inverse identification
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2021 ◽  
Vol 160 ◽  
pp. 107831
Author(s):  
Romain Beauvais ◽  
Adrien Pelat ◽  
François Gautier ◽  
Véronique Florquin ◽  
Guillaume Vandenbossche ◽  
...  

2021 ◽  
pp. 1-17
Author(s):  
Marthe Loiseau ◽  
Sylvain Chataigner ◽  
Romain Créac’hcadec ◽  
Jean-Philippe Court ◽  
Marie-Odette Quéméré

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6593
Author(s):  
Hua Liu ◽  
Xue Chen ◽  
Zhongcan Chen ◽  
Caobing Wei ◽  
Zuo Chen ◽  
...  

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.


2021 ◽  
pp. 104056
Author(s):  
Christian Gebhardt ◽  
Tobias Sedlatschek ◽  
Alexander Bezold ◽  
Christoph Broeckmann

2021 ◽  
Author(s):  
Evelyn Lunasin ◽  
Athanasios Iliopoulos ◽  
John Michopoulos ◽  
John Steuben

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