论文标题
包装床中粒子热辐射的深神经网络模型
A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed
论文作者
论文摘要
在大型离散颗粒系统中，粒子辐射传热通量的预测是一项重要任务，例如发电厂中的卵石床和工业流化的床。对于粒子运动和填料，现在被普遍接受为出色的拉格朗日方法。对于热辐射，传统方法着重于通过数值算法直接计算障碍物视图因子。模拟的主要挑战是该方法被证明是耗时的，并且在实际情况下不可行。在这项工作中，我们提出了一个分析模型，以计算粒子填料结构的宏观有效电导率，然后开发一个深神经网络（DNN）模型，用作复杂视图因子函数的预测指标。 DNN模型由大型数据集训练，并且准确的计算速度大大提高。使用DNN模型进行实时模拟是可行的，以在大卵石床中进行辐射热传递。训练有素的模型也可以与DEM结合，并用于有效地分析颗粒热辐射的辐射电导率，各向异性因子和壁效应。
Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.
