Search Results (3,392)

Multi-output, single-switch, hard-switched Pulse-Width Modulated (PWM) converters suffer from high switching losses, which strictly limit their power density. To significantly reduce these losses, this work proposes a novel family of non-isolated multi-output DC-DC converters based on a quasi-resonant, single-switch cell operating in the megahertz (MHz) range. Sixteen configurations are derived to enhance power density and minimize component stress. A comprehensive analysis derives the fundamental analytical expressions for operation, switching conditions, and power flow. These expressions form the basis of a design tool that facilitates parametric component selection and optimization. The developed tool calculates voltage and current stresses, alongside power losses, using RMS current analysis and user-defined parameters such as ESR and semiconductor non-idealities. Finally, experimental results from prototypes operating at approximately 1 MHz in both full-wave and half-wave modes, with step-up and step-down capabilities, confirm the accuracy of the analytical design tool and the simulation model. Full article

The International Civil Aviation Organization (ICAO) sets strict limits for aircraft ramp noise, a key source of which is Auxiliary Power Unit (APU) inlet noise. This paper presents a systematic and computationally efficient design methodology for APU inlet mufflers. The high-frequency noise necessitates validating a single-degree-of-freedom liner impedance model up to 10,000 Hz. The core innovation overcomes prohibitive full-passage simulation costs (days) by optimally selecting attenuation center frequencies from the source spectrum and implementing an axially segmented design. This approach enables efficient, targeted optimization (minutes per case) and leverages acoustic mode scattering at segment interfaces to enhance overall attenuation. The design is verified via high-fidelity, full-flow-path simulation. Experimental validation under various operating conditions shows good agreement with predictions, achieving approximately 9 dB reduction in overall A-weighted Sound Power Level (OASPL) with consistent performance. The results demonstrate the feasibility and effectiveness of the proposed rapid, precise, and efficient design framework. Full article

The global transition toward carbon-neutral energy solutions has established Solid Oxide Fuel Cells (SOFCs) as a key technology for next-generation power generation. This work presents a comprehensive numerical study and multi-factor statistical analysis of an anode-supported SOFC fueled by synthetic diesel. A three-dimensional computational fluid dynamics model, validated against experimental data, was integrated with a Taguchi L₂₇ orthogonal array to systematically evaluate the influence of six key parameters: temperature, fuel mass flow rate, operating pressure, current load, flow channel configuration, and methane molar fraction. Statistical analysis through the signal-to-noise ratio and analysis of variance identified the operating current as the most significant factor affecting cell voltage, followed by the fuel mass flow rate and temperature. The experiments showed that the highest levels of all factors (except for the current, which had the lowest level) maximize electrochemical performance while maintaining a steam-to-carbon ratio (S/C) within a range of 0.83 to 0.92, calculated based on total carbon content, ensuring sufficient humidification for internal reforming across all tested fuel compositions. Furthermore, a multiple linear regression model was developed as a computationally efficient surrogate, demonstrating exceptional predictive accuracy with an R² of 0.9954 and a mean relative error of 1.76% across independent validation cases. These results provide a robust methodology for rapid design and sensitivity analysis of internal-reforming SOFCs, offering a precise tool for optimizing fuel utilization in high-temperature electrochemical systems. Full article

Power flow (PF) analysis is fundamental for power system operation and planning, yet traditional methods like Newton–Raphson face problems in convergence and computational efficiency. While deep learning (DL) offers promising solutions, its “black-box” nature and unstable training dynamics hinder practical adoption. This paper proposes a physics-informed neural network (PINN) framework integrated with a novel half-inverse gradient (HIG) mechanism to address these limitations. First, a systematic study of gradient scaling in PF optimisation found that the lack of enough inverse matrix compensation was the main cause of training instability. Second, we design a residual-driven HIG method that compensates gradient matrices via inverse operations, enabling accelerated convergence while maintaining numerical stability. Third, we develop parameterized voltage variables with differentiable activation functions to enforce hard operational constraints. The HIG optimizer leverages automatic differentiation and truncated singular value decomposition to balance diagonal/non-diagonal gradient information, achieving 99% accuracy in case4gs and case30 studies. Experiments on case118 demonstrate the framework’s scalability, with 65% accuracy compared to about 38% for baseline physics-informed approaches. Full article

Laser assistance offers a promising pathway for high-efficiency and low-damage ultraprecision grinding for difficult-to-machine hard-brittle semiconductors. This study employs atomistic simulation to investigate the surface removal and subsurface damage mechanisms of C-, M-, and A-plane AlN workpieces during single-grit laser-assisted nanogrinding (LAG). The results indicate that LAG reduces material pileup, thereby decreasing the grit–workpiece contact area and grinding resistance. By leveraging laser-induced thermal effects to enhance atomic plastic flow, LAG evidently achieves a higher material removal rate than conventional grinding (CG). Grinding the C-plane along a <11–20> orientation yields the lowest surface roughness, although this improvement is not useful for the M- and A-planes. Tangential force increases linearly with grinding depth in both methods, but LAG exhibits a lower rate of increase. LAG consistently produces lower grinding forces and friction coefficients and results in lower dislocation densities in C- and A-plane AlN workpieces at nearly all grinding depths. The C-plane exhibits the thinnest damage layer, followed by the M-plane, with the A-plane the thickest. Increasing the laser power density lowers the grinding force and enhances the removal efficiency. Optimal power density minimizes subsurface damage and improves surface quality; however, excessive power density exacerbates damage. This work provides valuable insights for developing high-efficiency, low-damage LAG techniques for hard-brittle semiconductors. Full article

Decarbonizing air travel poses a major technological challenge, driven by the substantial power requirements of the drivetrain and the demanding weight and volume constraints of airborne systems. One promising avenue involves leveraging the high specific energy of hydrogen by designing compact, high-power fuel cell stacks to supply power for electric drivetrains. However, a key drawback of such propulsion architectures is the substantial heat generated within the fuel cells, which necessitates bulky and heavy thermal management systems to ensure safe and continuous operation. This study investigates a proposed air-based thermal management system, which operates by introducing pulsating heat pipes into the bipolar plates of a High-Temperature Polymer Electrolyte Membrane Fuel Cell (HT-PEM FC) stack. If proven to be feasible, heat pipe assisted air cooling may provide the benefit of reducing overall system complexity by decreasing the number of components in the thermal management system. To evaluate the thermal performance of the proposed system, a one-dimensional thermal model was initially developed in a previous study to describe the temperature distribution along the length of a heat pipe. Building upon this foundation, the present work extends the model by incorporating a two-dimensional Computational Fluid Dynamic (CFD) analysis to account for geometry-specific effects within the hexagonal design. Results indicate that the heat transfer from the hexagonal heat pipe geometry to the coolant air flow was marginally overestimated in previous analytical calculations. Revised heat transfer rates led to a shift in the predicted temperature distributions, resulting in the need for either increased external airflow, extended condenser sections, or reduced inlet temperatures to maintain target operating conditions. Although these adjustments may result in a slight increase in system mass and parasitic power consumption, the overall impact is limited, and the heat pipe-assisted air cooling approach remains theoretically feasible. Based on the results, design modifications are proposed and their impact on thermal performance is evaluated to address the challenges of heat rejection and temperature uniformity. A modification based on variation and optimization of PHP meander lengths was evaluated using the updated model and it significantly improved temperature homogeneity across the evaporator. Full article

High power servers are accelerating adoption of cold plate liquid cooling in data centers, but control-chain latency and thermal inertia can delay regulation after load changes and trigger transient swings that threaten temperature stability. This study develops a delay-aware Modelica model for a liquid cooled data center and validates it against measured operating conditions. To compare control options, a standardized percentage step-test protocol is proposed with three indicators—dynamic response time, dynamic fluctuation amplitude, and dynamic fluctuation ratio. Step-response simulations evaluate three single actuator strategies (constant differential pressure valve control, primary side variable flow pumping, and cooling tower outlet temperature control), and a combined condition database is built for coordinated pump–fan control with operating-point matching. Valve control responds fastest (38.3–41.3 s) but produces the largest fluctuations; variable flow pumping is smoother with response times of 44.2–72.9 s; and cooling tower temperature control is most stable but slowest (684–826 s). The optimized combined strategy reallocates control authority across operating conditions, reducing response time from 688.3 s to 73.7 s and lowering dynamic temperature swing risk by up to 1.3 °C. These results support load-responsive, plant-level transient-safe operation of liquid-cooled data-center cooling plants, particularly for secondary-side supply temperature control. Full article

Magnetoplasmadynamic thrusters represent a promising Electric Propulsion technology for future space missions; however, their optimization is hampered by the lack of accurate performance models in the emerging regime of low power (<12 kW) and high magnetic fields (>0.1 T), where traditional formulations prove inadequate. In this work, a new semi-empirical model for predicting the thrust and discharge voltage of argon-fed MPD thrusters was developed and validated. Starting from state-of-the-art physical models, multi-factorial correction factors were introduced to account for the coupled effects of discharge current (8–180 A), mass flow rate (3–21 mg/s), and applied magnetic field (up to 0.6 T). The model was calibrated and validated using a comprehensive and homogeneous collection of experimental data from the literature. A comparative analysis demonstrates that the corrected model significantly reduces prediction errors (0–9%) compared to reference models available in the literature (8–50%). In particular, the model exhibits remarkably superior accuracy in both the Self-Field and Applied-Field regimes, overcoming the main limitations of previous formulations and providing more robust estimates across a wide operational envelope. The developed model constitutes a reliable and physically consistent tool for the analysis and preliminary design of low-power, argon-fed magnetoplasmadynamic thrusters, enabling more effective optimization for this class of propulsion systems. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

The eddy current braking system (ECBS) is a crucial non-contact technology for high-speed railway. Conventional DC-excited systems face significant challenges such as excessive rail heating and high-capacity power supply requirements. This paper proposes a novel ECBS with AC excitation and auxiliary capacitor to achieve integrated energy recovery and power supply optimization. To evaluate its performance, a rigorous analytical framework is developed. First, a 2D subdomain model is established by incorporating the longitudinal end effect to solve the magnetic field distribution. Subsequently, an equivalent circuit is derived from the subdomain results to investigate steady-state braking characteristics and power flow. Analysis results demonstrate that the proposed system not only generates controllable braking force but also converts a portion of kinetic energy into storable electrical energy, effectively mitigating secondary rail heating. Most significantly, the implementation of an optimal auxiliary capacitor (134 μF) is found to reduce the required inverter capacity compared to inverter-only conditions. These findings provide a theoretical foundation and a practical design tool for developing high-performance, energy-efficient braking systems in high-speed transportation. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

The integration of distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems, into distribution networks necessitates accurate estimation of hosting capacity (HC). This paper presents an optimization-based approach for HC assessment and enhancement, which considers both overvoltage and line overload constraints and incorporates the reactive power (VAR) capabilities of DER inverters. Furthermore, the methodology is extended to include network reconfiguration, leveraging switchable branches to alleviate network congestion and further enhance DER integration. The proposed method utilizes a linearized power flow model to ensure computational efficiency and formulates the problem as a convex optimization task when considering only inverter VAR capabilities. The framework jointly addresses overvoltage, line overload, and inverter VAR capability constraints through linear and second-order cone constraints. In the extended formulation that includes network reconfiguration, binary decision variables are introduced to model switch statuses, resulting in a mixed-integer optimization problem. Simulation results based on the IEEE 33-bus system demonstrate that reactive power optimization can effectively redistribute HC across nodes, improving power quality in congested networks. Additionally, the incorporation of network reconfiguration provides further HC enhancement, particularly in scenarios where fixed network topology severely limits DER integration. Simulation studies are further extended to the UKGDS 95-bus system, which is derived from a real UK distribution network and incorporates a 33/11 kV on-load tap changer (OLTC) transformer, thereby providing a more practically representative validation platform. The results demonstrate that the proposed framework is effective across networks of different scales and complexities. The proposed approach offers a flexible and efficient tool for modern distribution network planning, supporting high-penetration DER integration while maintaining grid stability and operational reliability. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

A coordinated optimization method for emergency repair scheduling and system operation restoration is proposed to address large-scale transmission line outages caused by extreme weather events such as ice and snow disasters. First, an active outage scenario for transmission lines is constructed based on the Jones icing thickness model and an exponential failure probability model, while incorporating the spatial distribution characteristics of ice disasters. Subsequently, a bi-level optimization model with repair resource constraints is developed. The upper-level model determines the transmission line repair schedule with the objective of minimizing the total repair time while taking system power supply restoration efficiency into account. Based on the completion times of line repairs, the lower-level model optimizes the system restoration process by considering power flow constraints, generator start-up processes, and load restoration characteristics. To address the challenges posed by discrete operational states and strongly coupled bi-level constraints that are difficult to solve using conventional approaches, a logic-based Integer L-shaped coordinated solution method is proposed. Finally, the effectiveness of the proposed method is validated through case studies based on the IEEE New England 10-unit 39-bus system. The results demonstrate that the proposed method can significantly improve system load restoration levels while maintaining high repair efficiency. Full article

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article