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The importance of propeller effects and power contribution to the aerodynamics of small aircraft and unmanned aerial vehicles (UAVs) is indispensable. The aerodynamic analysis of wings in flight varies from rigid wing analysis due to wing deflection caused by transferred aerodynamic loads. This paper investigates the intertwined influence of propeller effects and elasticity on the aerodynamics of small propeller-driven aircraft and UAVs. Through a detailed methodology, a twin-engine propeller-driven aircraft is analyzed as a case study, providing insights into the proposed approach. Two critical analyses are presented: an examination of propeller effects in rigid aircraft and the incorporation of elastic wing properties. The former establishes a foundational understanding of aerodynamic behavior, while the latter explores the impact of wing elasticity on performance. Validation is achieved through comparative analysis with wind tunnel test results from a similar rigid structure aircraft. Utilizing NASTRAN software V2010.1, aerodynamic analysis of the elastic aircraft is conducted, complemented by semi-empirical insights. The results highlight the importance of these factors across different angles of attack. Furthermore, deviations from the rigid aircraft configuration emphasize the considerable influence of static aeroelasticity analysis, notably increasing longitudinal characteristics by approximately 20%, while showing a lower impact of 5% in lateral-directional characteristics. This study contributes to enhanced design and operational considerations for small propeller-driven aircraft, with implications for future research and innovation, particularly for the purpose of efficient concepts in advanced air mobility.

Unmanned aerial vehicles (UAVs) are increasingly being powered by fuel cells, which provide a zero-emission green energy source, improve endurance, and reduce charging/refuelling times. These gadgets are used for anything from aerial photography to military activities. By tackling two problems—power management (PM) of the resources and design optimization (DO) of a hybrid electric source (HES) made up of a fuel cell (FC) and a battery—this study seeks to increase endurance and energy efficiency. It is designed for a fixed-wing electric unmanned aerial vehicle (EUAV) and uses a novel method to lighten the drone. For hybrid electric drones, energy management techniques are crucial. Using fuzzy logic-based programming and Multi-Factor Reinforcement Learning (MFRL), we will apply a reinforcement learning system to regulate the drone’s fuel consumption between the fuel cell and the battery. The Harris Hawk Optimization (HHO) algorithm is used by DO to determine the fuel cell and battery’s maximum power and capacity in order to reduce resource use. In order to choose the best management system, this PMS will use the HHO method to optimize the MFRL parameters and membership functions in the fuzzy logic structure. We have considered the uncertainty that governs the drone’s mobility and the effect of variations in wind speed in order to produce a realistic model. In the fuzzy system, we have also incorporated the wind speed variable for the energy management problem. Through the use of a modelling platform that integrates the UAV and hybrid power system models with a Matlab tool, the proposed method is assessed and yields an 8% weight reduction, saving a total of 70.43 kJ of energy, which can extend the “endurance phase” by more than 30 min. Additionally, the proposed method reduces the amplitude of SoC fluctuations, saving up to 40% of FC energy and enabling the UAV to operate for longer missions.

Cogeneration systems for simultaneous supply of power and hydrogen have been studied extensively because of their great potentials. Accordingly, in the present study, an innovative cogeneration system consisting of a gas turbine, a gasifier, a transcritical Rankine cycle, and a proton exchange membrane electrolyzer is proposed. The system operates on municipal solid waste (MSW) with constant power output. The proposed cogeneration system is simulated under steady-state condition using Engineering Equation Solver (EES) software, and its performance is evaluated from the first and second laws of thermodynamics. The proposed system produced 3.92 MW power and 608.8 m 3 /h hydrogen under biomass feed of 1.155 kg/s. Under this design condition, the energy utilization factor (EUF), the total exergy efficiency, and the overall exergy destruction rate are calculated 34.71%, 29.44%, and 11,854 kW, respectively. There components of gasifier, gas turbine, and combustion chamber were introduced for owning the highest exergy destruction rate. A comprehensive parametric study was carried out, and it was concluded that the exergy efficiency of condenser has the lowest value among all components. Also, results indicate that the EUF and the total exergy efficiency can be increased by increasing the inlet temperature of the gas turbine or by decreasing the maximum pressure of the transcritical CO 2 cycle. In conclusion, the proposed biomass-driven cogeneration system can produce clean electricity and hydrogen by consuming CO 2 . The strengths of this system are consumption of municipal waste as the main fuel, simplicity in design, as well as high productivity of hydrogen gas. Graphic abstract

Herein, the design optimization of multi-objective controllers for the lateral–directional motion using proportional–integral–derivative controllers for a twin-engine, propeller-driven airplane is presented. The design optimization has been accomplished using the genetic algorithm and the main goal was to enhance the handling quality of the aircraft. The proportional–integral–derivative controllers have been designed such that not only the stability of the lateral–directional motion was satisfied but also the optimum result in longitudinal trim condition was achieved through genetic algorithm. Using genetic algorithm optimization, the handling quality was improved and placed in level 1 from level 2 for the proposed aircraft. A comprehensive sensitivity analysis to different velocities, altitudes and centre of mass positions is presented. Also, the performance of the genetic algorithm has been compared to the case where the particle swarm optimization tool is implemented. In this work, the aerodynamic coefficients as well as the stability and control derivatives were predicted using analytical and semi-empirical methods validated for this type of aircraft.

Recent development in Electric Vertical Take-off and Landing (eVTOL) aircraft makes it a popular design approach for urban air mobility (UAM). When designing these configurations, due to the uncertainty present in semi-empirical estimations, often used for aerodynamic characteristics during the conceptual design phase, results can only be trusted to approximately 80% accuracy. Accordingly, an optimized aircraft using semi-empirical estimations and deterministic multi-disciplinary design optimization (MDO) approaches can be at risk of not being certifiable in the detailed design phase of the life cycle. The focus of this study was to implement a robust and efficient possibility-based design optimization (PBDO) method for the MDO of an eVTOL tilt-wing aircraft in the conceptual design phase, using existing conventional designs as an initial configuration. As implemented, the optimization framework utilizes a deterministic gradient-based optimizer, run sequentially with a possibility assessment algorithm, to select an optimal design. To achieve this, the uncertainties which arise from multi-fidelity calculations, such as semi-empirical methods, are considered and used to modify the final design such that its viability is guaranteed in the detailed design phase. With respect to various requirements, including trim, stability, and control behaviors, the optimized eVTOL tilt-wing aircraft design offers the preferred results which ensure that airworthiness criteria are met whilst complying with predefined constraints. The proposed approach may be used to revise currently available light aircraft and develop eVTOL versions from the original light aircraft. The resulting aircraft is not only an optimized layout but one where the stability of the eVTOL tilt-wing aircraft has been guaranteed.

Employing fossil fuels in electricity generation increases carbon emissions and worsens global warming. Relying solely on fossil fuels is not a sustainable solution, which is making renewable energy sources (RESs) an essential part of a sustainable power system. However, high RES integration into power systems poses stability challenges, primarily due to issues with balancing supply and demand. One technical solution is to transfer RES unpredictability to the natural gas network, thereby enhancing power system stability. Other complementary solutions include energy storage systems (ESS), such as power-to-gas (P2G) conversion and battery energy storage systems (BESS), which provide additional balancing capabilities. Peer-to-peer (P2P) energy trading facilitates the integration of distributed energy resources (DERs), while microgrid architectures enable their implementation in medium-voltage (MV) distribution systems. However, neglecting grid constraints—particularly active power losses and voltage stability limits—may compromise the sustainability and cost-effectiveness of RES integration. This paper proposes a multi-objective decentralized P2P energy transactive market framework for the integrated multi-energy microgrid (IEM). The proposed framework employs a double-objective optimization (DOO) approach, minimizing both operating costs and active power losses while incorporating AC power flow constraints and gas pipeline dynamics. A Continuous Double Auction (CDA) mechanism facilitates dynamic energy trading among various energy resources (ERs), containing gas-fired generators (GFGs), prosumers (PRs), P2G systems, and RES-based energy producers. The DOO P2P market outperforms single-objective approaches, achieving 40% lower energy losses and 47% lower peak power demand. Additionally, since the DOO framework reduced energy losses and peak demand significantly, the loss-aware energy dispatch improves the average nodal voltage by 1.5%.

Integration of distributed generation (DG) units in distribution networks (DNs) has some benefits, such as improvement in voltage profile, decrease in power losses, and reduction in operation costs. In line with this concern, the achievement of these advantages, along with environmental benefits, can be further strengthened by the optimal placement and sizing of renewable‐based DGs. Reconfiguration is well known as another approach for optimizing the voltage profile and reducing energy losses in DNs. In this paper, a comprehensive model of simultaneous optimal reconfiguration and allocation of renewable energy DGs, including wind turbines (WTs) and biomass (BM) units in DNs, is presented, considering uncertainties of renewables and hourly load demands. In addition, environmental aspects of the proposed problem are taken into consideration by including emissions resulting from the use of other fossil fuel generations in the objective function. To cope with uncertainties in a robust framework, the information gap decision theory (IGDT) method is implemented. The proposed robust optimization model is examined on the IEEE‐33 node DN as a benchmark based on a discrete particle swarm optimization (DPSO) algorithm in MATLAB platform software. Various cases are considered to examine the impact of uncertainty budgets and robustness indices of different parameters on the results. The achieved simulation results are analyzed and compared with the other existing algorithms to verify the accuracy of the proposed method and its superiority over other algorithms in reducing costs and losses. In this paper, a comprehensive model of simultaneous optimal reconfiguration and allocation of renewable energy DGs including wind turbines (WTs) and biomass (BM) units in power distribution networks is presented considering uncertainties of renewables and hourly load demands.

A real-time flight simulation tool is proposed using a virtual reality head-mounted display (VR-HMD) for remotely piloted airships operating in beyond-line-of-sight (BLOS) conditions. In particular, the VR-HMD was developed for stratospheric airships flying at low/high altitudes. The proposed flight simulation tool uses the corresponding aerodynamics characteristics of the airship, the buoyancy effect, mass balance, added mass, propulsion contributions and ground reactions in the FlightGear Flight Simulator (FGFS). The VR headset was connected to the FGFS along with the radio controller containing the real-time orientation/state of each button, which is also simulated to provide better situational awareness, and a head-up display (HUD) that was developed to provide the required flight data. In this work, a system was developed to connect the FGFS and the VR-capable graphics engine Unity to a PC and a wireless VR-HMD in real time with minimal lag between data transmission. A balance was found for FGFS to write to a CSV file at a period of 0.01 s. For Unity, the file was read every frame, which translates to around 0.0167 s (60 Hz). A test procedure was also conducted with a similar rating technique based on the NASA TLX questionnaire, which identifies the pilot’s available mental capacity when completing an assigned task to assure the comfortability of the proposed VR-HMD. Accordingly, a comparison was made for the aircraft control using the desktop simulator and the VR-HMD tool. The results showed that the current iteration of the system is ideal to train pilots on using similar systems in a safe and immersive environment. Furthermore, such an advanced portable system may even increase the situational awareness of pilots and allow them to complete a sizeable portion of actual flight tests with the same data transmission procedures in simulation. The VR-HMD flight simulator was also conceived to express the ground control station (GCS) concept and transmit flight information as well as the point of view (POV) visuals in real-time using the real environment broadcast using an onboard camera.

Purpose To investigate the prevalence of low back pain (LBP) and its absence rate among female university student athletes in different types of sports. Methods A cross-sectional study based on a standard self-reporting questionnaire was performed among 1335 athletes. Participants were female athletes who attended the National Sports Olympiad of Female University Students in basketball, volleyball, futsal, tennis, badminton, swimming, track and field, shooting, and karate. Results One thousand and fifty-nine athletes with the mean (SD) age of 23.1 (3.8) years responded to the questionnaire (response rate 79 %). The 12-month prevalence of LBP was 39.0 %; in addition, lifetime and point prevalence of LBP were 59.7 and 17.8 %, respectively. Basketball (47.9 %) and karate (44.0 %) players had reported the highest 12-month prevalence of LBP. Also, LBP prevalences in shooting (29.7 %) and badminton (42.4 %) players were not negligible. Results show that, LBP led to relatively high absence rate from training sessions (27.9 %) and matches (13.0 %). Conclusion While most of the existing literatures regarding female athletes’ LBP have focused on particular sports with specific low back demands (such as skiing and rowing), many other sports have not been studied very well in this regard. Investigating LBP prevalence and related factors in other types of sports, such as combat sports, badminton and shooting, can help us better understand the prevalence of low back pain and provide us with necessary insight to take effective steps towards its prevention in athletes.