Performance Prediction Of Pump Research Articles

Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

Pressure swing adsorption (PSA) is a promising technology for gas separation and purification, receiving considerable attention in the past decades. Existing studies on simulating PSA involving a vacuum pump, the vacuum (pressure) swing adsorption [V(P)SA] process, typically estimate vacuum pump energy consumption using an approximate constant energy efficiency or an empirical energy efficiency correlation, leading to inaccurate representation of realistic vacuum pump performance. In this work, we propose an enhanced computational approach for simulation and optimisation of V(P)SA processes through simultaneous incorporation of realistic vacuum pump performance prediction models and adsorption bed fluidisation limits. The vacuum pump prediction models are developed using data-driven modelling techniques with realistic vacuum pump performance data. The enhanced computational approach more accurately accounts for the V(P)SA performance, without relying on an estimated vacuum pump efficiency and assumed pressure/flow rate boundary conditions at the vacuum pump end of the adsorption bed. Furthermore, this approach prevents the bed fluidisation. The computational results show that the developed prediction models accurately represent the actual performance curves of the vacuum pump. It is also demonstrated that incorporating the vacuum pump prediction models and fluidisation constraints in PSA optimisation leads to significantly different optimal solutions compared to when these factors are not considered.

Energy performance prediction of pump as turbine (PAT) based on PIWOA-BP neural network

The greenhouse effect and the depletion of fossil energy sources have emerged as significant challenges impacting development of society. The development of micro hydropower stations is crucial in reducing carbon emissions and achieving carbon neutrality. Among them, pump as turbine (PAT) technology is considered one of the most promising alternatives in small and micro hydropower. However, pump manufacturers don't provide performance curves in turbine conditions, leading to issues such as difficulty in selecting a model and inaccuracy in calculating the turbine's high-efficiency zone when employing PAT in practice. This study proposes a new solution to these problems by introducing a new PIWOA-BPNN model. The model integrates the enhanced WOA algorithm with the parameter-optimized multilayer BPNN and employs the innovative spiral guidance vector p to direct the random search within the WOA algorithm, significantly enhancing the prediction capability of the neural network while reducing time cost. Additionally, the internal parameters of the turbine are analyzed, and a network model incorporating the geometric parameters of the impeller as auxiliary input is proposed. Several experimental tests demonstrate that the method offers greater accuracy and reliability compared to current mainstream algorithms. The prediction accuracy of the proposed model is approximately 4 %, meeting engineering requirements.

Experimental and CFD modeling of a progressive cavity pump (PCP) using overset unstructured mesh part 2: Two-phase flow.

In oil well operations, accurate modeling of two-phase liquid-gas flow in Progressive Cavity Pumps (PCPs) is vital. This study aimed to comprehensively analyze such flow in a metal-metal PCP using computational fluid dynamics (CFD) simulations validated by experimental data. The methodology applied to the High-Resolution Interface Capturing (HRIC) scheme is to compute the convective transport of phase fractions, yielding insights into the intricate dynamics of these flows. Validation was achieved using laboratory-acquired data and comparisons with extant publications. The findings confirmed the CFD simulations' alignment with field data for two-phase liquid-gas flows. Gas compression and effective viscosity gradient were crucial for accurate pump performance prediction. An investigation into the “gas seal” phenomenon using the Eulerian Multiphase Model elucidated the distinct separation of gas and liquid phases. The influence of the gas volume fraction on pump metrics like torque, power, and efficiency was explored, with CFD predictions exhibiting less than 8% error. The study underscores the pivotal role of CFD tools in predicting pump behavior under diverse conditions. It advances our understanding of two-phase flows in oil wells, especially regarding the gas's impact on pump components. This research offers the oil industry a refined modeling tool, enhancing the comprehension of oil well pump dynamics.

Analysis of Cavitation Flow Performance in Centrifugal Pump Using OpenFOAM

In order to explore the cavitation mechanism and cavitation cloud development process of centrifugal pump, OpenFOAM7.0 open-source software was used to simulate the unsteady cavitation gas-liquid two-phase flow of IS125 centrifugal pump under different flow conditions. Based on the flow field simulation results, the relationship between the flow rate of the centrifugal pump and the NPSHr was predicted and compared with the experimental data. The comparison showed that it is feasible to use OpenFOAM software to simulate the cavitation flow of centrifugal pump under design condition. Under the design condition, when NPSHa was different, the distribution of cavitation clouds in each blade channel was asymmetric. With the decrease of NPSHa, cavitation clouds continue to develop downstream in the impeller channel and appear on the blade pressure surface. Regardless of whether cavitation occurred in the centrifugal pump, the pressure coefficient showed periodic pulsation, and the pulsation number of the impeller in a rotation cycle was equal to the number of blades of the impeller. The research results can provide an effective open-source software calculation method for centrifugal pump performance prediction.

Numerical Assessment of a Two-Phase Model for Propulsive Pump Performance Prediction

The present work provides a detailed numerical investigation of a turbopump for waterjet applications in cavitating conditions. In particular, the study focuses on the complexities of cavitation modelling, serving as a pivotal reference for future computational research, especially in off-design hydro-jet scenarios, and it aims to extend current model assessments of the existing methods, by disputing their standard formulations. Thus, a computational domain of a single rotor-stator blade passage is solved using steady-state Reynolds-Averaged Navier–Stokes equations, coupled with one-, two-, and four-equation turbulence models, and compared with available measurements, encompassing both nominal and thrust breakdown conditions. Through grid dependency analysis, a medium refinement with the Shear Stress Transport turbulence model is chosen as the optimal configuration, reducing either computational time and relative error in breakdown efficiency to 1%. This arrangement is coupled with a systematic study of the Zwart cavitation model parameters through multipliers ranging from 10−2 to 102. Results reveal that properly tuning these values allows for a more accurate reconstruction of the initial phases of cavitation up to breakdown. Notably, increasing the nucleation radius reduces the difference between the estimated head rise and experimental values near breakdown, reducing the maximum error by 4%. This variation constrains vapour concentration, promoting cavitation volume extension in the passage. A similar observation occurs when modifying the condensation coefficient, whereas altering the vaporization coefficient yields opposite effects.

A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process

Traditional centrifugal pump performance prediction (CPPP) employs the semi-theoretical and semi-empirical approaches; however, it can lead to many prediction errors. Considering the superiority of deep learning when applied to nonlinear systems, in this paper, a method combining hydraulic loss and convolutional neural network (HLCNN) is applied to CPPP. Head and efficiency were selected as two variables for demonstrating the energy performance of the centrifugal pump in order to reflect the prediction ability of the proposed model. The evaluation results indicate that the predicted head and efficiency are accurate, compared with the experimental results. Furthermore, the HLCNN prediction model was compared against machine learning methods and the computational fluid dynamic method. The proposed HLCNN model obtained a better AREmean, root mean square error, sum of squares due to error, and mean absolute error for centrifugal pump energy performance. The research revealed that the HLCNN model achieves accurate energy performance prediction in the design of centrifugal pumps, reducing the development time and costs.

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

Ground Source and Sewage Water Source Heat Pump Systems for Block Heating and Cooling Network

The demand for district heating and cooling systems in block units with a heat pump that utilizes various unused energy sources for energy supply has been increasing. This study investigated experimentally the ground source heat pump (GSHP) and sewage water source heat pump (SWSHP) facilities used in block cooling and heating networks. Then, a heat pump performance prediction model was derived for utilization in future designs. Operational data for heating and cooling energy supply from an experimental site were investigated for the period between 2018 and 2020. During the cooling season, the coefficient of performance (COP) of the GSHP was approximately 4.1, and that of the SWSHP was approximately 2.9. The cooling performance of the SWSHP gradually decreased because of the fouling. The COP of the GSHP and SWSHP during the heating season was approximately 3.6 and 3.4, respectively. The results also demonstrated that, if fouling in the SWSHP can be prevented or reduced, the acquired COP can be similar to that of the GSHP. The derived prediction model serves as a good reference for engineers who require information on the performance of field operations.

Analisis Kinerja Pompa Sentrifugal pada Variasi Trim Diameter Menggunakan Simulasi Numerik

Diameter trimming is one of the most common modification on centrifugal pump impeller aimed to keep conformity between pump performance and required head and flow rate. In its application, centrifugal pump performance with trimmed diameter could be predicted by using affinity equations which based on geometrical similarity between pre- and post-trimming impeller. However, diameter trimming also alter the dimension ratio in blade passage which prompt further investigation on performance prediction of pump with trimmed impeller diameter. This research is carried out by using numerical simulation to analyze performance of pump with trimmed impeller diameter. The simulation is conducted on radial-type centrifugal pump with impeller diameter 105 mm, inlet blade angle 200, outlet blade angle 280, and operating on mass flow rate 1.5 kg/s at rotational speed 2800 rpm. RNG k-e model is used to model turbulence while trimmed diameter values are 100 mm and 95 mm. Results indicate that there is significant differences on head and consumed power between predicted value by simulation and predicted value obtained by employing affinity equations.

3D simulation of gas-laden liquid flows in centrifugal pumps and the assessment of two-fluid CFD methods

An assessment of a two-fluid model assuming a continuous liquid and a dispersed gas phase for 3D computational fluid dynamics (CFD) simulations of gas/liquid flow in a centrifugal research pump is performed. A monodisperse two-fluid model, in conjunction with a statistical eddy-viscosity turbulence model, is utilized. By a comprehensive measurement database, a thorough assessment of model inaccuracies is enabled. The results on a horizontal diffuser flow reveal that the turbulence model is one main limitation of simulation accuracy for gas/liquid flows. Regarding pump flows, distinctions of single-phase and two-phase flow in a closed and semi-open impeller are figured out. Even single-phase flow simulations reveal challenging requirements on a high spatial resolution, e.g., of the rounded blade trailing edge and the tip clearance gap flow. In two-phase pump operation, gas accumulations lead to coherent gas pockets that are predicted partly at wrong locations within the blade channel. At best, a qualitative prediction of gas accumulations and the head drop towards increasing inlet gas volume fractions (IGVF) can be obtained. One main limitation of two-fluid methods for pump flow is figured out in terms of the violation of the dilute, disperse phase assumption due to locally high disperse phase loading within coherent gas accumulations. In these circumstances, bubble population models do not appear beneficial compared to a monodisperse bubble distribution. Volume-of-Fluid (VOF) methods may be utilized to capture the phase interface at large accumulated gas cavities, requiring a high spatial resolution. Thus, a hybrid model, i.e., a dispersed phase two-fluid model including polydispersity for flow regions with a dilute gas phase, should be combined with an interphase capturing model, e.g., in terms of VOF. This hybrid model, together with scale-resolving turbulence models, seems to be indispensable for a quantitative two-phase pump performance prediction.

Investigation of the influence of various numbers of impeller blades on internal flow field analysis and the pressure pulsation of an axial pump based on transient flow behavior

AbstractIn the current study, the flow behavior in an axial pump through changing the number of impeller blades is analyzed. Due to the number of blades being very important geometrical parameters in the pump, the study of the influence of various numbers of blades on flow and pressure pulsation in the pump is carried out using the computational fluid dynamics technique. The sliding mesh with the standard turbulence k‐ε model is used to investigate the unsteady flow with several flows and impeller blades. Pump performance prediction results with available experimental data indicate reasonable and good agreement with each other. Static pressure, shear stress, and different velocity compounds are qualitatively analyzed. Moreover, the fluctuation pressure and average pressure under different operating conditions and impeller blades are quantitatively investigated. The numerical results show that the impeller blade has a high impact on pressure, shear stress, magnitude velocity, axial velocity, radial velocity, tangential velocity, and average pressure. Furthermore, this numerical study provides good and useful information for the hydraulic design of axial pumps.

PERFORMANCE EVALUATION OF END SUCTION, SINGLE STAGE, RADIAL DISCHARGE CENTRIFUGAL PUMP IN TURBINE MODE OPERATION

Pump as Turbine (PAT) provides a cost-effective alternative to conventional hydro turbines for high terrain rural areas where grid supply is not feasible. PAT technology is simple, low in cost and easy to harness. End suction centrifugal pumps are readily available as compared to conventional custom made hydro turbines. This paper presents theoretical, numerical and experimental study of radial discharge centrifugal pump in hydro turbine mode. Experimental setup is used to investigate performance of radial discharge end suction mono block centrifugal pump in pump and turbine mode and the results are compared with theoretical and numerical results. Performance characteristic of mono block pump of specific speed 20.28 (m, m3 /s) in turbine mode operation are determined through experiment. The experiment results reveal suitability of using a mono block centrifugal pump in hydro turbine mode. The results confirm that the pump in hydro turbine mode operate at higher heads and discharge as compared to the pump mode operation. BEP (best efficiency point) for the pump in hydro turbine mode is lower than in pump mode operation. Correlations proposed by earlier researchers for performance prediction of pump in hydro turbine mode are also tested.

Theoretical, Numerical and Experimental Research of Single Stage, Radial Discharge Centrifugal Pump Operating in Turbine Mode

In this study, the best efficiency point of end suction, radial discharge, centrifugal pump operated in turbine mode was arrived applying numerical and experimental analysis. The pump was simulated both in direct and turbine modes using Star CCM+ CFD software. Characteristic curves were developed for the pump in direct and turbine modes. A monoblock centrifugal pump of specific speed 35.89 (m, m3/s) was used for this study. The pump was tested experimentally in turbine and pump mode .The theoretical and numerical results were verified by those obtained through experimentation. Some of the correlations proposed by earlier researchers for performance prediction of pump in reverse mode were also tested.

On the assessment of lumped parameter models for gear pump performance prediction

The present work describes a statistical approach for the assessment of discrete models for gear pump efficiency prediction. A critical discussion is performed on the input data assumptions that are commonly adopted to carry out the analysis, with particular regards to the actual bearing clearances and casing radial clearances. The proposed model adopts well-established techniques for simulating the pump fluid-dynamics in association with a novel approach, which allows us to take into account the effects produced by the gearpair micromotions. Moreover, the possibility to study both spur and helical gears, as well as non-unitary transmission ratio gearpairs, has been included, in order to ensure the wide applicability of the model in modern design solutions. Measured data obtained from an extended experimental campaign, involving 20 nominally identical samples of the same pump design, are used to establish the assessment procedure. Each sample is geometrically characterized by measuring the actual clearances at the end of the production process and then tested at 14 different working conditions, leading to 280 tests. The entire set of test conditions is then adopted to carry out a trace-driven simulation analysis, showing that the lumped parameter approach may reach different levels of accuracy depending on both the analyzed working conditions and the simulated pump samples. The results underline that reliability and accuracy of this kind of model should be evaluated with respect to a population of pumps, defined on the basis of a statistical approach, since referring to a single pump sample may easily lead to an over/under-estimate of the quality of the proposed model. In addition, they also demonstrate that real clearance values need to be included in the model to obtain high fidelity estimations.

Numerical Simulation and Performance Prediction of Centrifugal Pump’s Full Flow Field Based on OpenFOAM

The open-source software OpenFOAM 5.0 was used as a platform to perform steady-state and transient numerical simulation for full flow field of a pipeline centrifugal pump (specific speed ns = 65) in a wide operating capacity range of 0.3Qd~1.4Qd. The standard k-ε and k-ω SST (Shear-Stress Transport) turbulence models were selected in the flow governing equations. The simpleFoam and pimpleDyMFoam solvers were used for the steady-state and transient calculations, respectively. ParaView, the postprocessor in OpenFOAM, was used to display the calculated flow velocity, pressure and streamline distributions, and to analyze the relationship between the vortex and the hydraulic loss in the pump. The external performance parameters of the pump such as head, input power and efficiency were also calculated based on the simulated flow fields. The predicted pump performances by OpenFOAM and Ansys-Fluent are compared with the test results under the same calculation model, grids and boundary conditions. The comparison indicates that OpenFOAM had high accuracy in the prediction of pump performance in the current case.

Performance prediction of pump and pumping system based on combination of numerical simulation and non-full passage model test

The design scheme of open intake will often increase the shaft length and the height of wet-pit pump house when the range of intake water level varies relatively large. And if the pump house is far away from the discharge bay, a longer discharge pipe may be inevitable, and the arrangement of full passage model pumping system may exceed the allowed size of a specific test bench. Aiming at a long-shaft vertical pumping system with open intake design, larger water level variation and longer discharge pipe, a methodology of performance prediction for the pump and pumping system was proposed by combining numerical simulation and non-full passage model test because of less experiment funds, shorter research periods and restricted test bench. Through numerical simulation, the hydraulic losses and drag coefficients of sump and discharge pipe were calculated and followed by the experiment design and model test of non-full passage pumping system. Based on the calibration accuracy of measurement instruments and test results, the experimental uncertainties were analyzed. Through combination of numerical simulation and non-full passage model test, the prediction of model pump and pumping system performance was realized. The performance of prototype pump and pumping system was obtained by applying the similarity law of pump. When operated under the designed head of 13.59 m, the flow rate and efficiency of the prototype pumping system reached 11.86 m3/s and 86.56%, respectively, exceeding the requirements stipulated in the bidding documents and showing that it possessed greater pumping capacity and higher efficiency. The methodology proposed in this paper can be referenced and applied to the engineering design of similar pump stations.

Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.

Hill chart modelling using the Hermite polynomial chaos expansion for the performance prediction of pumps running as turbines

Pumps running as turbines are suitable hydraulic machines for micro hydropower applications. The selection of the proper pumps to install in a given site still remains a major challenge, as pump manufacturers do not provide the characteristic curves data in the turbine mode. Also, the accurate prediction and modelling of the pumps running as turbines characteristic curves still remain a major difficulty as existing methodologies still lack accuracy, especially in the part load and full load operating regions. This paper proposes a new two-step methodology based on the Hermite polynomial chaos expansion for predicting the characteristic curves of pumps running as turbines and modelling their variable speed operation, aiming at improving the prediction accuracy. Firstly, bivariate continuous surrogate functions are established for predicting the turbine mode and the extended operation mode characteristic curves inside a closed interval of unit specific speed values. These surrogate functions are developed by calibrating empirical coefficients based on collected experimental data. Secondly, a hill chart model is determined for describing the variable speed operation of a given pump running as a turbine. This hill chart model allows identifying the discharge and the rotational speed set points for maximising efficiency for a given operating condition. The proposed prediction surrogate functions and the variable speed hill chart model are useful engineering tools for improving the design of pump as turbine hydropower plants and for optimising the pump running as turbine control settings to maximise the produced energy.

Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN

In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller’s performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by ∼10% and the efficiency has increased by ∼3%, which is in keeping with our optimal result and confirms our method is feasible.

Prediction of Head, Efficiency, and Power Characteristics in a Semi-Open Impeller

Artificial Neural Network (ANN) was used to predict the effects of splitter blades in a semi-open impeller on centrifugal pump performance. The characteristics of this impeller were compared with those of impellers without splitter blades. Experimental results for lengths of splitter blades in ratio of 1/3, 2/3, and 3/3 of the main blade length were evaluated by different ANN training algorithm. Training and test data were obtained from experimental studies. The best training algorithm and number of neurons were determined. The values of head, efficiency, and effective power were estimated in a semi-open impeller with splitter blades in ratio of 3/6 and 5/6 of the main blade length at the best efficiency point (b.e.p.). Here, as the splitter blade length increases; the flow rate and power increases, the efficiency decrease. All of the estimated values of performance in a semi-open impeller with splitter blades indicate the model works in line with expectations. Experimental studies to determine head, efficiency and effective power consumption in different types of pumps are complex, time consuming, and costly. It also requires specific measurement tools to obtain the characteristics values of pump. To overcome these difficulties, an ANN can be used for prediction of pump performance in semi open impeller.