Assessment of RANS-based turbulence models for isothermal confined swirling flow in a realistic can-type gas turbine combustor application

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-06-12 DOI:10.1016/j.jocs.2024.102362
Aishvarya Kumar , Ram Prakash Bharti
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Abstract

The present study assesses RANS-based turbulence models to simulate the isothermal confined swirling flow in a combustor representing a constituent can combustor of the can-annular configuration used in jet engines. The two-equation models (standard kϵ, realizable kϵ, standard kω, SST kω), and seven-equation model (linear pressure strain-Reynolds stress model, LPS-RSM), are assessed by comparing their predictions of mean axial velocity, mean transverse velocity, turbulent kinetic energy, and shear stress with the experimental data at two different positions (i.e., the primary and dilution hole planes) in the combustor. While the two-equation models generally have failed to predict the confined swirling flow at both positions accurately, the SST kω model yielded the most accurate, followed by standard kω and realizable kϵ models. The discrepancies between the computational and experimental results could be attributed to the isotropic turbulence assumptions, which, however, are invalid for confined swirling flows. Further, the two-equation model formulations cannot capture the intricacies of vortex flow and its interaction with the surroundings in confined swirling flows. The LPS-RSM, which considers turbulence anisotropy, showed some promise, although overpredicted results follow the trend with experimental values at the primary holes plane. However, at the dilution holes plane, the model overpredicted the velocity field, and underestimated the turbulence field, including turbulent kinetic energy and shear stress. These observed discrepancies can be ascribed to the pressure-strain correlation in the LPS-RSM, which assumes the pressure is a linear function of the strain-rate tensor. However, this linear assumption is quite simplistic for complex flows. Further, the influence of discretization (SOU and third-order MUSCL) schemes of convective terms is also assessed, and the differences in predictions resulted from MUSCL scheme having lower diffusion and superior ability to capture sharper gradients, however, did not translate into improving the solution accuracy. Hence, this study suggests that more advanced high-fidelity turbulence models (e.g., hybrid RANS-LES, LES, DNS) are needed to accurately predict the confined swirling flow in combustors.

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评估基于 RANS 的湍流模型在实际罐式燃气轮机燃烧器应用中的等温封闭漩涡流效果
本研究对基于 RANS 的湍流模型进行了评估,以模拟喷气式发动机中使用的罐环形构型成分罐燃烧器中的等温封闭漩涡流。通过比较两方程模型(标准 k-ϵ、可实现 k-ϵ、标准 k-ω、SST k-ω)和七方程模型(线性压力应变-雷诺应力模型,LPS-RSM)对平均轴向速度、平均横向速度、湍流动能和剪应力的预测值与燃烧器中两个不同位置(即主孔平面和稀释孔平面)的实验数据,对它们进行了评估。虽然两方程模型一般都无法准确预测两个位置的约束漩涡流,但 SST k-ω 模型的结果最为准确,其次是标准 k-ω 模型和可实现 k-ϵ 模型。计算结果与实验结果之间的差异可归因于各向同性湍流假设,然而,这些假设对封闭漩涡流无效。此外,双方程模型公式无法捕捉到漩涡流的复杂性及其在封闭漩涡流中与周围环境的相互作用。考虑了湍流各向异性的 LPS-RSM 显示出了一些前景,尽管在主孔平面上的预测结果与实验值的趋势一致。然而,在稀释孔平面,模型高估了速度场,低估了湍流场,包括湍流动能和剪应力。这些观察到的差异可归因于 LPS-RSM 中的压力-应变相关性,它假定压力是应变速率张量的线性函数。然而,这种线性假设对于复杂的流动来说非常简单。此外,还评估了对流项离散化(SOU 和三阶 MUSCL)方案的影响,MUSCL 方案具有较低的扩散性和捕捉较尖锐梯度的卓越能力,这导致了预测结果的差异,但并没有提高求解精度。因此,这项研究表明,需要更先进的高保真湍流模型(如混合 RANS-LES、LES、DNS)来准确预测燃烧器中的约束漩涡流。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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