Critical parameters for nucleate boiling and liquid film dynamics are estimated from temporal experimental temperature observations at fixed locations, rationalized with a relatively low-order physics-based predictive OHP model. Based on the results of several OHPs with different boundary conditions and two different working fluids, we report progress in building adaptive prediction models for thermofluid system performance with limited initial training.