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.

该提案解决了一个对水力发电行业至关重要的问题，即水力发电行业，即准确的模型，用于预测流量在粗糙表面上的模型，远离平衡的湍流。将考虑双重酶：弯曲导管中的分离流，以及经过系统切入的粗糙通道中的流动。我们将使用湍流的直接和大涡模拟（DNS和LES）来验证工业常用的雷诺平均的Navier-Stokes方程的模型。尽管DNSAND LES将在雷诺数的数字低于实际应用中的雷诺数字上执行，但LES应该挑战以实现完全粗糙的制度，从而可以将模拟结果扩展到实际应用。该研究的研究将以几种方式使加拿大受益：首先，它将培训一名博士学位学生和一名PDF，以竞争性科学；其次，它将在存在漩涡，曲率，系统旋转和分离的情况下对粗糙层层的物理学产生基本知识。最后，它将开发预定模型，从而使发电行业受益（并可能扩展到其他领域），并可能导致效率的大幅提高和财务节省。