Badenhorst, HeinrichSandrock, Carl2016-03-172016-05Badenhorst, H & Sandrock, C 2016, 'Novel method for thermal conductivity measurement through flux signal deconvolution', Journal of Energy Storage, vol. 6, pp. 32-39.2352-152X10.1016/j.est.2016.02.010http://hdl.handle.net/2263/51896Rapid thermal conductivity measurement of porous solids and composites remains a challenge. A modified steady state technique has been proposed which uses two heat flux sensors instead of one. The parameter estimation is achieved through the deconvolution of these signals and the identification of the system impulse response. A detailed derivation of the theoretically expected behaviour has been done, which provides a basis for fitting the measured impulse response. A six term expansion is required for the theoretical model to achieve full convergence. The unit requires a calibration step to measure the convective boundary condition. A signal validity check has been built into the approach through the use of the energy balance which detects any drift due to ambient losses or other factors. Through suitable choice of the mathematical algorithm rapid convergence of the non-linear fitting procedure is achieved. The parameter estimates of the standard test samples are excellent, with average errors of 2.3% for brass and 6.3% for aluminium. The system has several advantages in addition to the short measurement time, including low cost and no guard furnace or insulation requirement for room temperature measurements. The approach is suitable for measuring the overall behaviour of practical, composite systems.en© 2016 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Journal of Energy Storage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Energy Storage, vol. 6, pp. 32-39, 2016. doi : 10.1016/j.est.2016.02.010.Energy materialsAnalytical modellingThermal propertiesPostprint Article