Small Aircraft Research Articles

A new scoring system to predict fatal accidents in General Aviation and to facilitate emergency control centre response.

Numerous accidents occur with General Aviation aircraft every year. To date, pre-emptive prediction of survival or death is impossible. The current study aims to identify significant factors elementary to predict survival after General Aviation (GA) aircraft accidents. The Implementation of a scoring system, including these factors, may facilitate emergency control centre response. Data of flight accidents over a 20-year period (extracted from the German Federal Bureau of Aircraft Accident Investigation [BFU]) was analysed for fixed-wing motorized small aircrafts below 5,700kg MTOW. Factors of interest were analysed using Chi2- and Mann-Whitney-U-Tests. Logistic regression was used to establish a score to calculate the probability of a fatal outcome after an aircraft accident. The BFU lists 1,595 GA aircraft accidents between 2000 and 2019. The factors "third quarter of the year" (p = 0.04), "last quarter of the year" (p = 0.002), "fire" (p < 0.0001), "distance from airport > 10km" (p < 0.0001), "landing" (p < 0.0001) and "cruise" (p < 0.0001), significantly correlated positively or negatively with a fatal outcome. "Take-off", "approach", "month", "day of the week", "persons on board above three", "night-time" and "icing conditions" showed no significant correlation. Using logistic regression "third quarter of the year" and "cruise" were excluded when using the B-STEP method. Including the four significant parameters, the score showed a strong effect with f2 = 0.709. The analysis of GA aircraft accidents in Germany enabled the identification of relevant factors and establishment of a new scoring system for survival prediction after small aircrafts accidents below 5,700kg MTOW. The implementation of the scoring system in emergency control centres in the context of digital development and artificial intelligence can improve emergency response planning and distribution.

Small aircraft detection in infrared aerial imagery based on deep neural network

Detection of aerial target is an important part of infrared image processing. Both neural network method and traditional method can be used in infrared object detection. Neural network method has many advantages such as high accuracy and good portability compared with traditional object detection method. Since the features extracted by neural network method can change over detection target, automatic feature extraction makes neural network based detection method more effective. In recent years deep learning method has been also found wide use for object detection in images. In this paper, an object detection model based on the deep learning network YOLO is constructed for solving the infrared aircraft detection problem. We construct the dataset used for training and testing with recognized features being iteratively learned. The task of infrared otject detection is sensitive to model size and detection speed. There is a requirement of using quantization method to reduce the storage space and the computation complexity. We propose a quantized model with appropriate accuracy for infrared object detection task. To solve the detection task for multiple extremely small aircrafts, model adjustment and quantization are used in proposed model and it gets a better performance. Experimental results on the constructed dataset show that the storage space for weight after quantization shrinks to a quarter, and there is no precision loss for extremely small aircrafts compared to the original model. The optimized YOLO-based deep learning model is effective to detect the small aircraft target in infrared aerial imagery.

The Development of Simulated Dust-Devil-Like Vortices

Abstract In this study, the cause of rotation in simulated dust-devil-like vortices is investigated. The analysis uses a numerical simulation of an initially resting, dry, atmosphere, in which uniform surface heating leads to the development of a growing convective boundary layer (CBL). As soon as convective mixing sets in, regions of weak vertical vorticity develop at the lowest model level. Using forward trajectories, this vorticity is shown to originate from horizontal baroclinic production and simultaneous reorientation into the vertical within the descending branches of the convective cells. The requirement for vertical vorticity production in the downdraft cells is shown to be a nonaxisymmetric horizontal footprint of the downdraft regions. The resulting vertical vorticity is not initially associated with rotation. However, as the CBL matures, like-signed vortex patches merge, the vertical vorticity magnitude increases due to stretching, and deformation in the vortex patch decreases, leading to the development of vortices. The ultimate origin of the vortices is thus initially horizontal vorticity that has been produced baroclinically and that has subsequently been reoriented into the vertical in sinking air. Significance Statement Dust devils are concentrated vortices consisting of rapidly rising buoyant air, which may pose a risk to small aircraft and light structures on the ground. Although these vortices are a common occurrence in convective boundary layers, the origin of the vorticity within these vortices has not yet been fully established. The present study uses a numerical simulation of an evolving convective boundary layer and analyzes air parcel trajectories to identify the origin of vertical vorticity at the surface during dust-devil formation. The work contributes an answer to the long-standing question of what causes dust devils to spin.

Investigation of Onboard Power Generation Method Using Waste Energy at Altitude Conditions

Abstract Demand for increased onboard power generation on small aircraft is ever growing due to increased electrical power demand. The objective of this study is to investigate onboard power generation using waste energy at various altitude and ambient conditions. Experiments were performed on a 2-liter turbocharged direct-injection intermittent combustion engine fueled with jet fuel (F-24). The turbocharger has an integrated motor-generator system that is designed to generate electricity using the enthalpy in the engine exhaust, or to use as a motor to rotate the turbocharger using the energy stored in onboard energy storage device. An electrically actuated wastegate was used to control useful exhaust flowrate that is used to generate electricity. An external power supply system was used to emulate the energy storage. The engine was installed in the US DEVCOM Army Research Laboratory?s altitude chamber in which absolute outside air pressure was controlled up to 37.6 kPa to simulate the altitude conditions up to 7,620 m (25,000 ft). Three different ambient conditions were selected to represent International Standard Atmosphere (ISA), polar, and tropical conditions. Based on the experimental results, a response surface model was developed to predict the power generation of the motor-generator integrated turbocharger using waste energy as a function of altitude, engine power, and the wastegate position. The result from this study provides an insight into onboard power generation method using waste energy at altitude conditions.

Assessing the Impact of Hybrid Propulsion Systems on the Range and Efficiency of Aircraft

The demand for aviation continues to grow, posing issues in terms of fuel consumption, environmental effect, and operational efficiency. In addition, the COVID-19 pandemic has also highlighted the need for sustainable solutions in the aviation sector. To address these issues, hybrid electric propulsion systems have emerged as a potential option. Hybrid electric propulsion systems have the potential to improve airplane performance while reducing environmental impact. This article looks into the effects of hybrid electric propulsion technologies on optimal aircraft range. The study looks at aviation's environmental impact, several hybrid aircraft prototypes, and battery capacity and density challenges. Fuel usage increases in proportion to the weight of the aircraft. As a result, the range is shorter. In modern technology, along with to the added weight of batteries used as energy storage in hybrid propulsion systems, there are low battery densities and capacities. When the researches were reviewed, it was discovered that overcoming these limitations was easier for small aircraft and more difficult for large aircraft. As a consequence of the studies and research conducted, the development of light and reliable batteries with high energy density and capacity would expand the range of hybrid aircraft and allow them to be used more efficiently over long distances.

Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

0-D Dynamic Performance Simulation of Hydrogen-Fueled Turboshaft Engine

In the last few decades, the problem of pollution resulting from human activities has pushed research toward zero or net-zero carbon solutions for transportation. The main objective of this paper is to perform a preliminary performance assessment of the use of hydrogen in conventional turbine engines for aeronautical applications. A 0-D dynamic model of the Allison 250 C-18 turboshaft engine was designed and validated using conventional aviation fuel (kerosene Jet A-1). A dedicated, experimental campaign covering the whole engine operating range was conducted to obtain the thermodynamic data for the main engine components: the compressor, lateral ducts, combustion chamber, high- and low-pressure turbines, and exhaust nozzle. A theoretical chemical combustion model based on the NASA-CEA database was used to account for the energy conversion process in the combustor and to obtain quantitative feedback from the model in terms of fuel consumption. Once the engine and the turbomachinery of the engine were characterized, the work focused on designing a 0-D dynamic engine model based on the engine’s characteristics and the experimental data using the MATLAB/Simulink environment, which is capable of replicating the real engine behavior. Then, the 0-D dynamic model was validated by the acquired data and used to predict the engine’s performance with a different throttle profile (close to realistic request profiles during flight). Finally, the 0-D dynamic engine model was used to predict the performance of the engine using hydrogen as the input of the theoretical combustion model. The outputs of simulations running conventional kerosene Jet A-1 and hydrogen using different throttle profiles were compared, showing up to a 64% reduction in fuel mass flow rate and a 3% increase in thermal efficiency using hydrogen in flight-like conditions. The results confirm the potential of hydrogen as a suitable alternative fuel for small turbine engines and aircraft.

Optimisation of bioaerosol sampling using an ultralight aircraft: A novel approach in determining the 3-D atmospheric biodiversity

Bioaerosols, such as pollen and fungal spores, are routinely monitored for agricultural, medical or urban greening practices, but sampling methodology is largely relying on techniques more than half a century old. Moreover, biomonitoring campaigns often take place in urban environments, although sources can be located outside cities’ borders with ampler vegetation. Therefore, the question arises whether we are accurately picturing the biodiversity and abundance of regional bioaerosols and whether those locally detected might derive from long-distance transport, horizontally or vertically. To answer the above, we used novel, mobile monitoring devices, and aerial measurement units, like aircrafts, so as to explore bioaerosol concentrations at a variety of altitudes.An ultralight aircraft was equipped with a sampling device for bioaerosols. The device consisted of duplicate isokinetic impactors that match the physical functioning and the microscopic quantification method of the widely used ground-based Hirst-type impactors. Isokinetic airflow was realized by adjusting the air flux at the impactors’ inlet to the airspeed of the aircraft. Three campaigns were made, where the comparability, efficiency and accuracy of different sampling devices were determined, namely of the abovementioned impactor, and of the mobile conventional Hirst-type pollen sampler. The campaigns involved measurements from ground level (0 m altitude) up to 900 m (above ground level (agl)) via flights.Our results showed that aircraft-based airborne pollen concentration measurements were consistently higher than those of all other devices, regardless of the altitude and sampling time. It is noteworthy that the pollen concentration exceeded 500 pollen grains/m3 at >900 m of altitude, this concentration being 1.77 times higher than that simultaneously measured at ground level. Likewise, the diversity of pollen was also higher at higher altitude.Our results indicate the usability and superiority of small aircraft and high-flow impactors for research, achieving higher biodiversity and abundance over a shorter sampling interval compared to conventional volumetric techniques. Higher pollen amounts at higher altitudes also point at the necessity to monitor bioaerosols across the vertical dimension, especially in densely populated areas and high-traffic air space.

Aircraft Electrothermal Pulse Deicing

Abstract Ice formation and accumulation on aircraft is a major problem in aviation. Icing is directly responsible for aircraft incidents, limiting the safety of air travel and requiring expensive, and sometimes ineffective deicing strategies. Furthermore, electrification of aircraft platforms leads to difficulties with integration of legacy deicing methods such as pneumatic boots. In this work, we study electrothermal pulse deicing capable of efficient and rapid removal of ice from aircraft wings. The pulse approach enables the efficient melting of a thin (&amp;lt;100 μm) ice layer on the wing surface to limit parasitic heat losses. Only the interface is melted, with the rest of the ice sliding on the melt lubrication layer due to aerodynamic forces. To study pulse deicing, we developed a transient thermal-hydrodynamic numerical model that accounts for multiple phases and materials, specific and latent heating effects, melt layer hydrodynamics, as well as boundary layer effects. To identify optimal deicing strategies, we use our model to study the effects of heater thickness (50 μm &amp;lt; th &amp;lt; 1 mm), substrate electrical insulation thickness (10 μm &amp;lt; ti &amp;lt; 1 mm), pulse duration (0.4 s &amp;lt; Δtpulse &amp;lt; 4.5 s), and pulse energy. Optimum operating points are identified for large (Boeing-747), midsize (Embraer-E175), and small (Cessna-172) aircraft. The scale-dependent thermal-hydraulic model results are used to estimate input conditions required for deicing and integrated into an electrical model considering energy storage, power electronics, integration, and layout, to achieve overall volumetric and gravimetric power density optimization.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

A clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network

The current paper presents a clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network (FANET). The technological advances of the last decade gave rise to emerging technologies. Unmanned aerial vehicles (UAVs) are small aircraft that proved their usefulness for different tasks nowadays. They can collaborate to achieve missions especially in areas where traditional networks cannot work or cannot accede. A FANET is composed by a number of these aircraft. For alike networks, the resources are limited. Indeed, an efficient energy management is required to extend the life of the network. This work is a clustering method for heterogeneous nodes of a FANET, each node is equipped with one sensor, and four different sensors are used. Clustering is grouping nodes with the aim of efficiency improvement. The clustering is done before the beginning of the rescue mission and depends on the types of sensors the nodes are equipped with and. The master election depends on the available energy of each one of the nodes. The simulation is done with a discrete event simulator (DES) and the results are compared to the algorithm of glowworm swarm optimization (GSO) to demonstrate the effectiveness of the suggested technique.

SE-CBAM-YOLOv7: An Improved Lightweight Attention Mechanism-Based YOLOv7 for Real-Time Detection of Small Aircraft Targets in Microsatellite Remote Sensing Imaging

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional squeeze-and-excitation convolution (SEConv) to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. In addition, the SEConv-based spatial pyramid pooling and connected spatial pyramid convolution (SPPCSPC) module extracts image features. It improves detection accuracy while the feature fusion section integrates the convolutional block attention module (CBAM) hybrid attention network, forming the convolutional block attention module Concat (CBAMCAT) module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model’s feature fusion capabilities. Experiments on public remote sensing datasets reveal that the proposed SE-CBAM-YOLOv7 improves detection accuracy by 0.5% and the mAP value by 1.7% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging.

Biofuel–Electric Hybrid Aircraft Application—A Way to Reduce Carbon Emissions in Aviation

As global warming intensifies, the world is increasingly concerned about carbon emissions. As an important industry that affects carbon emissions, the air transportation industry takes on the important task of energy saving and emission reduction. For this reason, major airlines have designed or will design different kinds of new-energy aircraft; however, each aircraft has a different scope of application according to its energy source. Biofuels have an obvious carbon emission reduction effect in the whole life cycle, which can offset the drawback of the high pollutant emission of traditional fossil fuels in the preparation and combustion stages. At the same time, a battery has zero emissions in the operating condition, while the low energy density also makes it more applicable to short-range navigation in small aircraft. In this paper, the development direction of a biofuel–electric hybrid aircraft is proposed based on the current development of green aviation, combining the characteristics of biofuel and electric aircraft.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.

Possibilities of small robotic UAVS for surveillance of agricultural areas in Southern Dobruja

Timely diagnosis of trends in the development of agricultural crops is essential for precision agriculture. The use of unmanned aerial vehicles (UAV) allows us to quickly and accurately collect information about crops. The study tests the capabilities of a small aircraft in the fields of Dobruja, Bulgaria. We tested two small aircrafts with their built-in cameras and a near-infrared camera. An early diagnosis of a reduced vegetation index due to pathogens and a defect in the planter was confirmed. The obtained dependencies show the possibility of using aircraft in the agrometeorological situation of Dobruja.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400–1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green–red range (550–650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Stand characteristics of the GTM 400 MOD turbojet engine

Miniaturization of turbine jet engines not only enables testing of fuel mixtures but also opens up new possibilities for their use in smaller aircraft. In this work, measurements were carried out in the GTM 400 MOD engine in order to create the stand characteristics of unit thrust and specific fuel consumption. For both parameters, polynomials were determined describing their changes in the range of rotational speeds used. The work is the first stage of research under the rector's grant. Its goal is to create a hybrid turbojet engine powered by aviation kerosene and hydrogen. The reason for the research is to check the possibility of using hydrogen in turbomachinery engines. Hydrogen is one of the fuel additives approved for use by the European Union, which forces the aviation industry to reduce exhaust emissions into the atmosphere. Hydrogen can not only enrich aviation kerosene but also become an alternative fuel.

Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)

Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)

Infrastructural and operational availability for air taxi operations at German aerodromes

According to Flightpath 2050, door-to-door travel times of less than four hours are aspired for journeys within Europe. To accomplish this goal, on-demand air mobility (ODAM) can improve the conventional means of transportation with short travel times and comfort gains. This paper outlines a methodology for analyzing the availability of German aerodromes for air taxi operations. Analyses are based on the usage of hybrid-electric fixed-wing aircraft under visual flight rules (VFR) for ODAM. This future use-case is applied for the evaluation of infrastructural availability (quality) and operational availability in Germany. An aerodrome database and METAR weather database are developed and evaluated for the availability analyses. The main research finding is the availability of German aerodromes for small fixed-wing aircraft (according to EASA CS-23) operation under VFR for ODAM. The lack of suitable infrastructure for air taxi services (e.g., terminals, paved runways) at many small airfields limits the availability of the service. Furthermore, unsuitable weather conditions reduce operational availability, especially in winter, due to lack of visibility, e.g., snowfall. Current ODAM research has assumed continuous availability, causing an overestimation of availability. This paper provides a holistic approach in which the current state of aerodrome infrastructure and operational constraints are considered by the example of German aerodromes. Minimal infrastructure needed for this use-case is determined, main weather parameters causing operational constraints are detected and possibilities for increasing operational availability at aerodromes are revealed.