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: $ 3.9万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696334
• 关键词: Numerical; simulation; modelling; non; equilibrium

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

该提案解决了水电行业的一个至关重要的问题， 在远离平衡的湍流中，预测粗糙表面上的流动的精确模型。两 本文将考虑两种情况：弯曲管道中的分离流和粗糙通道中的流动， 旋转我们将使用湍流的直接和大涡模拟（DNS和LES）来验证模型 对于工业中常用的雷诺平均纳维尔-斯托克斯方程。虽然DNS 并且LES将在低于实际应用中的雷诺数下进行，LES应 能够实现允许将模拟结果扩展到实际应用的完全粗糙的制度。的 拟议中的研究将在几个方面使加拿大受益：首先，它将培养一名博士生和一名PDF， 计算科学;其次，它将产生关于粗糙壁物理学的基础知识 存在涡流、曲率、系统旋转和分离时的边界层。最后，它将发展 预测模型，将有利于发电行业（并可能扩展到其他领域， 应用程序），并可能导致显着的效率提高和财务节省。