Electromagnetic-Thermal Integrated Design Optimization for Hypersonic Vehicle Short-Time Duty PM Brushless DC Motor

High reliability is required for the permanent magnet brushless DC motor (PM-BLDCM) in an electrical pump of hypersonic vehicle. The PM-BLDCM is a short-time duty motor with high-power-density. Since thermal equilibrium is not reached for the PM-BLDCM, the temperature distribution is not uniform and there is a risk of local overheating. The winding is a main heat source and its insulation is thermally sensitive, so reducing the winding temperature rise is the key to the improvement of the reliability. In order to reduce the winding temperature rise, an electromagnetic-thermal integrated design optimization method is proposed. The method is based on electromagnetic analysis and thermal transient analysis. The requirements and constraints of electromagnetic and thermal design are considered in this method. The split ratio and the maximum flux density in stator lamination, which are highly relevant to the windings temperature rise, are optimized analytically. The analytical results are verified by finite element analysis (FEA) and experiments. The maximum error between the analytical and the FEA results is 4%. The errors between the analytical and measured windings temperature rise are less than 8%. It can be proved that the method can obtain the optimal design accurately to reduce the winding temperature rise

Investigation on thermal and electrical performance of late-model plate-and-tube in water-based PVT-PCM collectors

A large amount of redundant energy gained from incident solar energy is dissipated into the environment in the form of low-grade heat, which significantly reduces and limits the performance of photovoltaic cells, so removing or storing redundant heat and converting it back into available thermal energy is a promising way to improve the utilization of solar energy. A new combined water-based solar photovoltaic-thermophotovoltaic system embedded in the phase change material (PCM) mainly is proposed and designed. The effects of the water flow rate, cell operating temperature, the presence of PCM, and the thickness of the PCM factor on the overall module performance are explored comprehensively. The maximum thermal power output and the corresponding efficiency of the combined-system-embedded PCM are calculated numerically, The results obtained are compared with those of the PV (photovoltaic) and PVT(photovoltaic-thermal) cells with the same solar operating conditions. In addition, the PVT-PCM system possesses a higher power output and overall efficiency in comparison with the PVT and PV system, and the maximum cell temperature reduction of 12.54 °C and 42.28 °C is observed compared with PVT and PV systems. Moreover, an increased average power of 1.13 W and 4.59 in PVT-PCM systems is obtained compared with the PVT system and the PV system. Numerical calculation results illustrate that the maximum power output density and efficiency of the PVT-PCM are 3.06% and 16.15% greater than those of a single PVT system and PV system in the working time range, respectively. The obtained findings show the effectiveness of using PCM to improve power output and overall efficiency

Stability of Uncertain Impulsive Stochastic Genetic Regulatory Networks with Time-Varying Delay in the Leakage Term

This paper is concerned with the stability problem for a class of uncertain impulsive stochastic genetic regulatory networks (UISGRNs) with time-varying delays both in the leakage term and in the regulator function. By constructing a suitable Lyapunov-Krasovskii functional which uses the information on the lower bound of the delay sufficiently, a delay-dependent stability criterion is derived for the proposed UISGRNs model by using the free-weighting matrices method and convex combination technique. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI control toolbox. In addition, three numerical examples are given to justify the obtained stability results

Numerical simulation of PV/T air collector with gradually expanding and shrinking channels

CFD technology isused to simulate the PV/T air collector with gradually expanding and shrinking channels, and thegradually expanding and shrinking channels have influence on the temperature of the heat absorbing plate and the temperature outlet air of the PV/T air collector is analyzed.The resultshowsthat the expanding and shrinking channels have adverse effectson the massairflow in the collector,causing the temperature of the heat absorption plate to rise.Increasing the mcan effectively reduce the temperature of the heat absorption plate and enhancethe photoelectricefficiency of the system,and thetemperatureof air outlet will be enhanced.However, themassflowhas greater influence on the outlet temperature, and the increase of the mass flowmakes the outlet temperature decreasemore obvious. As the air mass flow increases, the temperature difference between m=0.0081and m=0.0169will increase. When the massflow is 0.0029kg/s, the temperature difference is 1.69℃; when the massflow is 0.0169, the temperature difference is 2.35℃

A hybrid method for remaining useful life prediction of fuel cells under variable loads

Cost and durability are crucial factors limiting the widespread commercialization of proton exchange membrane fuel cells (PEMFC). Prognostics, aiming at health indicator extraction and remaining useful life prediction, is a key issue for PEMFC durability enhancement. However, deploying prognostics for PEMFC under dynamic load still faces challenges in extracting health indicators reliably and predicting the degradation evolution efficiently. This work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to predict the remaining useful life. The proposed method is tested using long-term dynamic load ageing experiments. The results show that the method can provide reliable prognostics horizons and improve real-time performance

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

International audienceHealth indicator of fuel cell under dynamic operating conditions is extracted. • Extracted health indicator has clear trend and can also indicate various faults. • Symbolic-based deep learning captures historical trend and retains it in prediction. • Hybrid approach provides wide prognostic horizon and credible lifetime estimation

Technical and economic analysis of multi-energy complementary systems for net-zero energy consumption combining wind, solar, hydrogen, geothermal, and storage energy

An integrated renewable energy supply system is designed and proposed to effectively address high building energy consumption in Zhengzhou, China. This system effectively provides cold, heat, and electricity by incorporating various clean energy sources such as wind, solar, hydrogen, and geothermal energy. Technical and economic analyses are conducted to optimize the integration of these renewable sources. Technical and economic analyses are conducted to optimize the integration of these renewable sources. Rigorous system modeling and dynamic simulation using TRNSYS software evaluate the seamless integration and optimal functioning of the PV/T subsystem within the CCHP system. The interaction between Photovoltaic/Thermal (PV/T) and borehole heat exchanger (BHE) coupling is investigated, analyzing their impact on individual system performance. Furthermore, key indicators, including overall electricity consumption (OEC), life cycle cost (LCC), heat pump coefficient of performance (COPHP), and system coefficient of performance (COPSYS) are analyzed. The robust response surface methodology (RSM) and Box-Behnken experimental design approach are employed to show remarkable agreement between predicted and simulated values, with a maximum deviation of only 1.45%. The optimal configuration consists of a PV/T area of 132 m2, 20 wind turbines, 12 alkaline fuel cells, and 17 borehole heat exchangers, resulting in highly favorable outcomes: an OEC of −35648.72 kW∙h/year, an LCC of $209113.85, a COPSYS of 2.91, and a COPHP of 3.82. Moreover, detailed assessments of each subsystem's performance enhances our understanding of the system's overall operation, affirming the feasibility of the proposed integrated energy supply system for buildings

Three-Dimensional Printed Biomimetic Robotic Fish for Dynamic Monitoring of Water Quality in Aquaculture

The extensive water pollution caused by production activities is a key issue that needs to be addressed in the aquaculture industry. The dynamic monitoring of water quality is essential for understanding water quality and the growth of fish fry. Here, a low-cost, low-noise, real-time monitoring and automatic feedback biomimetic robotic fish was proposed for the dynamic monitoring of multiple water quality parameters in aquaculture. The biomimetic robotic fish achieved a faster swimming speed and more stable posture control at a swing angular velocity of 16 rad/s by using simulation analysis. A fast swimming speed (0.4 m/s) was achieved through the control of double-jointed pectoral and caudal fins, exhibiting various types of movements, such as straight swimming, obstacle avoidance, turning, diving, and surfacing. As a demonstration of application, bionic robotic fish were placed in a lake for on-site water sampling and parameter detection. The relative average deviations in water quality parameters, such as water temperature, acidity and alkalinity, and turbidity, were 1.25%, 0.07%, and 0.94%, respectively, meeting the accuracy requirements for water quality parameter detection. In the future, bionic robotic fish are beneficial for monitoring water quality, fish populations, and behaviors, improving the efficiency and productivity of aquaculture, and also providing interesting tools and technologies for science education and ocean exploration

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

International audienceProton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness