Core Equipment Research Articles

Study on the cantilever ratio optimization of high-temperature molten salt pump for molten salt reactor based on structural integrity

The high-temperature molten salt pump is the core equipment in the small modular molten salt reactor with media temperatures up to 700 °C. The cantilever ratio of the molten salt pump is usually large. Excessively large cantilever ratios cause increased deformations and rotational amplitudes at the impeller, thus affecting the operational stability of the main pump; small cantilever ratios cause heavy temperature gradients, thus affecting the structural integrity evaluation. This paper used numerical simulation methods to calculate and analyze the temperature field, stress, and structural integrity, optimized the pump shaft cantilever length of the original scheme based on structural integrity using the dichotomy method, and analyzed the rotor dynamics of the optimization results. The results of this study show that the thermal expansion load caused by the temperature difference has a significant mechanical effect on the structure; the first-order critical speed of the rotor system of the optimized schemes has been improved, and the amplitude of the unbalanced response has been significantly reduced, which not only improves the operational stability of the rotor, also contributes to the compact design of the main pump of a small modular molten salt reactor.

The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data

As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Structural optimization design of connection between centrifugal impeller and shaft end based on finite element method

A centrifugal compressor is the core equipment of the chemical industry. To solve the problems of assembly difficulties and poor mechanical properties caused by the connection between impeller and shaft end in existing compressor, this paper takes a small geared air compressor designed by Shenyang Borui Turbine Technical Service Co., Ltd. as an example and designs its first-stage impeller and shaft end into three structural forms of tie rod–pin connection, tie rod–end key connection, and flange connection, respectively, to find an optimal connection mode between the centrifugal impeller and shaft end. Firstly, the calculation method is verified. Then, the stress, strain, deformation, and critical rotor speed of these three structures are compared and analyzed based on multi-parameter genetic optimization method. The results show that when the impeller and shaft end are connected by the tie rod–end key structure, compared to the tie rod–pin connection structure and flange connection structure, the maximum stress is reduced by 43.8% and 61.2%, the strain is reduced by 43.8% and 61.8%, the deformation is reduced by 3.64 times and 6.27 times, respectively, and the resonance margin will also be greatly improved. The research results in this paper provide a theoretical basis for the reliable design of the connection between the centrifugal impeller and shaft end.

A general energy modeling network for serial industrial robots integrating physical mechanism priors

Industrial robots (IRs), as the core equipment of intelligent manufacturing, play increasingly important roles in various industrial scenarios such as assembly, welding, handling, and spraying, significantly improving production efficiency and product quality. The massive popularization and application of IRs have brought about a sharp increase in energy consumption (EC), and the modeling and optimization of EC is becoming imperative. In this paper, a general energy modeling network DM-PLM for serial IRs based on Dynamic Model (DM) and Power Loss Model (PLM) is proposed by integrating the prior knowledge of IR power composition and dynamic mechanism, enabling efficient and accurate modeling of dynamics, power, and EC under multi-load conditions. Considering the force transmission characteristics of serial robots, this paper proposes an improved bidirectional recurrent neural network (BiRNN) to model the joint dynamics. Additionally, a power loss model based on the ResNet convolutional neural network is employed. Experiments are carried out with a KUKA KR210 heavy-duty robot and a UR5 collaborative robot. The results show that the DM-PLM model incorporating the physical mechanism priors achieves 97 %, 98 %, and 99 % modeling accuracy in joint torques, total power, and EC for both robots under multi-load conditions. In addition, the proposed DM-PLM model is applied to the EC optimization of KUKA KR210 through trajectory planning, which achieves over 30 % EC reduction with the genetic algorithm, providing an effective approach to improving the energy efficiency of serial IRs.

Thermoelectric performance analysis of the novel direct-expansion photovoltaic thermal heat pump/power heat pipe compound cycle system in summer

Photovoltaic thermal heat pump technology has outstanding cogeneration ability by utilizing renewable energy to be accepted as a decarbonization pathway in the residential sector. However, existing photovoltaic thermal heat pumps consume higher energy under better external heat source conditions. To address this problem, a novel direct-expansion photovoltaic thermal heat pump/power heat pipe compound cycle system for heating and power generation as well as refrigeration, whose core equipment was a photovoltaic thermal module with straggled honeycomb fluid channel pattern on the serpentine arrangement, was proposed, manufactured, and studied with the method of experiment. The thermoelectric performance was compared between the heat pump and power heat pipe cycle modes, investigated under typical weather conditions, and discussed in contrast to the literature. The results showed that the average photoelectric conversion efficiency and coefficient of photothermal performance of the PVT modules, as well as COP of the novel system were 10.6%, 51.0%, and 6.19, respectively, at the average ambient temperature of 27.0 °C, average solar radiation intensity of 581 W/m2 and water temperature of 18.3–55.6 °C. And daily average COPs of the novel system under sunny, cloudy, and overcast conditions were 15.12, 7.40, and 3.17, respectively. The conclusion drawn from the experimental results is that the COP of the novel system was 10.0%–60.0% higher than that in the literature under similar outdoor conditions. The payback period of the additional cost of the novel system compared with the existing direct-expansion photovoltaic thermal heat pump was within 2.5 years. The novel system represents a practical response to the need to achieve total decarbonization of the residential sector.

Numerical simulation of the optimization for the inlet structure of the furnace in the Phosphogypsum reduction process by sulfur vapor

Amidst the backdrop of carbon peaking and the pursuit of carbon neutrality targets, the co-production of sulfuric acid and cement from phosphogypsum through sulfur reduction in a dilute phase suspended gas system has emerged as the focal point in recent research, aiming to harness the high-value potential of solid waste phosphogypsum. This paper employs a combination of numerical simulation and pilot testing to optimize the material inlet structure of the core equipment, the reduction furnace, ensuring it aligns with the requisite phosphogypsum decomposition rate in practical production settings. Utilizing the Euler-Lagrange method, the study examines gas-solid two-phase flow and chemical reactions within the reduction furnace across various inlet structures. Coupling this with the Box-Behnken Design (BBD) in Response Surface Methodology (RSM) highlighted pivotal factors influencing calcium sulfate decomposition, ranking them as B > A > AC > AB>C > BC. Ultimately, the regression equation estimation forecasts a composite of material inlet structural parameters—A (Material Inlet Height) = 2352.12 mm, B (Material Inlet Diameter) = 315.89 mm, and C (Material Inlet Angle) = 30.59°—corresponding to a 23% calcium sulfate decomposition rate, exhibiting merely a 4.62% deviation from the numerical simulation's calculated rate of 23.98% under similar conditions.

Analysis of vortex characteristics in hump region of reversible pump-turbine based on omega vortex identification method

As the core equipment of pumped storage power stations, reversible pump-turbines will frequently switch between different working modes during operation, and it is easy to appear hump region under pump condition, which will greatly affect the performance of the pumped storage unit. Therefore, in order to explore the causes of the hump region, this paper takes the model reversible pump-turbine as the research object. First, the unit speed–unit flow characteristics are compared with the model test results under different working conditions. Then, based on the omega vortex identification method, the vortex distribution in the flow channel of the unit is analyzed. By analyzing the flow characteristics of the flow components of the unit under different flow rates in the hump region, the internal flow law of the hump region is revealed. It is found that when the flow rate decreases, the rising head cannot offset the head lost by the hydraulic loss, thus forming the hump region, and the unstable flow gradually appears in the guide vane and runner area. There are unstable phenomena, such as flow separation, in the guide vane area. There is a sudden change of radial pressure in the vaneless area between the guide vane and the runner; that is, the unstable vortex almost occupies the whole flow channel due to the change of pressure in this area, resulting in the deterioration of the instability of the unit operation. When the flow rate is large, the radial pressure mutation zone mainly exists between the guide vane cascades, making it easy to form an unstable vortex. The research results can provide a theoretical reference for improving the stability of reversible pump-turbines.

Development of SyntheticTest System for Thyristor Voltage Monitoring of Converter Valve

Thyristor voltage monitoring is one core equipment for a light-controlled converter valve, and its performance directly affects the reliability, stability, and efficiency of the whole power system. However, the field test equipment for thyristor voltage monitoring is limited by import, which is disadvantageous to the operation and maintenance of the converter valve. Thus, developing a synthetic test system for thyristor voltage monitoring is particularly urgent. The synthetic test system for thyristor voltage monitoring is developed based on the operational principle of thyristor voltage monitoring and the technical standard IEC 60700-1, which is subsequently applied to the thyristor valve test. The simulation and test demonstrate this synthetic test system’s reasonability, meeting the need for engineering applications.

Methodology, application and platform for urban security planning and management

With the acceleration of economic development and urban construction, urban security accidents have occurred around the world with alarming frequency, causing serious casualties and economic losses. Urban security planning and management as emerging areas of research have drawn widespread attention. For city development plans, urban security planning and management have become one of major topics. This paper first outlines the principles of urban security planning and management, combined with the construction of a digital and intelligent platform for urban emergency management. This research then analyzes the core technology and equipment support system of urban security management and its practical application. It also presents a new model based on urban security planning and management, followed by examples of its application in some mega infrastructure development for security planning and design (for example, Singapore Changi Airport and Shanghai Hongqiao Airport Transportation Hub). Additionally, a blast protection concept of urban security planning and management is provided.

Numerical theory and method on the modal behavior of a pump-turbine rotor system considering gyro-effect and added mass effect

The pump-turbine unit is the core equipment of pumped storage power station, and the rotor system is the key component of the pump-turbine unit. In this paper, the numerical method considering the gyro-effect and added mass effect is proposed, the effect of the added mass, gyro-effect and bearing stiffness are discussed, respectively, and a “transfer” phenomenon of the mode shape is found. A coupling influence coefficient is proposed to analyze the coupling influence of gyro-effect and added mass effect on the rotor system mode, and the nodal diameter (ND) mode of runner is discussed. For the forward whirling ND mode, the coupling influence coefficient increases with the increase of the rotational speed, while for the reverse whirling ND mode, the coupling influence coefficient decreases with the increase of the rotational speed. The results in this paper can provide a theoretical basis for revealing the complex dynamic behaviors of the rotor system, and provide scientific guidance for the research and development, design, and safe and stable operation of pump-turbine units.

Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models

In the process of coal mining, one of the main reasons for the high labor intensity of workers and the frequent occurrence of casualties is the low level of intelligence of coal mining equipment. As the core equipment in the process of coal mining, the intelligence level of shearers directly affects the safety production and mining efficiency of coal mines. Coal and rock recognition technology is the core technology used to realize the intelligentization of shearers, which is an urgent technical problem to be solved in the field of coal mining. In this paper, coal seam images, rock stratum images, and coal–rock mixed-layer images of a coal mining area are taken as the research object, and key technologies such as the construction of a sample image library, classification and recognition, and semantic segmentation are studied by using the relevant theoretical knowledge of artificial neural network models. Firstly, the BP neural network is used to classify and identify coal seam images, rock stratum images, and coal–rock mixed-layer images, so as to distinguish which of the current mining targets of a shearer is the coal seam, rock stratum, or coal–rock mixed layer. Because different mining objectives will lead to different working modes of a shearer, it is necessary to maintain normal power to cut coal when encountering a coal seam, to stop working when encountering rock stratum, and to cut coal along the boundary between a coal seam and rock stratum when encountering a coal–rock mixed stratum. Secondly, the DeepLabv3+ model is used to perform semantic segmentation experiments on the coal–rock mixed-layer images. The purpose is to find out the distribution of coal and rocks in the coal–rock mixed layer in the coal mining area, so as to provide technical support for the automatic adjustment height of the shearer. Finally, the research in this paper achieved a 97.16% recognition rate in the classification and recognition experiment of the coal seam images, rock stratum images, and coal–rock mixed-layer images and a 91.2% accuracy in the semantic segmentation experiment of the coal–rock mixed-layer images. The research results of the two experiments provide key technical support for improving the intelligence level of shearers.

Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network

The hydro-turbine is the core equipment of the hydropower station, and it is essential to diagnose and identify its faults. A fault diagnosis model based on Bayesian optimization (BO), which incorporates convolutional neural network (CNN) and long short-term memory (LSTM) methods for the hydro-turbine, is proposed (BO–CNN-LSTM). CNN adaptively extracts and down-scales fault features, fed into the LSTM model for feature learning and training. The BO algorithm is employed to address the challenge of model hyperparameter selection. A hydro-turbine fault experiment bench is constructed to train and validate the model. Experimental results demonstrate the superior performance of the proposed BO-CNN-LSTM model in hydro-turbine fault diagnosis, achieving accuracies of 92.7 %, 98.4 %, and 90.4 %, respectively, surpassing CNN, LSTM, and CNN-LSTM models. The BO-CNN-LSTM model improves accuracy by 5.5 %, 6.3 %, and 9.0 %, respectively, Compared to the unoptimized CNN-LSTM model. The BO algorithm is introduced to optimize CNN-LSTM from the perspective of acoustic vibration signals, which can be a beneficial supplement to the existing hydro-turbine fault diagnosis.

Low-Carbon Economic Dispatch of Virtual Power Plant Considering Hydrogen Energy Storage and Tiered Carbon Trading in Multiple Scenarios

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Analysis and Testing of Variable Height Operating Characteristics of Super-Pressure Balloon Airbag Fan

A fan is part of the core equipment of a super-pressure balloon altitude control system, and high-performance fans have a significant impact on the altitude control capability and flight safety of super-pressure balloons. This paper proposes a mixed-flow MIX-140 fan for use with super-pressure balloons. Changes in the fan’s operating characteristics at various flight altitudes of a super-pressure balloon were investigated. First, the performance of the fan at ground level was obtained through numerical simulation and compared with measured data of the prototype to verify the accuracy of the simulation analysis. On this basis, the influences of changes in the atmospheric pressure, temperature, and fan speed on fan performance were investigated through numerical simulation. Furthermore, the MIX-140 fan was compared with an existing fan, and the variation of two parameters, namely, the ratio of inflation volume per unit time and the ratio of inflation volume per unit power, were investigated at different altitudes. Finally, the changes in the operating characteristics of the fan under different high-altitude environments were investigated through actual testing. The results reveal that changes in altitude can lead to significant changes in fan performance, and changes in the atmospheric pressure, temperature, and fan speed affect the fan’s working characteristics. Compared with the existing fan, the MIX-140 fan achieves an average increase of 295.8% in the inflation volume per unit of time, and 14.6% in the inflation volume per unit of power at altitudes of 16–20 km. The performance variation characteristics and testing methods of this proposed super-pressure balloon fan can provide a foundation and reference for the design of a super-pressure balloon control system.

Principles, development, and utilization of silicon-based solar cell

Photovoltaic technology is a technology that uses the photoelectric conversion properties of semiconductor materials to convert solar energy into electricity. Photovoltaic technology is a kind of renewable energy technology that does not produce pollution and greenhouse gas emissions and has many application prospects. According to data, from 1985 to 2017, the cumulative total installed photovoltaic capacity globally exceeded 400 GW and is expected to grow in the next few years. Photovoltaic technology has become an essential part of renewable energy worldwide. Photovoltaic cells are the core equipment of photovoltaic technology. There are mainly monocrystalline silicon, polysilicon, amorphous silicon, organic solar cells, and other types. Among them, monocrystalline silicon photovoltaic cells have high photoelectric conversion efficiency, but high cost, mainly used in high-end applications; Polysilicon photovoltaic cells are cost-effective and used primarily in large-scale photovoltaic power station construction; Photovoltaic technology can also be used in mobile chargers, solar streetlights, solar pumps, and other aspects. In addition, photovoltaic technology also has a wide range of application prospects in urban rail transit, automobiles, robots, and other fields also appear. Although photovoltaic technology has many advantages, there are still some problems, such as panel cost, reliability, stability, and storage of photovoltaic power generation. Solving these problems requires continuous upgrading and development of technology. With the progress of technology and the support of policies, it is believed that photovoltaic technology will continue to develop and make more significant contributions to global energy transformation and sustainable development.

Effects of Different Die Metals on the Performance and Friction and Wear of Composite Materials during the Extrusion Process.

Extrusion technology is widely utilized in the rubber processing industry, with the extruder serving as the core equipment. As mixed rubber enters the extruder, it undergoes conveyance and plasticization, ultimately forming specific shapes and dimensions upon extrusion. The extruder head is a crucial component, playing a key role in achieving the final product's required size and shape. Factors such as its structure, materials, and manufacturing processes significantly impact the efficiency, product quality, and sustainability of the extrusion process. However, prolonged operation leads to severe wear of the extruder head, adversely affecting rubber product quality. Additionally, extruder head processing poses challenges, with maintenance and repair being complex procedures. Therefore, exploring a wear-resistant, long-lasting metal material for the extruder head without compromising mixed rubber performance is essential. This study focuses on severely worn extruder head metal materials, comparing wear levels after friction with STELLITE 6 alloy, Hastelloy C-276 alloy, 38CrMoAlA, and tungsten carbide with composite rubber. Results show that compared to the NR/BR composite material after Hastelloy C-276 alloy friction, rubber Payne effect increased by 4.4% (38CrMoAl), 3.2% (STELLITE 6), and 4.6% (tungsten carbide). Similarly, rubber dispersion decreased by 9.4% (38CrMoAl), 4.7% (STELLITE 6), and 9.8% (tungsten carbide). Rolling resistance increased by 18.1% (38CrMoAl), 16% (STELLITE 6), and 23.4% (tungsten carbide). Friction coefficient increased by 3.5% (38CrMoAl), 2.8% (STELLITE 6), and 4.3% (tungsten carbide). Wear volume increased by 39.3% (38CrMoAl), 45.3% (STELLITE 6), and 48.9% (tungsten carbide). Specifically, using Hastelloy C-276 alloy as the extruder head metal material yields the best NR/BR composite material dispersion, highest ten times tear strength, excellent anti-wet skid resistance, and minimum rolling resistance. Conversely, using the other alloys results in varying reductions in the physical and mechanical properties of NR/BR composite materials. This research is crucial for improving rubber product quality and extending extruder head lifespan.

Fault diagnosis for multiple current sensors in grid‐connected inverter based on average modulation voltage

AbstractGrid‐connected inverters are the core equipment in the renewable power system. There are multiple current sensors which may affect the driving module of the switch in inverter. In multiple current sensor fault diagnosis, the coupling between fault components makes fault diagnosis difficult. This paper presents an offset fault diagnosis method of multiple current sensors based on the average modulation voltage model. Based on the influence of current sensor offset fault in the system, the average modulation voltage model is established in three‐phase stationary coordinates. The difference between the measured value of the current and the actual value is estimated through the model when the offset fault occurs. And then the fault is located. Experimental results show that the fault can be located accurately and fault tolerant control can be performed by this method when there are offset faults in multiple sensors simultaneously.

Modeling and Reliability Constraint Analysis of MMC Valve

The VSC-converter valve is the core equipment of the VSC-HVDC transmission system, and its reliability directly affects the availability of the VSC-HVDC system. The reliability model of the existing VSC-converter valve mainly considers the relationship between different redundancy and the reliability of the VSC-converter valve. This paper first analyzes the impact of the failure of key components of the VSC-converter valve. According to the different effects, the arm reliability model of the VSC-converter valve is established, and finally, the comprehensive reliability model of the VSC-converter valve is established. Then, based on the model of the VSC-converter valve, the reliability constraints of the VSC-converter valve that meet the engineering requirements are finally derived. An example is given to verify the correctness of the classification reliability evaluation. Finally, according to the reliability model proposed in this paper, the design recommendations for a highly reliable VSC converter valve are given.

Converter valve state evaluation method based on hierarchical graded fuzzy theory

As the core equipment of the DC power transmission project, the operation status of the converter valve is the key to the safety and stability of the DC system. Existing assessment methods cannot accurately assess the health status of the converter valve in project operation. According to the independence of the function and structure of each component of the converter valve, this paper proposes a converter valve state evaluation method based on hierarchical grading fuzzy theory. The data used are various information characterizing the state of the converter valve, such as voltage, current, sound, image, and other data. According to hierarchical grading, the evaluation index system is constructed. Based on the fuzzy theory, the affiliation function is proposed, and the affiliation probability of four hierarchical states is output for the state evaluation results of the converter valve and its group components. Experiments show that the method improves the accuracy and practicability of converter valve evaluation in engineering sites.