EAGER:Predictive Surrogate Modeling and Analysis of Radiative Heat transfer in Porous Media

多孔介质中的辐射换热是复杂而模糊的，然而，测量目标多孔介质的辐射特性是至关重要的。辐射性能的测量和预测对于涉及多孔材料结构的能源技术的模拟和设计至关重要，这些技术包括核反应堆中的卵石床、选择性激光烧结技术、太阳能吸收器、太阳能热化学反应装置、生物组织、喷气发动机和空间飞行器的热障、用于催化燃烧的泡沫陶瓷等等。目前，预测随机填充床的辐射特性需要大量耗时的光线跟踪模拟。该项目用高效的基于机器学习的方法取代了这些计算，以彻底改变一系列相关的应用程序和底层技术。这一变革性的项目表明，代理模型可以可靠而有效地逼近和预测随机堆积结构的辐射特性的概率分布函数。大量耗时的光线跟踪蒙特卡罗模拟被基于机器学习方法的预测模型所取代。模型的输入是与空洞、实心、边界条件和尺寸以及介质形状的物理配置有关的各种变量的统计数据。研究了用于数据拟合的各种学习模型，并对每种模型的精度与计算成本进行了分析。数据采样、模型选择和模型拟合都经过精心设计，以提供准确、高效、可扩展和可推广的代理模型。对每个代理模型的抽样、实验设计和模型拟合进行了研究，以减少计算量，同时最小化数据收集和学习的成本。通过与直接蒙特卡罗模拟和已建立的实验室实验的比较，验证了所提模型的实际精度。建议的预测模型被应用于计算机断层扫描，以推断各种应用中的多孔介质结构。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Radiative heat transfer in porous media is complex and ambiguous; yet, measuring the radiative properties of a target porous media is of vital importance. Measurement and prediction of the radiative properties is critical for simulation and design of energy technologies that involve porous material structures, including pebble beds in nuclear reactors, selective laser sintering technology, solar absorbers, solar thermochemical reactors, biological tissues, thermal barriers for jet engines and space vehicles, ceramic foams for catalytic combustion and many more. At present, predicting radiative properties of randomly packed beds requires large time-consuming ray-tracing simulations. This project replaces these computations with efficient machine learning based methods to revolutionize a wide range of related applications and underlying technologies. This transformative project demonstrates that surrogate models can approximate and predict the probability distribution functions of radiative properties of randomly packed structures reliably and efficiently. Large time-consuming ray-tracing Monte Carlo simulations are replaced by predictive models based on machine learning methods. The inputs to the models are statistics of a wide range of variables pertaining to the physical configurations of void, solid, boundary conditions and dimensions and the medium shape. Various learning models are studied for data fitting, and an analysis of accuracy versus the cost of computation is performed for each. Data sampling, model selection and model fitting are all engineered to render surrogate models that are accurate, efficient, scalable and generalizable. Sampling, design of experiment and model fitting is studied for each surrogate model to reduce the computational load while minimizing the cost of data collection and learning. The practical accuracy of the proposed models is validated based on comparison with direct Monte Carlo simulations and previously established laboratory-based experiments. The proposed predictive models are applied in computed tomography for inference of porous media structures in various applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.