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

EAGER：多孔介质中辐射传热的预测替代模型和分析

• 批准号: 1926882
• 负责人: Shima Hajimirza
• 金额: $ 16.58万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926882&HistoricalAwards=false
• 关键词: EAGER; Predictive; Surrogate; Modeling; Analysis

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.

多孔介质中的辐射传热是一个复杂而模糊的问题，而测量目标多孔介质的辐射特性是至关重要的。辐射特性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，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、太阳能热化学反应器、生物组织、喷气发动机和航天器的热障、催化燃烧的陶瓷泡沫等等。目前，预测随机填充床的辐射特性需要大量耗时的射线跟踪模拟。该项目将这些计算替换为基于高效机器学习的方法，以彻底改变广泛的相关应用和底层技术。这个变革性的项目表明，代理模型可以可靠有效地近似和预测随机堆积结构辐射特性的概率分布函数。基于机器学习方法的预测模型取代了耗时的光线跟踪蒙特卡罗模拟。模型的输入是与空隙、固体、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。各种学习模型的数据拟合进行了研究，并进行了分析的准确性与计算成本的每一个。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、可扩展和可推广的替代模型。为每个代理模型研究了采样，实验设计和模型拟合，以减少计算量，同时最大限度地减少数据收集和学习的成本。所提出的模型的实际精度进行了验证的基础上与直接蒙特卡罗模拟和先前建立的实验室为基础的实验比较。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。