2006
DOI: 10.1088/0022-3727/39/18/020
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An inverse hyperbolic heat conduction problem in estimating surface heat flux by the conjugate gradient method

Abstract: In the present study an inverse hyperbolic heat conduction problem is solved by the conjugate gradient method (CGM) in estimating the unknown boundary heat flux based on the boundary temperature measurements. Results obtained in this inverse problem will be justified based on the numerical experiments where three different heat flux distributions are to be determined. Results show that the inverse solutions can always be obtained with any arbitrary initial guesses of the boundary heat flux. Moreover, the drawb… Show more

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Cited by 28 publications
(12 citation statements)
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“…Continuous-time analogue Hopfield neural network based inverse solution algorithm has been proposed by Deng and Hwang (2006). Conjugate gradient method for the estimation of surface heat flux has been used by Huang and Wu (2006) and Xue and Yang (2005). A non-iterative least square minimization technique along with FEM proposed by Ling et al (2003) simplifies the inverse problem computation and produces consistent results.…”
Section: Introductionmentioning
confidence: 99%
“…Continuous-time analogue Hopfield neural network based inverse solution algorithm has been proposed by Deng and Hwang (2006). Conjugate gradient method for the estimation of surface heat flux has been used by Huang and Wu (2006) and Xue and Yang (2005). A non-iterative least square minimization technique along with FEM proposed by Ling et al (2003) simplifies the inverse problem computation and produces consistent results.…”
Section: Introductionmentioning
confidence: 99%
“…The phase lag times, s q1 , s T1 , s q2 and s T2 , become the target estimated parameters of the present work, and the number of parameters, N, is 4. The instruments have a measurement error of 3% is common [19,20]. Therefore, in the process of inverse analysis, the standard deviation of the measurements was assumed to be a constant 0.03.The standard deviation of the measurements gets a smaller value, the estimated values of the phase lags will make the predicted temperature increase closer to the experimental data, but the inverse computation is harder to converge.…”
Section: Resultsmentioning
confidence: 99%
“…Ijaz et al (2007) [10] have presented an adaptive state estimator for the estimation of input heat flux and measurement sensor bias in a two-dimensional inverse heat conduction problem. Continuous-time analogue hop-field neural network based inverse solution algorithm has been proposed by Hwang (2006) [11] and Deng (2006) [12] and. Gradeck et al (2011) [13] used temperature data from infrared camera to obtain transient heat flux with an inverse heat conduction problem formulated using an analytical solution of the direct problem expressed in Hankel space.…”
Section: Introductionmentioning
confidence: 99%