Search Results (255)

To improve the predictive accuracy of wind-field distributions during fires in high-rise buildings, this study targets the shortcomings of traditional prediction methods, including insufficient information fusion and dispersed feature representations under high-rise fire conditions. An efficient attention mechanism, termed Adaptive Channel and Multi-Scale Spatial Fusion Attention Mechanism (CSFAM), is proposed, which endows the model with enhanced adaptive focusing and multi-scale integration capabilities. CSFAM can account for environmental features across multiple dimensions to enable high-spatial-resolution wind-field reconstruction, thereby improving robustness and prediction accuracy in complex environments. To validate the effectiveness of CSFAM for predicting wind fields under high-rise-fire conditions, CFD-based scenario modeling was employed to generate a dataset of 1050 CFD-derived wind-field distributions across diverse inflow-wind and fire-source scenarios, partitioned into training, testing, and validation sets according to the fire-source size. When applying the CSFAM-enhanced multi-layer perceptron (MLP), the wind-field predictions achieved a mean squared error (MSE) of 0.0004, a mean absolute error (MAE) of 0.0141, and an R² of 0.9766, outperforming state-of-the-art methods. The results demonstrate that CSFAM plays a significant role in markedly improving wind-speed prediction accuracy during high-rise-building fires, and enhances the model’s ability to identify and express vortex-like and other key aerodynamic features generated by the fire, thereby improving the capture of the complex nonlinear aerodynamic structures induced by fire. Full article

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

Shock-aware aerodynamic shape optimization of transonic airfoils requires surrogate models that capture both integral aerodynamic trends and shock-relevant pressure distribution features. This study addresses drag-oriented optimization of the RAE2822 transonic airfoil under a lift-targeted condition with baseline relative thickness feasibility, rather than strict target pressure inverse design. Each airfoil is parameterized by a 16-dimensional CST vector and mapped to a two-channel vertical signed distance field representation of the upper- and lower-surface $C_p$ curves, from which shock descriptors, including the shock location indicator $x_s$ and the pressure jump magnitude $\Delta C_p$, are extracted in a deterministic, implementation-consistent manner. To quantify the reliability of surrogate-derived shock metrics, a held-out uncertainty analysis is performed on 500 samples. The surrogate achieves MAE/RMSE values of 0.00474/0.00602 for $C_L$ and $4.66\times {10}^{-4}/6.33\times {10}^{-4}$ for $C_D$, while the recovered shock-related quantities yield 0.00201/0.01598 for $x_s$ and 0.00200/0.00336 for $\Delta C_p$. Scatter plots and error histograms show tight one-to-one trends for most samples, with limited outliers mainly associated with locally ambiguous pressure gradient patterns. Overall, the surrogate is more reliable for capturing shock intensity trends than for prescribing an exact shock location; accordingly, $x_s$ is interpreted as a trend-level descriptor, whereas $\Delta C_p$ is treated as the more stable engineering indicator inside the optimization loop. The trained surrogate is embedded in a differential evolution optimizer with soft penalties on lift deviation and thickness feasibility violation, and selected designs are re-evaluated through closed-loop SU2 RANS simulations. CFD verification shows that the optimized design reduces drag from $C_D=0.01463$ to $C_D=0.01229$ (a 16.0% reduction) and reduces the shock jump from $\Delta C_p=0.239$ to $\Delta C_p=0.046$ (an 80.7% reduction). For the optimized design, the prediction-to-CFD differences are $\Delta C_L=+0.0042$ and $\Delta C_D=+0.00012$. These results support an engineering-oriented and auditable shock-aware closed-loop optimization workflow, with final design conclusions established by CFD verification rather than surrogate-predicted shock location alone. Full article

Machine learning has demonstrated significant potential as a valuable tool for aerodynamic design. However, collecting an abundant training set is usually computationally expensive and time-consuming. To address this data scarcity, meta-learning and transfer learning offer viable strategies. Meta-learning enables models to learn efficiently from limited data by leveraging experience across related tasks, while transfer learning reduces data requirements by reusing knowledge from pre-trained models. In addition, integrating physics knowledge into the models provides a complementary path to enhance the reliability and generalizability under data-scarce conditions. This paper studies meta-learning and transfer learning strategies to realize the prediction of supercritical airfoil pressure distribution under multiple free stream conditions with a small-scale dataset. All the models are tested both in the source domain and the target domain. Then, a systematic comparative analysis of different models across different target domain training sample scales is studied. Results show that meta-learning and transfer learning both improve target-domain performance compared to the baseline model. Yet, meta-learning still achieves limited accuracy in the target domain, and data-driven transfer learning exhibits poor generalization. Compared with data-driven models, the Mach number weighted transfer learning model provides more generalized results and higher accuracy. Full article

The design of millimeter-scale micro-turbine blades is challenging due to conflicting requirements: achieving aerodynamic performance while remaining compatible with microfabrication, and exploring high-dimensional morphological design spaces without prohibitive computational cost. To address these challenges, this study proposes an intelligent framework for the design and optimization of the three-dimensional blade profile of a 3 mm diameter micro-turbine. The blade morphology is parameterized using 22 variables, ensuring geometric feasibility for micro-EDM (Electrical Discharge Machining) fabrication. A physics-informed neural network (PINN) surrogate model, efficiently trained through a two-stage active learning strategy combining KD-tree exploration and residual-based sampling, provides accurate predictions of flow fields. Multi-objective optimization using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then performed to maximize torque and thrust. Experimental results show that the optimized blade achieves a 38.6% increase in rotational speed while retaining 75.1% of thrust at 0.2 MPa inlet pressure, validating the framework’s effectiveness. This methodology offers a systematic solution for designing microfluidic devices characterized by high-dimensional parameters and high-fidelity simulation requirements. Full article

Wind-assisted ship propulsion (WASP) systems are gaining importance due to their contribution to reducing greenhouse gases and saving fuel. Existing studies mostly focus on the aerodynamics of sailing systems, the integration of sails and ship dynamics, and the prediction of fuel savings. The present study extends the use case of sailing systems by proposing a new control logic that improves maneuvering performance. Determining the spin ratio of rotor sails not only with thrust but also with side forces and moments is also included as an objective function. Using numerous random weights for each term and environmental conditions, the turning performance of the target ship was evaluated. Then, an artificial neural network (ANN) model was trained to decide on the optimal weights, depending on the environmental conditions. Finally, the performance of the new control approach was evaluated based on turning and zigzag test simulations. It was found that the advance, transfer, and tactical diameters dropped by up to 5%, 7% and 7%, respectively, compared to those of a conventional ship. When it comes to the zigzag performance, it was revealed that the overshoot angles dropped even though there was no simulation data about zigzag tests in the trained ANN model. Thus, it was shown that sails improve the maneuverability of ships in addition to providing additional thrust if a proper control approach is adopted. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

Trajectory optimization of hypersonic vehicles face challenges from complex aerodynamic environments and multiple constraints, where traditional offline optimization methods struggle to meet real-time requirements. This study proposes a novel online trajectory optimization framework for hypersonic vehicles that integrates a multi-strategy improved whale optimization algorithm (MWOA) with an attention-mechanism Long Short-Term Memory (AM-LSTM) network. First, an offline trajectory dataset under aerodynamic uncertainties is generated using sequential second-order cone programming (SOCP). Subsequently, a multi-head attention mechanism is incorporated into the LSTM network to effectively capture sequential dependencies within the trajectory data. To automate the hyperparameter tuning of the AM-LSTM architecture, a multi-strategy improved whale optimization algorithm is developed, which incorporates circle chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic golden-sine mutation strategy to enhance optimization robustness. The trained MWOA-AM-LSTM hybrid model is then employed for real-time trajectory generation. Numerical simulation results demonstrate that the proposed framework achieves superior terminal accuracy under aerodynamic perturbations, validating its effectiveness and robustness for hypersonic vehicle reentry trajectory optimization. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

High−Altitude wind is a critical factor affecting the recovery safety of reusable rockets, significantly altering aerodynamic loads, flight attitudes, and trajectories—especially during the aerodynamic deceleration phase (engine shutdown) of reentry, posing severe challenges to high-precision guidance and stable control. Currently, accurate advance prediction of landing site wind fields is difficult with poor real-time performance, necessitating a real-time estimation and prediction method independent of additional measurement equipment. This study addresses this gap by proposing a deep learning-based approach for wind field estimation and prediction, using directly measurable attitude angles and apparent acceleration deviations of the rocket as inputs to train a dedicated deep neural network. Furthermore, to solve the attitude control problem of Reusable Launch Vehicles (RLVs) during recovery, a non-recursive simplified high-order sliding mode control method with online wind disturbance compensation is designed to achieve finite-time convergence. First, a dynamic model for the attitude control of RLVs during recovery is established; second, based on homogeneity theory, a non-recursive simplified homogeneous high-order sliding mode controller is developed to realize finite-time tracking control during RLV recovery with uncertainties, effectively suppressing the chattering inherent in sliding mode control; finally, simulation results verify the effectiveness and engineering feasibility of the proposed method. The combined approach significantly reduces wind-induced disturbance torque and required control torque, enhancing the adaptability and control robustness of vertically recoverable rockets to wind fields. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

Axial flow fans are widely used in high-speed train cooling and ventilation systems, where both static efficiency and noise reduction are critical performance requirements. In this study, the effects of hub geometry variation on the aerodynamic and acoustic characteristics of an axial flow fan are numerically investigated through three-dimensional simulations. Five fan configurations with different hub angles are analyzed under identical operating conditions. Steady aerodynamic performance is first evaluated using the Reynolds-averaged Navier–Stokes (RANS) approach with the k-ω shear stress transport (SST) turbulence model. The unsteady flow field is then resolved using large eddy simulation (LES) to capture the vortex structures and blade surface pressure fluctuations responsible for noise generation. The far-field aerodynamic noise is predicted based on the Ffowcs Williams–Hawkings (FW–H) acoustic analogy, and both tonal and broadband noise characteristics are analyzed using multiple virtual microphones. The results show that reducing the hub angle leads to improved aerodynamic performance at lower volumetric flow rates. Meanwhile, a reduction in tonal noise at the blade-passing frequency (BPF) and broadband noise at higher frequencies is observed. The findings demonstrate that appropriate hub angle design provides an effective approach for the simultaneous improvement of static efficiency and the reduction of aerodynamic noise of axial-flow fans used in high-speed train applications. Full article

Centrifugal compressors (CCs) are key components of marine power plants (MPPs), supporting engine boosting, boil-off gas (BOG) handling on liquefied natural gas (LNG) carriers, and auxiliary services such as heating, ventilation, and air conditioning (HVAC). However, recent publications are often fragmented by domain (aerodynamics, mechanical design, standards, and digitalization), complicating cross-domain engineering decisions for marine duty cycles. This structured review follows an explicit protocol to synthesize peer-reviewed studies (2015–2025) retrieved from Scopus and Web of Science and organizes the evidence by application class: turbocharger-integrated stages for marine diesel and gas-turbine engines, LNG/BOG compression trains, and auxiliary onboard services. The synthesis consolidates (i) aerodynamic KPIs (pressure ratio, efficiency, surge and stall margins, and operating range), (ii) mechanical and lifecycle enablers (seals, bearings, and rotordynamics), and (iii) quantified impacts of digital methods (control, diagnostics, and digital twins). Reported trends include single-stage pressure ratios of ~5.4–5.7, multistage overall pressure ratios exceeding 10, and surge-margin improvements of ~40–44% associated with advanced diffusers as well as casing and endwall treatments. Industrial case studies (non-marine) report downtime reductions of ~25–35% and maintenance-cost reductions of ~25%, while evaluated diagnostic datasets show high accuracy. Key gaps remain in marine-specific validation datasets and harmonized testing and data standards. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article