湍流燃烧封闭中PDF制表的有效机器学习方法
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure
摘要：
基于概率密度函数（PDF）的湍流燃烧建模受限于需要存储可能占用大量存储器的多维PDF表。通过使用各种机器学习技术可以实现存储的显著节省，这些技术使用数学函数表示PDF表的热化学量。这些函数在计算上可能比用于热化学量的现有插值方法更昂贵。更重要的是，训练时间可能占模拟时间的相当大的一部分。在这项工作中，我们通过引入一种自适应训练算法来解决这些问题，该算法依赖于多层感知（MLP）神经网络进行回归和自组织映射（SOM）对数据进行聚类，以使用不同的网络进行制表。该算法的目的是解决PDF表的多维性以及所提出的算法的计算效率。SOM聚类根据数据的相似性将PDF表分为几个部分。每个数据簇使用简单网络架构上的MLP算法进行训练，以生成热化学量的局部函数。该算法被验证为所谓的DLR-A湍流射流扩散火焰使用RANS和LES模拟和PDF表格的结果进行了比较，标准的线性插值方法。比较产生了很好的协议之间的两个制表技术，并建立了MLP-SOM方法作为一个可行的方法PDF制表。
英文摘要：
Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various machine-learning techniques that represent the thermo-chemical quantities of a PDF table using mathematical functions. These functions can be computationally more expensive than the existing interpolation methods used for thermo-chemical quantities. More importantly, the training time can amount to a considerable portion of the simulation time. In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is designed to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm. SOM clustering divides the PDF table into several parts based on similarities in data. Each cluster of data is trained using an MLP algorithm on simple network architectures to generate local functions for thermo-chemical quantities. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations and the results of the PDF tabulation are compared to the standard linear interpolation method. The comparison yields a very good agreement between the two tabulation techniques and establishes the MLP-SOM approach as a viable method for PDF tabulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
