Abstract
A new and simple method for solving linear, inverse heat conduction problems using temperature data containing significant noise is presented in this paper. The method consists in a straightforward application of singular-value decomposition to the matrix form of Duhamel's principle. A physical interpretation of the method is given by discussing the frequency-domain interpretation of the decomposition. Basically, rows and columns are removed from the decomposed matrices that are associated with small singular values that are shown to be associated with frequencies where the signal-to-noise ratio is small. The technique is demonstrated by considering a standard one-dimensional example. Advantages of the new method are reduction in matrix size, robust treatment of noisy temperature data, optimal in the least-squares sense, and lack of ad hoc parameters.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.