Search Results (47)

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings. Full article

The growing demand for renewable energy has underscored the importance of wind power, with wind turbines playing a pivotal role in sustainable electricity generation. However, wind turbine blades are exposed to various challenges, particularly flow-induced vibrations (FIVs), including vortex-induced vibrations, flutter, and galloping, which significantly impact the performance, efficiency, reliability, and lifespan of turbines. This review presents an in-depth analysis of wind turbine blade technology, covering the fundamental principles of operation, aerodynamic characteristics, material selection, and failure mechanisms. It examines the effects of these vibrations on blade integrity and turbine performance, highlighting the need for effective vibration suppression techniques. The paper also discusses current advancements in maintenance strategies, including active and passive vibration control methods, sensor networks, and drone-based inspections, aimed at improving turbine reliability and reducing operational costs. Furthermore, emerging technologies, such as artificial intelligence (AI)-driven prognostic assessments and novel materials for vibration damping, are explored as potential solutions to enhance turbine performance. The review emphasizes the importance of continued research in addressing the challenges posed by FIVs, particularly for offshore turbines operating in harsh environments. Full article

Unmanned aerial vehicles, particularly drones, have been increasingly deployed for different tasks in the community. They have become an important part of the economic and social benefits that society is exploiting from modern technology development. However, efforts are still required to further develop technologies which can mitigate the negative impacts. Among them, drone noise is considered a major health concern for the community. The present study undertakes an experimental investigation of the effectiveness of blade modifications on drone noise in an aeroacoustic wind tunnel facility. A quadcopter drone is programmed to operate in both hover and forward flights. Three modified blade configurations, including trailing-edge serrations combined serration–finlets, and an unmodified (baseline) blade, are manufactured. The far-field noise signals are recorded by two polar microphone arrays to quantify both the magnitude and directivity. The results show that all modified blades are able to reduce the drone noise at mid-to-high frequencies in both hover and forward flights, and this leads to a noticeable reduction in the overall sound pressure level. More importantly, the combined serration–finlet configuration outperforms all the other blades. Psychoacoustic analysis is also performed using the far-field acoustic time series. Interestingly, only the serration–finlet combination demonstrates a consistent reduction in the psychoacoustic annoyance levels, suggesting that it is important to use metrics from both acoustic and psychoacoustic analysis when developing noise mitigation strategies in the socio-economic context. Full article

Following the rising popularity of Unmanned Aerial Vehicles (UAVs) among large-scale users, in the form of domestic as well as professional drones, with applications in domains such as safety (e.g., surveillance drones), terrain mapping (using geo-scanning UAVs), videography drones, and high performance drones used in FPV (First Person View) drone competitions—as well as the rising wide accessibility of Fused Filament Fabrication (FFF)—especially considering the recent apparition and popularization of 3D printers capable of displaying exponential increases in performance metrics, the present work takes into consideration the practice of fabricating UAV propellers by means of FFF, focusing on the theoretical, as well as on the practical aspects of the roughness and quality observed at the level of the resulting surfaces. The paper proposes a set of propeller configurations obtained by combining popular propeller geometries, such as the Gemfan 51466-3 three-bladed propeller and the novel Toroidal propeller model, with a range of different fabrication materials, such as the Polyethylene Terephthalate Glycol (PETG) filament and the Polylactic Acid (PLA) filament. The main aim of the study is to reveal observations on the influence that the surface quality has on the performance metrics of a propeller. Based on the practical work, which aims to develop a comparative study between two drone propeller geometries manufactured by a nonconventional process, 3D printing, the practical applications in the study were carried out using low-cost equipment in order to evaluate the results obtained in a domestic setting. The study involves the identification of the noise values produced by the two geometries due to the roughness of the propeller surfaces. Full article

The static efficiency of a propeller cannot be determined in the same way as for propellers operating in the presence of freestream airflow. As various kinds of multirotor drones and small UAVs operate in hovering or nearly hovering modes, it is necessary to develop methods for determining and measuring the static aerodynamic efficiency of small-scale propellers. Propellers with a Reynolds number near the 0.75 R, where the blade section is less than 500,000, are considered to be at a critical value, i.e., the estimated border between two flow modes—laminar and turbulent. The efficiency of small-scale propellers may be hard to predict through modeling, making direct empirical measurements invaluable in this situation. Full article

Wind turbine inspections are traditionally performed by certified rope teams, a manual process that poses safety risks to personnel and leads to operational downtime, resulting in revenue loss. To address some of these challenges, this study explores the use of deep learning and drones for automated inspections. Three Mask R-CNN models, leveraging different convolutional neural network (CNN) backbones—VGG19, Xception, and ResNet-50—were constructed and trained on a novel dataset of 3000 RGB images (size 300 × 300 pixels) annotated with defects, including cracks, holes, and edge erosion. To improve defect detection performance, a multi-variable fuzzy (MVF) voting system is proposed. This method demonstrated superior accuracy compared to the individual models. The best-performing standalone model, Mask R-CNN with Xception, achieved an mAP of 77.48%, while the MVF system achieved an mAP of 80.10%. These findings highlight the effectiveness of combining fuzzy voting systems with Mask R-CNN models for defect detection on wind turbine blades, offering a safer and more efficient alternative to traditional inspection methods. Full article

This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs). Full article

Wind energy is one of the most rapidly growing sectors in renewable energy generation, with wind turbines being central to this expansion. Regular maintenance, particularly the inspection of wind turbine blades, is critical to ensure operational efficiency and prevent catastrophic failures. Conventional methods of blade inspection, including ground-based visual inspections, rope-access inspections, and cranes, are time-consuming, expensive, and often hazardous. In recent years, drone-based technologies have emerged as a promising alternative for wind turbine blade inspection. This paper provides a comprehensive review of current drone-based technologies for wind turbine blade inspection, highlighting their advantages, challenges, and future prospects. Full article

The shift towards renewable energy, particularly wind energy, is rapidly advancing globally, with Southeastern Europe and Croatia, in particular, experiencing a notable increase in wind turbine construction. The frequent exposure of wind turbine blades to environmental stressors and operational forces requires regular inspections to identify defects, such as erosion, cracks, and lightning damage, in order to minimize maintenance costs and operational downtime. This study aims to develop a machine learning model using convolutional neural networks to simplify the defect detection process for wind turbine blades, enhancing the efficiency and accuracy of inspections conducted by drones. The model leverages transfer learning on the YOLOv7 architecture and is trained on a dataset of 231 images with 246 annotated defects across eight categories, achieving a mean average precision of 0.76 at an intersection over the union threshold of 0.5. This research not only presents a robust framework for automated defect detection but also proposes a methodological approach for future studies in deep learning for structural inspections, highlighting significant economic benefits and improvements in inspection quality and speed. Full article

In the present study, a performance analysis of three different VTOL configurations is presented within an urban air mobility context. A classical lightweight helicopter was employed as a reference configuration to design a dual-rotor side-by-side helicopter and a hexacopter drone layout. An analytical model based on general momentum and blade element theories was developed for single- and multiple-rotor configurations in horizontal and vertical flight conditions. Suitable battery pack and electric motor designs were produced to evaluate the endurance and range of the different configurations for a specific mission. This paper provides fundamental insights into the endurance and range capabilities of multiple-rotor unmanned aerial vehicles (UAVs) and a qualitative discussion on the safety and acceptability features of each configuration implemented in an advanced air mobility context. As a result, the side-by-side helicopter configuration was identified as the best solution to be introduced within urban environments, fulfilling all the performance and mission requirements. Full article

The cooperative transportation of a cable-suspended load by two unmanned rotorcraft is analyzed. Initially, the equations describing a system composed of three point masses and two rigid cables are derived. The model is then linearized about the hovering condition, and analytical expressions are derived to describe the eigenstructure of the open-loop system. Thanks to the specific parameterization of the problem, the different dynamic modes are outlined and discussed within an analytical framework. A novel controller is designed to enable the UAVs in the formation to perform trajectory tracking, maintain formation geometry, and stabilize payload swing simultaneously. A preliminary investigation of closed-loop stability is conducted using a linear approach. Validation is performed in a realistic simulation scenario where two drones are modeled as rigid bodies under the effect of external disturbances and rotor-generated forces and moments, as obtained by Blade Element Theory. The proposed method demonstrates relative simplicity and significantly improves the flying qualities of delivery operations while minimizing hazardous payload oscillations and reducing energy demand. Full article

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions. Full article

The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity which is vital to drone reliability. The proposed method, which provides a systematic procedure to determine blade load carrying capacity, consists of three parts, namely, a procedure to determine the distributed quasi-static blade aerodynamic load via the Blade Element Momentum (BEM) approach, a finite element-based failure analysis method to identify the actual blade failure mode, and an optimization method to determine the actual blade load carrying capacity. The experimental failure characteristics (failure mode, failure thrust, failure location) of two types of composite sandwich rotor blades with different skin lamination arrangements have been used to verify the accuracy of the theoretical results obtained using the proposed load carrying capacity determination method. The skin lamination arrangement for attaining the optimal blade-specific load carrying capacity and the blade incipient rotational speed for safe drone operation has been determined using the proposed method. Full article

This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. Recent special operations has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (>100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” × 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability. Full article

This paper introduces a drone-based surrogate project aimed at serving as a preliminary educational platform for undergraduate students in the Electrical and Computer Engineering (ECE) fields. Utilizing small Unmanned Aerial Vehicles (sUAVs), this project serves as a surrogate for the inspection of wind turbines using scaled-down pedestal fans to replace actual turbines. This approach significantly reduces the costs, risks, and logistical complexities, enabling feasible and safe on-campus experiments. Through this project, students engage in hands-on applications of Python programming, computer vision, and machine learning algorithms to detect and classify simulated defects in pedestal fan blade (PFB) images. The primary educational objectives are to equip students with foundational skills in autonomous systems and data analysis, critical for their progression to larger scale projects involving professional drones and actual wind turbines in wind farm settings. This surrogate setup not only provides practical experience in a controlled learning environment, but also prepares students for real-world challenges in renewable energy technologies, emphasizing the transition from theoretical knowledge to practical skills. Full article