使用降阶方法的逆传热问题优化贝叶斯框架
Optimized Bayesian Framework for Inverse Heat Transfer Problems Using Reduced Order Methods
AI总结：
针对连铸结晶器不可测瞬态热通量反演，提出结合径向基函数降阶与集合同步输入状态滤波的贝叶斯数据同化框架，实现噪声和模型误差下的实时概率估计。
摘要：
本文构建了一个随机逆传热问题，用于推断被视为未知 Neumann 边界条件的瞬态热通量。因此，采用基于集合的同步输入与状态滤波作为一种数据同化技术，以同时进行温度分布预测和热通量估计。该方法与径向基函数相结合，不仅减少了未知输入的规模，还减轻了该技术的计算负担。该流程适用于连铸机械中使用的结晶器这一特定案例，并基于结晶器内部热电偶按顺序提供的温度数据。我们的研究对实现概率边界条件实时估计作出了重要贡献，能够处理含噪测量和模型误差。我们还证明了该流程对现有文献中未记载的若干超参数的依赖性。在连铸结晶器与钢水接触的边界区域，热通量无法进行物理测量，而准确的实时热通量预测对于连铸机械的平稳运行至关重要。因此，这为高效的实时监测与控制铺平了道路，而这对于防止连铸机停机至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
