Data Filtering Techniques Applied to the Solution of the Inverse Heat Conduction Problem

Form the engineering point of view, one of the most relevant Inverse Heat Conduction Problem (IHCP) consists in the reconstruction of the heat flux distribution over a given solid domain. The well known difficulties involved in the IHCP stem primarily from the fact that they are ill-posed. This issue is generally performed by measuring the temperature distribution over an accessible boundary wall of the system under investigation. In particular, the temperature field can be acquired either with traditional contact sensors or, more suitably, with non-intrusive thermographic techniques, which allow to reach both high spatial and temporal resolution. A great deal of intensive and very fruitful work has been devoted to the development of solution strategies of the IHCP but most of the solution techniques available literature have been tested by considering their possible application to problems characterized by a limited number of both input and output variables, while their application to problems characterized by a high number of degrees of freedom is often impracticable. This aspect may prevent their application to problems which require thermograhic detectors to acquire the temperature distribution. In order to solve IHCPs based on thermographic temperature maps, a competitive and challenging approach is represented by the possibility of pre treating the raw input temperature data by some suitable filtering techniques. This approach is still not well-established, and, therefore, it is desirable that some further research efforts will be devoted to this research topic. The present contribution has been developed in this direction and the effect of filtering techniques on noisy data to be used as input data in a typical IHCP with an high number of unknowns has been considered.