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Optimized Bayesian Framework for Inverse Heat Transfer Problems Using Reduced Order Methods (2402.19381v1)

Published 29 Feb 2024 in math.NA and cs.NA

Abstract: A stochastic inverse heat transfer problem is formulated to infer the transient heat flux, treated as an unknown Neumann boundary condition. Therefore, an Ensemble-based Simultaneous Input and State Filtering as a Data Assimilation technique is utilized for simultaneous temperature distribution prediction and heat flux estimation. This approach is incorporated with Radial Basis Functions not only to lessen the size of unknown inputs but also to mitigate the computational burden of this technique. The procedure applies to the specific case of a mold used in Continuous Casting machinery, and it is based on the sequential availability of temperature provided by thermocouples inside the mold. Our research represents a significant contribution to achieving probabilistic boundary condition estimation in real-time handling with noisy measurements and errors in the model. We additionally demonstrate the procedure's dependence on some hyperparameters that are not documented in the existing literature. Accurate real-time prediction of the heat flux is imperative for the smooth operation of Continuous Casting machinery at the boundary region where the Continuous Casting mold and the molten steel meet which is not also physically measurable. Thus, this paves the way for efficient real-time monitoring and control, which is critical for preventing caster shutdowns.

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Citations (1)

Summary

  • The paper introduces an optimized Bayesian framework using Radial Basis Functions (RBFs) and Ensemble-based Simultaneous Input and State Filtering (EnSISF) to estimate transient heat flux in inverse heat transfer problems.
  • Numerical results show that integrating EnSISF with Multiquadric RBFs achieves lower spatiotemporal errors in heat flux estimation (6.31%) than Gaussian RBFs (7.59%).
  • The method provides a practical, real-time heat flux prediction tool for steel continuous casting, optimizing processes and preventing shutdowns, and is a blueprint for other inverse problems.

Optimized Bayesian Framework for Inverse Heat Transfer Problems Using Reduced Order Methods

The paper provides a comprehensive exploration of a stochastic inverse heat transfer problem, emphasizing the reconstruction of transient heat flux treated as a Neumann boundary condition. This problem has critical implications for the continuous casting (CC) process in steel manufacturing. The researchers have devised a method to estimate this heat flux, which is crucial for the operational integrity and efficiency of CC machinery, from noisy temperature data collected by thermocouples during the process.

The methodology is anchored in an ensemble-based Bayesian estimation framework known as Ensemble-based Simultaneous Input and State Filtering with direct feed-through (EnSISF-wDF), enhanced by the incorporation of Radial Basis Functions (RBFs). The primary advantage of this approach is its capability to simultaneously predict the temperature distribution and estimate the unknown heat flux in terms of a posterior distribution, which highlights the uncertainty estimation in the solution. The use of RBFs in this context serves not only to reduce the dimensionality of the problem space but also to alleviate the computational burden inherent in Bayesian inversion methods.

One of the paper's notable contributions is the development of a sequential data assimilation technique, which integrates RBFs with EnSISF-wDF, to improve the computational efficiency in handling real-time mould state estimation under noisy observations. This contribution is further solidified by a sensitivity analysis of various hyperparameters not extensively documented in past literature, including the number of ensemble members, RBF shape parameters, and time discretization elements, among others.

In terms of numerical performance, the paper evaluated optimal settings for various hyperparameters and demonstrated that integrating the EnSISF approach with Multiquadric RBFs yields lower spatiotemporal errors in heat flux estimation than using Gaussian RBFs. For example, with Multiquadric kernels, the approach achieved a spatiotemporal error of 6.31% compared to 7.59% with Gaussian kernels, indicating higher accuracy and reduced computational requirements with Multiquadric RBFs for the case investigated.

Practically, achieving accurate real-time heat flux predictions at the boundary between the CC mold and molten steel confreres significant benefits for the steel manufacturing industry, particularly in optimizing and controlling the continuous casting process. These predictions assist in preventing potential issues that may otherwise necessitate unscheduled shutdowns of the caster. Theoretically, the integration of RBFs with Bayesian frameworks for such estimations provides a blueprint for tackling other inverse problems characterized by uncertainty and nonlinearity.

The paper also opens pathways for future work. This includes exploring other reduced-order modeling or surrogate strategies to minimize the computational demands of solving forward dynamic problems and evaluating the method's adaptability across different boundary heat flux scenarios. Such future directions could enhance both the robustness and versatility of the proposed modeling and estimation framework.

Overall, this paper makes a significant contribution to the domain of inverse heat transfer problem-solving and its application in industrial processes by providing a detailed, efficient, and probabilistic estimation procedure to handle real-time, noisy environmental data. This work will likely inform future analytical and computational tools designed to improve the reliability of the continuous casting process in industrial settings.

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