Low-flying Aircraft Research Articles

Determining the aerodynamic performance of a high-speed unmanned marine wig craft

The study object of this paper is the aerodynamic processes during the movement of an unmanned aerial vehicle flying low over the underlying surface. The ground effect is known to enhance the aerodynamic performance of low-flying aircraft, particularly larger ones. However, this effect is most noticeable for large objects. Unmanned vehicles are typically characterized by their relatively compact geometry. This study explores the aerodynamic processes involved in the flight of a small-sized vehicle using the principle of dynamic support over a surface. The particular prototype of the unmanned aerial vehicle suggested by the authors has been examined herein. The aim of the study is to evaluate the aerodynamic forces affecting a small-sized unmanned high-speed vehicle that employs the dynamic principle of support over the surface (WIG craft) by using CFD modeling. In contrast to most available studies on the ground effect, a 3D problem statement was used in this work. Computational experiments have visualized the physical fields surrounding an aircraft in flight over the ground. This study determines how the distance from the surface affects the aerodynamic properties of a small-sized aircraft, as well as the height of the effective zone where ground effect influences the small-sized WIG craft within . It has been shown that approaching the surface leads to a shift in the center of pressure of the vehicle, which leads to a change in aerodynamic momentum. This phenomenon must be taken into account when designing a control system to provide a stable flight. The study's findings are directly relevant for the development of a type of unmanned vehicles that use the dynamic principle of support over the surface.

Enhancing Power Lines Detection Using Deep Learning and Feature-Level Fusion of Infrared and Visible Light Images

AbstractThe detection of power lines is critical for flight safety, especially for drones and low-flying aircraft. Power line detection models help prevent collisions, reducing potential damage and preserving lives, while also safeguarding critical infrastructure. This has led to significant research efforts to develop accurate detection models. In this study, we employ paired infrared–visible power line datasets to train three distinct deep learning models. The first two models are sequential deep learning models based on VGG16 and AlexNet networks. They are tailored for detection in visible images, while they were optimized again for infrared images. For the third model, we introduce an innovative deep learning architecture utilizing Functional Application Programming Interface, affording us the flexibility to construct a multi-input model with shared layers. Our proposed model accepts paired images (visible and infrared) as inputs. Then, a feature-level fusion process is applied to merge the extracted features from both inputs and generate an enriched feature map. This approach amalgamates the advantages of visible images, which boast high resolution and rich texture features, with infrared images, which excel in high contrast and clear vision under adverse environmental conditions. Comparing the outcomes of the three models, our proposed model emerges as the front runner, boasting an impressive accuracy rate of 99.37%. Moreover, real-time processing was adopted by conducting ablation experiments to optimize the model and reduce the number of trainable parameters, resulting in an inference speed of 2.7 milliseconds per frame.

Extending air traffic control tasks to control air taxis and to warn of critical wildlife: an impact analysis

AbstractDespite large and successful efforts to keep wildlife off airports, many collisions between aircraft and animals still happen at low altitudes outside the airport boundaries, where wildlife is most abundant. These wildlife strikes are especially threatening for aircraft departing from airports as well as for envisioned low-flying urban air mobility aircraft. The here presented study evaluates the operational feasibility of a concept to provide tower air traffic controllers with wildlife strike risk information to contribute in a reduction of these critical strikes. For this purpose, real-time human-in-the-loop simulations with ten air traffic controllers were performed in a 360° tower simulator. Based on controller feedback as well as measures for safety and efficiency, the findings indicate feasibility of providing controllers with wildlife strike risk information. It is expected that the refinement of the procedures presented here will pave the way towards field-tests and eventually actual implementation of air traffic control involvement in wildlife strike prevention.

Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC.

Optimization of the parameters of a ground speed meter of low-flying aircraft based on the application of the theory of optimal filtration

The basic principle of parametric optimization according to the criterion of minimax of variance of the resulting error of estimation of the measured parameter is considered using the example of one of the main elements of an integrated measuring system – a ground speed meter. The meter consists of a Doppler ground speed sensor (location) and an accelerometer with a longitudinal axis of sensitivity (inertial). Based on the data on their measurement errors, the coefficients of the transfer functions of each channel of the meter containing these sensors were optimized. The result of the research is a comparative analysis of the resulting accuracy of the optimized meter for the invariant and non-invariant cases, based on the obtained graphs of the frequency distribution of variances. A reasonable conclusion is made about the advantage of an invariant or non-invariant meter depending on the frequency of the signal. The Matlab R2015a software was used for calculations.

Model, analyse and prevent fatal aircraft manoeuvres

On 24 June 1994 at Fairchild Air Force Base, during practice for an air show, a low-flying B-52H aircraft banked its wings vertically and crashed. Emphasizing the activity of modeling drag and gravity, these notes examine the possibility of recovery with several models. First, with algebra, historical data lead to a model where in a free fall near Earth's surface, the distance fallen is proportional to time squared. Calculus then gives an ordinary differential equation to model free fall near Earth's surface. Second, with calculus, historical data lead to various models of drag on objects moving through air. Third, combining models of drag and gravity with Newton's Laws of Motion leads to ordinary differential equations that model free fall in air. Fourth, predictions from such models with ordinary differential equations are consistent with the aircraft crash. Further models examine the possibility of increasing engine thrust to regain the vertical component of lift. All models fit in a first course in differential equations, without requiring any computational machinery. However, numerical experiments show how uninformed use of professional software can produce rounding errors to cause the modelled aircraft to plunge to the bottom of the deepest oceans and shoot up into space.

Prophesying the End of Human Time: Eco-Anxiety and Regress in J. G. Ballard’s Short Fiction

Despite being written half a century before the term “eco-anxiety” (Gifford and Gifford) was coined, J. G. Ballard’s disaster fictions can be read in the context of the social psychodynamics of climate change. My aim in this article is to demonstrate that in J. G. Ballard’s fiction, climate catastrophes and the devastation of nature cause the characters to realize that the Earth is not going to be able to sustain human life much longer, and their psychological reaction is either subdued anger or strange numbness. In order to do this, I analyze two short stories by Ballard: “Deep End” (1961) and “Low-Flying Aircraft” (1975) and show how their protagonists are affected by the landscape they inhabit: de-populated wastelands whose wildlife is extinct or mutated. I argue that it is their awareness that human civilization on earth is coming to its end that results in the state of mind akin to eco-anxiety. The characters are immersed in their own inner space and in these stories clocks mark not the passage from past to future but a countdown to the end.

Improvements of the Stevens drone acoustic detection system

Stevens Institute of Technology (SIT) designed and built multiple acoustic sensors to detect and track drones using Steered-Response Phase Transform (SRP-PHAT) and classify them using narrow-band frequencies. SIT improved a previously built four-microphone system by increasing the number of microphones to seven, modifying the software, and improving the testing algorithm for system performance estimation. The four- and the seven-microphone systems were deployed during several tests conducted with multi-rotor UAVs of different sizes, including the DJI Inspire 2, DJI Mavic 2 Pro, Autel Robotics EVO II Pro, and the Intel Falcon 8 performing flight patterns at various distances, elevations, and speeds. The tests were conducted in an environment with low-flying aircraft and vehicular traffic noise. Acoustic signatures were collected and detection distances were compared for the tested UAVs. The improved seven-microphone system provided farther detection and classification distances than the previous four-microphone system. Typical detection distances were between 300 and 500 m with a bearing accuracy within five degrees. An analysis of the effect of external noise on performance was also performed.

Sound Propagation Modelling for Manned and Unmanned Aircraft Noise Assessment and Mitigation: A Review

This paper addresses one of the recognized barriers to the unrestricted adoption of Unmanned Aircraft (UA) in mainstream urban use—noise—and reviews existing approaches for estimating and mitigating this problem. The aircraft noise problem is discussed upfront in general terms by introducing the sound emission, propagation, and psychoacoustic effects. The propagation of sound in the atmosphere, which is the focus of this paper, is then analysed in detail to isolate the environmental and operational factors that predominantly influence the perceived noise on the ground, especially looking at large-scale low-altitude UA operations, such as in the envisioned Urban Air Mobility (UAM) concepts. The physics of sound propagation are presented, considering all attenuation effects and the anomalies due to Doppler and atmospheric effects, such as wind, thermal inversion, and turbulence. The analysis allows to highlight the limitations of current mainstream aircraft noise modelling and certification approaches and, in particular, their inadequacy in addressing the noise of UA and, more generally, UAM vehicles. This finding is important considering that, although reducing noise at the source has remained a priority for manufacturers to enable the scaling up of UAM and drone delivery operations in the near future, the impact of poorly considered propagation and psychoacoustic effects on the actual perceived noise on the ground is equally important for the same objective. For instance, optimizing the flight paths as a function of local weather conditions can significantly contribute to minimizing the impact of noise on communities, thus paving the way for the introduction of full-scale UAM operations. A more reliable and accurate modelling of noise ground signatures for both manned and unmanned low-flying aircraft will aid in identifying the real-time data stream requirements from distributed sensors on the ground. New developments in surrogate sound propagation models, more pervasive real-time sensor data, and suitable computing resources are expected to both yield more reliable and effective estimates of noise reaching the ground listeners and support a dynamic planning of flight paths.

Spatially and temporally resolved measurements of NO<sub><i>x</i></sub> fluxes by airborne eddy covariance over Greater London

Abstract. Flux measurements of nitrogen oxides (NOx) were made over London using airborne eddy covariance from a low-flying aircraft. Seven low-altitude flights were conducted over Greater London, performing multiple overpasses across the city during eight days in July 2014. NOx fluxes across the Greater London region (GLR) exhibited high heterogeneity and strong diurnal variability, with central areas responsible for the highest emission rates (20–30 mg m−2 h−1). Other high-emission areas included the M25 orbital motorway. The complexity of London's emission characteristics makes it challenging to pinpoint single emissions sources definitively using airborne measurements. Multiple sources, including road transport and residential, commercial and industrial combustion sources, are all likely to contribute to measured fluxes. Measured flux estimates were compared to scaled National Atmospheric Emissions Inventory (NAEI) estimates, accounting for monthly, daily and hourly variability. Significant differences were found between the flux-driven emissions and the NAEI estimates across Greater London, with measured values up to 2 times higher in Central London than those predicted by the inventory. To overcome the limitations of using the national inventory to contextualise measured fluxes, we used physics-guided flux data fusion to train environmental response functions (ERFs) between measured flux and environmental drivers (meteorological and surface). The aim was to generate time-of-day emission surfaces using calculated ERF relationships for the entire GLR; 98 % spatial coverage was achieved across the GLR at 400 m2 spatial resolution. All flight leg projections showed substantial heterogeneity across the domain, with high emissions emanating from Central London and major road infrastructure. The diurnal emission structure of the GLR was also investigated, through ERF, with the morning rush hour distinguished from lower emissions during the early afternoon. Overall, the integration of airborne fluxes with an ERF-driven strategy enabled the first independent generation of surface NOx emissions, at high resolution using an eddy-covariance approach, for an entire city region.

Tracking Light Aircraft with Smartphones at Low Altitudes

Most aircraft in the world are tracked by various surveillance radar systems. Currently there is no legal requirement for light aircraft to be fitted with a transponder; however, this does not mean light aircraft should not be tracked. By adding a cheap, live tracking solution for light aircraft, the safety of low-flying aircraft pilots can be greatly increased. The radio operators who coordinate the aircraft can have an improved understanding of the air traffic and in the event of an emergency, the position of the aircraft can be relayed to emergency services. This paper proposes an approach to use a smartphone as an aircraft transponder to improve the radar tracking capabilities of low-flying aircraft. This study presents a practical and effective approach as well as a prototype implementation. The study includes the development of the three main components: (1) A mobile application that transforms a smartphone into an aircraft transponder; exploiting the GPS functionalities, (2) a desktop application that visualizes the aircraft data in real time on a map, and (3) a backend that bridges the mobile and the desktop application. To evaluate the study, flight tests were performed in a real aircraft over the Isle of Wight in the UK.

Assessment of Regional Methane Emission Inventories through Airborne Quantification in the San Francisco Bay Area.

This study derives methane emission rates from 92 airborne observations collected over 23 facilities including 5 refineries, 10 landfills, 4 wastewater treatment plants (POTWs), 2 composting operations, and 2 dairies in the San Francisco Bay Area. Emission rates are measured using an airborne mass-balance technique from a low-flying aircraft. Annual measurement-based sectorwide methane emissions are 19,000 ± 2300 Mg for refineries, 136,700 ± 25,900 Mg for landfills, 11,900 ± 1,500 Mg for POTWs, and 11,100 ± 3,400 Mg for composting. The average of measured emissions for each refinery ranges from 4 to 23 times larger than the corresponding emissions reported to regulatory agencies, while measurement-derived landfill and POTW estimates are approximately twice the current inventory estimates. Significant methane emissions at composting facilities indicate that a California mandate to divert organics from landfills to composting may not be an effective measure for mitigating methane emissions unless best management practices are instituted at composting facilities. Complementary evidence from airborne remote sensing imagery indicates atmospheric venting from refinery hydrogen plants, landfill working surfaces, composting stockpiles, etc., to be among the specific source types responsible for the observed discrepancies. This work highlights the value of multiple measurement approaches to accurately estimate facility-scale methane emissions and perform source attribution at subfacility scales to guide and verify effective mitigation policy and action.

Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning

Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia.

Detection and Speed Estimation of Moving Target Based on Phase Compensation and Coherent Accumulation Using Fractional Fourier Transform.

As unmanned aerial vehicles and other small, low-flying, and low-speed aircrafts are being extensively used, studies on their detection are being extensively conducted in radar application research. However, weak echoes, low Doppler frequencies, and target echoes mixed with ground clutter can considerably degrade the detection performance. Therefore, specific methods for the detection of such targets should be devised. We propose herein a phase compensation and coherent accumulation algorithm based on the fractional Fourier transform (FRFT) for detection and speed estimation of this type of target. First, the energy of the target echo is converged using the FRFT. Next, the phase between the peaks of the target echo is analyzed. Phase compensation and coherent accumulation determined from the expected target speed in the fractional domain eliminate ground clutter and further improve the signal-to-interference-plus-noise ratio. Finally, constant false alarm rate detection is used to identify the target, for which radial speed can be estimated directly according to the peak coordinates. The validity of the algorithm is verified via data simulation and application to real data.

A Review of Aircraft-Subsistence Harvester Conflict in Arctic Alaska + Supplementary Appendix 1

The traditional harvest of wild resources carries significant nutritional, economic, and sociocultural values for rural residents in the Arctic, especially for Indigenous subsistence communities. Rural communities in the Alaskan Arctic have expressed concern that aircraft activity from industry, commercial hunting, research, and tourism disrupts their harvest of wildlife, particularly caribou (Rangifer tarandus). However, little research exists on how aircraft impact harvest opportunities. Our objective was to assess the extent of scientific knowledge on aircraft-harvester interaction in the Arctic through a systematic search of the available literature. We found that no peer-reviewed publications addressed the conflict between aircraft and harvesters in the region. Some literature addressed aircraft impacts to subsistence species, but did not discuss how those impacts would affect local harvesters. Most research has been directed towards studying aircraft impacts on wildlife or humans in urbanized areas rather than in rural, subsistence communities. Therefore, we expanded our review to draw from gray literature (e.g., public records, government documents) to synthesize the current state of concern and perceptions on aircraft disturbance to subsistence harvesters. Based on the gray literature, we found that harvester frustrations were primarily directed toward low-flying aircraft and non-local operations. However, an absence of quantitative information on the extent of interaction between aircraft activity and harvesters hinders an objective assessment of the conflict. Mitigating conflict will require research focused on this data gap and may begin with better cooperation among rural communities, aircraft users, and decision-makers.

Flying, health and the city: sensing aeromobility and risk in an informal settlement

ABSTRACT This research explores the relationships between flying, health and the city from the perspective of an informal settlement called Freedom Farm in Cape Town, South Africa. Located underneath the flight path for Cape Town International Airport, Freedom Farm has a low-income population whose proximity to and experience of aeromobility serve to complicate the relationship that humans have with commercial aviation, and our understanding of the health impacts of living near airports. Using semi-structured interviews with Freedom Farm residents and participant observation, this study explores the terrestrial experience and associated perceptions of health risks of aeromobility from the vantage point of informal dwellers. Findings indicate that the perceived risks from aeromobility are numerous and are exacerbated by the proximity of low-flying aircraft. They conclude that the relationship between flying and informal living is complex, and understood through relational geographies articulated by materiality and proximity. While the residents of Freedom Farm are not themselves air passengers or crew, this research also finds that residents nonetheless have unique experiences of aeromobility that they use to construct knowledge of a mobility practice that boosts the health of the city-region while it impacts negatively on their well-being.

Permafrost nitrous oxide emissions observed on a landscape scale using the airborne eddy-covariance method

Abstract. The microbial by-product nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance, has conventionally been assumed to have minimal emissions in permafrost regions. This assumption has been questioned by recent in situ studies which have demonstrated that some geologic features in permafrost may, in fact, have elevated emissions comparable to those of tropical soils. However, these recent studies, along with every known in situ study focused on permafrost N2O fluxes, have used chambers to examine small areas (<50 m2). In late August 2013, we used the airborne eddy-covariance technique to make in situ N2O flux measurements over the North Slope of Alaska from a low-flying aircraft spanning a much larger area: around 310 km2. We observed large variability of N2O fluxes with many areas exhibiting negligible emissions. Still, the daily mean averaged over our flight campaign was 3.8 (2.2–4.7) mg N2O m−2 d−1 with the 90 % confidence interval shown in parentheses. If these measurements are representative of the whole month, then the permafrost areas we observed emitted a total of around 0.04–0.09 g m−2 for August, which is comparable to what is typically assumed to be the upper limit of yearly emissions for these regions.

Onopvallend turen naar de Russen

Secretly watching the Russians. Cold War aircraft observation posts on existing buildings During the 1950s a network of aircraft observation post was built in The Netherlands, as a detection/observation system against low-flying hostile aircraft during the Cold War. Preferably, these were placed on highrise buildings. 134 of these 276 observation posts were built on existing buildings, on factories, mills, water towers, monasteries, government buildings and bunkers. Since their decommissioning in 1964-1968, many posts have been demolished. Approximately 37 posts on existing buildings remain, but mostly go unnoticed and many risk demolition in the future. These remaining aircraft observation posts are remarkable relics of our military heritage from the Cold War.

Aircraft Disturbance to Common Murres (Uria aalge) at a Breeding Colony in Central California, USA

Devil's Slide Rock, a rock sea stack on the central California coast near San Francisco, USA, hosts a Common Murre (Uria aalge) colony that was recently restored after it was extirpated in the mid-1980s. Since the late 1990s, low-flying aircraft have threatened the colony at Devil's Slide Rock. To assess variables contributing to Common Murre disturbance, multinomial log-linear models were constructed and evaluated using reproductive timing and aircraft characteristics for 7 years (2008–2014). The top model included seven variables (year, reproductive timing, time of day, aircraft type, aircraft category, aircraft altitude and aircraft lateral distance from Devil's Slide Rock) and the interaction effects between aircraft type and aircraft category, and between altitude and distance. The odds of disturbance varied temporally, with 2010, 2011, 2013 and 2014 exhibiting greater odds of disturbance than 2008. Disturbance response was more likely during the pre-egg lay period and post-chick hatch period than during incubation; 47%, 51%, and 40% of over-flights, respectively, resulted in agitation or flushing. Helicopters were more likely to cause disturbance than fixed-wing aircraft; 72% of helicopter over-flights and 39% of fixed-wing aircraft resulted in agitation or flushing. Flushing was more likely to occur at lower aircraft altitudes and closer lateral distances; all flushing from over-flights occurred below 305 m, and 91% of agitation events occurred below 305 m. These results support the need for measures to reduce over-flight disturbance of the Devil's Slide Rock colony.

Remote Diagnostics Efficiency in Case of Plants Nitrogen Nutrition

The article summarizes foreign and own research on the development of photometric methods for the diagnosis of nitrogen nutrition of plants. Previous own research has shown that ground and aerospace surveys can be successfully used to diagnose nitrogen nutrition of plants. However, despite significant advantages over traditional chemical diagnostics, both terrestrial and space indications of the availability of agricultural crops with nitrogen have certain limitations: terrestrial - by the scale of coverage, aerospace - by time parameters. At the same time, it was shown that the use of low-flying aircraft, in particular helicopters, is most suitable for photometric inspection of crops, although its application has a significant limitation - at the cost of diagnostic work In this regard, the most promising for the rapid diagnosis of nitrogen nutrition of plants is the use of unmanned aerial vehicles - UAV, equipped with appropriate photometric equipment. Testing the effectiveness of remote, using unmanned aerial vehicles, diagnosis of nitrogen nutrition test culture - winter wheat in the field experiment with increasing doses of nitrogen fertilizers showed a close dependence of the UAV instrument readings, namely the vegetative index (NDVI), with the supply of plants with nitrogen. Efficiency and technological simplicity, as well as the economic component of the use of unmanned aerial vehicles for monitoring crops of crops, essentially open a new page in solving the problem of nitrogen nutrient nutrition in plants.