Efficiency Point Research Articles

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

Exploring the mechanisms behind dual-peak formation in energy harvesting efficiency of semi-passive flapping airfoils with varied spring stiffness

Flapping airfoil energy harvesting has emerged as a promising paradigm inspired by birds and marine organisms. In this investigation, a semi-passive flapping airfoil device with a predetermined pitching motion was examined using a transient numerical simulation method employing an overlapping grid technique. The impact of the spring stiffness on the energy harvesting characteristics of the flapping airfoil was analyzed through the implementation of the integral momentum theorem and dynamic mode decomposition. The results demonstrated that the efficiency and power curves of the flapping airfoil exhibited a distinctive bimodal M shape in response to variations of the spring stiffness. The initial peak efficiency point, which corresponded to the system's natural frequency in proximity to the pitching frequency, was found to be heavily influenced by the presence of leading-edge vortexes, thereby influencing lift generation. By utilizing the integral momentum theorem, it was determined that the Lamb vector term, fluid acceleration term, and total pressure term predominantly contributed to the lift generation. The emergence of the second peak efficiency point was primarily linked to torque variations induced by trailing-edge vortexes.

Optimality and KKT conditions for interval valued optimization problems on Hadamard manifolds

Recently, a new type of optimization problems, the so-called interval optimization problems on Hadamard manifolds, is introduced by the authors in Nguyen et al. [Interval optimization problems on Hadamard manifolds. J Nonlinear Convex Anal. 2023;24(11):2489–2511]. In this follow-up, we further offer the algorithmic bricks for these problems. More specifically, we characterize the optimality and KKT conditions for the interval valued optimization problems on Hadamard manifolds. For unconstrained problems, the existence of efficient points and the steepest descent algorithm are investigated. To the contrast, the KKT conditions and exact penalty approach are explored in the ones involving inequality constraints. These results pave the foundations for the solvability of interval valued optimization problems on Hadamard manifolds.

A comparative study of different algorithms and scalarization techniques for constructing the Pareto front of multiobjective optimization problems

This paper presents an analysis and comparison of various algorithms applied to different scalarization methods for multiobjective optimization problems (MOPs). At first, some theoretical results are provided on the relation between (weakly) efficient solutions of an MOP and optimal solutions of the related numerical method. Moreover, we consider some deficiencies in different scalarization approaches given in the literature and try to fill some gaps in these works. Hence, by considering appropriate limitations, we provide sufficient conditions for (ε-)properly efficient and (ε-)efficient solutions of an MOP via scalarization techniques. Then, an algorithm for approximating the Pareto front of MOPs is presented. The main purpose of this algorithm is to generate efficient points on the Pareto front with a uniform distribution. One advantage of this algorithm, compared to some other algorithms, is that in each iteration of the algorithm, more than one efficient point located on the Pareto border can be generated. The new algorithm is implemented by applying the modified Pascoletti–Serafini, the unified direction, and the modified normal boundary intersection scalarization techniques, and then the procedures are considered on different test problems including (non-)convex and discrete Pareto fronts. The capability of the numerical method is shown by comparing the results with the algorithms in the literature. To compare the algorithms, measures of coverage and spacing metric indicators are used.

Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency

Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.

On Necessary Optimality Conditions for Sets of Points in Multiobjective Optimization

AbstractTaking inspiration from what is commonly done in single-objective optimization, most local algorithms proposed for multiobjective optimization extend the classical iterative scalar methods and produce sequences of points able to converge to single efficient points. Recently, a growing number of local algorithms that build sequences of sets has been devised, following the real nature of multiobjective optimization, where the aim is that of approximating the efficient set. This calls for a new analysis of the necessary optimality conditions for multiobjective optimization. We explore conditions for sets of points that share the same features of the necessary optimality conditions for single-objective optimization. On the one hand, from a theoretical point of view, these conditions define properties that are necessarily satisfied by the (weakly) efficient set. On the other hand, from an algorithmic point of view, any set that does not satisfy the proposed conditions can be easily improved by using first-order information on some objective functions. We analyse both the unconstrained and the constrained case, giving some examples.

Multiobjective hydraulic optimization of the diffuser vane in an axial flow pump

Separation flows tend to induce a chaotic flow field that eventually leads to energy losses and reduced efficiency. The present study performed a multiobjective optimization to improve the hydraulic performance of an axial flow pump at the best efficiency point (BEP) and critical stall point based on the diffuser vane (DV) geometry. Computational fluid dynamics were applied to predict the hydraulic performance of a series of DV models with design points generated through design of experiment. Six different surrogate models were evaluated based on the R-squared criteria. The nondominated sorting genetic algorithm II was also employed to search for optimum solutions for design variables. Hydraulic performance balance between low and high flow rate conditions was analyzed based on the velocity triangle. After optimization, the efficiency and total head at the BEP of the optimum model were increased by 2.341% and 2.779%, respectively, compared to the reference model. Despite the minimal changes to the hydraulic performance at the critical stall point, the optimal operating range was notably expanded in the high flow rate region. Thorough evaluation of losses attributed to horseshoe, corner, and trailing-edge vortices was conducted in meridional planes, multiple spans, and various cross sections in the DV domain. Additionally, the formation and development of turbulent flow were analyzed in detail by transient simulation. Vibration and noise caused by instabilities in the flow characteristics of the reference model were substantially reduced by 36.76% and 67.342% at the first higher-harmonic frequencies at the BEP and the critical stall point, respectively.

OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

An Online Maximum Efficiency Point Tracking Technique for Bidirectional Noninverting Buck–Boost Converter Over Wide Power Range

An Online Maximum Efficiency Point Tracking Technique for Bidirectional Noninverting Buck–Boost Converter Over Wide Power Range

A generalized prediction model for complete performance investigation of pump operated as turbine in microhydro applications using novel approach for improving sustainability

Centrifugal pumps operated in reverse mode (popularly called pump as turbines (PATs)) are well-established as green energy converters, mainly used in microhydro applications due to their lower capital cost. However, the appropriate selection of the pump for reverse-mode application is a critical issue due to the unavailability of its characteristic curve in turbine mode. Therefore, various researchers have proposed models to predict PAT parameters from pump characteristics based on diverse approaches. Still, researchers are striving to develop a prediction model, which can predict the head and flow rates for the PAT with less than ±20 % deviation for both. This work proposed a novel approach to predict the characteristics of PAT with the integration of ensemble regression modelling for Best Efficiency Point (BEP) and from it, by polynomial equations complete characteristics curve. The data from in-house experiments and open literature are used to develop the proposed model excluding the data of four pumps, which are typically used for validation of it. The predicted parameters of PAT from pump characteristics show good agreement for four selected PATs with <10 % maximum deviation. Compared to the literature prediction models, it shows a lower deviation for the same four PATs. A case study based on the field data is carried out to justify the applicability of the proposed model. By using this model selected pump when operated in PAT will produce higher power and generate more revenue compared to literature models, resulting in the economical utilization of resources and improving sustainability. Though the savings are lower, it will be significant for a large number of PAT applications.

Change point detection of events in molecular simulations using dupin

Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, and even larger objects. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynamic phase transitions, structural transitions, protein folding, crystallization, gelation, swarming, avalanches and fracture. A common challenge in studies of these systems involves change detection. Change point detection discerns when a temporal signal undergoes a change in distribution. These changes can be local or global, instantaneous or prolonged, obvious or subtle. Moreover, system-wide changes marking an interesting physical or chemical phenomenon (e.g. crystallization of a liquid) are often preceded by events (e.g. pre-nucleation clusters) that are localized and can occur anywhere at anytime in the system. For these reasons, detecting events in particle trajectories generated by molecular simulation is challenging and typically accomplished via ad hoc solutions unique to the behavior and system under study. Consequently, methods for event detection lack generality, and those used in one field are not easily used by scientists in other fields. Here we present a new Python-based tool, dupin, that allows for universal event detection from particle trajectory data irrespective of the system details. dupin works by creating a signal representing the simulation and partitioning the signal based on events (changes within the trajectory). This approach allows for studies where manual annotating of event boundaries would require a prohibitive amount of time. Furthermore, dupin can serve as a tool in automated and reproducible workflows. We demonstrate the application of dupin using three examples and discuss its applicability to a wider class of problems. Program summaryProgram Title:dupinCPC Library link to program files:https://doi.org/10.17632/kjcn97zc46.1%Developer's repository link::https://github.com/glotzerlab/dupinLicensing provisions: BSD 3-clauseProgramming language: PythonNature of problem: In the field of molecular simulations, detecting structural transitions or events within trajectories can be both challenging and time-consuming for larger studies due to the requirement of a manual approach. This issue is particularly pronounced in studies involving hundreds or thousands of simulations, where manual detection and analysis of transitions become infeasible. Our goal is to develop an automated, accurate and efficient method for detecting transition points in simulation trajectories, which both saves time and aids researchers in uncovering important events and their underlying causes in various systems. Additionally, we aim to facilitate new machine learning applications to important materials problems such as predicting and designing crystallization pathways, predicting defect formation, and describing the behavior of active matter, all of which involve structural transitions occurring over time. The developed method should be applicable to offline and online detection, enabling event-dependent triggers for advanced simulation/experimental protocols and efficient processing and storing of data.Solution method: We develop a versatile python package called dupin for detecting molecular events and structural transitions in simulation trajectories. dupin's workflow pipeline includes three major stages: data preprocessing, data augmentation, and detection. The components of this pipeline collectively improve the accuracy and efficiency of identifying structural changes in particle trajectory data. In data preprocessing, we generate and aggregate data into a comprehensive representation of the system. Data augmentation techniques such as feature selection and dimensionality reduction counteract the noise arising from high-dimensional data and enhance computational performance. We detect change points within the trajectory indicating transition events using a cost-based event detection method. In dupin, we implement two cost functions based on piecewise linear fits, which offer different levels of sensitivity to sudden shifts and changes in the signal. The package can use any cost-based detection algorithm but has a special interface for the Python package ruptures. Regardless of detection algorithm, we use the cost function and “elbow” detection to determine the correct number of change points. The detection scheme can be applied both offline and online, enabling real-time analysis of molecular events as simulations progress. As an example, dupin may be used to trigger a high frequency storage of frames within a simulation upon nucleation and subsequent solidification of a liquid into a crystal. Our method demonstrates a high degree of accuracy in detecting transition points within simulation trajectories when provided with informative descriptors. By automating the detection process, our solution enables efficient change point detection for studies with large-scale simulations.Additional comments including restrictions and unusual features: Our package, dupin, has great promise in detecting transition points within simulation trajectories with a high degree of accuracy; nonetheless, it is essential to note that it relies heavily on the selection of informative descriptors. The accuracy of the detection may be compromised if the chosen descriptors do not effectively capture the changes in a system's properties. However, this restriction can be mitigated by selecting a diverse range of descriptors and applying a feature selection tool to refine the signal.

Research on the efficient process-oriented structural optimization method of the large-scale vacuum cryostat for fusion reactors

An efficient optimization design for the large and complex components of fusion reactors is crucial to address the engineering design requirements and further promote technical standardization. Based on research status, current engineering designs for fusion reactors have some deficiencies, such as time and energy wastage, inefficiency, and difficulties in covering the typical ‘multi-variable multi-objective’ design requirements. These are pressing and common problems that urgently need to be overcome. To deal with the aforementioned technical challenges, it is vitally important to design an efficient, precise, and normalized approach that is tailored for the development of future fusion reactors. Therefore, this paper proposes a process-oriented optimization design method, which involves Coupled external parameterized modeling, Experimental points design, Response surface optimization, and Structural integrity validation (CERS), to improve the currently inefficient design methods. And the vacuum cryostat, the largest and complex component of a tokamak, is taken as an example to present the basic procedures of CERS. Firstly, the functions, basic structures, load types, analysis methods, and verification criteria of the cryostat are presented in detail. Then, real-time data interaction between external global parametric variables and ANSYS via coupling is established by CERS, which achieves parametric modeling of the cryostat and efficient experimental point design and optimization analysis with multi-variables and multi-objectives in an automatic way. Subsequently, this study demonstrates the significance and sensitivity of various structural parameters of the cryostat from such objectives as maximum deformation, maximum equivalent stress, and total mass. And the optimal set of its structural parameters is obtained by establishing a mathematical optimization model. Finally, the structural integrity is verified. The results indicate that the optimized cryostat maintains a minimum safety margin of 23% and will not suffer fatigue damage under various load events during its service. Moreover, the nonlinear buckling load multiplier ∅ is 5.4, obtained by analyzing the load-displacement curve of the cryostat according to the zero-curvature criterion. This shows that the designed cryostat is stable enough. The proposed method is simple, efficient, and reliable, and can be applied to both the cryostat and other complex components of fusion reactors in engineering design fields. It has great value of practical technical reference and can further promote the standardization of engineering design technology for future fusion reactors.

Basic performance, cavitation and runaway investigations of a high specific speed Francis turbine with emphasis on cavitation analysis: A case study

The paper presents comprehensive experimental research on a high specific speed model Francis turbine with a characteristic runner diameter of 250 mm under the head of 12 m. Essentially, the turbine was designed for use at heads between 10 and 50 m, which are typical for installations with low specific speed Kaplan turbines. The basic performance, cavitation and runaway characteristics of the machine, which were determined as a result of the work, are quite rarely available in the literature in the case of high specific speed Francis turbines. The turbine achieved the maximum efficiency of 91%, kinematic specific speed of 82, and relatively wide operation range, so that it can be attractive as an alternative to the low specific speed Kaplan, semi-Kaplan or propeller turbines.In result of the basic laboratory tests, the performance characteristics of the machine operation in a wide range of guide vane opening angles between 9° and 36° as well as the shell efficiency characteristics were determined. On the characteristics plotted vs. rotational speed, the saddle effect was identified for the guide vane opening angles with values greater than the optimal one (22°). During the operation of the machine in the area of saddles, the change in the flow rate took quite surprising patterns. The share of the turbine thrust torque in the total torque was also determined, the characteristics of which are also burdened with a similar saddle effect.In the case of cavitation tests, a multi-aspect approach to cavitation diagnostics was used to characterize the cavitation phenomenon from the viewpoint of various criteria. Following this approach, the signals of the draft tube wall acceleration, acoustic emission at the draft tube cone upper flange, and pressure fluctuations inside the draft tube were used. The determined cavitation characteristics indicate little cavitation susceptibility of the Francis turbine blade system, despite the presented turbine having been designed to operate at higher flow rates than the classic high head Francis turbines with long interblade channels. The cavitating flow in the draft tube, directly below the runner, was also visualized during the cavitation tests. The paper presents the results of visualization for all cavitation states at five selected settings of the guide vane opening angle (two states in Partial Load, two states in High Load area and one state in the Best Efficiency Point), thanks to which it was possible to track the development of vortices during pressure reduction in the flow system.The runaway coefficient was determined as a result of the runaway tests. The value of 1.497 was reached at the optimum guide vane opening angle.Additionally, the work also presents in detail the efficiency scale effects to be expected at prototype turbines, i.e. those intended for operation at a higher head and with larger runner diameters. The efficiency of the machine with a runner diameter of 1.5 m and working at a head of 50 m reaches ∼93%. The influence of water viscosity on the change in efficiency of the prototype turbine is also shown.The presented methodology can be used for the proper selection of turbines intended for hydropower plants and for the proper installation of the runner in respect to the tailwater level in order to avoid the phenomenon of cavitation erosion.

Maximum power extraction of partially shaded solar photovoltaic array using reconfiguration method

ABSTRACT This article presents a reconfiguration strategy, named the Cheetah Optimization Algorithm (COA), for maximizing power generation in partially shaded solar photovoltaic (PSPV) arrays. The proposed method’s originality aims to mitigate the negative impacts of partial shading, improve the output power of the PV system, and prevent thermal failure of shaded solar panels. The strategy combines efficient maximum power point tracking techniques with suitable photovoltaic system design topologies. The COA is done on the MATLAB platform and is analyzed with the existing methods such as the Wild Horse Optimizer (WHO), Salp Swarm Algorithm and Heap-based Optimizer. The outcome shows the superior performance of the COA in achieving the extraction of maximum power compared to the other methods. Additionally, by comparing the two patterns of partial shading, it is shown that the COA extracts more power than the existing methods and gets to the global power value faster. The COA is compared with existing methods, including WHO, SSA, and HBO. In terms of accuracy, the proposed system achieves the highest value of 0.93, surpassing the accuracy of HBO, WHO, and SSA, which stand at 0.8, 0.9, and 0.9, respectively. The specificity, precision and recall are also reflect the superiority of the proposed system with the values of 0.87, 0.9, and 0.86, respectively, outperforming the existing algorithms in most categories.

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry.

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

Numerical study of rotor-stator interaction in variable speed reversible pump-turbine in turbine mode at low head

Abstract Pumped storage hydropower (PSH) offers the cost-effective storage of large quantities of energy with high efficiency. Reversible pump-turbines (RPT) are used in PSPs having large head variations and where variable speed operation can enhance its efficiency and grid stability. The RPT may experience higher fatigue and vibrations due to hydraulic instabilities caused by pressure fluctuations in the vaneless space between runner blades and guide vane interaction, known as rotor-stator interaction (RSI). Because of this instability, the local components of the powerhouse vibrate severely. Therefore, there is a need to study the pressure fluctuations caused by RSI in the variable speed RPTs. The present numerical study focuses on the pressure fluctuations due to RSI in turbine mode conditions having low available head. The present study used a reduced scale high-head variable speed RPT model, and the SST k-ω turbulence model was used to carry out numerical calculations. The analysis was performed at the best efficiency point and low head operating conditions having optimized rotational speed. The analysis showed that the main source of pressure fluctuations in the RPT at all operating conditions is the RSI, where the dominant frequency is BPF and its harmonics.

Centrifugal pump performance improvement using back cavity filling

Centrifugal pumps consume majority of energy used by the electric motor driven systems among all end users of the industrial sector. Only in European region, a 1% improvement in efficiency (η) of the centrifugal pump is capable enough to reduce CO2 emissions by at least 572 tons per day. The prime motive of this research is to assess the applicability of the novel back cavity filling (BCF) in reducing the disk friction losses of centrifugal pumps without changing the pump's original design. By adding a solid ring to the bearing housing's back-cover plate, the BCF was applied to a medium specific speed pump (Ns = 54 rpm), resulting in a 1 mm back axial clearance from 13 mm. Numerical simulations and experiments were carried out to investigate the effect of BCF on pump performance under two conditions: with and without BCF at best efficient point (BEP) and various partload conditions for 1450 rpm (rated) as well as 1000 rpm. Pump performance characteristics were enhanced by BCF at both rpms for the whole flow rate range. At 1450 rpm, BCF resulted in an average 0.74% increment in total head, 2.08% reduction in input torque, and 2.70% increment in overall efficiency over the flow rate range. While an average 2.04% improvement in total head, 1.33% reduction in input torque, a 3.30% enhancement in overall efficiency were obtained at 1000 rpm. Performance parameter enhancement was higher at lowest part load compared to the BEP on rated rpm.

Assessment of Fiber Bragg Grating sensors for monitoring induced strains on the draft tube cone of hydraulic turbines

Abstract In hydraulic turbines, several flow instabilities can take place inside the draft tube cone during off-design and transient operating conditions such as the rotating vortex rope which can severely damage the structure if sustained in time. In the frame of the AFC4Hydro H2020 research project, an extensive measurement campaign has been carried out to monitor and predict this rotating vortex rope phenomenon in a reduced scale Kaplan turbine model at the Vattenfall Research and Development facility in Älvkarleby, Sweden. The hydraulic turbine model has been operated in propeller mode with a fixed blade angle corresponding to its best efficiency point. Several sensors have been placed along the test stand to monitor vibrations, strains and pressures. Concretely, the present paper assesses the performance of using Fiber Bragg Grating sensors to measure the strains induced on the draft tube cone walls with high-spatial resolution in three different zones of influence: the upper and lower flanges and the vertical cone wall between the runner outlet and the elbow. To do so, a total of 3 arrays embedding a total of 48 Fiber Bragg Grating sensors were glued inside three grooves previously machined on these particular areas of the draft tube cone. Analysing the frequency response of the different Fiber Bragg Grating sensors, the strain patterns induced by the rotating and plunging components of the rotating vortex rope have been precisely determined. Moreover, their impacts at the different part load conditions tested have also been quantified.

Numerical Investigation of Impeller Parameters on the Performance and Internal Flow Characteristics in Regenerative Flow Pumps

Abstract As a particular vane pump, regenerative flow pump (RFP) is not only featured by relatively high head under low flow rate, but also is characterized by compact volume and simple structure. However, the efficiency of RFP is quite low. For the purpose of improving RFP’s hydraulic performance and internal flow, this paper investigates the influence of the matching relation between impeller diameter and height on RFP’s performance. Two cases through changing impeller’s diameter and height are designed and compared. The results show that, as impeller’s diameter increases while keeping the blade’s radial length constant, RFP’s head and efficiency are improved, and the best efficiency point (BEP) shifts gradually from lower flow rate to larger flow rate. Additionally, the ratio of the flow rates at BEP is approximately equal to the ratio of the blade’s middle diameters. On the other hand, as impeller’s height increases, the BEPs among different pump models stay in same flow rate, and there is always an optimal impeller’s height for this RFP model to achieve optimal performance. Finally, the pressure distribution, velocity distribution and mass exchange of different models are analysed to reveal the differences.

Energy loss analysis of a double-suction centrifugal pump using in pump mode and turbine mode

As an economical energy recovery device, pump as turbine (PAT) is widely used in micro-hydropower stations and the chemical industry. The inlet and outlet pipelines connected to the double-suction pump are on the same horizontal line. As the pipeline layout is very convenient, in some chemical industries, the way of residual pressure energy utilization is increasing using the double-suction pump as a turbine. Based on numerical simulation, experimental verification, and entropy generation theory, the energy loss rule of each flow component in the pump mode and turbine mode under different flow rates is compared and analyzed. The results show that when the double-suction pump is used as a turbine, the flow rate at the best efficiency point (BEP) in the turbine mode shifts to a large flow rate by 30.89% and the BEP efficiency decreases by 1.30%. In the pump mode and turbine mode, the main energy loss component is the impeller, and the turbulent entropy generation power and the wall entropy generation power are the main sources of energy loss. The energy loss in the suction chamber and impeller increases sharply, and the energy loss is primarily enhanced in the blade trailing edge and the tongue near due to the unsteady flow in the turbine mode. Due to the complex structure, the spiral suction chamber is not suitable for the flow direction of the fluid flow out of the impeller, and the flow state inside the impeller is negatively affected by the suction chamber in the turbine mode. This paper provides a theoretical basis for the design and application of double-suction centrifugal PAT.