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With the technology of enhanced oil recovery by supercritical CO2 flooding (scCO2-EOR) popularized and applied, the water-in-oil (W/O) emulsion can be easily formed in the surface gathering pipeline when the produced crude oil and water flows through the pump and throttle valve. The formation of emulsion increases the resistance and cost of pipeline transportation tremendously. Considering the detrimental effect of emulsion formation, we focus on the effect of CO2 treatment on rheological properties and stability of emulsion within the pipeline. It is hoped to provide some guidance for the increase in the efficiency of pipeline transportation and emulsion treatment. In this work, a device was developed to simulate the scCO2 treatment conditions in the reservoir. The variations in gelation process, pour point, yield stress, viscosity-temperature property and coalescence stability of emulsion were studied experimentally under treatment conditions (without any treatment, treated by 80 ℃, 5 MPa, treated by 80 ℃, 15 MPa, treated by 80 ℃, 25 MPa). The effect of scCO2 treatment on gelation property of emulsion was reported for the first time. The results show that the storage modulus and gelation temperature are increased obviously after the scCO2 treatment, which is owing to the influence of scCO2 treatment on wax crystals morphology and gelation structure of system. As for the pour point, yield stress, and viscosity/apparent viscosity, they are all enlarged greatly after scCO2 treatment, due to the scCO2 extraction effect. Finally, the sizes of emulsion droplets gradually decrease with the treatment pressure, leading to the improvement of emulsion stability.

Abstract Strain gauge plays vital roles in various fields as structural health monitoring, aerospace engineering, and civil infrastructure. However, traditional flexible strain gauge inevitably brings the pseudo-signal caused by the substrate temperature effect and determines its accuracy. Here, we present an anisotropic composite substrate designed to modify the thermal expansion performance via Micro-electro-mechanical System (MEMS) technology, which facilitates the development of strain gauges that are minimally affected by substrate temperature-induced effect. Compared to the isotropic flexible substrate, the simulated expansion displacement in the thermal insensitive direction is reduced by 53.6% via introducing an anisotropic thermal expansion structure. The developed strain gauge exhibits significantly reduced sensitivity to temperature-induced effect, with a temperature coefficient of resistance decreasing from 87.3% to 10%, along with a notable 47.1% improvement in TCR stability. In addition, the strain gauge displays a sensitivity of 1.99 and boasts a wide strain operational range of 0–6000 µε, while maintaining excellent linearity. Furthermore, stress response conducted on a model of an aircraft wing illustrates the rapid monitoring of the strain gauge, which can detect strain as low as 100 µε. This study strongly highlights the potential applicability of the developed strain gauge in the aircraft, ships, and bridges for monitoring stress.

Abstract Reductive amination by amine dehydrogenases is a green and sustainable process that produces only water as the by-product. In this study, a continuous flow process was designed utilizing a packed bed reactor filled with co-immobilized amine dehydrogenase wh84 and glucose dehydrogenase for the highly efficient biocatalytic synthesis of chiral amino alcohols. The immobilized amine dehydrogenase wh84 exhibited better thermo-, pH and solvent stability with high activity recovery. (S)-2-aminobutan-1-ol was produced in up to 99% conversion and 99% ee in the continuous flow processes, and the space-time yields were up to 124.5 g L-1 d-1. The continuous reactions were also extended to 48 h affording up to 91.8% average conversions. This study showcased the important potential to sustainable production of chiral amino alcohols in continuous flow processes.

Tunnel excavation in water-rich and saturated loess layers often encounters a series of engineering disasters, including surface settlement, large deformations of surrounding rock, collapses, water inrushes, mud inrushes, and lining cracks. This paper presents an analogy of 16 cases of instability and collapse of surrounding rock during the excavation of water-rich loess tunnels in China’s loess regions. The weight of influence of various factors affecting the stability of surrounding rocks has been analyzed based on the Fuzzy Analytic Hierarchy Process (FAHP), addressing the engineering challenges encountered during the construction of the Tuanjie Tunnel. Measures such as deep well-point dewatering of the surface, reinforcement of locking foot anchors, and construction treatment with large arch feet are proposed. The effectiveness of these treatments is then monitored and analyzed. The results show that after 30 days of dewatering, the average water content of the surrounding rock decreased from 28.8% to 22.3%, transforming the surrounding rock from a soft plastic state to a hard plastic state. Phenomena such as mud inrushes at the tunnel face and water seepage through the lining are significantly reduced, and the self-stabilizing capacity of the surrounding rock is markedly improved. By optimizing the excavation method and enhancing support parameters, the construction progress rate for Grade VI surrounding rock has increased from 10–15 m per month to 40 m per month, validating the effectiveness of the proposed measures.

Low-carbon design has become increasingly important in the cement and concrete industry. Previous studies have primarily focused on the impact of different types of admixtures on the carbon emissions of concrete while overlooking the influence of the water-to-cementitious materials ratio on concrete carbon emissions. To address this gap, this study aims to investigate the synergistic effects of the water-to-binder ratio and quartz powder dosage on concrete hydration, strength, and carbon emissions and to propose a comprehensive performance prediction model. Our proposed performance prediction model highlights the critical role of the water-to-cementitious materials ratio in low-carbon concrete design. It distinguishes between the dilution and nucleation effects of the quartz filler and evaluates the impact of quartz content (10% and 20%) and water-to-binder ratios (0.5 and 0.2) on the cement hydration rate; consequently, it is able to predict the concrete’s thermal, chemical, mechanical, and environmental properties. The model specifics are as follows: the parameters were determined using hydration heat data from a paste with a water-to-binder ratio of 0.5 over the first 3 days, and the chemically combined water and portlandite production was calculated up to 28 days. The water availability coefficient, derived from hydration product measurements with a ratio of 0.2, shows that lower water-to-binder ratios slow hydration as the coefficient exceeds 1. A linear equation predicts strength development based on the mix ratio and reaction degree. The CO2 emission analysis shows that when the water/binder ratio is 0.50, with a compressive strength of 1 MPa, the carbon emissions change little with an increase in the quartz powder replacement rate. However, when the water/binder ratio is 0.2, the percentage reductions in CO2 emissions per unit strength are 11% and 20% for the 10% and 20% quartz powder replacement rates, respectively, compared with the specimen without quartz powder. The model’s regression method is simple, versatile across mix ratios, and capable of predicting hydration heat, product composition, strength, and CO2 emissions.

Innovation in key low-carbon technologies plays a supporting role in achieving a high-quality low-carbon transition in the power sector. This paper aims to integrate research on the power transition pathway under the “dual carbon” goals with key technological innovation layouts. First, it deeply analyzes the development trends of three key low-carbon technologies in the power sector—new energy storage, CCUS, and hydrogen energy—and establishes a quantitative model for their technological support in the low-carbon transition of the power sector. On this basis, the objective function and constraints of traditional power planning models are improved to create an integrated optimization model for the power transition pathway and key low-carbon technologies. Finally, a simulation analysis is conducted using China’s power industry “dual carbon” pathway as a case study. The optimization results include the power generation capacity structure, power generation mix, carbon reduction pathway, and key low-carbon technology development path for China from 2020 to 2060. Additionally, the impact of uncertainties in breakthroughs in new energy storage, CCUS, and hydrogen technologies on the power “dual carbon” pathway is analyzed, providing technological and decision-making support for the low-carbon transition of the power sector.

This paper illustrates a new bi-objective optimization model of real-time automatic generation control dispatch in a performance-based frequency regulation market. It attempts to simultaneously minimize the total power deviation and the regulation mileage payment via optimally distributing the real-time total generation command to different regulation units. To handle this problem, an efficient non-dominated sorting genetic algorithm II is adopted to rapidly obtain a high-quality Pareto front for real-time AGC dispatch. A dynamic ideal point based decision making technique is designed to select the best compromise solution from the obtained Pareto front according to the minimization of the total regulation variation, which can effectively avoid an excessive regulation variation for each unit. Finally, two testing systems are used to verify the performance of the proposed technique.

The inherent passive impedance characteristics of soft robotic arms provide excellent environmental adaptability. When a soft robotic arm interacts with its surroundings, its passive impedance responds swiftly, preventing rigid collisions that could damage the arm and ensuring high safety. However, during the movement of the soft robotic arm, these passive impedance properties are uncontrollable, making it impossible to achieve precise impedance control in constrained environments by relying solely on passive mechanisms. Therefore, this paper integrated active impedance control with the passive impedance characteristics of soft robotic arms, proposing an active–passive composite impedance controller. Additionally, a position-based impedance controller was designed for comparative analysis. Finally, this article developed both control systems and conducted simulations and experiments, demonstrating that the composite active–passive impedance controller offers superior control performance and environmental adaptability.

The gallated catechin content in Wuyi Rock Tea is higher in the summer, leading to a more bitter and less desirable taste. Here, tannase, tyrosinase, and laccase were used to treat summer Rougui Wuyi Rock Tea extracts. The effects of single and combined enzymes on the taste of the tea extracts and their mechanisms of action were analyzed. Compared with the no-enzyme-treated sample, the sensory score results showed that tannase was the most effective, increasing the bitterness and astringency scores by 113.9% and 255.3%, respectively. Among the combined enzyme treatments, the samples treated with tannase and tyrosinase yielded the best sensory scores, with bitterness and astringency scores increasing by 141.2% and 289.0%, respectively. Data obtained using an electronic tongue confirmed the role of these enzymes, showing that, in addition to bitterness and astringency, enzyme treatment also influenced the bitterness aftertaste and astringency aftertaste, as well as its sourness and sweetness. Further product analysis revealed that tannase hydrolyzes the ester bonds on the gallacyl groups in gallated catechins, converting them to non-gallated catechins, while tyrosinase and laccase oxidize the phenolic hydroxyl groups on catechins to form o-quinone, leading to the production of theaflavins and improved tea quality. This study presents an effective approach to improving the quality of summer tea using biological enzymes.

The fracture network of the Y214 block in the Changning area of China is complex, and there are significant differences in the productivity of different shale gas wells. However, traditional machine learning models have problems such as missing key parameters, poor fitting effects and low prediction accuracy, which make it difficult to effectively evaluate the impact of crack network complexity on productivity. Therefore, the Pearson correlation coefficient was used to analyze the correlation between evaluation parameters, such as mineral content, horizontal stress difference, natural fractures and gas production. Combined with the improved particle swarm optimization (IPSO) algorithm and support vector machine (SVM) algorithm, a fracture network index (FNI) model was proposed to effectively evaluate the complexity of fracture networks, and the model was verified by comparing it with the performance evaluation results from the other two traditional models. Finally, the correlation between the fracture network index and the actual average daily gas production of different fracturing sections was calculated and analyzed. The results showed that the density of natural fractures was the key factor in controlling gas production (the Pearson correlation coefficient was 0.39), and the correlation between other factors was weak. In the process of fitting the actual data, the coefficient of determination, R², of the IPSO-SVM-FNI model training set increased by 8% and 24% compared with the two traditional models, and the fitting effect was greatly improved. In the prediction process based on actual data, the R² of the IPSO-SVM-FNI model test set was improved by 22% and 20% compared with the two traditional models, and the prediction accuracy was also significantly improved. The fracture index was concentrated, and its main distribution range was in the range of [0.2, 0.8]. The fracturing section with a higher FNI showed higher average daily gas production, and there was a significant positive correlation between fracture network complexity and gas production. Indeed, the research results provide some ideas and references for the evaluation of fracturing effects in shale reservoirs.

This paper proposes an Adaptive Robust Control (ARC) strategy for pump-controlled pitch systems in large wind turbines to address challenges in control accuracy and energy efficiency. First, a mathematical model integrating pitch angle dynamics and hydraulic characteristics is established, with pitch angle, pitch angular velocity, and hydraulic cylinder thrust as state variables. Then, an ARC strategy is designed using the backstepping method and incorporating parameter adaptation to handle system nonlinearities and uncertainties. The controller parameters are optimized using Particle Swarm Optimization (PSO) under wind disturbance conditions, and comparative analyses are conducted with traditional PID control. The numerical simulation results show that both controllers achieve similar tracking performance under nominal conditions, with PID achieving a 0.08° maximum error and ARC showing a 0.1° maximum error. However, the ARC strategy demonstrates superior robustness under parameter variations, maintaining tracking errors below 0.15°, while the PID error increases to 1.5°. Physical test bench experiments further validate these findings, with ARC showing significantly better performance during cylinder retraction with 0.1° error compared to PID’s 0.7° error. The proposed control strategy effectively handles both the inherent nonlinearities of the pump-controlled system and external disturbances, providing a practical solution for precise pitch control in large wind turbines while maintaining energy efficiency through the pump-controlled approach.

Insulators and conductors in transmission line systems are susceptible to wind-induced movements, especially when insulators are closely positioned to transmission towers, potentially leading to electrical discharge. To address this issue, this study proposes a spring-pendulum dynamic vibration absorber (SPDVA) for windage suppression in transmission line systems. Using a finite element model based on an actual transmission line project, the study investigates SPDVA’s effectiveness in mitigating windage responses under varying wind speeds. The analysis considers mass and stiffness parameters to elucidate SPDVA’s suppression mechanism. The results indicate that SPDVA outperforms the additional heavy hammer method in suppressing insulator windage responses and also affects the peak acceleration response at the insulator’s bottom. Increasing stiffness initially enhances SPDVA’s suppression effectiveness on both peak and mean square deviation windage angle responses, peaking at 10 kN/m stiffness, while increasing the mass of the mass block also enhances suppression effectiveness.

Magnetotelluric (MT) sounding is a geophysical technique widely utilized in mineral resource surveys, where conductivity and magnetic permeability serve as essential physical parameters for forward modeling and inversion. However, the effects of conductive anisotropy and non-zero magnetic susceptibility are usually ignored. In this study, we present a three-dimensional (3D) MT modeling algorithm using Coulomb-gauged electromagnetic potentials, incorporating a mixed nodal and edge-based finite element method capable of simulating MT responses for conductive anisotropic and magnetic anomalies. Subsequently, the algorithm’s accuracy was validated in two steps: first, it was compared with analytical solutions for a 1D magnetic model; then, a comparison was made with previously published numerical results for a 3D generalized conductive anisotropic model. The results of two tests show that the maximum relative error is below 0.5% for both models. Furthermore, representative models were computed to comprehensively analyze the responses of MT. The findings illustrate the relationship between anisotropic parameters and electric fields and emphasize the significance of considering the impact of magnetic susceptibility in magnetite-rich regions.

Subdomain adaptation plays a significant role in the field of bearing fault diagnosis. It effectively aligns the pertinent distributions across subdomains and addresses the frequent issue of lacking local category information in domain adaptation. Nonetheless, this approach overlooks the quantitative discrepancies in distribution between samples from the source and target domains, leading to the vanishing gradient issue during the training of models. To tackle this challenge, this paper proposes a bearing fault diagnosis method based on Wasserstein metric residual adversarial subdomain adaptation. The Wasserstein metric is introduced as the optimized objective function of the domain discriminator in RASAN-W. The distribution discrepancy between the source domain and target domain samples is quantitatively measured, achieving the alignment of the relevant subdomain distributions between the source domain and the target domain. Ultimately, extensive experiments conducted on two real-world datasets reveal that the diagnostic accuracy of this method is significantly enhanced when compared to various leading bearing fault diagnosis techniques.

The Bozi 3 reservoir is an ultra-deep condensate reservoir (−7800 m) with a high temperature (138.24 °C) and high pressure (104.78 MPa), leading to complex phase behaviors. Few PVT studies could be referred in the literature to meet such high temperature and pressure conditions. Furthermore, it is questionable regarding the applicability of existing condensate production techniques to such a high temperature and pressure reservoir. This study first characterized the phase behavior via PVT experiments and EOS tuning. The operating conditions were then optimized through reservoir numerical simulation. Results showed that: (1) the critical condensate temperature and pressure of Bozi 3 condensate gas were 326.24 °C and 43.83 MPa, respectively; (2) four gases (methane, recycled dry gas, carbon dioxide, and nitrogen) were analyzed, and methane was identified as the optimal injection gas; (3) gas injection started when the production began to fall and achieved higher recovery than gas injection started when the pressure fell below the dew-point pressure; (4) simultaneous injection of methane at both the upper and lower parts of the reservoir can effectively produce condensate oil over the entire block. This scheme achieved 8690.43 m3 more oil production and 2.75% higher recovery factor in comparison with depletion production.

This paper measures the coupling coordination development level between digital finance and green finance in Chinese provinces and investigates their carbon emission reduction effects. The study reveals that green digital finance has a significant carbon emission reduction effect, displaying an inverted “U” shape with a marginal effect that first increases and then decreases. There are substantial differences in the moderating effects of formal environmental regulation and informal environmental regulation. Furthermore, in the central and western regions, areas with higher financial regulatory intensity, and non-resource-based provinces, the carbon emission reduction effect of green digital finance is more pronounced.

Electrically conductive elastomer composites (CECs) with segregated networks of conductive nanofillers show high potential in stretchable strain sensors due to balanced mechanical and electrical properties, yet the sensitivity at low strain is generally insufficient for practical application. Herein, we report an easy and effective way to improve the resistive response to low strain for CECs with segregated network structure via adding stiff alumina into carbon nanostructures (CNS). The CEC containing 0.7 ​wt% CNS and 5 ​wt% Al2O3 almost sustains the same elasticity (elongation at break of ∼900%) and conductivity (0.8 ​S/m) as the control, while the piezoresistive sensitivity is significantly improved. Thermoplastic polyurethane (TPU) composites with a segregated network of hybrid nanofillers (CNS and Al2O3) show much higher strain sensitivity (Gauge factor, GF ​= ​566) at low strain (45% strain) due to a local stress concentration effect, this sensitivity is superior to that of TPU/CNS composites (GF ​= ​11). Such a local stress concentration effect depends on alumina content and its distribution at the TPU particle interface. In addition, CECs with hybrid fillers show better reproducibility in cyclic piezoresistive behavior testing than the control. This work offers an easy method for fabricating CECs with a segregated filler network offering stretchable strain sensors with a high strain sensitivity.

As the main inter-domain routing protocol in today’s internet, the Border Gateway Protocol (BGP) faces serious security risks during actual usage. Research on BGP malicious attack methods requires a realistic network environment, and evaluation methods based on physical networks often suffer from high costs and insufficient flexibility. Thus, we propose an efficient BGP simulated network deployment system based on a virtualization technology called the SOD–BGP. This system, combining cloud computing and virtualization technologies, creates a scalable, highly flexible basic network environment that allows for the automated simulation and evaluation of actual BGP prefix hijacking attack scenarios. A Resource Public Key Infrastructure (RPKI) simulation suite is introduced into the system, emulating a certificate issuance system, certificate storage, and a certificate synchronization verification mechanism, thus aligning the simulation environment with real-world usage scenarios. Finally, we propose a data collection and performance evaluation technique to evaluate BGP networks deploying RPKI under different attack scenarios and to explore the effectiveness of RPKI defense mechanisms at various deployment rates. A comparative analysis with other simulation techniques demonstrates that our approach achieves a balanced performance in terms of deployment speed, complexity, and RPKI integrity, providing a solid simulation technology foundation for large-scale BGP security defense strategies.

Vision-based weld seam extraction poses a significant challenge for weldments with complex spatial structures in automated welding. Existing research primarily focuses on identifying weld seams from weldments with given positions and postures, while practical weld path planning requires multiple weld seams identified within arbitrarily placed weldments. This paper proposes a methodology that identifies weld seams from arbitrarily placed spatial planar weldments in a single run. First, by introducing a turntable calibrated with respect to a 3D camera, we perform 3D reconstruction on an arbitrarily placed spatial planar weldment. Second, an improved RANSAC algorithm based on Euclidean clustering is proposed to carry out plane segmentation, focusing on segmentation accuracy. Finally, we present a novel weld seam extraction algorithm leveraging the half-edge data structure to efficiently compute weld seams from the segmented planes. The experiments conducted in this study demonstrate that the average segmentation errors (as an indirect indicator of weld seam extraction error) are reduced by 90.3% to 99.8% over conventional segmentation methods, and the standard deviations are reduced by 64.8% to 97.0%.

Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model’s robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications.

In the context of achieving carbon neutrality, the substantial integration of high proportions of renewable energy sources has significantly impacted the dynamic characteristics of power systems, including frequency stability, voltage security, and synchronous stability, thereby posing formidable challenges to the secure and stable operation of power systems [...]

The energy consumption of air-jet looms mainly comes from air compressors. Predicting the air consumption of air-jet looms for the upcoming period is significant for the variable frequency adjustment of air compressors, thereby aiding in energy saving and reducing fabric costs. This paper proposes an innovative method that combines Sliding Time Windows (STW), feature analysis, and incremental learning to improve the accuracy of short-term air consumption prediction. First, the STW method is employed during the data collection phase to enhance data reliability. Through feature analysis, significant factors affecting the air consumption of air-jet looms, beyond traditional research, are explored and incorporated into the prediction model. The experimental results indicate that the introduction of new features improved the model’s R2 from 0.905 to 0.950 and reduced the MSE from 32.369 to 16.239. The STW method applied to the same random forest model increased the R2 from 0.906 to 0.950 and decreased the MSE from 32.244 to 16.239. The decision tree method, compared to the linear regression model, improved the R2 from 0.928 to 0.950 and reduced the MSE from 23.541 to 16.239, demonstrating significant predictive performance enhancement. After establishing the optimal model, incremental learning is used to continuously improve the reliability and accuracy of short-term predictions. Experiments show that the incremental learning method, compared to static models, offers better resilience and reliability when new data are collected. The proposed method significantly improves the accuracy of air consumption prediction for air-jet looms, providing strong support for the variable frequency adjustment of air compressors, and contributes to the goals of energy saving and cost reduction. The research results indicate that this method not only enhances prediction accuracy but also provides new insights and methods for future energy-saving research.

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.

The backflow of high-temperature products in an engine’s combustion chamber is a key issue which can significantly reduce combustion efficiency. This is particularly problematic for hypergolic propellants, as the high-temperature products may still contain fuel or an oxidizer. If either the fuel or the oxidizer backflows into the manifold of the other, it can easily lead to micro-explosions, thereby creating a threat. To address this problem, this paper proposes a new design of an anti-backflow injector, aimed at effectively preventing the backflow of combustion products to the propellant manifold. A steady-state, non-reactive Computational Fluid Dynamics (CFD) model is employed to evaluate the steady internal flow characteristics of the proposed anti-backflow injector. Additionally, a complementary transient, multiphase fluid dynamics simulation is carried out to assess the response characteristics of the anti-backflow injector. Our analysis focuses on the response characteristics of the concave valve core. The study also explores the impact of different expansion port angles on the injection effect, finding that the vortex diameter at the injector outlet is positively related to the expansion port angle. It is also shown that the injection angle becomes more stable as the expansion angle increases. When the expansion port angles are 10° and 20°, the injection angles show similar trends. In terms of anti-backflow effect, taking the injection stiffness of 150% and a time range of 300 µs as examples, the response time of the anti-backflow models with expansion ports of 10°, 15°, and 20° is increased by 67 µs, 99 µs, and 213 µs, respectively, compared to the base models with the same expansion angles. Meanwhile, when the injection stiffness is 50%, the response time of the anti-backflow models with expansion ports of 10°, 15°, and 20° is increased by 207 µs, 210 µs, and 207 µs, respectively. When the injection stiffness is 20%, the response recovery speed of the anti-backflow models with expansion ports of 10° and 15° is increased by 41 µs and 96 µs, respectively. However, the performance of the anti-backflow model with a 20° expansion port is 216 µs of the base model. The optimized design of the anti-backflow injector has potential applications in solving the propellant backflow problem and contributes to the advancement of combustors.

Perception of digitized depth is a prerequisite for enabling the intelligence of three-dimensional (3D) laparoscopic systems. In this context, stereo matching of laparoscopic stereoscopic images presents a promising solution. However, the current research in this field still faces challenges. First, the acquisition of accurate depth labels in a laparoscopic environment proves to be a difficult task. Second, errors in the correction of laparoscopic images are prevalent. Finally, laparoscopic image registration suffers from ill-posed regions such as specular highlights and textureless areas. In this paper, we make significant contributions by developing (1) a correction compensation module to overcome correction errors; (2) an adaptive cost aggregation module to improve prediction performance in ill-posed regions; (3) a novel self-supervised stereo matching framework based on these two modules. Specifically, our framework rectifies features and images based on learned pixel offsets, and performs differentiated aggregation on cost volumes based on their value. The experimental results demonstrate the effectiveness of the proposed modules. On the SCARED dataset, our model reduces the mean depth error by 12.6% compared to the baseline model and outperforms the state-of-the-art unsupervised methods and well-generalized models.

Loop closure detection (LCD), also known as place recognition, is a crucial component of visual simultaneous localization and mapping (vSLAM) systems, aiding in the reduction of cumulative localization errors on a global scale. However, changes in environmental appearance and differing viewpoints pose significant challenges to the accuracy of the LCD algorithm. Addressing this issue, this paper presents a novel end-to-end framework (MetricNet) for LCDs to enhance detection performance in complex scenes with distinct appearance variations. Focusing on deep features with high distinguishability, an attention-based Channel Weighting Module(CWM) is designed to adaptively detect salient regions of interest. In addition, a patch-by-patch Similarity Measurement Module (SMM) is incorporated to steer the network for handling challenging situations that tend to cause perceptual aliasing. Experiments on three typical datasets have demonstrated MetricNet’s appealing detection performance and generalization ability compared to many state-of-the-art learning-based methods, where the mean average precision is increased by up to 11.92%, 18.10%, and 5.33% respectively. Moreover, the detection results on additional open datasets with apparent viewpoint variations and the odometry dataset for localization problems have also revealed the dependability of MetricNet under different adaptation scenarios.

The nozzle guide vane, which is a stationary part of a gas turbine, is a critical component of gas turbine engines because it must operate under harsh conditions with high pressure and temperature. Unfortunately, when a gas turbine runs for a long time, the turbine vane is subjected to repeated thermal load. This increases the possibility of fatigue damage and crack failure, thereby reducing the vane material's lifespan. In practice, the risk of failure at the trailing edge (TE) of a turbine vane is very high due to the reasons of shape configuration and cooling performance. The TE damage disturbs the flow physics of compressible air passing the vane TE, resulting in flow phenomena and heat convection. The study aims to numerically investigate the effects of damaged surfaces at the TE of a turbine vane on its flow behavior using computational fluid dynamics (CFD) with the SST k-w turbulence model. To simplify the simulation, the effects of the TE failure are presented by using two basic patterns, i.e., long (continuous) cutback damage, and two-short (discrete) cutback damage. To complete the investigation, a further study on the effects of repaired surfaces is included as well. The numerical results show the effects of damaged and repaired surfaces on flow behavior, particularly the vortex formation and the level of turbulent kinetic energy (TKE) in the TE region. Specifically, the damaged vane surface significantly increases the TKE level in the TE region, particularly the two-short damaged surface, which TKE shoots up to 7000-8000 m2/s2. Meanwhile, TKE in the normal and long damaged case is around 1500 and 4000 m2/s2. With the restoration of the vane surfaces, it can reduce the TKE level in the TE region. For instance, TKE is uniformly around 1750 m2/s2 for the long repaired surface.

Abstract Based on reasonable simplifications of a two‐dimensional (2D) energy equation, a basic heat transfer equation in 1D form is obtained for the hot crude oil transportation in a long‐buried pipeline. To enclose the heat transfer equation, the overall heat transfer coefficient and the numerical simulation of temperature fields of pipe wall and soil are introduced, and they together with the basic heat transfer equation constitute 1D and cross‐dimensional heat transfer models of crude oil transportation, respectively. Some numerical procedures, including grid generation, partial differential equation discretization, and algebraic equation solution, are combined to solve the two heat transfer models. Based on the designed cases with different pipeline parameters, the relative deviation between the numerical results from the two heat transfer models does not exceed 1%, and the two heat transfer models agree well. Furthermore, the nonuniform natural soil temperature field is designed, and its influence on the oil temperature and the deviation of the 1D heat transfer model is investigated. Under the condition of the nonuniform natural soil temperature field, the atmospheric temperature as the ambient temperature in the 1D heat transfer model causes an apparent deviation, whereas the soil temperature at the buried depth of the pipeline in the natural soil temperature field as the ambient temperature does not. This study can provide a reference for the reasonable selection and use of the heat transfer models of the buried hot oil pipeline.

Highlights The crystal facets featured with facet-dependent physical and chemical properties can exhibit varied electrocatalytic activity toward hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) attributed to their anisotropy. The highly active exposed crystal facets enable increased mass activity of active sites, lower reaction energy barriers, and enhanced catalytic reaction rates for HER and OER. The formation mechanism and control strategy of the crystal facet, significant contributions as well as challenges and perspectives of facet-engineered catalysts for HER and OER are provided.

Abstract A two‐dimensional model of the charging process on a heat storage unit in a shell‐and‐tube type latent heat subsystem of a solar power plant with direct steam generation was constructed in this study. The effects of the outer diameter to inner diameter ratio, aspect ratio, phase change material (PCM) thermal conductivity, and heat transfer fluid (HTF) mass flow rate were investigated. Results show that increasing the PCM thermal conductivity, HTF mass flow rate, and aspect ratio of the heat storage unit can shorten heat storage time, but the ratio of the outer diameter to the inner diameter of the heat storage unit has an optimal value of 6 in this problem. Using response surface methodology analysis, the influence of the aspect ratio, outer‐to‐inner‐diameter ratio, PCM thermal conductivity, and HTF mass flow rate on the storage time of the phase change heat storage unit is in descending order. After a genetic algorithm optimization, the storage rate of the heat storage unit increased by 35%. The results of this study can guide the heat storage unit to achieve a better practical application performance.

Abstract Organic photodiodes (OPDs) have recently garnered attention as a competitive alternative to their inorganic counterparts, given their inherent advantages in solution processability, mechanical flexibility, and cost‐effective manufacturing. In this work, a novel method for fabricating high‐performance semitransparent color OPDs by integrating Fabry–Perot (FP) interferometer‐based color‐filtering electrodes and solution‐processed distributed Bragg reflectors (sDBRs) is introduced. The FP electrode provides color control by modulating the metal oxide layer thickness, irrespective of the photoactive layer's color. To overcome limitations related to light absorption and device transparency, this work employs a sDBR as a selective window reflector, allowing the OPD to retain its color while preserving semitransparency. The experimental findings demonstrate the successful integration of these components, resulting in semitransparent red, green, and blue (RGB) OPDs exhibiting significantly improved detectivity. The fabricated RGB‐OPDs achieve detectivity values of 4.07, 3.49, and 4.22 × 1010 cm Hz1/2 W−1 for red, green, and blue, respectively. This research highlights the efficacy of FP and sDBR color filters in realizing high‐performance color sensors and offer novel opportunities for semitransparent OPD integration with other optoelectronic devices.

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk.

This research investigates the impact of nonlinear clearance factors on position tracking accuracy in the servo drive system of a harmonic reducer. The study introduces a technique for modeling and compensating for transmission errors, thereby improving position tracking accuracy through online compensation combined with an auto-disturbance rejection controller. Initially, the mathematical model of the permanent magnet synchronous motor is outlined, and the current loop and speed loop control models are derived. Subsequently, an electromechanical actuator (EMA) simulation model with clearance is established, and detailed simulation analysis is conducted to verify the impact of clearance on tracking accuracy. A model for online compensation of transmission errors is then developed. Following the principles of active disturbance rejection control (ADRC), a second-order ADRC is formulated for real-time compensation of transmission errors in EMA position mode. Finally, through no-load and load experiments, the change in position tracking error with and without transmission error compensation is compared and analyzed. The results demonstrate that utilizing automatic disturbance rejection control with transmission error compensation achieves the highest position tracking accuracy. Compared to the proportion integration differentiation (PID) control method, the root mean square of position tracking error is reduced by approximately 12.8% and 17.3% under no-load and load conditions, respectively. By compensating for position errors online, the accuracy of the EMA position can be improved.

The demand for non-destructive testing of carbon fiber-reinforced polymer (CFRP) is becoming increasingly pressing to ensure its safety and reliability across different fields of use. However, the complex structural characteristics and anisotropic bulk conductivity of CFRP make achieving high sensitivity in detecting internal defects such as delamination extremely challenging. To address this issue, a novel triple rectangular coil probe with high sensitivity developed for detecting delamination in CFRP is presented in this paper. A finite element model using COMSOL Multiphysics was developed for CFRP delamination eddy current testing with the designed probe. Based on this model, the probe parameters were determined through orthogonal experiments. By analyzing the eddy current distribution in CFRP samples, the scanning mode was defined. Following this, the detection voltage was evaluated for various delamination parameters, and the sensitivity of different probes was compared. Results indicate that, under the same excitation coil parameters, for a 5 mm delamination lateral dimension change, the single pancake and single rectangular coil probes exhibit sensitivities of 88.24% and 72.55%, respectively, compared with the designed probe. For a 0.5 mm delamination thickness change, their sensitivities are 49.04% and 56.69% of those of the designed probe. The designed probe meets the demand for high-sensitivity detection.

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values.

Abstract Stratified thermal energy storage (TES) tanks are widely used in thermal power plants to enhance the electric power peak load shifting capability and integrate high renewable energy shares. In this study, a data‐driven surrogate modeling and optimization study of the unequal diameter radial diffuser previously proposed by the present authors is conducted. First, based on the orthogonal experimental design, numerical experiments are performed to generate the performance database. Then, the database is used to establish the data‐driven surrogate model via the support vector machine. Subsequently, the single‐objective optimization and multiobjective optimization of an unequal diameter radial diffuser are conducted using the genetic algorithm. For the single‐objective optimization, the optimal thermocline thickness is 0.829 m when the diameter ratio of the long baffle and the tank is 0.426, the diameter ratio of the short baffle and the long baffle is 0.823, and the distance between the two baffles is 228.51 mm. For multiobjective optimization, the obtained Pareto optimal solutions are obtained. Under the premise of maintaining excellent thermal stratification, the selected Point C can reduce the steel cost by 88.1%. The research results are helpful for designing efficient and economical unequal diameter radial diffusers for TES tanks.

Multimodal sarcasm detection is a developing research field in social Internet of Things, which is the foundation of artificial intelligence and human psychology research. Sarcastic comments issued on social media often imply people’s real attitudes toward the events they are commenting on, reflecting their current emotional and psychological state. Additionally, the limited memory of Internet of Things mobile devices has posed challenges in deploying sarcastic detection models. An abundance of parameters also leads to an increase in the model’s inference time. Social networking platforms such as Twitter and WeChat have generated a large amount of multimodal data. Compared to unimodal data, multimodal data can provide more comprehensive information. Therefore, when studying sarcasm detection on social Internet of Things, it is necessary to simultaneously consider the inter-modal interaction and the number of model parameters. In this paper, we propose a lightweight multimodal interaction model with knowledge enhancement based on deep learning. By integrating visual commonsense knowledge into the sarcasm detection model, we can enrich the semantic information of image and text modal representation. Additionally, we develop a multi-view interaction method to facilitate the interaction between modalities from different modal perspectives. The experimental results indicate that the model proposed in this paper outperforms the unimodal baselines. Compared to multimodal baselines, it also has similar performance with a small number of parameters.

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

Quality management in the design phase is crucial for determining the overall quality of prefabricated buildings. However, traditional design methods can no longer meet the complex design, component, and nodal requirements of prefabricated buildings. This study proposes a quality management framework for the prefabricated building design phase based on building information modeling (BIM) and virtual reality (VR) technologies to enhance design precision and satisfaction. Applying this framework to a prefabricated building project in Shenzhen, China, it was found that compared to traditional 2D drawing methods, the design issues feedback during drawing reviews decreased by 41.35%. Compared to solely using BIM technology, the number of design collisions identified through collision detection increased by 28.35%, and feedback on design issues during drawing reviews decreased by 15%. Furthermore, the framework was tested to prove its usability, effectiveness, and functionality. The framework, integrating the rich architectural information of BIM with the immersive experience of VR, contributes to focusing on the design process, improving design tools, optimizing design workflows, significantly reducing design errors, and enhancing the quality of prefabricated buildings.

When multiple Virtual Synchronous Generators (VSGs) operate in parallel in an islanded grid, power and frequency oscillations will occur when one VSG goes offline. However, the existing literature does not cover the related analysis and transient suppression schemes for this scenario. To analyze these complex high-order system dynamics, this paper first establishes an intuitive equivalent circuit model for multiple VSGs, and the frequency domain expressions of the multi-machine VSG system during the VSG offline transient simulation are then derived. Based on the multi-VSG model and its transient oscillations analysis, this paper further proposes a configuration scheme for the equivalent circuit parameters. Equivalently, the virtual inertia, damping coefficient and virtual impedance of the VSGs can be configured. With the proposed parameter configuration scheme, the power oscillation during the VSG offline transient can be eliminated, as verified by experiments with a microgrid lab platform using three VSGs. Compared with the existing multi-VSG studies, the proposed scheme is not only the first attempt to study the transient suppression when a VSG goes offline, but also is more intuitive in analysis and less complicated in the controller parameter tuning.

Building point cloud completion is the process of reconstructing missing parts of a building’s point cloud, which have been affected by external factors during data collection, to restore the original geometric shape of the building. However, the uncertainty in filling point positions in the areas where building features are missing makes it challenging to recover the original distribution of the building’s point cloud shape. To address this issue, we propose a point cloud generation diffusion probability model based on building outline constraints. This method constructs building-outline-constrained regions using information related to the walls on the building’s surface and adjacent roofs. These constraints are encoded by an encoder and fused into latent codes representing the incomplete building point cloud shape. This ensures that the completed point cloud adheres closely to the real geometric shape of the building by constraining the generated points within the missing areas. The quantitative and qualitative results of the experiment clearly show that our method performs better than other methods in building point cloud completion.

Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of “deeper is better”. However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efficient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic filtered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efficient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.

Abstract Vapor‐compression refrigeration systems are widely used in refrigeration equipment. Theoretically, the process is typically divided into two isobaric processes: an adiabatic isentropic compression process and adiabatic isentropic throttling process. The refrigeration compressor is the main energy‐consuming component in vapor‐compression refrigeration systems. However, this device has a large energy loss and low overall efficiency in the adiabatic isentropic compression process. In this study, a modified vapor‐compression refrigeration cycle with an isothermal piston is proposed to realize near‐isothermal compression of a refrigerator to significantly reduce the energy loss in the compression process and improve the system performance. A real‐gas compression process model is established, and the heat transfer index Hex is set. By changing the heat transfer index Hex, the performances of the vapor‐compression refrigeration system under ideal and real compression conditions are compared and analyzed. Compared with a traditional vapor‐compression refrigeration system, the coefficient of performance of the compressor with an isothermal compression process is increased by approximately 17%. The results also demonstrate that the lower the evaporation temperature Te and higher the condensation temperature Tc, the greater the optimization effect of the isothermal compression.

Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with “better” quality to improve the chance of faulty statements being ranked as highly suspicious. In this paper, we propose a software fault localization approach based on feedback ranking information, namely FLFR, according to an improved genetic algorithm. The starting point of the new method is the execution of a set of test cases, which gives a preliminary suspiciousness ranking of program statements. The improved genetic algorithm is iteratively applied to generate new test cases. The new method is evaluated through a series of experiments on four C programs and two Java programs. The experimental results show that the test cases automatically generated by the method can improve the suspiciousness ranking of the faulty statement, and thus enhance the performance of fault localization.

High-rise teaching buildings are complex public buildings that combine the evacuation risks of school buildings and high-rise buildings. In this regard, studying fire product transport patterns and personnel evacuation characteristics of high-rise school buildings is crucial for safe and rapid evacuation. In this paper, we applied Pyrosim2018 software to build a model of a teaching building and performed numerical fire simulation to analyze temperature, CO gas, and visibility to determine the available evacuation time ASET; meanwhile, we performed evacuation simulation by Pathfinder 2019 to determine the required evacuation time and analyze the congestion problem during evacuation. By improving the evacuation route, secondary simulations were conducted and compared with the previous results. The results show that visibility is the main factor affecting evacuation in of the event of a fire in this school building. Based on the visibility analysis, it is recommended that personnel evacuate from floors four and above within 709.2 S when the fire location is at a specific position on the third floor. While the original safety exits of the school building can avoid a large number of casualties, they cannot guarantee the safe evacuation of all people, and planning a reasonable evacuation route can obviously relieve the evacuation pressure in the high-rise corridor.

Reasonable end effect equivalence is of vital importance in the 2-D finite element analysis (FEA) for a line-start permanent magnet synchronous motor (LSPMSM) equipped with a hybrid solid rotor for improved starting and synchronous capabilities. In this work, a series of correction factors are applied to the rotor conductivity to include the end effect in the 2-D FEA model. Derivation of these correction factors completes after the separation of different torque components. A dynamic motor model of the LSPMSM is established with some parameters extracted from the 2-D FEA using the conductivity correction. Outcomes of this model exhibit good agreements with the 3-D FEA results, by which the introduced correction factors are validated to possess the acceptable precision and effectiveness.

The fractal dimension is widely used in many fields as a parameter to characterize the geometric complexity and geometric distribution relationship of research objects. To study the surface characteristics of sulfide ore particles, the fractal theory was applied to quantitatively characterize the surface fractal dimension Ds of sulfide ore microparticles in three particle size ranges, 60–100 mesh, 100–140 mesh and >200 mesh, based on the area–perimeter method. Using an optical microscope, grain projection images of the particles were obtained. The grain shape and characteristics of sulfide ore particles were studied by means of an image processing system. The results demonstrate that the grain shape of sulfide ore particles can be expressed by fractal dimension, and the particle surface fractal dimension ranges from 2.4392 to 2.5492. It was found that the fractal properties begin to decrease due to the increasing of the particle size. The larger the fractal dimension, the finer the particles are. The fractal dimension of sulfide ore particles can be used as an important indicator of their particle shape distribution characteristics, which can provide important information for further study of the relevant physical and chemical properties of sulfide ore particles and provide a new theoretical method and basis for the adhesion and removal of sulfide ore dust. With the quantitative description of the fractal distribution of sulfide ore particles, a new way to study the adhesive force between particles is offered for further research.