A robust and fast algorithm for three-dimensional transient inverse heat conduction problems

Despite numerous studies of inverse heat conduction problems (IHCP) over the last several decades, their solutions still suffer from the mathematical difficulties and the bottleneck of currently available numerical methods for large-scale problems. In this paper, we present a robust and efficient algorithm for the solution of a specific type of three-dimensional (3D) IHCP commonly involved in various engineering applications. The solution method incorporates the Tikhonov regularization for tackling the severe ill-posedness and the conjugate gradient (CG) method for solving the resulting minimization problems. A model function approach is used to significantly reduce the effort needed to find the optimal Tikhonov regularization parameter. The proposed solution method requires no a priori knowledge of the measurement noise and is much more computationally efficient than the traditional Tikhonov regularization-based inversion approaches. Thus, it can be used for the efficient solution of large-scale practical problems. Two simulation case studies of practical significance are presented to validate and assess the performance of the proposed method. Finally, the solution method is successfully applied to the reconstruction of instantaneous heat fluxes from experimentally measured temperature data.