A modified sequential gradient-based method for the inverse estimation of transient heat transfer coefficients in non-linear one-dimensional heat conduction problems

Tourn, Benjamín A.; Hostos, Juan C. Álvarez; Fachinotti, Víctor D.

doi:10.1016/j.icheatmasstransfer.2021.105488

Cited by 26 publications

(2 citation statements)

References 42 publications

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“…On the other hand, the whole-time domain approach (3) is powerful because it allows for very small-time steps, but it may not be as computationally efficient. Most stabilization techniques, such as function specification and regularization methods, can be applied in both sequential and whole-domain estimation forms [18][19][20]24].…”

Section: Introductionmentioning

confidence: 99%

“…Precisely online, determining the mold heat flux remains a focal point of research and presents a substantial challenge [41][42][43][44]. The heat flux in the mold exhibits variations in both the vertical and horizontal directions across the mold surface [24,[45][46][47]. Monitoring the temperature of the continuous casting mold typically involves the use of numerous thermal sensors, such as thermocouples and fiber-optic temperature sensors [48].…”

Section: Introductionmentioning

confidence: 99%

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Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques

Zhang,

Zou,

Xiao

2023

Metals

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A two-dimensional transient inverse heat-conduction problem (2DIHCP) was established to determine the mold heat flux using observed temperatures. The sequential regularization method (SRM) was used with zeroth-, first-, and second-order spatial regularization to solve the 2DIHCP. The accuracy of the 2DIHCP was investigated under two strict test conditions (Case 1: heat flux with time-spatial periodically varying, and Case 2: that with sharp variations). The effects of the number of future time steps, regularization parameters, order of regularization, discrete grids, and time step size on the accuracy of the 2DIHCP were analyzed. The results showed that the minimum relative error (epred) of the predicted Case 1 heat flux is 5.05%, 5.39%, and 5.88% for zeroth-, first-, and second-order spatial regularization, respectively. The corresponding values for the predicted Case 2 heat flux are 6.31%, 6.30%, and 6.36%. Notably, zeroth- and first-order spatial regularization had higher accuracy than second-order spatial regularization, while zeroth-order spatial regularization was comparable to first-order. Additionally, first-order spatial regularization was more accurate in reconstructing heat flux containing sharp spatial variations. The CPU time of the predicted Case 2 heat flux is 1.71, 1.71, and 1.70 s for zeroth-, first-, and second-order spatial regularization, respectively. The corresponding values for the predicted Case 1 heat flux are 6.18, 6.15, and 6.17 s. It is noteworthy that the choice of spatial regularization order does not significantly impact the required computing time. Lastly, the minimum epred of Case 2 heat flux with zeroth-order spatial regularization is 7.96%, 6.42%, and 7.87% for time step sizes of 1/fs, 1/2fs, and 1/5fs, respectively. The accuracy of the inverse analysis displays an initial improvement followed by degradation as the time step size decreases. A recommended time step size is 1/2fs, where fs denotes the temperature-sampling rate.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques

Zhang,

Zou,

Xiao

2023

Metals

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Heat Transfer Coefficient Estimation

2023

Inverse Heat Conduction

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Analysis of transient heat conduction in tubes under convective boundary conditions

Camaraza‐Medina,

Hernandez‐Guerrero,

Luviano‐Ortiz

2024

Heat Trans

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In this work, six analytical solutions are given to estimate the energy exchange by transient conduction in pipes with convection conditions. The developed models were adjusted for an interval from 0.2 to 0.8, dimensionless Fourier (Fo) and Biot (Bi) numbers, from 0.05 to 50 and 0.005 to 50, respectively. In each case, 616 tests of temperature distributions were computed using the Heisler approximate method (HAM) and the exact models proposed, for different combinations of . For the comparison made between the analytical solutions and the HAM, 3696 tests were used, detecting that the HAM correlates with the analytical method with an average deviation of and for 71.2%, and 90.4% of the different values of combinations evaluated. The best fit was found for Case 5, with a mean deviation of and for 81.1% and 92.3% of the data used, respectively, while the weaker fit was detected for Case 2, with a mean deviation of and for 67.9% and 88.2% of the available tests.

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A modified sequential gradient-based method for the inverse estimation of transient heat transfer coefficients in non-linear one-dimensional heat conduction problems

Cited by 26 publications

References 42 publications

Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques

Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques

Heat Transfer Coefficient Estimation

Analysis of transient heat conduction in tubes under convective boundary conditions

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