Search Results (398)

The Composite Cycle Engine (CCE) enhances the conventional Joule/Brayton cycle by replacing the high-pressure compressor with a high-quality piston-based gas generator that enables extremely high compression, combustion, and expansion of the working fluid before entering the classic Joule burner. This piston-based topping cycle unlocks much more efficient fuel utilization. This paper studies a CCE concept featuring a system of free double piston (FDP) units for a potential long-range (LR) application in 2045, benchmarked against an advanced turbofan engine representative of the same time frame. In-house-developed simulation tools for the piston system and the overall power plant, as well as aircraft non-linear trade factor analysis, are used for different levels of conceptual assessment. First, the cooling demand inside the FDP system is determined. An engine cycle parametric study is then performed for the design point top-of-climb (ToC). Off-design performance is further studied, demonstrating a 9.3% improvement in thrust-specific fuel consumption (TSFC) in cruise relative to the baseline engine. After incorporating the engine weight and nacelle geometry effects, the engine reaches a total mission fuel burn reduction of around 14.7% compared to the baseline engine. The concept evaluation shows the fuel burn potential of the CCE in the future LR aviation sector and lays the foundation for further climate impact analysis. Full article

To improve the aerodynamic design efficiency of nacelle intake systems for wing-mounted civil aero-engines under multiple operating conditions, an integrated multi-objective optimization method was developed to address the limited optimization efficiency and robustness encountered in conventional approaches. The proposed method employed parametric techniques to construct three-dimensional non-axisymmetric nacelle geometries and integrated flow-field simulations with performance evaluation modules, forming a hybrid optimization framework based on a Kriging surrogate model coupled with the NSGA-II genetic algorithm. Two-dimensional numerical analyses were employed to rapidly evaluate inlet profiles and constrain the three-dimensional design space. Following the reduction in the design space, the three-dimensional optimization simultaneously accounted for multiple performance objectives, including nacelle drag and block fuel consumption during cruise conditions, as well as inlet distortion and flow separation under off-design conditions. A set of Pareto-optimal solutions was obtained through surrogate-based prediction and validated using high-fidelity CFD simulations. The results indicate that the optimized nacelle configuration achieves a 0.933% reduction in drag coefficient and a 0.628% decrease in block fuel consumption under cruise conditions. Under crosswind conditions, the inlet total pressure recovery coefficient is increased by 2.76%, accompanied by a pronounced reduction in flow separation, while under maximum-lift coefficient conditions, the total pressure recovery remains above 99%. These results demonstrate that the proposed optimization approach enables coordinated aerodynamic performance improvements across multiple operating conditions while simultaneously enhancing overall aircraft fuel efficiency, providing an effective strategy for advanced nacelle aerodynamic shape design. Full article

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences and finite venue capacities, lacking predictive capability for the ultimate planning quality. To overcome these limitations, this study proposes a novel bio-inspired data-driven hybrid optimization framework for the cruise itinerary planning task unit partition. The framework innovatively integrates a Genetic Balanced Clustering Algorithm (GBCA) for multi-objective passenger grouping, Kernel Principal Component Analysis (KPCA) for feature extraction from preference data, an improved Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) for hyperparameter optimization, and a Kernel Extreme Learning Machine (KELM) for data-driven prediction of itinerary planning quality. This synergy enables the framework to dynamically allocate venue capacities based on group preferences and optimize partitioning towards maximizing overall benefits, ensuring load balance and fairness. Extensive experiments on simulated cruise scenarios demonstrate that the proposed framework significantly outperforms conventional methods, improving segmentation quality by at least 40% while exhibiting superior convergence speed and stability. This work provides a scalable, intelligent solution for complex resource-constrained scheduling problems, showcasing the effective application of bio-inspired data-driven methodologies in engineering optimization. Full article

In urban driving cycles, battery electric vehicles are subject to frequent start–stop operations, which lead to substantial braking energy losses. Although fuzzy control (FC) strategies are commonly employed for regenerative braking, their performance is often constrained by subjectively defined membership functions and rules. To address this limitation, this paper proposes an improved FC strategy that is optimized using the particle swarm optimization (PSO) algorithm. Focusing on a front-wheel-drive BEV, a three-input single-output fuzzy controller is developed in accordance with ECE regulations, where braking intensity, battery state of charge (SOC), and vehicle speed serve as inputs, and the motor braking force ratio serves as the output. A co-simulation platform based on AVL-Cruise 2019 and Matlab/Simulink 2017a is established to evaluate the strategy under the New European Driving Cycle (NEDC) and the Worldwide Light Vehicles Test Cycle (WLTC). Additionally, hardware-in-the-loop (HIL) tests are conducted to validate the practical feasibility and accuracy of the optimized strategy. The results demonstrate that the PSO-optimized FC strategy achieves a performance in real-world controllers that is comparable to that observed in a simulation, confirming its real-time applicability. Specifically, under the NEDC, the optimized strategy reduces battery SOC from 0.90 to 0.8795, representing improvements of 0.2515% and 0.4670% over the unoptimized FC strategy and the ideal distribution strategy, respectively. The regenerative braking efficiency is enhanced by 2.45% and 10.48%. Under the WLTC, the final SOC with the optimized strategy is 0.8488, reflecting gains of 0.5202% and 0.8380% over the two reference strategies, while regenerative braking efficiency improves by 2.32% and 8.95%. These findings indicate that the proposed strategy offers a safe and effective solution for improving the regenerative braking performance in electric vehicles. Full article

Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality. Full article

High-performance geared turbofan engines generate significant heat within the planetary power gearbox. This study presents the thermal design of an integrated fan guide vane heat exchanger aimed at recovering gearbox heat losses with minimal pressure loss and converting them into useful propulsive energy via the Junkers–Meredith Effect. Hot gearbox oil is routed through hollow fan static guide vanes, enabling heat transfer to the bypass airflow while simultaneously reducing oil temperature and augmenting thrust. A comprehensive analytical framework is applied, incorporating heat transfer modeling, guide vane geometry reconstruction, lubrication flow sizing, and propulsion performance evaluation for both take-off and cruise flight conditions, using the PW1127G-JM geared turbofan as the reference engine. The results indicate that the proposed system can achieve a thrust increase of up to 6.4% at the end of take-off and deliver a thrust-specific fuel consumption reduction of up to 5.6% during take-off and approximately 2% during cruise. While sufficient heat dissipation is achieved under cruise conditions, take-off operation requires a higher transient oil temperature. Overall, this study demonstrates that integrating heat recovery into existing engine structures offers a promising pathway to enhance propulsion efficiency, reduce fuel consumption, and support more sustainable aircraft engine designs. Full article

Hydrogen fuel-cell-powered all-electric aircraft are promising for decarbonizing short-range aviation, but the substantial low-temperature waste heat demands a compact thermal management system (TMS). This study presents a methodological framework for the integrated co-design of the TMS and powertrain using multi-objective optimization and holistic mission-level analysis to identify optimal TMS designs and operating strategies. Changes in TMS net drag translate into changes in required aircraft thrust, while changes in powertrain, TMS, and fuel mass affect the available payload under a constant maximum take-off mass assumption. This iterative process yields performance metrics across TMS cooling architectures (parallel or series), heat exchanger mass-drag characteristics, coolant temperature targets (50, 70, or 90 °C), and installation objectives (minimizing mass or ram-air duct length). The optimal design is a parallel cooling architecture that balances mass-specific heat rejection of 4.77 kW kg⁻¹ at hot-day take-off with drag-specific heat rejection of 1.29 kW N⁻¹ at standard-day cruise. A reduction in coolant temperature at standard-day missions entails no significant performance penalties and could improve the efficiency of electrical components. A shorter ram-air duct significantly decreases the available payload by 630 kg but may facilitate nacelle integration. The findings underscore that holistic TMS-powertrain co-design and optimization is essential for rigorous design of sustainable all-electric aircraft. Full article

Recently, the United States unveiled a conceptual design of an unmanned high-speed vehicle, the SR-72, which boasts a maximum flight speed of Mach 6, enabling rapid airspace dominance and superior combat performance. To this end, this study conducted a comprehensive review of publicly available data and employed 3D modeling software to reconstruct the SR-72 configuration, utilizing the supersonic thin airfoil NACA 16006 for the wing design. Subsequently, a meticulously structured computational mesh was generated. Numerical simulations were conducted across subsonic, transonic, supersonic, and high-Mach-number flow regimes. The results reveal that the vehicle exhibits high maneuverability in subsonic conditions, with a stall angle of attack reaching 24°. In transonic conditions, significant wave drag is observed, while, in supersonic and high-Mach-number flow regimes at Mach 6, the vehicle demonstrates excellent wave-riding performance, enabling extended cruise durations and improved fuel efficiency. Furthermore, the initial airfoil was optimized using the CST (Class-Shape Transformation) parameterization method and the SLSQP (Sequential Least Squares Programming) algorithm. Under the given constraints, the drag coefficient was reduced by 40%, demonstrating a significant optimization effect. Full article

This paper proposes a power architecture for a Compound-Wing Unmanned VTOL to supply power during the hovering state. To enhance the hovering efficiency of the UAV while considering the cruising efficiency, the layout structure of a traditional Compound-Wing Unmanned VTOL is optimized. A high-power-density hybrid-power range-extender using an ICE (internal combustion engine) suitable for the Compound-Wing Unmanned VTOL is designed. The engine electronic control unit (ECU) suitable for the range-extender is presented by using a locally linearized state-space equation and LQR (Linear-Quadratic Regulator). Simulation experiments, ground running tests, and flight tests have been conducted to verify the performance of the Compound-Wing Unmanned VTOL and its power architecture. Full article

This paper presents a comprehensive study aimed at improving the efficiency of unmanned aerial vehicles (UAVs) through the enhancement of their aerodynamic and mechanical structures. The research is based on coupled computational fluid dynamics (CFD) and finite element analysis (FEA). The airflow around the UAV was modeled using the Navier–Stokes equations, while the structural behavior was described by the equations of linear elasticity. A UAV configuration with a wingspan of 1.8 m and a mass-optimized structure was investigated for flight speeds in the range of 10–35 m/s and angles of attack from −5° to +15°. The results of the aerodynamic optimization, including airfoil thickness variation and smoothing of the wing–fuselage junction, showed a reduction in the drag coefficient by 9–12% and an increase in the lift-to-drag ratio by up to 11% in the cruise regime. The structural optimization based on replacing aluminum with a carbon-fiber composite material led to a reduction in the structural mass by 13–16%, a reduction in the structural strength criterion value by 18–22%, as confirmed by the Tsai–Wu failure analysis, and a reduction in wing-tip deflection by 20–25% under 3 g and 5 g load cases, while satisfying strength and stiffness requirements. The obtained results demonstrate that the proposed integrated aerodynamic and structural optimization approach significantly improves the overall performance, efficiency, and operational reliability of UAV systems. Full article

The decarbonization of large cruise ships is challenged by their extreme and tightly coupled electrical, thermal, and cooling demands. This study investigates a liquid hydrogen (LH₂)-based tri-generation system for cruise ships that simultaneously supplies electricity, heat, and cooling. Key novelties include the use of LH₂ as the onboard energy carrier for large cruise ships, the recovery of cooling energy from LH₂, a dynamic control strategy that synergistically modulates PEM fuel cell utilization to regulate downstream catalytic burner heat generation and balance heat and electricity generation and demand, and the first full-scale cruise-ship model of such a system, including hydrogen consumption and onboard storage sizing. A dynamic system-level model is applied to a representative 7-day voyage of a large cruise ship. The results show that the proposed system can meet combined peak demands of approximately 61 MW while achieving overall system efficiencies approaching 75%. Compared to traditional marine diesel-based power plants, the LH₂-based tri-generation configuration improves system efficiency by more than 20 percentage points. Total hydrogen consumption is estimated at approximately 240 t, which can be reduced by about 20% through shore-to-ship power, yielding a system volume comparable to that of a conventional diesel-based power plant. These results demonstrate the technical feasibility and system-level advantages of LH₂-based tri-generation for zero-emission cruise ships. Full article

Accurate perception of tugboat operational status is essential for optimising port scheduling efficiency and ensuring operational safety. However, existing AIS-based methods often struggle to capture the fine-grained and asymmetric manoeuvring characteristics of tugboats, particularly in distinguishing assisted berthing from unberthing operations. To address these limitations, this study proposes a hybrid recognition framework integrating multidimensional feature engineering with spatiotemporal dynamics. First, a speed-threshold-based sliding window algorithm segments trajectories into sailing and berthing states. Second, a 15-dimensional feature vector—comprising statistical and descriptive features from speed, heading, and trajectory morphology—is constructed to characterise tugboat behaviour. Notably, morpho-logical descriptors such as the ‘Overlap Ratio’ serve as implicit spatial proxies, capturing geographical constraints without reliance on Electronic Navigational Charts. A three-layer fully connected neural network (FCNN) is then developed to classify segments into “Cruising” and “Assisting in Berthing/Unberthing.” Finally, a speed-dynamics rule further distinguishes berthing from unberthing based on opposing temporal evolution patterns. Experiments on real AIS data from Ningbo–Zhoushan Port demonstrate that the model achieves an F1-score of 0.90 and a recall of 0.93 for assistance-related operations. Permutation importance analysis confirms that integrating kinematic and morphological features enables interpretable and precise intent inference. This study offers a high-precision, low-dependency solution for tugboat operation identification, supporting intelligent port surveillance and sustainable maritime management. Full article

Dimensioning the energy storage systems for a heavy-duty fuel cell hybrid electric vehicle is not straightforward. This study proposes a methodology to address this challenge, aiming to maximize efficiency while mitigating the aging effects on the energy storage systems. Various configurations of storage system ratios have been analyzed using the concept of hybridization percentage, which represents the ratio between the supercapacitor weight and the total weight of the energy storage elements. Simulations were conducted using models developed in AVL Cruise MTM. A case study is included to test the methodology, incorporating commercial components, a standard driving cycle, and a rule-based energy management strategy. The conclusions of this application example illustrate the types of results that can be obtained by using this hybrid energy storage system sizing methodology. Findings for this case study suggest that for cycles lacking extreme power peaks, non-hybridized configurations can be the optimal solution, as the battery size reduction outweighs the benefits of hybridization in terms of efficiency, achieving 76.08% without supercapacitors compared to 65.7% with a high hybridization grade of 32.4%, and overall cost. However, sensitivity analysis reveals that if the optimization weights are adjusted to prioritize aging over efficiency, the optimal configuration shifts to a 6.48% hybridization grade at a 0.3C threshold. Full article

Vertical takeoff and landing (VTOL) aircraft must balance the conflicting demands of hover and cruise performance. To address the lack of integrated design methodologies in the existing literature, a unified design-optimization framework is presented, coupling high-fidelity CFD simulations with a genetic algorithm to refine a gas-driven thrust fan (GDTF) VTOL nacelle. Key geometric parameters—fan pressure ratio pressure ratio, fan tilt, nozzle angle, tail inclination, and tip shape—were varied in a comprehensive parametric study to maximize lift-to-drag ratio and maintain constant mass flow. The optimization reveals that a nearly horizontal fan axis maximizes cruise efficiency (${\displaystyle \frac{L}{D}}\approx 2.98$), a nozzle angle of about $22°$ offers the best lift-vs-drag compromise during transition, and refining the tip geometry yields a $10$–$20\%$ performance boost. To validate the numerical predictions, a 1:1.05 scale VTOL nacelle model (fan diameter $D = 0.42 \mathrm{m}$) was fabricated and tested in a low-speed wind tunnel at $52 {\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$ ($Re \approx 5 \times {10}^6$, turbulence intensity ≈ 2%). Total-pressure probes at the intake exit plane and static taps along the inner cowl wall provided detailed pressure distributions, from which exit Mach number, velocity and the equivalent flow coefficient φ (≈0.68 under test conditions) were derived. Oil-flow visualization on the external cowl surface confirmed smooth, attached streamlines with no large separation bubbles. This dual validation combining surface-flow visualization and pressure-recovery mapping demonstrates the accuracy and reliability of the proposed simulation methodology. By successfully bridging detailed CFD with genetic-algorithm-driven design and validating against comprehensive wind-tunnel measurements, this integrated approach paves the way for next-generation VTOL configurations with longer range and lower fuel consumption. Full article

To optimize fuel economy for platooning plug-in hybrid electric trucks, this paper proposes a co-optimization framework that integrates cooperative adaptive cruise control and energy management to enhance driving safety and fuel efficiency in complex traffic environments. The control strategy is divided into two layers: in the upper layer, a cooperative adaptive cruise control model based on distributed model predictive control (DMPC) is used to achieve stable platoon following and vehicle spacing, thus improving the overall platoon efficiency. In the lower layer, a distributed soft actor-critic (DSAC) algorithm is used for the fine-grained power distribution of plug-in hybrid electric trucks, enabling efficient energy utilization. The results demonstrate that this strategy significantly enhances the fuel economy and vehicle-following performance of plug-in hybrid truck platoons. Compared with the classical deep deterministic policy gradient (DDPG) algorithm, the energy management strategy based on the distributed soft actor-critic offers higher computational efficiency. Full article