Research Article Thermophysical Property Estimation by Transient Experiments: The Effect of a Biased Initial Temperature Distribution Federico Scarpa and Luca A. Tagliafico DIME/TEC, Division of Termal Energy and Environmental Conditioning, University of Genoa, Via All’Opera Pia 15 A, 16145 Genoa, Italy Correspondence should be addressed to Federico Scarpa; fscarpa@ditec.unige.it Received 15 April 2015; Revised 5 July 2015; Accepted 6 July 2015 Academic Editor: Ivan D. Rukhlenko Copyright © 2015 F. Scarpa and L. A. Tagliafco. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te identifcation of thermophysical properties of materials in dynamic experiments can be conveniently performed by the inverse solution of the associated heat conduction problem (IHCP). Te inverse technique demands the knowledge of the initial temperature distribution within the material. As only a limited number of temperature sensors (or no sensor at all) are arranged inside the test specimen, the knowledge of the initial temperature distribution is afected by some uncertainty. Tis uncertainty, together with other possible sources of bias in the experimental procedure, will propagate in the estimation process and the accuracy of the reconstructed thermophysical property values could deteriorate. In this work the efect on the estimated thermophysical properties due to errors in the initial temperature distribution is investigated along with a practical method to quantify this efect. Furthermore, a technique for compensating this kind of bias is proposed. Te method consists in including the initial temperature distribution among the unknown functions to be estimated. In this way the efect of the initial bias is removed and the accuracy of the identifed thermophysical property values is highly improved. 1. Introduction Established techniques based on parameter estimation theory provide an efective tool for the identifcation of thermophys- ical properties of materials and/or other unknown system and measurement parameters by means of transient experiments [1, 2]. As the estimation process is usually based on some inverse solution (analytical or numerical) of a physical model, the unavoidable presence of errors in the measured data may have a detrimental efect in the fnal estimates because of the ill- posed nature of inverse heat conduction problems [3]. For this reason, besides a great precision in the measurement technique, the key to achieve a precise and reliable estimation of thermophysical properties from transient experiments is the adherence of the implemented physical and numeri- cal models to the actual phenomenon under investigation. However, as a general rule, most measurement and process mismatches can be compensated, if detected, by including, in the inverse solution, further additional models [4]. For example, the knowledge of the exact location where thermal sensors are placed inside the specimen can be identifed with great accuracy [4–7]. Errors (time lag) in temperature measurements by contact probes in a transient regime can be adequately compensated [8]. Te uncertainty of sensor calibration can be included in the inverse conduction prob- lem [9] to improve both the estimated thermophysical prop- erties and the calibration curve. According to this general approach, the biases are compensated by identifying, in the same experiment, both the thermophysical properties and the unknown parameters (e.g., lags, positioning errors, and calibration coefcients) appearing in the additional models. In this work, the focus is on errors in the initial tempera- ture distribution [10–16]. It is ofen possible to arrange in the interior of the specimen under test only a limited number of sensors (or no sensors at all in particular experiments [17, 18]). It follows that the initial temperature distribution, needed not only at the measuring points but also at each node of the spatial discretization grid of the numerical model, could be afected by signifcant uncertainty. Despite the smoothing efect of thermal conduction, errors of this type will propagate in the reconstruction algorithm, thus afecting Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 494051, 9 pages http://dx.doi.org/10.1155/2015/494051