Search Results (3,821)

Background/Objectives: Distal femoral physeal fractures are rare and particularly uncommon in very young patients, as they typically require a significant amount of kinetic energy. They carry a high risk of premature physeal closure and later growth disturbance. We aimed to describe the management and long-term outcome of an open distal femoral physeal fracture in a 6-year-old child. Methods: We report a previously healthy 6-year-old child sustained an open distal femoral physeal fracture in an electric scooter–motor vehicle collision. Emergency treatment included trauma assessment, resuscitation, intravenous cefazolin, urgent irrigation and debridement, open reduction, crossed smooth Kirschner-wire fixation, and immobilization. Long-term follow-up included growth prediction using the multiplier method. Results: The injury was classified intraoperatively as a Salter–Harris type I distal femoral physeal fracture. Despite timely surgical treatment, progressive limb-length discrepancy developed, increasing from 1.3 cm at 10 months to 6.5 cm over 5 years. Growth prediction estimated a final discrepancy of 7.32 cm at skeletal maturity, and contralateral distal femoral epiphysiodesis was performed. The literature confirms that displaced high-energy distal femoral physeal injuries in younger children carry a substantial risk of premature physeal closure and later corrective surgery. Conclusions: Open high-energy distal femoral physeal fractures in young children are limb-growth-threatening injuries. This case demonstrates that satisfactory initial fracture management does not eliminate the risk of later premature physeal closure, and that clinically important discrepancy evolves gradually over several years. Long-term follow-up and growth prediction are essential to guide timely corrective treatment to minimize the leg-length discrepancy in bone maturity. Full article

Electric motors that use neodymium–iron–boron (Nd–Fe–B) magnets are at the forefront of global efforts to reduce greenhouse gas emissions. However, a major problem associated with these motors is thermal demagnetization driven by eddy current (EC) losses in the magnets; the relatively low electrical resistivity of Nd–Fe–B magnets means that the magnetic fields in the motor generate considerable EC losses. In this study, Nd–Fe–B magnets with 0–20 wt% Si additives were produced through hot pressing to investigate the effects of Si addition on magnetic properties and electrical resistivity. Small amounts of Si significantly increased electrical resistivity without negatively affecting the magnetic properties. The high coercivity of the Nd–Fe–B magnets, 12.5 kOe, did not decrease even in the presence of up to 15 wt% Si content. The electrical resistivity of Nd–Fe–B magnets increased monotonically as the Si content increased, from 1.43 μΩm for pure Nd–Fe–B magnets to 8.17 μΩm with 20% Si. As the electrical resistivity increased, the associated EC losses decreased; the estimated EC losses were halved with the addition of ~8 wt% Si, and further decreased to one-third through the addition of ~12 wt% Si, while simultaneously maintaining high coercivity. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

With the increasing demand for higher efficiency and power density, innovative winding techniques have become crucial in modern electric machines. Hairpin windings are increasingly used in electric machines, particularly in high-current applications. A novel analytical model is proposed to estimate slot leakage inductance in hairpin windings. Traditional models are limited to random windings, which fail to capture the complex mutual inductance between multiple coil layers. This paper derives a generalized model to estimate specific permeance and total mutual specific permeance for the hairpin windings, which are key factors in determining slot leakage inductance. The proposed model is also valid for fractional-pitch windings. The derived analytical model is validated through finite element analysis (FEA) on an electric motor similar to that employed in Tesla Model S. In addition, experimental validation is performed to further validate the proposed model. Furthermore, parametric analysis is conducted to analyze the influence of slot geometry and conductor dimensions on the slot leakage inductance. This paper contributes an accurate method for predicting slot leakage inductance in hairpin windings; this provides electrical machine designers with a valuable tool for precise modeling and optimization for improved efficiency and performance in various applications. Full article

Vapor compression refrigeration systems account for substantial global electricity consumption, and improving compressor efficiency offers significant potential for energy conservation and climate change mitigation. Linear compressor technology, particularly moving magnet configurations, has attracted attention for its oil-free operation and reduced friction losses, yet comprehensive experimental data under realistic refrigeration cycle conditions remain limited. This study experimentally evaluates the operational characteristics and performance of a moving magnet linear compressor integrated into a complete R134a vapor compression refrigeration system. The investigation systematically varies compressor load from 65% to 85% and pressure ratio from 2.0 to 3.5 while maintaining a fixed condenser temperature of 45 °C. Key parameters, including resonant frequency, piston offset, matching capacitance, power input, mass flow rate, motor and volumetric efficiencies, refrigerant mass distribution, cooling capacity, and coefficient of performance (COP) were measured and analyzed. Results indicate that piston offset remains nearly constant under varying compressor loads, fluctuating around 0.39 mm, but increases by 36% as pressure ratio rises from 2.0 to 3.5, necessitating careful pressure ratio control to prevent mechanical interference. Motor efficiency decreases from 87.7% to 82.4% as the compressor load increases, suggesting favorable part-load operation for domestic energy consumption reduction. This potential remains to be verified through long-term cyclic tests and a full annual energy assessment. The condenser consistently stores over 70% of the refrigerant charge, with distribution most sensitive to operating condition changes. Cooling capacity reaches a maximum of 434.6 W at 85% load and a pressure ratio of 2.0, while the COP achieves approximately 4.5 under the same conditions and decreases to 2.4 at a pressure ratio of 3.5. Normalized COP remains relatively stable at approximately 0.33 across the tested conditions. These experimental findings provide a robust baseline for the design, integration, and control of moving magnet linear compressors in energy-efficient refrigeration applications. Full article

Although PI and PID controllers are mature control laws, their effect on energy-related variables is rarely isolated in a complete electric vehicle traction model when the plant, controller tuning basis and driving conditions are kept unchanged. A full-system MATLAB/Simulink model was developed, comprising a DC motor with PWM H-bridge, reduction gear, vehicle dynamics and a lithium-ion battery with SOC monitoring. Fixed-gain PI and PID configurations were compared under FTP75, with US06 added as a dynamic-cycle assessment. Speed tracking was evaluated using RMSE, MAE, IAE and ITAE, while energy behavior was assessed through SOC depletion, battery voltage, current and braking-command signals. Under FTP75, both controllers achieved nearly identical tracking accuracy, with an overall RMSE of 0.1525 km/h across the active intervals. Despite this kinematic equivalence, PID reduced SOC depletion by 0.980 percentage points over 4.963 km and produced a less intense but more distributed braking command. The additional 600 s US06 simulation did not confirm a general PID advantage: both controllers reached the same maximum speed and showed practically identical tracking accuracy, while PID did not reduce SOC depletion. The results show that the derivative channel changes the control-command pattern, but it does not automatically improve kinematic or energy performance under fixed-gain tuning. Full article

Distributed Electric Propulsion aircraft have gained significant attention for advancing green aviation. However, their application is constrained by the limited energy density of batteries, resulting in weight compensation and flight range limitation. Current research on DEP energy management predominantly focuses on thrust allocation during the cruise phase while largely neglecting the energy regeneration potential during the descent phase. Conventional all-motors active energy recovery strategies force the multi-motor array to operate within a low-efficiency region, since the required drag torque is small under low aerodynamic drag conditions. To solve this issue, this paper proposes an energy recovery strategy that dynamically adjusts the number of activated motors during the descent phase of aircraft. The proposed N-Active strategy can adaptively regulate the number of operating motors, shifting motor operating points from the low-efficiency region to the high-efficiency region, which effectively decouples energy regulation within the longitudinal symmetry plane and maximizes energy recovery benefits. In this study, a high-fidelity simulation platform is established, including nonlinear aerodynamic characteristics and propeller windmilling motor efficiency models. Moreover, the optimal performance of the N-Active multi-motor cooperative energy recovery optimization strategy is verified based on the constructed platform. Simulation results demonstrate that compared with the traditional all motors active strategy, the proposed method improves battery state of charge by 11.96% and reduces virtual weight of battery. This method can effectively alleviate the weight compensation effect of distributed electric propulsion aircraft without additional physical weight increment, thereby enhancing the loading capacity of aircraft. Full article

Traditional optimization methods for interior permanent magnet synchronous motors (IPMSMs) often treat the stator and rotor as independent design domains, which limits the potential for suppressing torque fluctuations due to the neglected electromagnetic coupling between these components. This paper proposes a synergistic optimization strategy for a 120 kW IPMSM, aiming to overcome the inherent limitations of conventional unilateral optimization in design space exploration and achieve global performance enhancement through cross-domain collaboration. By establishing a unified surrogate model incorporating both stator slot geometries and rotor pole topologies, the collaborative effect of seven high-sensitivity design variables is systematically analyzed. The NSGA-II algorithm, coupled with a Kriging surrogate model, is employed to navigate the complex trade-offs among average torque, torque ripple, and cogging torque. Results demonstrate that the synergistic approach achieves a 28.1% reduction in torque ripple while maintaining high average torque, demonstrating superior improvement over conventional stator-only or rotor-only optimization schemes. Analysis based on Maxwell stress tensors and air-gap permeance functions reveals that the proposed method achieves simultaneous suppression of cogging torque and torque ripple by effectively harmonizing the 24th and 48th spatial harmonics. This study provides an efficient synergistic design methodology for the comprehensive performance enhancement of traction motors, offering practical reference value for the engineering development of high-performance electric vehicles. 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

Today, renewable energy is receiving increasing global attention. However, the operation of such energy systems is associated with several challenges, including natural uncertainty and intermittency at different times of the day. Furthermore, to overcome these challenges, there is an increasing interest in developing energy storage systems. Compressed air energy storage (CAES) is considered a promising, cost-effective, and environmentally friendly technology. The present study proposes a novel CAES system distinct from conventional designs. The proposed storage system can store energy by feeding the excess electrical energy to a motor to drive a large-diameter piston to compress and store air in a container. Then, the energy is extracted when needed by releasing the piston to drive the generator back. This study evaluates the feasibility via a thermodynamic model of all components. We examine the effects of (i) piston speed and piston-air volume ratio, (ii) initial pressure, and (iii) container volume. We also assess how container volume scales with the maintained initial pressure. Results are compared against an adiabatic baseline. The results demonstrate that near-isothermal compression/expansion can improve energy density and storage efficiency by generating two times more recoverable work than the adiabatic in the same volume, and an efficiency of 76% can be reached, while the realistic efficiency achieves around 50%. It also shows that the volume of the container for an amount of energy depends on the initial pressure maintained before the charging cycle. As a result, when the initial pressure increases, the volume of the container required decreases, and for the same volume, the results show that more energy can be stored by maintaining the initial pressure. Therefore, this system could be considered an attractive solution to the integration of intermittent renewable energy sources. Full article

Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain–computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present a novel detection method commonly used in time-series forecasting (e.g., stock market trends), identifying crosses in fast (short) and slow (long) moving average windows to identify negative deflections in slow movement-related cortical potentials (MRCPs) or event-related desynchronization (ERD) within −400–+100 ms of movement onset. We recorded EEG data from the Cz electrode during our cued ankle dorsiflexion BCI-NFT paradigm in four adult participants, two neurotypical and two with cerebral palsy. Simulated real-time offline analyses demonstrated an 85.9% mean true positive rate and 14.1% false positive rate of detecting motor intent at a mean −182 ms from movement onset. We further evaluated whether the detection indicated a MRCP and/or ERD, with MRCP detected in 70–80% of trials in three participants, but high ERD detection (87%) instead in the other. Preliminary results indicate that this approach offers a straightforward, accurate, and well-timed method for real-time EEG detection during neurofeedback training and as a control signal for brain–computer interfaces. Full article

The design of special induction motors for variable-speed drives in pumping systems is carried out using the Design of induction machines for adjustable-speed drives (DIMASDrive 2022) software, based on the motor efficiency criterion. The quality of a variable-speed drive is fully determined by an innovative criterion of equivalent costs, which takes into account not only the cost and energy efficiency of the drive, but also the costs of compensating for reactive power and distortion power, which characterize the drive’s energy and electromagnetic compatibility with the grid. The MATLAB program enables the calculation of the innovative criterion of the drive’s reduced costs. Currently, the cost component of distortion power compensation is not taken into account in the reduced cost criterion; consequently, the quality of the drive in monetary terms is determined incompletely and is underestimated. A method is proposed for calculating this component and incorporating it into the reduced cost criterion. The presented results were obtained entirely through simulations conducted using validated software. Experimental studies of the prototype will provide the final answer regarding the solution. Full article

International and regional decarbonisation policies are accelerating the deployment of hybrid electric propulsion systems (HEPSs) in short-sea and coastal trades, yet most existing design studies focus on ferries or tugs, rely on stylised duty cycles, and treat battery degradation only superficially. This paper proposes an integrated, data-driven framework for the design and dynamic performance optimisation of a diesel–battery HEPS for a coastal general cargo vessel operating on short-sea routes. A multi-year automatic identification system (AIS) and logbook data are processed to derive route-specific, time-resolved operating profiles, which drive a DC-based hybrid propulsion model comprising diesel generator sets, propulsion motors and a lithium-ion battery energy storage system (ESS). A degradation-aware ESS model is embedded in a life-cycle cost (LCC) formulation that explicitly accounts for battery replacement timing and residual value. The hybrid design problem is cast as a bi-level optimisation: an upper level determines engine rating and ESS capacity to minimise LCC, while fuel savings and emissions are evaluated as key parallel performance indicators, while a lower level uses dynamic programming to compute optimal power split trajectories under state-of-charge, C-rate and power constraints. A surrogate-assisted global search with Kriging and Expected Improvement is employed to manage the computational burden of repeated lower-level optimisations. Case-study results for representative coastal routes show that the optimised hybrid configurations achieve fuel savings of 16–21%, CO₂ reductions of 17–20%, and LCC reductions of 8–14% relative to a conventional mechanical baseline, outperforming a rule-based hybrid design. Sensitivity analyses with varying fuel prices and ESS costs confirm the robustness of the proposed framework and highlight the importance of explicitly coupling degradation-aware ESS. Full article

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm. Full article