Search Results (114)

Floating vertical-axis wind turbines (VAWTs) couple intrinsically unsteady rotor aerodynamics with the motions of their supporting platforms, producing complex temporal variations in power output, aerodynamic loads, and wake transport. A structured search of the Web of Science Core Collection and Scopus, supplemented by citation tracking, identified peer-reviewed studies published from database inception to 30 June 2026. The reviewed computational fluid dynamics (CFD) studies were classified as decoupled or fully coupled according to whether bidirectional feedback between the flow field and platform response was resolved. The evidence shows that motion-induced velocities alter blade-relative inflow and effective angle of attack, thereby modifying dynamic stall, loads, and wake evolution. Scaled testing is limited by the incompatibility between Froude and Reynolds similitude. Under identical pitch conditions, the mean power coefficient increased by 16.42% at full scale but decreased by 56.71% at 1:100 scale. Platform motion generally increases power and load fluctuations but may accelerate wake recovery; effects on mean performance remain configuration- and scale-dependent, so no universally optimal rotor-platform design has emerged. Overall, this review provides an integrated understanding of the effects of platform motion on the unsteady aerodynamics, load responses, and wake evolution of floating VAWTs, and clarifies the applicability of decoupled and fully coupled CFD methods to mechanism identification and system-level assessment. Full article

This study systematically investigates the dynamic mechanical behavior and constitutive modeling of low-temperature quenched and tempered 42CrMo steel under high-strain-rate and high-temperature conditions. Dynamic compression tests were performed using a split Hopkinson pressure bar (SHPB) system over a strain rate range of 460–6450 s⁻¹ and a temperature range of 25–800 °C. The results show that the flow stress of quenched 42CrMo steel exhibits significant strain hardening and temperature softening effects, while its strain rate sensitivity is observed to be relatively low, especially under ultra-high-strain-rate conditions. Based on the experimental data, both the Power-Law and Johnson–Cook constitutive models were established. A hardness-based temperature softening coefficient was introduced to convert the experimental stress–strain curves into isothermal stress–strain curves, thereby effectively decoupling the coupled effects of strain rate and temperature. Error analysis indicates that the Power-Law model yields an average fitting error of 1.98%, which is superior to that of the Johnson–Cook model (3.23%), suggesting that the Power-Law model is more suitable for describing the dynamic mechanical behavior of low-temperature quenched and tempered 42CrMo steel. The findings of this study provide a reliable constitutive basis for numerical simulations of low-temperature quenched and tempered 42CrMo steel under extreme thermomechanical coupling conditions, such as high-speed cutting and impact forming. Full article

To address the challenge of balancing junction temperature prediction accuracy and computational efficiency for high-power multi-chip IGBT modules in automotive applications during complex electro-thermal conversion processes, this study proposes an improved three-dimensional thermal network model based on equivalent power loss injection. Firstly, the effective heat conduction area of each packaging layer under actual heat flow distribution is extracted through three-dimensional finite element simulation, and the single-chip self-heating network parameters are constructed. Secondly, targeting the thermal cross-coupling effect among multiple chips, an elliptical thermal diffusion model is applied to accurately define the thermal coupling region, and a dynamic equivalent power loss compensation mechanism is introduced. Efficient decoupling of multi-heat-source interference is achieved without increasing the state-space dimension of the model. An experimental benchmarking results comparison indicates that the absolute error of junction temperature prediction by this model under steady-state operating conditions is 0.5 °C. Further comparative analysis under the full CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car) cycle verifies that the improved model not only overcomes the shortcomings of the traditional Foster model, which severely underestimates the transient peak junction temperature and alternating stress amplitude, but also effectively filters out non-physical overshoots caused by short-term ultra-narrow pulses, thus reasonably estimating the device’s maximum junction temperature within the real physical boundary. This method provides efficient theoretical support for accurate dynamic junction temperature predictions and reliability evaluations of electric vehicles under complex operating conditions. Full article

This paper proposes an optimal reconfiguration methodology for electrical distribution systems aimed at improving operational efficiency and service quality. Traditionally, distribution network reconfiguration has focused on minimizing technical losses and improving the voltage profile; however, these approaches do not explicitly incorporate reliability criteria or geospatial aspects associated with the actual operation of distribution networks. The proposed methodology minimizes active power losses while incorporating reliability constraints through the calculation of Energy Not Supplied and a relative georeferenced spatial-operational indicator for the selected switching devices. The approach is based on a topological analysis combined with the Manta Ray Foraging Optimization metaheuristic algorithm, while the electrical evaluation is performed using the fast-decoupled power flow method with the FDXB formulation. The weighted scalar objective function considers active power losses, Energy Not Supplied associated with N–1 contingencies, and the relative georeferenced spatial-operational indicator associated with the selected switching devices. The voltage profile is subsequently evaluated as a technical performance indicator to verify the operational quality of the obtained configurations. The methodology is validated using test systems, achieving loss reductions, improvements in the voltage profile, and a decrease in Energy Not Supplied, thereby demonstrating configurations with improved electrical and reliability performance that are more representative of practical distribution network operation. Full article

The snapshot proper orthogonal decomposition (POD) method relies on synchronously sampled datasets, significantly limiting its utility for analyzing asynchronous measurements in unsteady flow studies. This paper proposes a frequency-domain proper orthogonal decomposition (FDPOD) method tailored for mode extraction and flow field reconstruction from asynchronously sampled data. The FDPOD framework integrates three key components: frequency-domain transformation to decouple phase discrepancies inherent in asynchronous sampling, power spectral density (PSD) analysis combined with segmented ensemble averaging to suppress spectral leakage errors, and eigenvalue decomposition of energy-ranked frequency components to identify dominant coherent structures. Validated through numerical simulations of a subsonic jet and experimental measurements from a low-speed mixed-flow fan, the method demonstrates exceptional performance under asynchronous conditions: cumulative energy errors are reduced to 0.3% across the first 50 modes, while flow field reconstruction achieves 99.5% accuracy. Dominant mode structures exhibit remarkable consistency with those derived from synchronous conditions, with hot-wire measurement errors remaining below 0.03% for both asynchronous and temporally shuffled datasets. These results position FDPOD as a robust and practical tool for analyzing complex unsteady flows where synchronous data acquisition proves impractical, particularly in large-scale or spatially distributed measurement systems. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

Decoupled maximum power point tracking control and output voltage control can be accomplished simultaneously using dual-duty cycle control. However, developed triple switch triple mode (TSTM) exhibits absence of the common ground between the solar panel and output load therefore causing the leakage current to flow which creates safety concern especially for household electrification. In addition to having a negative effect on the solar panel, leakage current increases power losses. Thus, this work proposes a unique TSTM dc-dc converter. The suggested converter has the following advantages: (1) The presence of a common ground between the output load and the solar panel eliminates the leakage current. (2) Reduced electromagnetic interference issues present due to leakage current. (3) Enhanced voltage gain over wider duty cycle. (4) Enables simultaneous decoupled control of MPPT and output voltage. (5) Absence of voltage oscillation across the switches. The proposed TSTM converter is an unique combination of switched inductor and switched capacitor. Both inductor and capacitors are connected in order to boost the level of voltage at the output terminal. The operating principle, design equations and device stress are analyzed in detail for the proposed TSTM. The comparison over existing converter in terms of voltage gain and switch stresses are highlighted in details. Lastly, a laboratory prototype (40/400 V) for 400 W is created and thoroughly tested in order to validate mathematical calculations. Full article

Modern distribution networks face dual challenges: extremely asymmetric spatial power flows caused by the high-penetration integration of distributed renewables under normal operating conditions and asymmetric system faults triggered by extreme weather such as blizzards under extreme conditions. To address these imbalances, this paper integrates distributed energy storage (DES) and soft open points (SOPs) as flexible resources to propose a two-stage joint optimal planning method that balances operational economy and resilience enhancement. First, by incorporating the spatiotemporal evolution trajectory and distance attenuation effects of blizzards, a multi-dimensional scenario sets characterizing asymmetric faults and normal source-load fluctuations are constructed. Second, a joint optimal planning model minimizing the total lifecycle cost is established. The progressive hedging algorithm is then adopted to decouple cross-scenario variables for efficient parallel solving. Verified on both the IEEE 33-node and large-scale 123-node systems, the coordinated planning strategy effectively avoids redundant investment in a single type of device. By establishing a symmetrical balance of flexible resources, the proposed method significantly reduces network losses and renewable curtailment during normal operation, while minimizing the amount of system load shedding under extreme asymmetric faults. Full article

Fault location in distribution networks is often unreliable when measurements are noisy or incomplete. In actual feeders, synchronized data may be missing or distorted because of unstable edge communication. The proposed method uses physics-aware decoupled inference to locate line faults. The method works on single-time snapshots that capture voltages, currents, power flows, and zero-sequence components. These quantities are organized into an ordered hybrid tensor representing the feeder state at that instant. A one-dimensional convolutional encoder extracts spatial context from the tensor. Node measurements are handled separately and fused at the two terminals of each candidate line. This structure removes dependence on recursive graph message passing and confines the effect of local noise. The method is evaluated on the IEEE 33-bus test system under multiple noise levels, random masking of node features, and different fault resistances. With additive noise ($\sigma =0.3$) and 50% random node loss, the model achieves 92.8% localization accuracy. Average inference time per event is 0.62 ms on the tested GPU. The current implementation assumes a fixed feeder topology and synchronized aggregated measurements at the feeder level. Full article

Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage “instantaneous load allocation—day-ahead scheduling” framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. Full article

The rapid growth of electric mobility is reshaping active distribution networks (ADNs), where electric vehicle charging stations (EVCS) introduce spatially concentrated, time-dependent, and highly simultaneous demand. This paper develops a network-oriented framework to evaluate EVCS integration in ADNs by coupling Colombian EV demand characterization, photovoltaic (PV) generation, battery energy storage system (BESS) operation, and AC power flow feasibility. The framework is applied to a 33-bus distribution feeder through four EVCS deployment cases and three support architectures: PV-only, PV–BESS colocated, and PV–BESS dispersed operation. The results show that non-coordinated EVCS deployment may increase losses, reduce voltage margins, and produce thermal overloads when feeder electrical sensitivity is ignored. They also reveal that optimized EVCS siting is insufficient under PV-only support, since PV generation lacks the controllability required to reshape feeder power flows during charging peaks. By contrast, BESS-assisted architectures substantially improve feeder operation, with dispersed storage achieving the best performance by decoupling charging demand locations from grid support locations. SOC and SOH analyses further demonstrate that storage feasibility and degradation must be assessed together with voltage, loading, and loss indicators. The proposed framework provides an operationally consistent basis for technically feasible EVCS planning in ADNs, linking local EV demand characterization, AC feasibility, support-architecture selection, and battery lifetime assessment. Full article

This study details the modelling and simulation analyses performed on a mathematically modelled permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) driven by a field-oriented controlled (FOC) voltage source inverter (VSI) coupled with a half-bridge bidirectional buck-boost DC/DC converter for two-wheeler electric vehicle (EV) applications. The 5 kW, 1500 rpm PMa-SynRM employed here has a shorter response time and is also naturally lighter and cost-effective, making it suitable for two-wheeler EVs. Field-oriented control simplifies the control strategy for PMa-SynRM by decoupling torque and flux, effectively matching the behaviour of a DC motor. A half-bridge buck-boost converter is a DC-DC converter capable of bidirectional power flow, stepping up and down voltages. This makes it ideal for both motoring and regenerative braking in electric vehicles. The buck-boost converter with its controller effectively adjusts the inverter and battery voltage for efficient power flow during motoring and maximum power recovery during regenerating braking. The developed model aims at demonstrating forward and reverse motoring, as well as forward and reverse braking to validate the four-quadrant torque-speed characteristics of two-wheeler EVs. The proposed model attains less than 2% torque ripple and less than 1% speed ripple, respectively. Further, the current ripples are minimised to reduce losses and to improve efficiency. The work presented in this paper implements a PMa-SynRM-based drive system for EVs, a technology which is in the exploratory stage and not commercially widespread. This adds novelty to the proposed work. A MATLAB Simulink environment was used for modelling and simulation. Full article

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster. Full article

The transition toward sustainable and low-carbon power systems increasingly depends on the efficient accommodation of high shares of renewable energy while maintaining secure and reliable grid operation. In interconnected power systems, this challenge is intensified by strong cross-regional coupling, tie-line flow violation risks, and the high computational burden of fully coupled probabilistic assessments. To support the sustainable operation of renewable-rich interconnected systems, this paper proposes a probabilistic production simulation method that incorporates risk-aware tie-line exchange optimization. Sequential random sample paths are constructed by considering load fluctuations, renewable energy output uncertainty, and random outages of conventional units. Using cross-regional exchange power as coupling variables, a conditional value-at-risk (CVaR)-based pre-scheduling model is established to control tie-line and interface flow tail risks. Given the scheduled exchange power, cross-regional exchanges are transformed into regional boundary power injections, enabling decoupled sequential probabilistic production simulation for each region. The exchange schedule is then iteratively updated through marginal-value feedback. A four-region interconnected system is used for case-study validation. Results show that the proposed method improves renewable energy accommodation, reduces renewable curtailment, suppresses tie-line flow violation risk, and maintains high reliability assessment accuracy. Compared with the region-decoupled benchmark with fixed exchange power, the proposed method increases the renewable energy accommodation rate from 93.82% to 95.41% and reduces renewable curtailment from 312,162 MWh to 231,284 MWh, while also lowering expected energy not served and loss of load expectation. In addition, under the reported case-study setting, the proposed RC-IEF-PPS reduces the computation time from 5216.24 s for Full-PPS to 4074.63 s, i.e., by 21.9%, while maintaining comparable reliability assessment accuracy. These results indicate that the proposed framework can support the sustainable integration of high-penetration renewable energy by improving clean-energy utilization, operational reliability, and computational tractability in interconnected power systems. Full article