Simultaneous Identification of Temperature-Dependent Thermal Properties via Enhanced Genetic Algorithm

Czél, Balázs; Gróf, Gyula

doi:10.1007/s10765-012-1226-9

Cited by 16 publications

(5 citation statements)

References 30 publications

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“…The genetic algorithm (GA), which is widely used for complex, ill-posed problems [25][26][27], was employed to solve the inverse identification problem. Figure 2 shows the block diagram of the GA-based inverse method used to determine parameter vector u.…”

Section: Inverse Methodsmentioning

confidence: 99%

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

Hua

Chen

et al. 2021

Energies

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The conductive and radiative properties of participating medium can be estimated by solving an inverse problem that combines transient temperature measurements and a forward model to predict the coupled conductive and radiative heat transfer. The procedure, as well as the estimates of parameters, are not only affected by the measurement noise that intrinsically exists in the experiment, but are also influenced by the known model parameters that are used as necessary inputs to solve the forward problem. In the present study, a stochastic Cramér–Rao bound (sCRB)-based error analysis method was employed for estimation of the errors of the retrieved conductive and radiative properties in an inverse identification process. The method took into account both the uncertainties of the experimental noise and the uncertain model parameter errors. Moreover, we applied the method to design the optimal location of the temperature probe, and to predict the relative error contribution of different error sources for combined conductive and radiative inverse problems. The results show that the proposed methodology is able to determine, a priori, the errors of the retrieved parameters, and that the accuracy of the retrieved parameters can be improved by setting the temperature probe at an optimal sensor position.

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Section: Inverse Methodsmentioning

confidence: 99%

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

Hua

Chen

et al. 2021

Energies

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“…To solve the inverse problem and reconstruct the unknown parameters, various methods are at our disposal from classic gradient methods to artificial neural networks [23] to genetic algorithms [24].…”

Section: Compensation Of the Initial Temperature Biasmentioning

confidence: 99%

Thermophysical Property Estimation by Transient Experiments: The Effect of a Biased Initial Temperature Distribution

Scarpa

Tagliafico

2015

Mathematical Problems in Engineering

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The identification of thermophysical properties of materials in dynamic experiments can be conveniently performed by the inverse solution of the associated heat conduction problem (IHCP). The 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 affected by some uncertainty. This 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 effect on the estimated thermophysical properties due to errors in the initial temperature distribution is investigated along with a practical method to quantify this effect. Furthermore, a technique for compensating this kind of bias is proposed. The method consists in including the initial temperature distribution among the unknown functions to be estimated. In this way the effect of the initial bias is removed and the accuracy of the identified thermophysical property values is highly improved.

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“…Czél and Gróf [16] proposed a method for identification of the thermal conductivity without the heat source by means of genetic algorithms and the BICOND measurement technique. The authors in their next paper [17] solved a similar problem to determine simultaneously the specific heat capacity and the thermal conductivity. The inverse analysis along with genetic algorithms were used by Koči [18] to analyse heat release of hydrating cement.…”

Section: Introductionmentioning

confidence: 99%

Numerical identification of the thermal properties of early age concrete using inverse heat transfer problem

et al. 2018

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The procedure of numerical identification of thermophysical properties of concrete during its hardening is presented. Heat of cement hydration, thermal conductivity and specific heat are determined for purpose of modelling temperature evolution in massive concrete elements. The developed method is based on point temperature measurements in a cylindrical mould and the numerical solution of the inverse heat transfer problem by means of direct search optimization algorithm. The determined thermal characteristics of concrete are not constant and depend on the maturity of concrete. The procedure was verified on set of concrete mixes designed with Portland cement CEM I 42.5R and Portland-slag cement CEM II/B-S 32.5 N. Calcareous fly ash was also used for partial replacement of cement in the mixtures. The obtained results have been compared with experimentally measured temperature in concrete and a fair agreement has been found.

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Simultaneous Identification of Temperature-Dependent Thermal Properties via Enhanced Genetic Algorithm

Cited by 16 publications

References 30 publications

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

Thermophysical Property Estimation by Transient Experiments: The Effect of a Biased Initial Temperature Distribution

Numerical identification of the thermal properties of early age concrete using inverse heat transfer problem

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