湍流喷雾中的亚网格条件混合统计建模：一种机器学习方法-POF-Yao2020a

题目：湍流喷雾中的亚网格条件混合统计建模：一种机器学习方法-POF-Yao2020a

摘要：本文利用
文本利用机器学习方法封闭湍流喷雾火焰中的条件标量耗散率。由气相直接数值模拟(CP-DNS)得到的结果提取的统计数据作为神经网络的训练集，并将得到的智能体作为预测喷雾燃烧条件标量耗散率的大涡模拟下的亚格子模型。定量误差评估表明，条件平均耗散率的预测与CP-DNS数据非常吻合。与常用模型相比，如果使用人工神经网络(ANN)可以显著提高预测无条件滤波耗散率的精度。ANN还有助于识别影响当地耗散率的关键因素，结果表明，仅需少数（多与液滴相关）的变量就足以满足ANN对输入参数的精度需求，这与传统仅依赖气相参数封闭标量耗散率的方法相矛盾，说明如果想利用解析式对喷雾燃烧中的标量耗散率建模，需要重新审视建模的思路。

Notes:

1. 利用CP-DNS数据训练ANN模化大涡模拟的亚格子模型
2. 通过控制变量法研究了不同物理量对标量耗散率预测精度的影响，并利用R2量化
3. 结果表明，通常被忽略的液滴蒸发对标量耗散率的影响，是在ANN模型中最重要的影响因素

Fig. Simulation set-up of CP-DNS (solid points: droplets; the gas phase is coloured by temperature) and an LES filter box.

Title: Conditional scalar dissipation rate modeling for turbulent spray flames using artificial neural networks

Abstract: Machine learning techniques have been used for the closure of the conditional scalar dissipation rate in turbulent spray flames. Statistical data are extracted from carrier-phase direct numerical simulation (CP-DNS) results for the generation of artificial neural networks that are trained to predict the conditional scalar dissipation rate such that they could serve as a sub-grid model for large eddy simulations of spray combustion. A quantitative error assessment suggests that predictions of the conditionally averaged dissipation rate are in excellent agreement with CP-DNS data. Further comparison with commonly used models for the unconditionally filtered dissipation rate promises significant improvements if ANNs are used for closure. The artificial neural networks also help to identify the important features that affect the local dissipation rates. The results suggest that few - mostly droplet related - parameters suffice as input features for accurate ANNs. This is in contradiction to standard modeling techniques that are solely based on gas phase properties and highlights the need to revisit scalar dissipation rate modeling for spray flames if analytical expressions are to be used.

原文连接, PROCI, IF 5.627

相关文献

1. 机器学习+DNS：利用机器学习方法简化化学反应机理：以侧壁作用下的湍流混合全氧火焰DNS为例

2. 机器学习+小火焰库：一种用于提高层流火焰模拟精度的机器学习方法

3. 用于预测煤燃烧CFD模拟的机器学习方法：从详细动力学到HDMR降阶模型-Fuel-Debiagi2020