A new algorithm for solving an inverse transient heat conduction problem by dividing a complex domain into parts

Duda, Piotr; Konieczny, Mariusz

doi:10.1016/j.ijheatmasstransfer.2018.09.064

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…In this work, smoothing filters are used. The filters are based on local polynomial approximation (3 rd order kind and eleven subsequent measurement points) and they smooth out the measured temperature curves [7].…”

Section: Formulation Of the Methodsmentioning

confidence: 99%

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Identification of Transient Temperature Distribution in a Thick-Walled Component

Duda¹,

Konieczny²

2019

Proceedings of the 5th World Congress on Mechanical, Chemical, and Material Engineering

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The paper presents a method of transient temperature estimation in a thick-walled pressure component. Despite the unknown boundary condition on the internal surface of the component, the method allows to determine the temperature distribution thanks to "measured" temperature histories determined in easily accessible points located on the component outer surface. On the outer component surface, heat exchange by radiation and convection is assumed. The proposed method is verified numerically. The presented method for controlling temperature is also suitable for nuclear power plants because it does not require drilling holes for sensors in the pressure element walls.

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Section: Formulation Of the Methodsmentioning

confidence: 99%

“…On the other hand, analysing the phenomena that take place in the flowing fluid numerically [1] is very time-consuming. Another way to determine the temperature distribution without the need to approximate HTC is finding a solution of the inverse heat conduction problem (IHCP) in the device under analysis [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Transient Temperature Distribution in a Thick-Walled Component

Duda¹,

Konieczny²

2019

Proceedings of the 5th World Congress on Mechanical, Chemical, and Material Engineering

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“…After disturbance with measurement errors, slight differences appeared between the right and the left part, which can be seen in Figure 7. Using the proposed inverse method, the temperature was identified with t = 10 s. The measured temperature transients in nodes 37-48 were smoothed using a third-degree polynomial with 11 subsequent points and the same filter was used to calculate temperature derivative dTP/dt [20]. After the temperature derivative was determined, Equation (11) became algebraic.…”

Section: Numerical Verificationmentioning

confidence: 99%

Experimental Verification of the Inverse Method of the Heat Transfer Coefficient Calculation

Duda¹,

Konieczny²

2020

Energies

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The purpose of this work is to formulate a method which can be used to solve nonlinear inverse heat conduction problems and to calculate the heat transfer coefficient distribution on the unknown boundary. The domain under consideration is divided into control volumes in polar coordinates, and heat balance equations are written. Based on temperature transients measured in selected points on the outer surface, temperature values in other points of the domain are determined. Finally, the heat transfer coefficient distribution on the inner surface with the unknown boundary condition is calculated from the presented heat balance equation. The proposed inverse method was verified experimentally using a collector that is part of a semi-industrial laboratory system. This collector is a horizontal, cylindrical thick-walled tank with flat side walls with outlets that enable oil supply and removal. Each side wall has an additional connector to ensure venting. The calculations made it possible to identify the phenomena occurring inside the collector during the experiment. The transient temperature distribution identified by the proposed inverse method was verified by a comparison of the calculated and the measured temperature transients in points inside the collector wall. Very good agreement is observed between the calculated and the measured temperature transients, which confirms the correctness of the identification. This proposed inverse method of the temperature and the heat transfer coefficient calculation is fast enough to apply in online thermal state monitoring systems. The proposed algorithm presented in this paper can easily be implemented industrially.

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“…Therefore, it is hard for this method to solve problems within seconds. Duda et al [26,27]. efficiently solved the nonlinear IHCPs by dividing a complex domain into sample parts.…”

Section: Introductionmentioning

confidence: 99%

Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

Wang¹,

Wang²,

Ren³

2021

Preprint

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Existing algorithms with iterations as the principle for 3D inverse heat conduction problems (IHCPs) are usually time-consuming. With the recent advancements in deep learning techniques, it is possible to apply the neural network to compute IHCPs. In this paper, a new framework based on Convolutional-LSTM is introduced to predict the transient heat flux via measured temperature. The inverse heat conduction models concerned in this work have 3D complex structures with non-linear boundary conditions and thermophysical parameters. In order to reach high precision, a forward solver based on the finite element method is utilized to generate sufficient data for training. The fully trained framework can provide accurate predictions efficiently once the measured temperature and models are acquired. It is believed that the proposed framework offers a new pattern for real-time heat flux inversion.

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A new algorithm for solving an inverse transient heat conduction problem by dividing a complex domain into parts

Cited by 14 publications

References 22 publications

Identification of Transient Temperature Distribution in a Thick-Walled Component

Identification of Transient Temperature Distribution in a Thick-Walled Component

Experimental Verification of the Inverse Method of the Heat Transfer Coefficient Calculation

Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

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