Research Reports: Power, Propulsion & Turbomachinery

Research progress on laminar separation bubble dynamics and aerodynamic optimization of low-Reynolds-number airfoils

2026-08-26T07:10:24+00:00

This review examines the research progress on flow mechanisms and aerodynamic optimization of airfoils under low-Reynolds-number conditions, with emphasis on the formation and evolution of laminar separation bubbles and their effects on aerodynamic performance. Based on experimental studies, numerical simulations, and optimization design research, the characteristics of the separation, transition, and reattachment processes are analyzed, as well as the controlling effects of laminar separation bubbles on typical aerodynamic responses such as lift, drag, stall, and static hysteresis under different operating conditions. On this basis, the main technical approaches for low-Reynolds-number airfoil optimization are summarized, including geometry design based on pressure distribution regulation, numerical optimization considering transition effects, passive and active flow control, and data-driven optimization methods. Finally, the challenges faced by current research in transition prediction, multicondition robustness, and physics-constrained modeling are discussed. Overall, the research framework centered on laminar separation bubble control provides a unified physical understanding and design strategy for improving the
performance of low-Reynolds-number airfoils.

A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential

2026-05-20T09:56:07+00:00

Hand lay-up and vacuum resin infusion are two labor-intensive, time-consuming, and expensive traditional manufacturing methods used for wind turbine (WT) blades. With the ability to reduce mold manufacturing costs by up to 50%, additive manufacturing (AM) has become an attractive alternative for blade tooling and component fabrication. In 2024, the global market for 3D-printed turbine components reached USD 1.2 billion and is expected to increase to USD 3.8 billion by 2033. This review investigates the integration of AM and artificial intelligence in WT blade manufacturing. AI-assisted defect detection has shown great accuracy in controlled experimental investigations, with some research showing classification accuracies exceeding 90% under controlled laboratory conditions. In several studies, multimodal sensing approaches outperformed single-sensor systems by around 20%. Furthermore, machine learning models have demonstrated excellent prediction ability for composite blade production quality in small-scale experimental datasets. While these findings are promising, further validation under full-scale industrial conditions is required. The synergy of artificial intelligence and additive manufacturing under Industry 4.0 can provide scalable, lightweight, sustainable production as well as enabling defect monitoring, optimization, and adaptive control. Moreover, this integration will improve sustainability through the use of recycled thermoplastic polymers as additive manufacturing feedstocks for blade tooling and small components, thereby reducing energy consumption and material waste compared to thermoset-based processes. However, current limitations include scalability constraints for blades beyond 12 m and a lack of standardized datasets. Research should focus on the development of hybrid artificial intelligence–additive manufacturing frameworks, digital-twin integration, and full-scale validation to accelerate the implementation of these technologies for wind turbine blade manufacturing.

Health status monitoring of ship rotating machinery based on sliding window and long-short term generative adversarial network

2026-08-26T07:17:02+00:00

Introduction: Ship rotating machinery is the core of the ship’s power system, and its health is crucial to the safety and economy of ship operation. However, the traditional operation and maintenance model is difficult to meet the intelligent needs of modern ships.

Methods: This study proposes an integrated solution of “data preprocessing-fault diagnosis-life prediction” to finish real-time monitoring and accurate prediction of the health condition of ship rotating machinery. First, the solution uses the Mobile Grid-Based Density Clustering (MGBDC) algorithm for data preprocessing, and then integrates VMD, Autoencoder (AE), and BiGRU to establish a FD model. The model innovatively integrates SW and LSGAN to enhance the prediction precision of RUL, and finally builds a full-process intelligent monitoring system.

Results: Experiments showed that the mean fault recognition rate of the FD model reached 97.7%, which was 4.2%–6.4% higher than the comparison models. The designed algorithm selected MSE, RMSE, and MAE for remaining service life prediction as low as 0.017, 0.130, and 0.098, and the correlation coefficient reached 0.986. In practical applications, the system FD accuracy increased to 97.7%, the remaining service life prediction MAE was only 1.2 days, and the annual operation and maintenance cost of a single ship was reduced by 48.6%.

Discussion: The system provides full-process support for shipping companies and promotes the intelligent transformation of rotating machinery operation and maintenance.

Numerical investigation of macroscopic and microscopic characteristics of an air-assisted spray from a multi-hole nozzle

2026-08-26T06:58:55+00:00

To facilitate the transition of marine energy systems toward low-carbon and environmentally sustainable solutions, this study numerically investigates the medium-assisted atomization characteristics of a marine methanol tri-fuel boiler (methanol, diesel, and heavy fuel oil) under realistic operating conditions. A CFD-based VOF-DPM framework coupled with adaptive mesh refinement (AMR) is employed, in which the VOF method captures the evolution and breakup of the continuous liquid phase, while the DPM tracks the dispersed droplets and their size characteristics, with AMR applied to both phases to ensure adequate spatial resolution. Key parameters analyzed include the evolution of co-current atomization for different fuels, velocity-field distribution, droplet-size distribution, and atomization angle. The results demonstrate that methanol, owing to its low viscosity and surface tension, generates fine and uniformly distributed droplets predominantly within the 0–10 μm range, with a maximum atomization angle of 25^°–27^° and a Sauter mean diameter (SMD) consistently below 100 μm. Diesel produces droplets concentrated mainly between 40 and 50 μm, accompanied by noticeable fluctuations in atomization uniformity, whereas heavy fuel oil exhibits pronounced primary fragmentation after saturated-steam-assisted atomization, yet its velocity decays rapidly. Distinct differences are observed among the three fuels in terms of spray-penetration distance and atomization angle: methanol shows the strongest lateral dispersion but the weakest axial penetration, heavy fuel oil displays the opposite trend, and diesel lies between the two. This study elucidates the effects of fuel properties and atomizing media on the spray performance of marine methanol-fired triple-fuel boilers, offering theoretical insights for nozzle optimization and the practical application of low-carbon fuels in marine energy systems.

Explainable machine learning for condition monitoring of aircraft electromechanical actuators under variable loads: health state diagnosis from multi-sensor dynamic responses

2026-08-26T07:28:18+00:00

Electro-Mechanical Actuators (EMAs) are being adopted in small-aircraft primary flight-surface control due to their compactness and efficiency; however, their mechanical transmission components remain vulnerable to progressive degradation under variable loads and harsh operating conditions. This study proposes an explainable machine learning framework for health-state diagnosis of aircraft EMAs using multi-sensor dynamic responses acquired from the EU-H2020 REPRISE endurance campaign dataset. The approach is aligned with applied mechanics by exploiting degradation-sensitive dynamic signatures extracted from both electrical and mechanical domains, including three-phase motor currents, load and temperature measurements, and vibration-related indicators derived from position signals via numerical differentiation. To ensure interpretability and robust feature relevance assessment, SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of each sensor variable to the diagnostic decision process and to identify the most informative features. Based on SHAP-driven feature ranking, the diagnostic model is refined to primarily utilize current- and vibration-related features, which exhibit the highest sensitivity to friction evolution, load-dependent nonlinearities, and transmission wear. Four machine learning classifiers—k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and a Deep Neural Network (DNN)—are trained and used. Comparative evaluation demonstrates that RF and DNN achieve superior classification accuracy, reaching CA = 96.4% and CA = 97.8%, respectively, while SVM and kNN yield competitive performance with CA = 94.1% and CA = 91.6%. The results confirm that explainable AI enhances diagnostic reliability and provides mechanics-consistent insights into the degradation process that support the development of interpretable and scalable health monitoring strategies for next-generation aerospace electromechanical systems.