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arXiv 2409.03395physics.flu-dyn
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多保真度输入的数据驱动湍流热通量建模

Data-driven turbulent heat flux modeling with inputs of multiple fidelity

Matilde Fiore, Enrico Saccaggi, Lilla Koloszar, Yann Bartosiewicz, Miguel Alfonso Mendez

AI总结:

本文研究多保真度输入下数据驱动热湍流闭合建模,提出利用DNS与RANS输入空间分离的异构训练方法,使模型能识别保真度并自适应调整预测,从而提高鲁棒性。

摘要:

数据驱动的RANS建模正在成为一种利用高保真数据所提供信息的有前景的方法。然而,其广泛应用受到泛化能力以及在面对不同保真度水平输入数据之间不一致性时鲁棒性方面挑战的限制。对于热湍流闭合模型而言尤其如此,这类模型本质上依赖于由低保真或高保真湍流动量模型提供的动量统计量。本工作研究了动量建模不一致性对使用多保真度(DNS和RANS)数据集训练的数据驱动热闭合模型的影响。对模型输入的分析表明,两种保真度水平对应于输入空间中的不同区域。本文表明,可以通过使用异构数据进行训练来利用这种分离,使模型能够检测其输入中的保真度水平并相应地调整其预测。特别是,敏感性分析和验证表明,这种模型可以利用数据不一致性来提高其鲁棒性。最后,通过CFD模拟进行的验证显示了这种多保真度训练方法在传统模型提供的动量统计量受模型不确定性影响的流动中的潜力。

英文摘要:

Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challenges in generalization and robustness to inconsistencies between input data of varying fidelity levels. This is especially true for thermal turbulent closures, which inherently depend on momentum statistics provided by low or high fidelity turbulence momentum models. This work investigates the impact of momentum modeling inconsistencies on a data-driven thermal closure trained with a dataset with multiple fidelity (DNS and RANS). The analysis of the model inputs shows that the two fidelity levels correspond to separate regions in the input space. It is here shown that such separation can be exploited by a training with heterogeneous data, allowing the model to detect the level of fidelity in its inputs and adjust its prediction accordingly. In particular, a sensitivity analysis and verification shows that such a model can leverage the data inconsistencies to increase its robustness. Finally, the verification with a CFD simulation shows the potential of this multi-fidelity training approach for flows in which momentum statistics provided by traditional models are affected by model uncertainties.

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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