System Performance Research Articles

Orientation optimization of the Halbach PMG for the levitation and guidance performance of the HTS maglev system

High-temperature superconducting maglev technology has great application potential in rail transit applications due to its advantages such as passive stable levitation and green environmental protection. As one of the key components of the HTS maglev system, the Halbach-type permanent magnet guideway (PMG) enhances the magnetic field on one side of the guideway and weakens that on the other side. Optimizing the PMG, including the geometry and magnetic properties of the magnet, is an effective method to improve the levitation and guidance performance of the system. In addition, since NdFeB permanent magnets are expensive, optimizing the design of the PMG for the HTS maglev system is also an important work to reduce the application cost. In this paper, the Nelder–Mead Simplex method (NMS) in the optimization solver in COMSOL software is used to optimize the geometric parameters of the Halbach PMG according to the different objective requirements of the HTS maglev system. The optimization objectives include the maximum levitation force and guidance force under the constraint of the maximum cross-sectional area of the PMG, as well as the minimum cross-sectional area of the PMG with a minimum levitation force constraint. The results indicate that the NMS method is effective. By comparing four PMGs during the optimization process, it is found that this method can fully improve the utilization of the magnetic field by changing the geometric parameters of the Halbach PMG. This study can provide a valuable reference for the optimal design of the PMG for HTS maglev systems in the future.

Influence of Hot Water Pipe Position on the Performance of Radiant Floor Heating Systems Integrated with PCM

To address the slow heating and low efficiency issues of traditional macro-packed phase change material (PCM) radiant floor heating systems, this study innovatively explores the impact of altering the eccentric positions of hot water pipes within the PCM encasement, based on computational fluid dynamics (CFD) numerical simulations. Starting with a concentric case as a baseline, the investigation extends to six different configurations achieved by displacing the hot water pipes vertically (upward and downward) and horizontally. The analysis encompasses the PCM's thermal state, temperature dynamics, spatial distribution, and heat flux. The results indicate that the arrangement of hot water pipes with upward and horizontal displacements demonstrates a more positive impact on improving the thermal performance of the PCM radiant floor heating system, with the effect becoming more pronounced as the displacement distance increases. Cases with upward eccentric movement of r/4 (where r is the encasement radius) and r/2 reached the optimal floor temperature of 297.15 K faster than the concentric case by 2 hours and 3.8 hours, respectively, during the heating phase. Cases with horizontal eccentric movement advanced by 0.5 hours and 2.5 hours, respectively. Cases with horizontal displacements of r/4 and r/2 caused smaller temperature fluctuations and provided greater comfort and energy efficiency than those with upward displacements, with average heat flux values being 4.75% and 11.82% lower, respectively. These findings offer theoretical support for the optimization and application of macro-packed PCM floor heating systems.

Performance evaluation of PV configurations considering degradation rate and hot spots

<p>The rapid emergence and evolution of renewable energy sources such as solar energy has become a vital component of the global effort to meet the energy needs of the future. The major concerns for continuous solar photovoltaic (PV) generation are degradation rate, hot spots. These factors lead to the negative impact on PV mismatch losses, fill factor, maximum power and efficiency. To improve the performance of PV system, the simplest solution is PV panel configuration hence in this paper spider web tie (SWT) based PV Panel configuration in proposed. The proposed configuration is implemented on KC200GT PV Panel of 5×5 size PV panels considering degradation rate, hot spot. The performance of SWT configuration is compared with series-parallel (SP), bridge-link (BL), triple tied (TT), and photovoltaic (PV) panel configurations and performance parameters such as Vmp, Imp, Pmp, Voc, Isc. FF, ∆Pml, and η are calculated in all the cases. In all the cases the proposed SWT configuration exhibited the improved performance.</p>

A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods

<p>The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.</p>

Unreliable M[X]/G(P1,P2)/1 Feedback Retrial Queues with Combined Working Vacation

The study examines M[X]/G(P1,P2)/1 feedback retrial queues coupled with starting failure, repair, delay to repair, working vacation, and general retrial times. Also, it explores how different batch sizes affect performance and how bulk arrival affects system behavior. When the server is not in use, a single customer initiates the system while the remaining customers transition to a state of orbit. A new customer must turn on the server to provide two phases of mandatory service at any time. The server could have starting issues. If the service is successfully started (with likelihood α), the customer receives service immediately. In the absence of that, the likelihood of starting failure happens (with likelihood 1−α=α¯). The server was taken for repair with some delay and that customer was transported to an orbital location. When the server was busy or unavailable, the arriving customers queued by FCFS in the orbit. We also discuss the idea of reworking with probability p, and restarting unsuccessful service attempts to improve customer happiness and service efficiency. We also introduce the concept of working vacation, which permits servers to temporarily stop providing services, affecting system performance and availability at both peak and off-peak times. A supplementary variable technique was adopted for the system's and orbit size's probability-generating function. Various performance measurements were provided with appropriate numerical examples.

Transformasi Proteksi Tegangan: Sistem Monitoring IoT untuk Pemantauan Real-Time

Electric energy is a vital resource extensively used in various sectors such as industry, offices, agriculture, and trade. Its significance in supporting a nation's economy is undeniable, making the monitoring of power consumption and quality crucial. This study focuses on designing and implementing an Internet of Things (IoT)-based monitoring system for overvoltage protection in an electrical power system. The power system incorporates an Over Voltage Relay (OVR) as a protective measure against voltage fluctuations that may harm the equipment. The OVR operates based on electromagnetic principles, promptly responding to deviations in voltage beyond specified limits. Additionally, the system integrates the PZEM-004T module for measuring voltage, current, power, frequency, energy, and power factor, providing comprehensive insights into the electrical system's performance. The heart of the IoT-based monitoring system is the ESP32-32D module, facilitating real-time monitoring and remote control through the Blynk application. The implemented system successfully detects overvoltage conditions, automatically triggers protective measures, and allows users to monitor the power system through an intuitive interface.The study concludes with an evaluation of the average voltage measured by the OVR, emphasizing the system's reliability and potential for further industrial applications. The average OVR voltage, determined to be approximately 239 Volts, provides a comprehensive overview of measurement consistency throughout the experiments. This research contributes to the enhancement of electrical power management, ensuring the reliability and efficiency of overvoltage protection through IoT-based monitoring systems. Further evaluations and advancements could optimize the system's performance for broader industrial applications.

The Study on the Nonlinear Effects of Soliton in Optical Fibre

Abstract: In optical fibres, the chirps generated by GVD and SPM, which both limit the system's performance when operating independently, balance each other out to produce soliton formation. To comprehend how such a balance is possible, we shall study the nonlinear optical effects and the dispersion-induced pulse broadening. If an optical pulse is not adequately chirped before it propagates inside an optical fibre, the GVD broadens the pulse. More specifically, an early stage of transmission compresses a chirped pulse whenever β2 and the chirp parameter C have opposite signs and β2C is negative. The optical pulse chirps due to SPM, ensuring that C > 0. If β2 < 0, it is easy to meet the criteria β2C < 0. Moreover, because the SPM-induced chirp depends on power, it is possible that in some situations, SPM and GVD could cooperate to the extent that the SPM-induced chirp is precisely the right amount to cancel out the pulse broadening caused by GVD. Here, an optical pulse propagates distortion-free as a soliton [27].

Preparation and photocatalytic mechanism of magnetic Ag2S/CoFe1.95Dy0.05O4 Z‐scheme heterojunction

AbstractThe synthesis of high‐efficiency magnetic composite photocatalyst by doping magnetic cobalt ferrite and compounding single semiconductor photocatalyst is a promising strategy to improve the oxidation ability of photocatalytic systems. In this paper, CoFe1.95Dy0.05O4 (CFDO) was prepared by doping Dy element into magnetic CoFe2O4, and Ag2S (AS)/CFDO with high‐efficiency magnetic photocatalyst was synthesized by compounding AS with CFDO as the substrate. The photocatalytic samples were characterized by different advanced characterization methods, and their photocatalytic degradation of methylene blue (MB) was studied. The results show that AS/CFDO exhibits higher visible light response, excellent photogenerated charge separation ability and migration efficiency, and excellent catalytic performance in the catalytic degradation system. The photocatalytic activity of AS/CFDO was the highest, and its photocatalytic degradation kinetic constant K was 2.48 and 1.54 times that of AS and CFDO, respectively. In addition, the catalyst contained in the catalytically contaminated solution can be effectively separated by an external magnetic field to achieve multiple cycles of degradation and recycling. The cyclic degradation experiments showed that AS/CFDO exhibited high degradation stability during the photodegradation process. After the fifth reuse, the degradation efficiency was still more than 88.0%. Finally, the possible photocatalytic mechanism of the samples was discussed. Therefore, this work provides an effective solution for the construction of photocatalysts with high efficiency, magnetic recovery, and cyclic degradation stability and avoids the secondary pollution of catalysts to organic wastewater. It is of great significance to create an environmentally friendly catalytic method for efficient cyclic degradation of organic wastewater.

Reducing Cluster Overlap in Movie Recommendations with IKSOM and Silhouette Clustering

The exponential growth of online material requires the implementation of effective and precise recommendation systems in order to optimize the user experience. Nevertheless, conventional approaches frequently encounter problems such as cluster overlap, which reduces the accuracy of suggestions. This paper presents a new method for minimising the overlap between clusters in movie recommendation systems. It achieves this by combining Improved Kohonen Self-Organizing Maps (IKSOM) with Silhouette Clustering. The proposed method utilises IKSOM to efficiently represent high-dimensional user-item interactions in a two-dimensional space, enabling the formation of distinct and meaningful clusters. Subsequently, Silhouette Clustering is utilised to enhance the separation and cohesion of clusters, hence reducing overlap. The experimental findings show that proposed hybrid model works much better than the baseline techniques, obtaining an RMSE of 0.423 and MAE of 0.216. Additionally, it improves precision (93.6%), recall (94.2%) and F1-score (93.4%). Additionally, the proposed technique demonstrates a high level of accuracy (97.3%) with a precision rate of 95.8%. These results emphasise the method's efficacy in minimising errors and enhancing the overall performance of the recommendation system. The results indicate that combining IKSOM with Silhouette Clustering can improve the precision and dependability of movie recommendation systems by resolving cluster overlap and offering more individualised user experiences. Subsequent research will investigate the implementation of this method in different fields and the integration of supplementary contextual information to enhance the accuracy of recommendations.

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1,723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25% of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98%), and the remaining 75% of the groups were of low mortality (n=1,292; 60-day mortality=2.75%). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups as high with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Improving energy-related efficiency towards SDG7 in China: What role does energy poverty play?

The improvement of energy-related efficiency has promoted energy-sustainable development in China; however, it may be inhibited by energy poverty. This paper constructs an index system to measure China's energy poverty. Subsequently, we evaluate energy-related efficiency towards SDG7 comprised of energy production efficiency and energy sustainable utilization efficiency using a dynamic two-stage data envelopment analysis model. Furthermore, we adopt fixed effect models to quantify the impact of energy poverty on improving energy-related efficiency and the underlying mechanism. The findings indicate that: (1) China's energy poverty situation gradually improves from 2011 to 2020. The energy-related system's performance is medium due to the poor performance in its energy production stage in 30 provinces during 2011-2020. Energy-related efficiency is greater in areas with a higher response to SDG7. (2) The impact of energy poverty on improving energy-related efficiency is significantly negative. Addressing energy poverty significantly improves energy-related efficiency in regions characterized by a high energy poverty index and low energy-related efficiency. However, it bears no impact in regions with a low energy poverty index and high energy-related efficiency. (3) The moderating effect of technological innovation effectively improves energy-related efficiency, whereas the marketization level has an inverse effect. Consequently, this paper suggests several policy implications.

Adaptive super-twisted sliding mode trajectory tracking control for quadrotor UAV with prescribed performance based on finite-time disturbance observer

This study proposes a finite-time disturbed observer-based prescribed performance adaptive super-twisted sliding mode trajectory tracking control scheme for quadrotor UAV. Based on the super-twisted sliding mode algorithm, adaptive control, finite-time theory, and prescribed performance control, this scheme aims to achieve stable tracking of a quadrotor UAV along a defined trajectory. This is achieved even when faced with challenges such as modeling uncertainty, external uncertainty, and complex interference environments. Firstly, the coupling between the channels and the disturbance effects are unified as the aggregate disturbance. To address the issue of the adverse impact of aggregate disturbance on system performance, a new finite-time disturbance observer is developed to estimate the overall disturbance. The observer can effectively eliminate the negative effects of the aggregate disturbance without considering the disturbed boundary conditions during the design process. Secondly, the disturbance estimation information and the adaptive super-twisted algorithm are used to construct the adaptive super-twisted sliding mode controllers for the position and attitude loops, respectively. Then, a prescribed performance control is designed to predefined the time for the position loop to effectively suppress the deleterious effects of interference. This ensures that the position system can quickly meet the prescribed tracking requirements. Finally, the stability of the designed trajectory tracking control system is demonstrated using the Lyapunov theorem. Numerical simulation results verify the effectiveness and superiority of the proposed controller.

A Dispatch Strategy for the Analysis of the Technical, Economic, and Environmental Performance of a Hybrid Renewable Energy System

A Dispatch Strategy for the Analysis of the Technical, Economic, and Environmental Performance of a Hybrid Renewable Energy System

Long-term outcomes of renal AL amyloidosis patients undergoing autologous stem cell transplantation: Validating the performance of the renal staging system.

Renal AL amyloidosis can be complicated by end-stage renal disease (ESRD) requiring renal replacement therapy (RRT). In this study, we describe the long-term outcomes of renal AL amyloidosis patients undergoing autologous stem cell transplantation (ASCT) and assess the utility of the renal staging system. Retrospective study of renal AL patients (n = 697; Mayo Clinic, Boston University) who underwent ASCT between 2003 and 2020. Renal stage was assigned based on 24-h proteinuria and estimated glomerular filtration rate measurements. Renal survival was defined as the time from ASCT until initiation of RRT, while patients who were not placed on RRT were censored at their last follow-up. With a median follow-up of 10.4 years, RRT was required in 149 patients (21%). The median time from ASCT to ESRD was 3.4 years, with late events of progression to ESRD seen >10 years from ASCT. Pre-ASCT renal stage was significantly associated with the cumulative incidence of RRT: 3-year RRT rate was 3%, 10%, and 37% for renal stages I, II, and III, respectively. However, in the 2012-2020 period subset, a significant decrease in ESRD risk was noted across all renal stages (3-year RRT 0%, 5%, and 24%, respectively). In multivariate analysis, renal survival was independently associated with the pre-ASCT renal stage, lambda isotype, bone marrow plasmacytosis ≥20%, post-ASCT hematologic response, and year of ASCT. Long-term outcomes of renal AL amyloidosis treated with ASCT are presented. Renal stage reliably predicts renal outcomes in patients with AL undergoing ASCT, despite a reduction in the proportion of patients progressing to RRT in recent years.

A linear compensation method for inference accuracy improvement of memristive in-memory computing.

Memristive computing system (MCS), with the feature of in-memory computing capability, for artificial neural networks (ANNs) deployment showing low power and massive parallelism, is a promising alternative for traditional Von-Neumann architecture computing system. However, because of the various non-idealities of both peripheral circuits and memristor array, the performance of the practical MCS tends to be significantly reduced. In this work, a linear compensation method (LCM) is proposed for the performance improvement of MCS under the effect of non-idealities. By considering the effects of various non-ideal states in the MCS as a whole, the output error of the MCS under different conditions is investigated. Then, a mathematic model for the output error is established based on the experimental data. Furthermore, the MCS is researched at the physical circuit level as well, in order to analyze the specific way in which the non-idealities affect the output current. Finally, based on the established mathematical model, the LCM output current is compensated in real time to improve the system performance. the effectiveness of LCM is verified and showing outstanding performance in the ResNet-34 network architecture, which is easily affected by the non-idealities in hardware. The proposed LCM can be naturally integrated into the operation processes of MCS, paving the way for optimizing the deployment on generic ANN hardware based on the memristor.&#xD.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products' biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method's reliability, security, and efficiency. The experimental results show that this research scheme's initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7tx/s. The data query throughput is 81.8tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

Novel design and performance analysis of a selectively filtered nanofluid spectrum semi-trough solar concentrated photovoltaic/thermal system

ABSTRACT In this paper, the main novelty is the proposal and performance analysis of a novel selectively filtered nanofluid spectrum semi-trough solar concentrated photovoltaic-thermal (SPVT) system. Experimental preparation and optical tests of Ag-CoSO4/water nanofluid are conducted. The selected nanofluid for the SPVT system has an average absorptance of 53.5% and an average transmittance of 46.5% in 250.0 ~ 2500.0 nm. The optical analysis results show that the optical efficiency of the SPVT system is 94.3% under the optical filtering condition. When the solar tracking error increases to 0.3°, the optical efficiency of the SPVT system can still be kept at 84.8%. This means that the SPVT system has relatively good adaptability on solar tracking error. The operation performance analysis results indicate that under the optical filtering condition, the electric power and photo-electric efficiency of the PV subsystem are 392.1 W and 30.9%, and the thermal efficiency of the SPVT system is 40.7%. Effects of four factors on the thermal performance of the SPVT system are evaluated, which are the inlet nanofluid flow velocity and temperature, environmental temperature and convection heat transfer coefficient between the nanofluid flow channel and air. The results show that the thermal performance of the SPVT system can be improved by properly increasing the inlet nanofluid flow velocity and environmental temperature or by reducing the inlet nanofluid temperature and convection heat transfer coefficient between the nanofluid flow channel and air. When the inlet nanofluid flow velocity increases from 0.002 m/s to 0.004 m/s, the thermal efficiency of the SPVT system increases from 40.9% to 50.7%, and with the inlet nanofluid temperature increased from 6.0°C to 14.0°C, the thermal efficiency decreases from 48.6% to 45.6%. The results of this paper can provide certain reference for research and development works of solar PVT systems in the future.

Proposal and thermo-economic analysis of a comprehensive framework for energy recovery and emission reduction in natural gas depressurization stations

One of the largest urban natural gas pressure reducing stations (CGS) with a nominal capacity of 300,000 (Nm3/h) in Iran has been investigated for the generation of electricity using the energy recovered in the gas depressurization process. Currently, natural gas pressure is reduced by using the regulating valve; consequently, the energy is wasted. To harness this wasted energy, an energy recovery plan was proposed that utilizes a turbo-expander. Comprehensive analytical environmental and thermo-economic analysis of the proposal was performed. Effects of various parameters, including turbo-expander and preheater efficiencies, electricity and natural gas price, nominal power of expansion turbine, turbine price and the temperature of gas leaving the expansion turbine was fully investigated on the system performance. The findings illustrated that 14.73 (GWh) of electricity can be produced annually using the recovered wasted energy in the studied station. The investment payback period was calculated as 4 years. The generalization of the results to all Iran’s CGS stations showed that 3.24 (TWh) of electrical energy and an average of 370 (MW) of electric power are possible to produce each year. Also, by using the proposed plan, 640,000 tons of CO2 emissions can be avoided annually.

Real-Time Monitoring of Human and Process Performance Parameters in Collaborative Assembly Systems using Multivariate Control Charts

AbstractWith the rise in customized product demands, the production of small batches with a wide variety of products is becoming more common. A high degree of flexibility is required from operators to manage changes in volumes and products, which has led to the use of Human-Robot Collaboration (HRC) systems for custom manufacturing. However, this variety introduces complexity that affects production time, cost, and quality. To address this issue, multivariate control charts are used as diagnostic tools to evaluate the stability of several parameters related to both product/process and human well-being in HRC systems. These key parameters monitored include assembly time, quality control time, total defects, and operator stress, providing a more holistic view of system performance. Real-time monitoring of process performance along with human-related factors, which is rarely considered in statistical process control, provides comprehensive stability control over all customized product variants produced in the HRC system. The proposed approach includes defining the parameters to be monitored, constructing control charts, collecting data after product variant assembly, and verifying that the set of parameters is under control via control charts. This increases the system's responsiveness to both process inefficiencies and human well-being. The procedure can be automated by embedding control chart routines in the software of the HRC system or its digital twin, without adding additional tasks to the operator's workload. Its practicality and effectiveness are evidenced in custom electronic board assembly, highlighting its role in optimizing HRC system performance.

Blade redesign based on inverse design method for energy performance improvement and hydro-induced vibration suppression of a multi-stage centrifugal pump

As the urgency of the global energy crisis escalates and environmental protection standards become more stringent, the industrial sector is placing increased emphasis on enhancing energy efficiency and advancing vibration control technologies. Within this context, multi-stage centrifugal pumps (MSCPs), pivotal in energy systems, play a crucial role in influencing both system performance and environmental impact. This paper embarked on employing an inverse design method (IDM) to innovate the redesign of MSCP blades, introducing a steady-state index for characterizing the intensity of unsteady pressure fluctuations, thereby streamlining optimization costs. Optimization techniques utilizing an artificial neural network model and a multi-objective evolutionary algorithm were employed to achieve an optimal balance between maximizing energy efficiency and reducing hydro-induced vibrations. The optimized MSCP enhanced not only boosting performance but also a 2.75 % increase in weighted efficiency, significantly widening the high-efficiency operation zone. The improvement in vibration characteristics was manifested by an average reduction of more than 25 % in the amplitude of the primary frequency of pressure pulsations within the double volute, with a maximum reduction reaching 50.9 %. Furthermore, the research delved into the analysis of the internal energy dissipation mechanisms of MSCPs, drawing upon the entropy production theory and the enstrophy concept. This analysis reveals that energy dissipation in the mainstream zones is predominantly affected by shear enstrophy, whereas, in the wall regions, it is primarily governed by fluid velocity. The comprehensive performance enhancement of the optimized model is significantly credited to the refinement of the blade profile.