Search Results (579)

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

Coronary microvascular obstruction and dysfunction (CMVO) frequently arise following primary percutaneous coronary intervention (PCI), particularly in individuals with myocardial infarction. Despite the restoration of epicardial blood flow, microvascular perfusion might still be compromised, resulting in negative clinical outcomes. CMVO is a complex condition resulting from a combination of ischemia, distal thrombotic embolization, reperfusion injury, and individual susceptibilities such as inflammation and endothelial dysfunction. The pathophysiological features of this condition include microvascular spasm, endothelial swelling, capillary plugging by leukocytes and platelets, and oxidative stress. Traditional angiographic assessments, such as Thrombolysis in Myocardial Infarction (TIMI) flow grade and myocardial blush grade, have limited sensitivity. Cardiac magnetic resonance imaging (CMR) stands as the gold standard for identifying CMVO, while the index of microvascular resistance (IMR) is a promising invasive option. Treatment approaches involve powerful antiplatelet drugs, anticoagulants, and supersaturated oxygen, yet no treatment has been definitively shown to reverse established CMVO. CMVO remains a significant therapeutic challenge in coronary artery disease management. Enhancing the comprehension of its core mechanisms is vital for the development of more effective and personalized treatment strategies. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

One of the methods for mitigating the duck curve phenomenon in photovoltaic (PV) energy systems is storing surplus energy in the form of hydrogen. However, there is a lack of studies focused on residential PV systems that assess the impact of hydrogen storage on the reduction of energy flow imbalance to and from the national grid. This study presents an analysis of hydrogen energy storage based on real-world data from a household PV installation. Using simulation methods grounded in actual electricity consumption and hourly PV production data, the research identified the storage requirements, including the required operating hours and the capacity of the hydrogen tank. The analysis was based on a 1 kW electrolyzer and a fuel cell, representing the smallest and most basic commercially available units, and included a sensitivity analysis. At the household level—represented by a single-family home with an annual energy consumption and PV production of approximately 4–5 MWh over a two-year period—hydrogen storage enabled the production of 49.8 kg and 44.6 kg of hydrogen in the first and second years, respectively. This corresponded to the use of 3303 kWh of PV-generated electricity and an increase in self-consumption from 30% to 64%. Hydrogen storage helped to smooth out peak energy flows from the PV system, decreasing the imbalance from 5.73 kWh to 4.42 kWh. However, while it greatly improves self-consumption, its capacity to mitigate power flow imbalance further is constrained; substantial improvements would necessitate a much larger electrolyzer proportional in size to the PV system’s output. Full article

As designers push engine efficiency closer to thermodynamic limits, the analysis of flow instabilities developed in a high-pressure turbine (HPT) is crucial to minimizing aerodynamic losses and optimizing secondary air systems. Purge flow, while essential for protecting turbine components from thermal stress, significantly impacts the overall efficiency of the engine and is strictly connected to cavity modes and rim-seal instabilities. This paper presents an experimental investigation of these instabilities in an HPT stage, tested under engine-representative flow conditions in the short-duration turbine rig of the von Karman Institute. As operating conditions significantly influence instability behavior, this study provides valuable insight for future turbine design. Fast-response pressure measurements reveal asynchronous flow instabilities linked to ingress–egress mechanisms, with intensities modulated by the purge rate (PR). The maximum strength is reached at PR = 1.0%, with comparable intensities persisting for higher rates. For lower PRs, the instability diminishes as the cavity becomes unsealed. An analysis based on the cross-power spectral density is applied to quantify the characteristics of the rotating instabilities. The speed of the asynchronous structures exhibits minimal sensitivity to the PR, approximately 65% of the rotor speed. In contrast, the structures’ length scale shows considerable variation, ranging from 11–12 lobes at PR = 1.0% to 14 lobes for PR = 1.74%. The frequency domain analysis reveals a complex modulation of these instabilities and suggests a potential correlation with low-engine-order fluctuations. Full article

Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)²INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios. Full article

Frequent extreme natural disasters can lead to large-scale power outages, significantly compromising the reliability and sustainability of urban power supply, as well as the sustainability of urban development. To address this issue, this paper proposes a two-layer resilience optimization method for distribution networks aimed at improving voltage quality during post-disaster power restoration, enhancing the resilience of the power grid, and thus improving the sustainability of urban development. Specifically, the upper-layer model determines the topology of the urban distribution network and dispatches emergency resources to restore power and reconstruct the original topology. Based on this restoration, the lower-layer model further enhances voltage quality by prioritizing the dispatch of flexible resources according to voltage sensitivity coefficients derived from power flow calculations. A larger voltage sensitivity coefficient indicates a stronger voltage optimization effect. Thus, the proposed method enables comparable voltage regulation performance with lower operational cost. Simulation findings on the IEEE-33 bus test system revealed that the proposed strategy minimized the impact of voltage fluctuations by 10.92 percent and cut the cost related to restoration by 31.25 percent, as compared to traditional post-disaster restoration plans, which do not entail optimization of system voltages. Full article

Pump-driven liquid cooling systems are widely utilized in unmanned aerial vehicle (UAV) electronic thermal management. As a critical power component, the miniaturization and lightweight design of the pump are essential. Increasing the operating speed of the pump allows for a reduction in impeller size while maintaining hydraulic performance, thereby significantly decreasing the overall volume and mass. However, high-speed operation introduces considerable internal flow losses, placing stricter demands on the geometric design and flow-field compatibility of the impeller. In this study, a miniature high-speed centrifugal pump (MHCP) was investigated, and a multi-objective optimization of the impeller was carried out using response surface methodology (RSM) to improve internal flow characteristics and overall hydraulic performance. Numerical simulations demonstrated strong predictive capability, and experimental results validated the model’s accuracy. At the design condition (10,000 rpm, 4.8 m³/h), the pump achieved a head of 46.1 m and an efficiency of 49.7%, corresponding to its best efficiency point (BEP). Sensitivity analysis revealed that impeller outlet diameter and blade outlet angle were the most influential parameters affecting pump performance. Following the optimization, the pump head increased by 3.7 m, and the hydraulic efficiency improved by 4.8%. In addition, the pressure distribution and streamlines within the impeller exhibited better uniformity, while the turbulent kinetic energy near the blade suction surface and at the impeller outlet was markedly decreased. This work provides theoretical support and design guidance for the efficient application of MHCPs in UAV thermal management systems. Full article

With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory behavior policies and experience-based data exploitation in practical applications, compounded by (b) users’ distrust from the opacity of model decision mechanics. To address these, a model–data hybrid-driven physics-informed reinforcement learning (PIRL) algorithm is proposed in this paper. Specifically, the proposed methodology uses the proximal policy optimization (PPO) algorithm as the agent’s foundational framework and constructs a PI-actor network embedded with prior model knowledge derived from power flow sensitivity into the agent’s actor network via the PINN method, which achieves dual optimization objectives: (a) enhanced environmental perceptibility to improve experience utilization efficiency via gradient-awareness from model knowledge during actor network updates, and (b) improved user trustworthiness through mathematically constrained action gradient information derived from explicit model knowledge, ensuring actor updates adhere to safety boundaries. The simulation and validation results show that the PIRL algorithm outperforms the baseline PPO algorithm in terms of training stability, exploration efficiency, economy, and security. Full article

This paper presents the airflow characteristics of humidity-sensitive air inlet. This type of air inlets and exhausts are often part of demand control ventilation, especially in dwellings where humidity is an important indicator of ventilation needs. Humidity-controlled ventilation is one of the simplest implementations of smart ventilation, even in the case of a natural ventilation system. This type of solution leads to decreased energy consumption and increases the indoor air quality. A description of airflow characteristics is crucial for resolving these issues. The presented characteristics are based on the measurements of the indoor/outdoor relative humidity, airflow, and pressure drop across the air inlet. The characteristics are described based on a general power law flow model (V = C·∆pⁿ), which is the most suitable, for example, for the CONTAM multizone indoor air quality and ventilation analysis computer program. The characteristics include relationships between the indoor and outdoor relative humidity, hysteresis, and dynamic changes in indoor relative humidity. The simplified and complex formulas are presented. The accuracy of the airflow calculation based on these formulas is discussed. Full article

Spectral flow cytometry has revolutionized traditional single-cell profiling to a new era of high-dimensional analysis, allowing for unprecedented deep phenotyping and more precise cell characterization, thereby significantly enhancing our multiplexing capability. The recent application of this technology in clinical settings has been redefining the landscape of clinical diagnostic panels and immune monitoring, particularly for hematologic malignancies, immunological disorders, and drug discovery. Emerging technologies like ghost cytometry, LASE, and imaging flow cytometry are advancing cytometry by improving sensitivity, throughput, and spatial resolution. In this review, we discuss the requirements, challenges, and considerations for spectral applications in clinical diagnostic laboratories and pharmaceutical/contract research organization (CRO) settings. We discuss how these recent innovations are set to push the boundaries of diagnostic accuracy and analytical power, heralding a new frontier in clinical cytometry with the potential to dramatically enhance patient care and treatment outcomes. Full article

The utilization of electric vehicles (EVs) has grown significantly and continuously in recent years, encouraging the creation of new implementation opportunities. The battery electric vehicle (BEV) charging system can be effectively used during peak load periods, for voltage regulation, and for the improvement of power system stability within the smart grid. It provides an efficient bidirectional interface for charging the battery from the grid and discharging the battery into the grid. These two operation modes are referred to as grid-to-vehicle (G2V) and vehicle-to-grid (V2G), respectively. The management of power flow in both directions is highly complex and sensitive, which requires employing a robust control scheme. In this paper, an Integral Fast Terminal Synergetic Control Scheme (IFTSC) is designed to control the BEV charger system through accurately tracking the required current and voltage in both G2V and V2G system modes. Moreover, the Black-Winged Kite Algorithm is introduced to select the optimal gains of the proposed IFTS control scheme. The system stability is checked using the Lyapunov stability method. Comprehensive simulations using MATLAB/Simulink are conducted to assess the safety and efficacy of the suggested optimal IFTSC in comparison with IFTSC, optimal integral synergetic, and conventional PID controllers. Furthermore, processor-in-the-loop (PIL) co-simulation is carried out for the studied system using the C2000 launchxl-f28379d digital signal processing (DSP) board to confirm the practicability and effectiveness of the proposed OIFTS. The analysis of the obtained quantitative comparison proves that the proposed optimal IFTSC provides higher control performance under several critical testing scenarios. Full article

To achieve efficient prediction and optimization of the energy consumption of ship central cooling systems, this paper first constructed and validated a high-precision multi-physical domain simulation model of the ship central cooling system based on fluid heat transfer principles and the physical network method. Then, simulation experiments were designed using the Box–Behnken design (BBD) method to study the effects of five key parameters—main engine power, seawater temperature, seawater pump speed, low-temperature fresh water three-way valve opening, and low-temperature fresh water flow rate—on system energy consumption. Based on the simulation data, an energy consumption prediction model was constructed using response surface methodology (RSM). This prediction model exhibited excellent goodness of fit and prediction ability (coefficient of determination R² = 0.9688, adjusted R²_adj = 0.9438, predicted R²_pred = 0.8752), with a maximum relative error of only 1.2% compared to the simulation data, confirming its high accuracy. Sensitivity analysis based on this prediction model indicated that main engine power, seawater pump speed, seawater temperature, and three-way valve opening were the dominant single factors affecting energy consumption. Further analysis revealed a significant interaction between main engine power and seawater pump speed. This interaction resulted in non-linear changes in system energy consumption, which were particularly prominent under operating conditions such as high power. This study provides an accurate prediction model and theoretical guidance on the influence patterns of key parameters for the simulation-driven design, operational optimization, and energy saving of ship central cooling systems. Full article

To address the thermal protection challenges of multiple high-temperature components and the electrical power deficiency in hypersonic vehicles, this study proposes twelve multi-heat-source thermoelectric conversion schemes based on the sCO₂ Brayton cycle. A three-dimensional evaluation system for thermal management is established, incorporating thermal efficiency, coolant mass flow rate, and system mass as key metrics. A comprehensive parameter sensitivity analysis was conducted on the twelve dual-heat-source cycle configurations. For systematic performance comparison, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for multi-objective optimization, with Pareto fronts analyzed to determine optimal configurations. The results demonstrate that appropriately increasing the minimum cycle temperature can significantly reduce coolant flow requirements. Multi-objective optimization reveals the following: (1) The pre-compressed aero-comb configuration achieves optimal performance in the efficiency-mass flow rate optimization scenario; (2) Both pre-compressed aero-comb and re-compressed comb-aero configurations show superiority in the efficiency-mass optimization scenario; (3) The pre-compressed aero-comb configuration exhibits lower system mass in low coolant flow regions for the mass flow rate-mass optimization scenario. Overall, the performance of the precompression aero-comb configuration is relatively superior. This work provides an important reference for the design of thermal management systems for hypersonic vehicles. Full article

To address the challenge of evaluating a radar cross-section (RCS) for a non-cooperative aircraft with limited aerodynamic shape information, this paper presents a multi-source, data-driven inverse reconstruction method. This approach integrates data fusion techniques to facilitate an initial shape reconstruction, followed by an iterative optimization process that utilizes computational fluid dynamics (CFD) to enhance the shape, accounting for the aerodynamic performance. Additionally, an inverse deduction analysis is effectively employed to ascertain the characteristics of the power system, leading to the design of a double S-curved tail nozzle layout with stealth capabilities. An aerodynamic analysis demonstrates that at Mach 0.6, the lift-to-drag ratio peaks at 27.3 for the attack angle of 4°, after which it declines as the angle increases. At higher angles of attack, complex flow separation occurs and expands with the increasing angle. The electromagnetic simulation results indicate that under vertical polarization, the omnidirectional RCS reaches its peak as the incident angle is deflected downward by 10° and reduces with the growth of the angle, demonstrating angular robustness. Conversely, under horizontal polarization, the RCS is more sensitive to edge-induced rounding. The findings illustrate that this methodology enables accurate shape modeling for non-cooperative targets, thereby providing a fairly solid basis for stealth performance evaluation and the assessment of surprise effectiveness. Full article