Implementation of total variation regularization-based approaches in the solution of linear inverse heat conduction problems concerning the estimation of surface heat fluxes

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

doi:10.1016/j.icheatmasstransfer.2021.105330

Cited by 14 publications

(1 citation statement)

References 43 publications

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“…Advances in continuous casting technology have given rise to fast mold thermal monitoring systems with temperature-sampling rates exceeding 10 Hz. These systems offer more detailed insights into mold breakout, the infiltration of liquid mold flux into the mold/shell gap, and the solidification behavior of liquid steel [49,50]. Subsequently, the requirements for addressing the inverse problem of online reconstructing mold heat flux using observed temperatures can be summarized as follows: ensuring the numerical solution's stability, achieving rapid heat flux reconstruction, and effectively handling the discontinuous functional form of the unknown.…”

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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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%

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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Rapid and Noise‐Resilient Mapping of Photogenerated Carrier Lifetime in Halide Perovskite Thin Films

Vidon,

Scrivanti,

Soret

et al. 2024

Adv Funct Materials

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Halide perovskite materials offer significant promise for solar energy and optoelectronics yet understanding and enhancing their efficiency and stability require addressing lateral inhomogeneity challenges. While photoluminescence imaging techniques are employed for the measurement of their opto‐electronic and transport properties, going further in terms of precision requires longer acquisition times. Prolonged exposure of perovskites to light, given their high reactivity, can substantially alter these layers, rendering the acquired data less meaningful for analysis. In this paper, a method to extract high‐quality lifetime images from rapidly acquired, noisy time‐resolved photoluminescence images is proposed. This method leverages concepts of the field of constrained reconstruction and includes the Huber loss function and a specific form of total variation regularization. Through both simulations and experiments, it is demonstrated that the approach outperforms conventional pointwise methods. Optimal acceleration and optimization parameters tailored for decay time imaging of perovskite materials, offering new perspectives for accelerated experiments crucial in degradation process characterization are identified. Importantly, this methodology holds the potential for broader applications: it can be extended to explore additional beam‐sensitive materials, and other imaging characterization techniques and employed with more complex physical models to treat time‐resolved decays.

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A novel defect identification design of gas pipeline based on inverse heat conduction problem

Wang

Xu²,

Zhou³

et al. 2023

J Therm Anal Calorim

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Implementation of total variation regularization-based approaches in the solution of linear inverse heat conduction problems concerning the estimation of surface heat fluxes

Cited by 14 publications

References 43 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

Rapid and Noise‐Resilient Mapping of Photogenerated Carrier Lifetime in Halide Perovskite Thin Films

A novel defect identification design of gas pipeline based on inverse heat conduction problem

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