The purpose of this paper is to reduce the destructive effects of existing unavoidable noises contaminating temperature data in inverse heat conduction problems (IHCP) utilizing the wavelets.
For noise reduction, sensor data were treated as input to the filter bank used for signal decomposition and implementation of discrete wavelet transform. This is followed by the application of wavelet denoising algorithm that is applied on the wavelet coefficients of signal components at different resolution levels. Both noisy and de‐noised measurement temperatures are then used as input data to a numerical experiment of IHCP. The inverse problem deals with an estimation of unknown surface heat flux in a 2D slab and is solved by the variable metric method.
Comparison of estimated heat fluxes obtained using denoised data with those using original sensor data indicates that noise reduction by wavelet has a potential to be a powerful tool for improvement of IHCP results.
Noise reduction using wavelets, while it can be implemented very easily, may also significantly relegate (or even eliminate) conventional regularization schemes commonly used in IHCP.
Ahmadi‐Noubari, H., Pourshaghaghy, A., Kowsary, F. and Hakkaki‐Fard, A. (2008), "Wavelet application for reduction of measurement noise effects in inverse boundary heat conduction problems", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 18 No. 2, pp. 217-236. https://doi.org/10.1108/09615530810846356Download as .RIS
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