Learning thermal radiative properties of porous media from engineered geometric features
Learning thermal radiative properties of porous media from engineered geometric features
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DOI：
10.1016/j.ijheatmasstransfer.2021.121668
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发表时间：
2021-11
影响因子：
5.2
通讯作者：
S. Hajimirza;Hussein Sharadga
中科院分区：
文献类型：
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作者：
S. Hajimirza;Hussein Sharadga
蒙特卡罗射线追踪（MCRT）模拟是预测随机堆积结构或多孔介质辐射特性的最可靠的非实验手段，特别是在多分散或非均匀环境中。这些方法可以近似的均匀或异质固体填充的空隙区域的辐射特性，平均跨越一个大池的独立随机射线跟踪模拟。然而，由于严格的精度要求，高计算成本的模拟设置和执行，包括处理，存储器和编程的要求和参数依赖的不确定性MC模拟，所需的时间和计算成本的这些方法是压倒性的高获得准确的估计。这项工作的目标是使用学习方法建立MCRT计算的近似模型。这项工作是以前的工作的扩展，与建设模型的目标是更普遍和更现实：而以前的工作是有限的圆形颗粒和假设的多孔介质的生成参数的知识，在这项工作中提出的学习模型是基于工程的几何featuresconstructed从最终配置。此外，地面实况计算目前的工作采取更准确的方法生成配置的基础上包装算法，而不是使用近似辐射分布函数识别方法（包免费）。此外，我们还研究了均匀和多分散的二维介质填充的圆形和正方形的随机取向。我们还考虑了不透明和透明材料，并研究了更大的各种折射率池。我们专注于几何光学尺寸制度，其中颗粒的大小是大的光波长相比，因此光线跟踪模拟呈现波的传播特性的准确估计。我们设计的学习模型的基础上，一个大的综合生成的数据库，使用内部包装算法和MCRT计算。我们还使用交叉验证和数据混合技术的组合，以确保模型不会过拟合到特定类别的配置。因此，我们证明，在这项工作中提出的模型可以估计的辐射性质的配置在基于不同的生成方案产生的样本外的数据具有较高的精度。
Monte Carlo ray tracing (MCRT) simulations are the most reliable non-experimental means for predicting radiative properties of randomly packed structures or porous media, particularly in polydisperse or heterogenous environments. These methods can approximate radiative properties of a void region filled with homogeneous or heterogenous solids by averaging across a large pool of independent random ray tracing simulations. However, due to stringent precision requirements, high computational cost of simulation setup and execution including processing, memory and programming requirements and parameter-dependent uncertainty of MC simulations, the required time and computational cost of these methods are overwhelmingly high for obtaining accurate estimations. The goal of this work is to build approximate models for MCRT calculations using learning methods. This work is an extension of a previous work, with the goal of building models that are more generalizable and more realistic: while previous work was limited to circular particles and assumed knowledge of the generating parameters of the porous media, the learning models proposed in this work are based onengineered geometric featuresconstructed from the final configuration. Furthermore, ground truth calculations of the current work take the more accurate approach of generating configurations based on a packing algorithm, rather than using the approximate radiation distribution function identification method (pack-free). In addition, we study both uniform and polydisperse 2D media packed with circles and squares of random orientation. We also consider both opaque and transparent materials and study a larger pool of various refractive indices. We focus on the geometric optics dimensions regime in which particle sizes are large compared to the light wavelength and thus the ray tracing simulations render an accurate estimation of the wave propagation properties. We design learning models based on a large synthetically generated database using in-house packing algorithms and MCRT computations. We also use combinations of cross-validation and data mixture techniques to assure that the models are not overfit to a particular class of configurations. As a result, we demonstrate that the models proposed in this work can estimate the radiative properties of configurations in out-sample data generated based on different generation schemes with high accuracy.