Slip Factor Correction in 1-D Performance Prediction Model for PaTs

In recent years, pumps operated as turbines (PaTs) have been gaining the interest of industry and academia. For instance, PaTs can be effectively used in micro hydropower plants (MHP) and water distribution systems (WDS). Therefore, further efforts are necessary to investigate their fluid dynamic behavior. Compared to conventional turbines, a lower number of blades is employed in PaTs, lowering their capability to correctly guide the flow, hence reducing the Euler’s work; thus, the slip phenomenon cannot be neglected at the outlet section of the runner. In the first part of the paper, the slip phenomenon is numerically investigated on a simplified geometry, evidencing the dependency of the lack in guiding the flow on the number of blades. Then, a commercial double suction centrifugal pump, characterized by the same specific speed, is considered, evaluating the dependency of the slip on the flow rate. In the last part, a slip factor correlation is introduced based on those CFD simulations. It is shown how the inclusion of this parameter in a 1-D performance prediction model allows us to reduce the performance prediction errors with respect to experiments on a pump with a similar specific speed by 5.5% at design point, compared to no slip model, and by 8% at part-loads, rather than using Busemann and Stodola formulas.

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Effect of Velocity Slip on Head Prediction for Centrifugal Pumps as Turbines

Mathematical Problems in Engineering ◽

10.1155/2019/5431047 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Xiaohui Wang ◽

Junhu Yang ◽

Zhengting Xia ◽

Yan Hao ◽

Xiaorui Cheng

Keyword(s):

Prediction Model ◽

Energy Recovery ◽

Velocity Slip ◽

Centrifugal Pumps ◽

Low Specific Speed ◽

Specific Speed ◽

Turbine Mode ◽

Recovery Schemes ◽

Slip Phenomenon ◽

Good Agreement

The application of pumps as turbines (PAT) has been developed in several applications for energy recovery schemes. Therefore, establishing a performance correlation between pump mode and turbine mode is essential for selecting the proper machine. However, slip phenomenon is the challenges of head prediction for PAT. In this paper, the slip phenomenon of pump and PAT was revealed, and the slip factor was studied using CFD. The effect of slip on head prediction for PAT was analyzed, and a theoretical prediction model was presented considering slip factors. In order to validate the head prediction model, six centrifugal pumps with specific speed (ns) from 9 to 54.8 were tested as turbines. Results showed that the predicted head by the proposed method was in good agreement with the experimental data, and it is more accurate than Stepanoff, Alatorre-Frenk, Sharma, and Derakhshan models. This method can be applied in head prediction for low specific speed PAT ( ns <60).

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A simple forecast technique for estimating the statistical characteristics of water consumption

Water Science & Technology Water Supply ◽

10.2166/ws.2014.013 ◽

2014 ◽

Vol 14 (4) ◽

pp. 593-600

Author(s):

József Bene ◽

Dávid Bóka ◽

Csaba Ho˝s ◽

Varga Roxána

Keyword(s):

Water Consumption ◽

Distribution Systems ◽

Water Distribution ◽

Computational Cost ◽

Real Life ◽

Forecast Model ◽

Mean Value ◽

Statistical Characteristics ◽

Prediction Errors ◽

Life Problems

The forecast water consumption values are the most critical input data in the pump schedule optimization of water distribution systems. The aim of this paper is to present a simple technique which is able to estimate the mean consumption and its distribution for a given demand zone with an hourly resolution. Simplicity (low computational cost) is advantageous since the forecast model needs to be run for any optimization computation. The proposed technique uses a recorded hourly consumption database and consists of two steps. First, the database content is automatically grouped based on the similarity of the elements (more precisely, their normality). This step is time-consuming but is performed only once for a given database independently of optimization. The second step – which is quick but has to be performed before the actual optimization – makes use of this grouping for forecasting mean value and standard deviation. The proposed technique provides hourly water consumption predictions independently; that is, the neighbouring hours do not effect each other, which prevents the accumulation of prediction errors. The daily overall consumption is computed a posteriori. The test results presented in this paper prove the applicability of the technique for real-life problems. Moreover, it is demonstrated that the confidence interval provided by the technique includes the actual measured data.

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Research on Performance Prediction of Highway Asphalt Pavement Based on Grey–Markov Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211057527 ◽

2021 ◽

pp. 036119812110575

Author(s):

Yunsheng Zhu ◽

Jinxu Chen ◽

Kaifeng Wang ◽

Yong Liu ◽

Yanting Wang

Keyword(s):

Markov Model ◽

Prediction Model ◽

Performance Prediction ◽

Asphalt Pavement ◽

Condition Index ◽

Economic Benefits ◽

Pavement Performance ◽

Prediction Errors ◽

Grey Prediction ◽

Relative Errors

Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which considers the fate of pavement performance prediction, are comprehensively applied to the combined forecasting model. The Grey GM (1, 1), Grey–Markov model and Liu-Yao model are adopted to predict the pavement condition index (PCI) based on the actual PCI values measured in Shanxi, Chongqing, and Shaoguan. The average relative errors of the above three models’ predicted values in Shanxi are 0.73%, 1.18%, and 0.67%, respectively, from 2012 to 2014. Thus, the prediction errors of the three models are relatively close. The average relative errors of the prediction values predicted by the three models are 3.89%, 0.67%, and 0.50%, respectively, from 2015 to 2019. The latter two errors are more minor than the Grey GM (1, 1) model. Two other regions have similar conclusions. The results show that the prediction accuracy of the combination Grey–Markov prediction model established in this paper is feasible to predict asphalt pavement performance in China.

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Performance Prediction and Geometry Optimization for Application of Pump as Turbine: A Review

Frontiers in Energy Research ◽

10.3389/fenrg.2021.818118 ◽

2022 ◽

Vol 9 ◽

Author(s):

Ming Liu ◽

Lei Tan ◽

Shuliang Cao

Keyword(s):

Performance Prediction ◽

Rural Areas ◽

Guide Vane ◽

Geometry Optimization ◽

Prediction Methods ◽

Prediction Errors ◽

Polynomial Fitting ◽

Specific Speed ◽

Loss Modeling ◽

The One

Pump as Turbine (PAT) is a technically and economically effective technology to utilize small/mini/micro/pico hydropower, especially in rural areas. There are two main subjects that influence the selection and application of PAT. On the one hand, manufacturers of pumps will not provide their characteristics under the turbine mode, which requires performance prediction methods. On the other hand, PAT efficiency is always slightly lower than that of pump, which requires further geometry optimization. This literature review summarized published research studies related to performance prediction and geometry optimization, aimed at guiding for selection and optimization of PAT. Currently, there exist four categories of performance prediction methods, namely, using BEP (Best Efficiency Point), using specific speed, loss modeling, and polynomial fitting. The using BEP and loss modeling methods are based on theoretical analysis, while using specific speed and polynomial fitting methods require statistical fitting. The prediction errors of published methods are within ±10% mostly. For geometry optimization, investigations mainly focus on impeller diameter and blade geometry. The influence of impeller trimming, blade rounding, blade wrap angle, blade profile, blade number, blade trailing edge position, and guide vane number has been studied. Among published methods, the blade rounding and forward-curved impellers are the most effective and feasible techniques.

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