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; 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复杂而模棱两可的。但是，测量目标多孔介质的辐射特性至关重要。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，包括核反应器中的卵石床，选择性激光烧结技术，太阳能吸收器，太阳能热化学反应堆，生物组织，生物组织，喷气发动机和太空车辆的热屏障，陶器和陶器的cat仪和cat构燃料的多种多样。目前，预测随机包装床的辐射特性需要大量耗时的射线追踪模拟。该项目用基于机器学习的方法取代了这些计算，以彻底改变广泛的相关应用程序和基础技术。这个变革性的项目表明，替代模型可以可靠，有效地近似和预测随机堆积结构的辐射特性的概率分布功能。大型耗时的射线追踪蒙特卡洛模拟被基于机器学习方法的预测模型所取代。模型的输入是与空隙，实心，边界条件和尺寸和中等形状的物理配置有关的多种变量的统计数据。研究了各种学习模型以进行数据拟合，并对每个学习模型进行了分析与每个计算成本进行分析。数据采样，模型选择和模型拟合均经过设计，以渲染准确，高效，可扩展且可推广的替代模型。为每个替代模型研究了采样，实验设计和模型拟合的设计，以减少计算负载，同时最大程度地减少数据收集和学习成本。根据与直接蒙特卡洛模拟和先前建立的基于实验室的实验的比较，对所提出的模型的实际精度进行了验证。所提出的预测模型用于计算机断层扫描中用于推断各种应用中多孔媒体结构的推断。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，认为NSF的法定任务值得通过评估。