Transient two-phase CFD simulation of overload operating conditions and load rejection in a prototype sized Francis turbine

Mössinger, Peter; Jung, Alexander

doi:10.1088/1755-1315/49/9/092003

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…CFD stands for computational fluid dynamics, which was developed with the advent of computer technology and computing technology [24]. To put it simply, CFD is equivalent to "virtual" computer tests for real-time stream simulation and comparison [25][26][27].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Optimal Design of Sliding Bearings Based on Artificial Intelligence Algorithm and CFD Simulation

Guo

2022

Wireless Communications and Mobile Computing

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Sliding bearings have a long history, simple manufacturing, and low cost. However, the function of sliding bearings is very large. As a key component of rotating machinery, thrust sliding bearing is directly related to the overall performance of the machine. The machines that can be seen in life, whether simple or complicated, have been used. However, with the development of science and technology and economy, all kinds of new things have been inoculated. One of the current issues is how sliding bearings can continue to keep machinery stable. CFD is the abbreviation of English computational fluid dynamics (computational fluid dynamics). It is developed with the development of computer technology and numerical computing technology. Simply put that CFD is equivalent to “virtually” doing experiments on the computer to simulate the actual fluid flow. The purpose of this article is to study the optimal design of sliding bearings by artificial intelligence algorithms and CFD simulation. This paper uses intelligent design methods to optimize the appearance, shape, and manufacturing process of sliding bearings and to carry the bearing capacity, friction coefficient and temperature rise of sliding bearings. Comprehensive research then establishes the objective function. The finite element algorithm, genetic algorithm, and matrix theory are used to calculate the stability of the sliding bearing under various conditions. The artificial intelligence algorithm is used to sort the calculated data. The CFD simulation is used to obtain the most reasonable result. Algorithm, as well as the appearance, shaft diameter, and material of the plain bearing, maintains stability under various conditions. The experimental data show that the classification using artificial intelligence algorithms and CFD simulation can significantly improve the performance of sliding bearings. It is found that the relationship between the sliding bearings is related to the speed, the diameter of the bearing, and other factors. Experimental data show that artificial intelligence algorithms and CFD simulation can provide reliable data references for the optimal design of sliding bearings, and the optimized sliding bearings can meet the stability under relevant conditions.

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Section: R E T R a C T E Dmentioning

confidence: 99%

Optimal Design of Sliding Bearings Based on Artificial Intelligence Algorithm and CFD Simulation

Guo

2022

Wireless Communications and Mobile Computing

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“…However numerical studies concerning full-scale hydraulic turbines are also sparse. Such simulations are challenging because of the large dimensions, high Reynolds numbers, and lack of experimental data to validate the results [19,20]. There have been no numerical studies referring to full-scale Kaplan prototype turbines.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Experimental Validation of a Kaplan Prototype Turbine Operating on a Cam Curve

Iovănel

Dehkharqani²,

Bucur

et al. 2022

Energies

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The role of hydropower has become increasingly essential following the introduction of intermittent renewable energies. Quickly regulating power is needed, and the transient operations of hydropower plants have consequently become more frequent. Large pressure fluctuations occur during transient operations, leading to the premature fatigue and wear of hydraulic turbines. Investigations of the transient flow phenomena developed in small-scale turbine models are useful and accessible but limited. On the other hand, experimental and numerical studies of full-scale large turbines are challenging due to production losses, large scales, high Reynolds numbers, and computational demands. In the present work, the operation of a 10 MW Kaplan prototype turbine was modelled for two operating points on a propeller curve corresponding to the best efficiency point and part-load conditions. First, an analysis of the possible means of reducing the model complexity is presented. The influence of the boundary conditions, runner blade clearance, blade geometry and mesh size on the numerical results is discussed. Secondly, the results of the numerical simulations are presented and compared to experimental measurements performed on the prototype in order to validate the numerical model. The mean torque and pressure values were reasonably predicted at both operating points with the simplified model. An analysis of the pressure fluctuations at part load demonstrated that the numerical simulation captured the rotating vortex rope developed in the draft tube. The frequencies of the rotating and plunging components of the rotating vortex were accurately captured, but the amplitudes were underestimated compared to the experimental data.

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“…However, the MOC technique generally focuses on the overall water conveyance system, including long penstocks or tail water channels, and therefore it cannot obtain detailed information regarding the internal flow field of the turbine. Currently, three dimensional (3D) numerical simulation methods that are based on computational fluid dynamics (CFD) have been increasingly applied for evaluating the transient characteristics of hydraulic turbines [26,27]. For example, Pavesi et al [28] conducted numerical simulations of a pump-turbine during the transient load following process in pump mode, and a full 3D flow structure was observed in the wicket gate channel, owing to boundary layer separation and stalling at lower rotational speeds.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on Flow Characteristics in a Francis Turbine during Load Rejection

et al. 2019

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Labyrinth seals are not usually included in the numerical models of hydraulic machinery to simplify the geometric modeling, and thereby reduce the calculation burden. However, this simplification affects the numerical results, especially in the load rejection process, because disc friction losses, volume losses, and pressure fluctuations in the seal ring (SR) clearance passage are neglected. This paper addresses the issue by considering all of the geometrical details of labyrinth seals when conducting multiscale flow simulations of a high head Francis turbine under a transient load rejection condition using the commercial software code. A comparison of the numerical results that were obtained with the experimental testing data indicates that the calculated values of both torque and mass discharge rate are 8.65% and 5% slightly less than the corresponding values that were obtained from experimental model testing, respectively. The obtained pressure fluctuations of the Francis turbine in the vaneless zone and the draft tube appear to more closely match with the experimental test data when including SR clearance. Moreover, the flow rates through SR clearance passages were very small, but the pressure fluctuations among them were significantly enhanced under the minimal load condition. The numerical model with SR clearance can more accurately reflect the fact that the water thrust on the runner only fluctuates from 800 N to 575 N during the load rejection process, even though the water thrust on the blades varies from −220 N to 1200 N. Therefore, multiscale flow study is of great significance in understanding the effect of clearance flow on the load rejection process in the Francis turbine.

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Transient two-phase CFD simulation of overload operating conditions and load rejection in a prototype sized Francis turbine

Cited by 12 publications

References 5 publications

Optimal Design of Sliding Bearings Based on Artificial Intelligence Algorithm and CFD Simulation

Optimal Design of Sliding Bearings Based on Artificial Intelligence Algorithm and CFD Simulation

Numerical Simulation and Experimental Validation of a Kaplan Prototype Turbine Operating on a Cam Curve

Numerical Study on Flow Characteristics in a Francis Turbine during Load Rejection

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