... are subsequently analyzed using two different iterative parameter estimation methods: a Levenberg scheme and a Tikhonov iterative scheme. The required sensitivity matrix is computed using forward finite difference approximations. The results for the heat transfer coefficients for this model problem suggest...

...), the method used here exploits a direct nonlinear numerical model associated with a parameter estimation technique to determine temperature dependent thermal conductivities. It has been applied to a water-agar gel during phase change and the thermal conductivities within the corresponding temperature interval...

... , J. , 2004 , Bayesian Inference, Kendall's Advanced Theory of Statistics , Vol. 2b , Oxford University , Oxford, UK . [13] Emery , A. F. , Bardot , D. , and Valenti , E. , 2007 , “ Using Bayesian Inference for Parameter Estimation When the System Response and Experimental...

... of the stochastic methods and of the fast convergence of the deterministic methods [( 30 )]. Inverse parameter estimation problems in heat transfer can be very ill-conditioned, creating difficulties in the application of Eqs. 12 , 13 . The Levenberg–Marquardt...

... in handling function estimation problems and BKM for parameter estimation problems. One of the unique features about the present method is that the correlation among the separate unknowns does not disrupt the convergence of the problem. Numerical experiments using measurement errors are performed to verify...

... with vectorial descent parameter. The experimental example developed herein uses one set of good data obtained by Beck and Arnold ( 1977, Parameter Estimation in Engineering and Science, Wiley, New York ). For this case, two measured temperatures in the solid are used to illustrate combined estimation...

... 2006 heat transfer Gaussian processes Monte Carlo methods response surface methodology sensitivity global sensitivity Gaussian processes parameter estimation variability uncertainty The responses, whether measured in real systems or predicted by simulation, are sensitive...

... algorithm. 24 06 2005 20 01 2006 emissivity inverse problems genetic algorithms neural nets minimisation physics computing inverse radiation problem parameter estimation discrete ordinate method Levenberg-Marquardt algorithm genetic algorithm artificial neural network...

...Ashley F. Emery Parameter estimation is based upon a comparison of predicted deterministic model responses to data. The models are often numerical, e.g., finite volume, with intrinsic inaccuracies. In addition, the models typically assume a full knowledge of the physical processes. By using...

... November 2002 21 03 2003 inverse problems parameter estimation heat transfer shape measurement Heat Transfer Image Processing Inverse Numerical Methods Optimization The results shown in Fig. 9 considering case 5 at Bi=1 and σ=0 exhibit a similar trend. The dashed curves...

... July 2, 2002. Associate Editor: A. F. Emery. 24 August 2001 02 July 2002 03 12 2002 information theory sampling methods parameter estimation Information Parameter Estimation Correlated Data Uncertainty Experiments are often performed to prove the accuracy...

... applications, such as parameter estimation, optimal experimental design, optimization, and uncertainty analysis, require sensitivity to the parameters describing temperature-dependent properties. A general procedure to compute the sensitivity of the temperature field to model parameters for nonlinear heat...