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arXiv 2312.13005physics.flu-dyn
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数据驱动的雷诺平均湍流模型中的非唯一机器学习映射

Non-Unique Machine Learning Mapping in Data-Driven Reynolds Averaged Turbulence Models

Anthony Man, Mohammad Jadidi, Amir Keshmiri, Hujun Yin, Yasser Mahmoudi

AI总结:

本研究首次针对二维流动引发的湍流模型非唯一映射(NUM)展开研究,提出NUM量化方法,证实二维流动数据会引发NUM且多源于低应变等区域,还验证了补充输入变量可减少NUM、提升张量基机器学习湍流模型精度。

摘要:

近期,人们对将机器学习用于湍流建模的兴趣日益浓厚,文献中已提出诸多数据驱动的湍流模型。然而,这些模型大多未将克服非唯一映射(non-unique mapping,NUM)作为开发考量,而该问题是训练与预测误差的重要来源。尽管多数数据驱动模型基于二维流动数据训练,但现有文献仅对一维通道流数据引发的NUM开展了充分研究。本工作旨在首次针对二维流动引发的NUM展开详细研究。本文提出了一种NUM量化方法,并在周期性山丘绕流与冲击射流的数据集上进行了验证:前者属于壁面约束分离流,后者为包含驻点与回流的剪切流。本研究证实,对于采用常用不变量输入的数据驱动湍流模型,二维流动数据会引发NUM。该结论在两类包含不同流动现象的算例中均得到验证,表明NUM并非局限于特定流动物理场景。此外,所提方法揭示,在两类算例中,低应变、低旋转区域或近纯剪切区域是NUM的主要产生区域:在周期性山丘绕流与冲击射流中,这类区域贡献的NUM占比分别约为76%和89%。基于上述结果,本研究选取黏度比作为补充输入变量(supplementary input variable,SIV),验证了SIV可减少二维流动数据引发的NUM,进而提升用于湍流建模的张量基机器学习模型的精度。

英文摘要:

Recent growing interest in using machine learning for turbulence modelling has led to many proposed data-driven turbulence models in the literature. However, most of these models have not been developed with overcoming non-unique mapping (NUM) in mind, which is a significant source of training and prediction error. Only NUM caused by one-dimensional channel flow data has been well studied in the literature, despite most data-driven models having been trained on two-dimensional flow data. The present work aims to be the first detailed investigation on NUM caused by two-dimensional flows. A method for quantifying NUM is proposed and demonstrated on data from a flow over periodic hills, and an impinging jet. The former is a wall-bounded separated flow, and the latter is a shear flow containing stagnation and recirculation. This work confirms that data from two-dimensional flows can cause NUM in data-driven turbulence models with the commonly used invariant inputs. This finding was verified with both cases, which contain different flow phenomena, hence showing that NUM is not limited to specific flow physics. Furthermore, the proposed method revealed that regions containing low strain and rotation or near pure shear cause the majority of NUM in both cases: approximately 76% and 89% in the flow over periodic hills and impinging jet, respectively. These results led to viscosity ratio being selected as a supplementary input variable (SIV), demonstrating that SIVs can reduce NUM caused by data from two-dimensional flows and subsequently improve the accuracy of tensor-basis machine learning models for turbulence modelling.

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。

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