Shape identification problems on detecting of defects in a solid body using inverse heat conduction approach

In this paper, a new shape identification method in the inverse heat conduction problem (IHCP) is applied to detect the location and size of defects in a solid body. Different defects are modeled in a solid body as an elliptical geometry whose parameters are estimated with a proposed inverse algorithm. The inverse algorithm consists of direct, inverse analysis, and gradient-based optimization method. The direct analysis is used a finite-element method in an unstructured grid system to solve the direct heat conduction problem. The inverse analysis is based on recording temperatures data on surface of solid body that calculates the objective function. The employed gradient-based optimization method is constructed using the adjoint, sensitivity, and conjugate gradient method (Powell-Beal's version) that are used to calculate the gradient of objective function, step size, and minimizing the objective function, respectively. The effects of different noisy temperature data, different cavities on some domains, and different type of defects such as poor cure, porosity, and crack are investigated in this work. The results show that this proposed inverse algorithm is more efficient in detection of defects.