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The extensive applications of double-suction centrifugal pumps consume a considerable amount of energy. It is urgent to reveal the detailed energy dissipation generation and find the critical factor for pump performance enhancement. In this investigation, the internal flow field of a double-suction centrifugal pump was obtained by solving the Reynolds averaged Navier–Stokes equations. The entropy production method was utilized to calculate and visualize irreversible energy dissipation. The Omega vortex method was utilized to identify vortical structures and determine the temporal and spatial relationship between entropy production and vortices. The results indicate that the entropy production in the main flow regions was critical in hydraulic loss, accounting for 54%–71% of the loss, and turbulent dissipation in the main flow regions of the impeller and volute casing dominated the variation of pump efficiency. The near-wall entropy production in the impeller positively correlated with the flow rate, but the impact was insignificant in volute casing. Although the suction chamber contributed minimally to the hydraulic loss, the backflows at the impeller inlet were relieved near the ribs, and the dissipation at the impeller inlet was reduced when the blade leading edges passed the ribs. By adopting Omega vortex identification, wake vortices, separation vortices, and their interactions were determined to correlate strongly with hydraulic loss in volute channels and near cutwaters. Furthermore, these vortices were influenced by the back flows from the impeller sidewall gaps. Additionally, this study can also provide the foundational principles for the optimal design of this type of pump. [ABSTRACT FROM AUTHOR]

Centrifugal pump optimization problems usually have strong nonlinear characteristics and are sometimes non-differentiable. The traditional multi-objective particle swarm optimization (MOPSO) algorithm was modified to solve this situation, and performed better with respect to both accuracy and search speed in validation experiments. Based on the modified algorithm and multi-layer artificial neural networks, the shape of the impeller blades of an industrial inline pump was optimized to improve the comprehensive performance under multiple operating conditions. The non-uniform rational B-spline was applied in the parametric design of the blade geometry, and 14 design variables of the spline were finally utilized in the iteration. With constraint of the computational head, the efficiencies of the part-load condition, the nominal condition, and the overload condition were selected as the objective functions. After optimization, a dramatic efficiency rise was obtained in all the three specified operating conditions, and correlation between the inflow conditions before the impeller and the performance of the inline pump was indicated. [ABSTRACT FROM AUTHOR]

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Qd, 1.0Qd, and 1.2Qd. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Qd, 1.0Qd, and 1.2Qd, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN). [ABSTRACT FROM AUTHOR]

In order to improve the operation performance of the multi-stage double-suction centrifugal pump and reduce the internal energy loss of the pump, this paper proposes a single-objective optimization design method based on the non-hierarchical response surface methodology (RSM) and the multi-island genetic algorithm (MIGA). Nine parameters, such as the blade outlet width and blade wrap angle, were used as design variables, and the optimization objective was efficiency under design conditions. In total, 149 sets of valid data were obtained under the Latin hypercube sampling method (LHS), the corresponding thresholds were set for efficiency and head, and 99 sets of valid data were obtained. A cross-validation analysis of the sieved data was carried out based on non-hierarchical RSM, global optimization of the efficiency was carried out using MIGA, and numerical verification was carried out via CFD. The research results show that compared with hierarchical RSM, non-hierarchical RSM can approximate the nonlinear relationship between the objective function and the design variables with higher accuracy, and the model fitting R2 value was 0.919. The efficiency was improved by 3.717% after optimization. The overall prewhirl of the impeller inlet after optimization decreased, the internal speed of the volute significantly improved, the large-area vortex at the volute and the outlet pipe was eliminated, the impact loss at the volute separating tongue disappeared, and the overall hydraulic performance of the pump was improved. The total entropy output value of the optimized pump was reduced by 4.79 (W/K), mainly concentrated on the reduction in the entropy output value of the double volute, and the overall energy dissipation of the pump was reduced. [ABSTRACT FROM AUTHOR]

The vertical inline pump is a single-stage single-suction centrifugal pump with a curved inlet pipe before the impeller, which is widely used in where the constraint is installation space. In this paper, with the objective functions of efficiencies at 0.5Qd, 1.0Qd, and 1.5Qd, a multi-objective optimization on inlet pipe and impeller was proposed to broaden the efficient operating period of a vertical inline pump. Two 5th order Bézier curves were adopted to fit the shape of the mid curve of the inlet pipe and the trend of the blade angle of the impeller. Fourteen design variables were selected after the data-mining process. 300 sample cases were generated using Latin hypercube sampling (LHS), and they were solved by 3D RANS code to obtain the objective functions. The feed-forward artificial neural network with a hidden layer and an output layer was adopted to fit the two objective functions and the 14 design variables. The Pareto frontiers were generated for the three objectives using multi-objective particle swarm optimization (MOPSO), and three different configurations on the Pareto front are selected for detailed study by computational fluid dynamics (CFD). The results showed that the profile of the inlet pipe and the blade have a dramatic impact on the performance of the vertical inline pump. The Pareto frontiers reported that the performance under the overload condition usually keeps stable when the nominal efficiency is lower than 82 %, or the part-load efficiency is lower than 62 %, and it will decrease rapidly after that. After optimization, the improvement of efficiencies at the part-load condition and nominal condition of the picked case were 9.65 % and 7.95 %, respectively. [ABSTRACT FROM AUTHOR]

Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions. To solve that problem, an optimization strategy has been designed to widen the high-efficiency range of the double-suction centrifugal pump at design (Qd) and nondesign flow conditions. An orthogonal experimental scheme is therefore designed with the impeller hub and shroud angles as the decision variables. Then, the "efficiency-house" theory is introduced to convert the multiple objectives into a single optimization target. A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. The pump performance is predicted using three-dimensional Reynolds-averaged Navier–Stokes equations which is validated by the experimental test. With ANN, Kriging, and a hybrid approximate model, an optimization strategy is built to widen the high-efficiency range of the double-suction centrifugal pump at overload conditions by 1.63%, 1.95%, and 4.94% for flow conditions 0.8Qd, 1.0Qd, and 1.2Qd, respectively. A higher fitting accuracy is achieved for the hybrid approximation model compared with the single approximation model. A complete optimization platform based on efficiency-house and the hybrid approximation model is built to optimize the model double-suction centrifugal pump, and the results are satisfactory. [ABSTRACT FROM AUTHOR]

The recent advances in centrifugal pump design do not only require a better suction performance but also there have been attempts to reduce design time at a lower cost. The traditional trial-and-error optimization design method, however, depends on the designer's experience, which requires longer cycles. This is because the computational process of calculating the net positive suction head required (NPSHr) involves several calculation steps and this consumes a lot of computational time. An investigation was therefore carried out to test a novel NPSHr prediction method in a double-suction centrifugal pump using unsteady numerical simulations. In the new approach, a new boundary pair was introduced and an algorithm was used to estimate a good value for a static pressure value that correlates to a 3% drop in pump head to determine the critical cavitation point. Experiments were conducted to validate the hydraulic performance and the cavitation model. The NPSHr and the characteristic "sudden" head-drop were very well predicted by the novel approach in only three simulation steps. The internal flow analysis showed that for 0.6 Q d , the flow around the volute tongue was uneven at NPSH = 10.06 m, inducing flow separation and recirculation at the tongue region. Attached cavities were also observed around the suction ring in the spiral suction domain. The pressure fluctuations were analyzed also and the dominant frequency at the pump outlet and tongue region was the blade passing frequency. Consequently, the novel approach proved very robust and efficient in NPSHr prediction and would be a good alternative to shorten simulation time during cavitation optimization design process in centrifugal pumps. [ABSTRACT FROM AUTHOR]

• Amplitude of high harmonic frequency of the blade-passing frequency increased with flow rate. • Rotor-stator interaction at the volute wall induced strong pressure fluctuations. • Propagation of circumferential pressure fluctuation was not dependent on flow rate. • The bispectrum analysis revealed the nonlinear frequency. • The spectral domain was dominated by high harmonic frequency of the blade-passing frequency. Residual Heat Removal Pumps have very strict requirements to maintain nuclear power plant safety and therefore unsteady pump operations are very critical in determining system reliability. The operational stability has however been affected by pressure pulsations that are induced by rotor-stator interactions. A pressure measurement test was undertaken to investigate the nonlinear pressure fluctuation characteristics at angular and axial positions within the annular volute wall. Diametric mode and bispectrum analysis were subsequently applied to reveal the dominant frequencies of the pressure pulsations induced at the stationary parts, and also to analyze the nonlinear frequencies in the frequency domain due to the impeller-diffuser and diffuser-volute interactions. The pressure pulsations on the volute wall were significantly affected not only by the flow rate, but also by the sensors' position. The highest amplitude was observed at a frequency that was four-fold the blade-passing frequency at a diameter mode of 1. When the flow rate increased, the amplitude of the high harmonics of the blade-passing frequency increased, whereas the amplitudes of the blade-passing and shaft rotating frequencies decreased. In the axial direction, flow rate greatly influenced the propagation of pressure fluctuations whose dominant frequency are blade-passing frequency, vane-passing frequency and their high harmonics. The spectral domain was dominated by high harmonics of the blade-passing frequency (3St and 4St). Conversely, the circumferential pressure fluctuation was significantly affected by rotor-stator interaction and was independent of flow rate. The study provided sufficient information for further work to reduce pressure pulsations in the residual heat removal pump. [ABSTRACT FROM AUTHOR]

To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance. [ABSTRACT FROM AUTHOR]

Clocking effects can generally have a great influence on the operational state of centrifugal pumps. In order to analyze the effects of the positions of the vaned diffuser relative to the annular volute on the performance characteristics and the pressure pulsations of a centrifugal pump, experimental tests were carried out at three different relative positions. Frequency domain and time-frequency domain of the pressure pulsations were obtained using the Welch’s power spectral density estimate and the short-time Fourier transform (STFT) methods, respectively. The diameter mode analysis was applied to explain the dominant frequency induced by rotor-stator interaction. The results showed that the centrifugal pump can obtain the highest efficiency when the diffuser was fixed at position A, where the diffuser was rotated 90 degrees in counterclockwise direction from position B. The maximum difference in the efficiency between 0.9 Q d and 1.1 Q d was 1.5% under three different diffuser positions. The pressure pulsations in the volute were determined not only by the impeller-diffuser interaction but also by the diffuser-volute interaction. Furthermore, nonlinear frequency existed in the frequency domain of the pressure pulsations at the volute outlet. The relative positions of the diffuser had great effect on the dominant frequency and the amplitudes of the decomposed frequencies of the pressure pulsations. A comparison of the amplitudes of the main frequencies of the pressure pulsations showed that the transient pressure on the volute had the lowest pulsations when the trailing edge of the diffuser reached the left corner of the volute outlet (position B). [ABSTRACT FROM AUTHOR]

Impeller trimming is an economical method for broadening the range of application of a given pump, but it can destroy operational stability and efficiency. In this study, entropy production theory was utilized to analyze the variation of energy loss caused by impeller trimming based on computational fluid dynamics. Experiments and numerical simulations were conducted to investigate the energy loss and fluid-induced radial forces. The pump's performance seriously deteriorated after impeller trimming, especially under overload conditions. Energy loss in the volute decreased after trimming under part-load conditions but increased under overload conditions, and this phenomenon made the pump head unable to be accurately predicted by empirical equations. With the help of entropy production theory, high-energy dissipation regions were mainly located in the volute discharge diffuser under overload conditions because of the flow separation and the mixing of the main flow and the stalled fluid. The increased incidence angle at the volute's tongue after impeller trimming resulted in more serious flow separation and higher energy loss. Furthermore, the radial forces and their fluctuation amplitudes decreased under all the investigated conditions. The horizontal components of the radial forces in all cases were much higher than the vertical components. [ABSTRACT FROM AUTHOR]

The high-pressure multistage centrifugal pump is the main piece of energy-consuming equipment in the reverse osmosis desalination process, and it consumes about 35% of the entire system's operating cost. The optimization process of multi-stage pumps undoubtedly requires the performance comparison of multiple schemes in order to verify the effectiveness of the optimized design and the optimization method. Therefore, based on ANSYS Workbench and an improved bat algorithm, an intelligent optimization scheme was designed and carried out on a three-stage reverse osmosis desalination high-pressure pump for efficiency improvement by optimizing the matching relationship between the impeller and the guide vane. An external characteristic test was carried out in an open test rig system in order to verify the numerical model. After modifying the positive guide vane structure, the efficiency was improved for both the rated design and the non-design flow conditions without obvious separation and backflow. With the improved bat algorithm, there was a 3.98% increase in the design point efficiency after the final optimization. Under the design conditions, all of the large vortices disappeared after the optimization. The study provides a reference for the optimization design of the impeller–guide vane matching effect in a multistage pump using an improved bat algorithm. [ABSTRACT FROM AUTHOR]

Double-suction centrifugal pumps form an integral part of power plant systems in maintaining operational stability. However, there has been a common problem of achieving a better cavitation performance over a wider operating range because the traditional approach for impeller design often leads to the design effect not meeting the operational needs at off-design conditions. In addressing the problem, an optimization scheme was designed with the hub and shroud inlet angles of the double-suction impeller to minimize the suction performance at non-design flow conditions. A practical approach that speeds up the cavitation simulation process was applied to solve the experimental design, and a multi-layer feed forward artificial neural network (ANN) was combined with the non-dominated sorting genetic algorithm II to solve the multi-objective problem into three-dimensional (3D) Pareto optimal solutions that meet the optimization objective. At the design point, the suction performance was improved by 6.9%. At non-design flow conditions, the cavitation performance was improved by 3.5% at 1.2Qd overload condition, 4% at 0.8Qd, and 5% at 0.6Qd. Additionally, there was significant reduction in the attached cavity distribution in the impeller and suction domains when the optimized model was compared to the original model at off-design points. Finally, the optimization established a faster method for a three-objective optimization of cavitation performance using ANN and 3D Pareto solutions. [ABSTRACT FROM AUTHOR]

The vertical inline pump is a single-suction single-stage centrifugal pump with a curved inlet pipe before the impeller, which usually causes a significant increase of hydraulic losses in the inline pump. Considering the matching relationship between the inlet pipe and impeller, a multi-objective direct optimization based on the MOPSO of the inlet pipe and impeller was carried out to broaden the efficient operating area of the vertical inline pump. Bezier curves were adopted to control the profiles of the inlet pipe and impeller and 39 coordinates of the control points and the blade number were selected as the optimization variables. The efficiencies of the inline pump at the part-load and nominal conditions were chosen as the objective functions, which were obtained by the automatic simulation program. A dramatic improvement in pump performance was found after optimization. In the set of Pareto solutions, the maximum increases of efficiency at part-load and nominal conditions were 8.06% and 7.33% respectively. It also reported that the inlet pipe with longer horizontal length and lower bend curvature could reduce the hydraulic losses of the inlet pipe and increase the pump performance. [ABSTRACT FROM AUTHOR]

Researches have over the past few years have been applying optimization algorithms to quickly find optimum parameter combinations during cavitation optimization design. This method, although better than the traditional trial-and-error design method, consumes lots of computational resources, since it involves several numerical simulations to determine the critical cavitation point for each test case. As such, the Traditional method for NPSHr prediction was compared to a novel and alternative approach in an axially-split double-suction centrifugal pump. The independent and dependent variables are interchanged at the inlet and outlet boundary conditions, and an algorithm adapted to estimate the static pressure at the pump outlet. Experiments were conducted on an original size pump, and the two numerical procedures agreed very well with the hydraulic and cavitation results. For every flow condition, the time used by the computational resource to calculate the NPSHr for each method was recorded and compared. The total number of hours used by the new and alternative approach to estimate the NPSHr was reduced by 54.55% at 0.6 Qd, 45.45% at 0.8 Qd, 50% at 1.0 Qd, and 44.44% at 1.2 Qd respectively. This new method was demonstrated to be very efficient and robust for real engineering applications and can, therefore, be applied to reduce the computation time during the application of intelligent cavitation optimization methods in pump design. [ABSTRACT FROM AUTHOR]

Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. A fast approach to predicting the Net Positive Suction Head required was applied to perform a multi-objective optimization on the double-suction centrifugal pump. An L32 (84) orthogonal array was designed to evaluate 8 geometrical parameters at 4 levels each. A two-layer feedforward neural network and genetic algorithm was applied to solve the multi-objective problem into pareto solutions. The results were validated by numerical simulation and compared to the original design. The suction performance was improved by 7.26%, 3.9%, 4.5% and 3.8% at flow conditions 0.6Qd, 0.8Qd, 1.0Qd and 1.2Qd respectively. The efficiency increased by 1.53% 1.0Qd and 1.1% at 0.8Qd. The streamline on the blade surface was improved and the vapor volume fraction of the optimized impeller was much smaller than that of the original impeller. This study established a fast approach to cavitation optimization and a parametric database for both hub and shroud blade angles for double suction centrifugal pump optimization design. [ABSTRACT FROM AUTHOR]