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Possible user-dependent CFD predictions of transitional flow in building ventilation

机译:建筑物通风中可能取决于用户的CFD预测过渡流

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A modified backward-facing step flow with a large expansion ratio of five (5) was modeled by 19 teams without benchmark solutions or experimental data for validation in an ISHVAC-COBEE July 2015 Tianjin Workshop, entitled as "to predict low turbulent flow". Different computational fluid dynamics (CFD) codes/software, turbulence models, boundary conditions, numerical schemes and convergent criteria were adopted based on the own CFD experience of each participating team. The largest coefficient of variation is larger than 50% and the largest relative maximum difference of penetration length is larger than 150%. The predicted non-dimensional penetration lengths as a function of the Reynolds number (1-10,000) are found to be significantly diverse among different teams. Even when the same turbulence model or even the laminar model is used, the difference in the predicted results is still notable among different teams. It indicates that the combined effects of a lack of general turbulence model, and possible errors in multiple decisions based on users' experience may have caused the observed significant difference. Prediction of transitional flows, as often observed in building ventilation, is shown to be still a very challenging task. This calls for a solid approach of validation and uncertainty assessment in CFD "experiments". The users are recommended to follow an existing guideline of uncertainty assessment of CFD predictions to minimize the errors and uncertainties in the future. (C) 2016 Elsevier Ltd. All rights reserved.
机译:由19个没有基准解决方案或实验数据的团队模拟了一个具有五(5)大膨胀比的改良后向步进流,以在ISHVAC-COBEE 2015年7月天津工作坊中进行验证,题为“预测低湍流”。根据每个参与团队各自的CFD经验,采用了不同的计算流体动力学(CFD)代码/软件,湍流模型,边界条件,数值方案和收敛准则。最大变异系数大于50%,并且穿透长度的最大相对最大差异大于150%。发现预测的无量纲穿透长度随雷诺数(1-10,000)的变化在不同团队之间差异很大。即使使用相同的湍流模型,甚至使用层流模型,不同团队之间的预测结果差异仍然很明显。这表明缺乏通用湍流模型的综合效果以及基于用户体验的多个决策中可能存在的错误可能导致了观察到的显着差异。如在建筑物通风中经常观察到的那样,对过渡流量的预测仍然是一项非常艰巨的任务。这就要求在CFD“实验”中采用可靠的验证和不确定性评估方法。建议用户遵循CFD预测不确定性评估的现有指南,以最大程度地减少将来的误差和不确定性。 (C)2016 Elsevier Ltd.保留所有权利。

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