Prediction of the coupled heat radiation and conduction parameters and boundary condition using the unscented Kalman filter

Wen, Shuangchun; Qi, Hong; Wang, Yifei; Ren, Ya-Tao; Wei, Linyang; Ruan, Li-Ming

doi:10.1007/s11431-019-1437-8

Cited by 8 publications

(4 citation statements)

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“…Within the framework of nonlinear Kalman filters, to overcome several limitations of the linear Kalman filter, the values of the initial or boundary conditions can be incorporated into the particles or members. Dubinkina and Ruchi (2020), 33 ) Wen et al (2020), 34 ) and Nakamura et al (2021) 35 ) have dealt with inverse analyses using nonlinear Kalman filters for which both the material parameters and boundary conditions are uncertain. Hence, the third category of the above classification corresponds to the prototype of the present DA technique.…”

Section: Kalman Filters and Bayesian Methodsmentioning

confidence: 99%

“…(2020), 34) and Nakamura et al (2021) 35) have dealt with inverse analyses using nonlinear Kalman filters for which both the material parameters and boundary conditions are uncertain. Hence, the third category of the above classification corresponds to the prototype of the present DA technique.…”

Section: ½9mentioning

confidence: 99%

“…Conversely, filtering via sequential importance sampling (SIS) preserves the weight set, fw ðiÞ k g, and w ðiÞ k is calculated by Eq. [34] instead of by resampling. Initial weight w ðiÞ 0 is usually set to 1/N.…”

Section: ) Resamplingmentioning

confidence: 99%

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Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

MURAKAMI,

FUJISAWA,

SHUKU

2023

Proceedings of the Japan Academy. Ser. B: Physical and Biologic

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The present paper reviews recent activities on inverse analysis strategies in geotechnical engineering using Kalman filters, nonlinear Kalman filters, and Markov chain Monte Carlo (MCMC)/Hamiltonian Monte Carlo (HMC) methods. Nonlinear Kalman filters with finite element method (FEM) broaden the choices of unknowns to be determined for not only parameters but also initial and/or boundary conditions, and the use of the posterior probability of the state variables can be widely applied to, for example, the decision making for design changes. The relevance of the unknowns and the observed values and the selection of the best sensor locations are some of the considerations made while using the Kalman filter FEM. This paper demonstrates several real-world geotechnical applications of the nonlinear Kalman filter and the MCMC with FEM. Future studies should focus on the following areas: attaining excellent performance for longterm forecasts using short-term observation and developing a viable method for selecting equations that describe physical phenomena and constitutive models.

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Section: Kalman Filters and Bayesian Methodsmentioning

confidence: 99%

Section: ½9mentioning

confidence: 99%

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Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

MURAKAMI,

FUJISAWA,

SHUKU

2023

Proceedings of the Japan Academy. Ser. B: Physical and Biologic

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“…Accurate measurement of time-and space-dependent heat flux is significant to numerous scientific researches and engineering applications, such as glass pressing, [1,2] combustion diagnosis, [3,4] tumor thermal therapy, [5,6] hot rolling, [7,8] to name a few. [9][10][11][12][13] Direct measurement of transient heat flux is a challenging task in many industry fields. For instance, the time-and space-dependent heat flux on the wall subjected to fire is difficult to identify in a combustion chamber.…”

Section: Introductionmentioning

confidence: 99%

An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium*

2020

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The decentralized fuzzy inference method (DFIM) is employed as an optimization technique to reconstruct time- and space-dependent heat flux of two-dimensional (2D) participating medium. The forward coupled radiative and conductive heat transfer problem is solved by a combination of finite volume method and discrete ordinate method. The reconstruction task is formulated as an inverse problem, and the DFIM is used to reconstruct the unknown heat flux. No prior information on the heat flux distribution is required for the inverse analysis. All retrieval results illustrate that the time- and space-dependent heat flux of participating medium can be exactly recovered by the DFIM. The present method is proved to be more efficient and accurate than other optimization techniques. The effects of heat flux form, initial guess, medium property, and measurement error on reconstruction results are investigated. Simulated results indicate that the DFIM is robust to reconstruct different kinds of heat fluxes even with noisy data.

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An escape strategy in orbital pursuit-evasion games with incomplete information

Zhu

Ya-zhong

2020

Sci. China Technol. Sci.

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Prediction of the coupled heat radiation and conduction parameters and boundary condition using the unscented Kalman filter

Cited by 8 publications

References 50 publications

Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium*

An escape strategy in orbital pursuit-evasion games with incomplete information

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