Sensitivity analysis and parameter estimation of heat transfer and material flow models in friction stir welding

Abstract

Although numerical models of heat transfer and material flow have contributed to understand the underlying mechanisms of friction stir welding (FSW), there are certain input model parameters that can not be easily determined. Thus, the model predictions do not always agree with experimental results. In this work, sensitivity analysis and parameter estimation were applied to test heat transfer and material flow models. A forward-difference approximation was used to compute the sensitivity of the solution with respect to the unknown model parameters. The Levenberg-Marquardt (LM) method was applied to solve the nonlinear parameter estimation problem. The numerical models were developed by the finite element method (FEM). The way in which the unknown model parameters independently affect the results and the importance of the location of reference points that take part in the objective function were determined.

A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data

“…They concluded that at high noise levels, Parallel Tempering (PT) and Evolutionary Monte Carlo (EMC) perform equally and estimate the parameter with the deviation of maximum 9%. Sensitivity analysis [15][16][17][18][19] is an important study that indicates the effectiveness of the change in parameter estimated on the temperature. Sensitivity analysis has control over the selection of the inverse method.…”