Prediction of short-duration transient surface heat flux using various analytical techniques

Abstract

Transient surface heat flux prediction, from temperature signals using one-dimensional heat conduction modeling, is the major objective of present investigation. The techniques reported in the literature based on spline fitting (linear and cubic) and least square polynomial fitting of temperature data are evaluated for prediction of surface heat flux through various analytical modeling. In addition, a Laplace-based technique is also incorporated here to predict surface heat flux where the least square polynomial fitting technique is used to discretize the temperature data. The temperature time histories obtained from an in-house built, one-dimensional finite volume computation solver, and experiments (i.e., shock tunnel testing and flight data) are considered for the performance assessment of these methods. Heat flux recovery from all the methods for smooth temperature signals is seen to be in good agreement with a reasonable accuracy of ±5%. However, it has been noticed that the spline based fitting techniques supersede the polynomial-based fitting techniques for prediction of heat flux from discontinuous or noisy temperature signals. © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library (wileyonlinelibrary.com/journal/htj). DOI 10.1002/htj.21050