Numerical simulation and modelling of non-equilibrium flows in hydro-power applications

水力发电应用中非平衡流的数值模拟和建模

• 批准号: 514642-2017
• 负责人: Piomelli, Ugo
• 金额: $ 3.68万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=719068
• 关键词: Numerical; simulation; modelling; non; equilibrium

This proposal addresses a problem of critical importance for the hydroelectric power industry, the developmentof accurate models for the prediction of the flow over rough surfaces, in turbulence far from equilibrium. Twocases will be considered: a separating flow in a curved duct, and the flow in a rough channel undergoing systemrotation. We will use direct and large-eddy simulations (DNS and LES) of turbulence to validate the modelsfor the Reynolds-Averaged Navier-Stokes equations that are commonly used in industry. Although the DNSand LES will be performed at Reynolds numbers lower than those in actual applications, the LES should beable to achieve the fully rough regime that allows to extend the simulation results to practical applications. Theproposed research will benefit Canada in several ways: first, it will train one PhD student and one PDF in thecomputational sciences; secondly, it will generate fundamental knowledge on the physics of rough-wallboundary layers in the presence of swirl, curvature, system rotation and separation. Finally, it will developprediction models that will benefit the power generation industry (and may be extended to other areas ofapplication), and can potentially result in significant efficiency increases and financial savings.

该提案解决了对水力发电行业至关重要的问题，即开发准确的模型来预测远离平衡的湍流中粗糙表面上的流动。将考虑两种情况：弯曲管道中的分离流和经历系统旋转的粗糙通道中的流。我们将使用湍流的直接大涡模拟（DNS 和 LES）来验证工业中常用的雷诺平均纳维-斯托克斯方程的模型。尽管 DNS 和 LES 将以低于实际应用中的雷诺数执行，但 LES 应该能够实现完全粗略的状态，从而允许将模拟结果扩展到实际应用。拟议的研究将从几个方面使加拿大受益：首先，它将在计算科学方面培训一名博士生和一名 PDF 生；其次，它将产生有关存在漩涡、曲率、系统旋转和分离的粗糙壁边界层物理学的基础知识。最后，它将开发出有利于发电行业的预测模型（并可能扩展到其他应用领域），并有可能显着提高效率并节省资金。