Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM.

Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs

Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making.

Evaluation of geocell-reinforced backfill for airfield pavement repair

After an airfield has been attacked, temporary airfield pavement repairs should be accomplished quickly to restore flight operations. Often, the repairs are made with inadequate materials and insufficient manpower due to limited available resources. Legacy airfield damage repair (ADR) methods for repairing bomb damage consist of using bomb damage debris to fill the crater, followed by placement of crushed stone or rapid-setting flowable fill backfill with a foreign object debris (FOD) cover. While these backfill methods have provided successful results, they are heavily dependent on specific material and equipment resources that are not always readily available. Under emergency conditions, it is desirable to reduce the logistical burden while providing a suitable repair, especially in areas with weak subgrades. Geocells are cellular confinement systems of interconnected cells that can be used to reinforce geotechnical materials. The primary benefit of geocells is that lower quality backfill materials can be used instead of crushed stone to provide a temporary repair. This report summarizes a series of laboratory and field experiments performed to evaluate different geocell materials and geometries in combinations with a variety of soils to verify their effectiveness at supporting heavy aircraft loads. Results provide specific recommendations for using geocell technology for backfill reinforcement for emergency airfield repairs.

Analysis of the Nicolaus Copernicus Airport Activity in Terms of the Flight Operations Impact on Air Pollution

The dynamic development of aviation is associated with many benefits, but also, unfortunately, with negative effects. One of the adverse consequences is the exhaust emissions that have a negative impact on human health. It particularly affects the residents of areas neighboring airports, as airport activity deteriorates local air quality. Using the Emissions and Dispersion Modeling System, the activity of the Nicolaus Copernicus Airport was assessed in terms of the flight operations’ contribution to air contamination in the area adjacent to the airport. Emissions from three sources were compared: aircraft, ground support equipment and auxiliary power units. The concentrations of pollutants in inhabited areas located in three different directions in relation to the airport were also estimated. In addition, the effect of distance from the airport on contaminant concentrations was assessed as a function of wind direction. It was noticed that small values of pollutant concentrations, originating from airport activity, appeared within a few kilometers from the airport, even if the prevailing wind direction on a given day was opposite to the analyzed dispersion direction.

Electric Aircraft Propulsion

This paper presents the state of the art in electric aircraft propulsion systems. The necessary reduction of greenhouse gas emissions on the global scale forces aviation engineers to search for ‘green’ solutions. Electric aircraft propulsion is a potential and relatively intuitive choice for a reduction of emissions in flight operations. This paper showcases four architectures of aircraft propulsion systems being now considered to utilise the advantages of electric propulsion with commercially profitable operating range and payload capabilities. One of the largest technological obstacles to the widespread use of electric propulsion in aviation is the low energy density of modern electric batteries. This paper presents the types of power supply which may achieve an energy density above the minimum threshold of 500 Wh/kg, and alternative onboard electrical power sources. The paper also shows novel designs of electric motors intended for aerospace applications. The final sections of this paper shows the implemented projects of aircraft with electric propulsion and the electric aircraft propulsion research projects underway around the world.

Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019

Volcanic ash advisories are produced by specialised forecasters who combine several sources of observational data and volcanic ash dispersion model outputs based on their subjective expertise. These advisories are used by the aviation industry to make decisions about where it is safe to fly. However, both observations and dispersion model simulations are subject to various sources of uncertainties that are not represented in operational forecasts. Quantification and communication of these uncertainties are fundamental for making more informed decisions. Here, we develop a data assimilation technique which combines satellite retrievals and volcanic ash transport and dispersion model (VATDM) output, considering uncertainties in both data sources. The methodology is applied to a case study of the 2019 Raikoke eruption. To represent uncertainty in the VATDM output, 1000 simulations are performed by simultaneously perturbing the eruption source parameters, meteorology and internal model parameters (known as the prior ensemble). The ensemble members are filtered, based on their level of agreement with Himawari satellite retrievals of ash column loading, to produce a posterior ensemble that is constrained by the satellite data and its uncertainty. For the Raikoke eruption, filtering the ensemble skews the values of mass eruption rate towards the lower values within the wider parameters ranges initially used in the prior ensemble (mean reduces from 1 Tg h−1 to 0.1 Tg h−1). Furthermore, including satellite observations from subsequent times increasingly constrains the posterior ensemble. These results suggest that the prior ensemble leads to an overestimate of both the magnitude and uncertainty in ash column loadings. Based on the prior ensemble, flight operations would have been severely disrupted over the Pacific Ocean. Using the constrained posterior ensemble, the regions where the risk is overestimated are reduced potentially resulting in fewer flight disruptions. The data assimilation methodology developed in this paper is easily generalisable to other short duration eruptions and to other VATDMs and retrievals of ash from other satellites.

Reinforcement Learning for Uncooperative Space Objects Smart Imaging Path-Planning

Leading space agencies are increasingly investing in the gradual automation of space missions. In fact, autonomous flight operations may be a key enabler for on-orbit servicing, assembly and manufacturing (OSAM) missions, carrying inherent benefits such as cost and risk reduction. Within the spectrum of proximity operations, this work focuses on autonomous path-planning for the reconstruction of geometry properties of an uncooperative target. The autonomous navigation problem is called active Simultaneous Localization and Mapping (SLAM) problem, and it has been largely studied within the field of robotics. Active SLAM problem may be formulated as a Partially Observable Markov Decision Process (POMDP). Previous works in astrodynamics have demonstrated that is possible to use Reinforcement Learning (RL) techniques to teach an agent that is moving along a pre-determined orbit when to collect measurements to optimize a given mapping goal. In this work, different RL methods are explored to develop an artificial intelligence agent capable of planning sub-optimal paths for autonomous shape reconstruction of an unknown and uncooperative object via imaging. Proximity orbit dynamics are linearized and include orbit eccentricity. The geometry of the target object is rendered by a polyhedron shaped with a triangular mesh. Artificial intelligent agents are created using both the Deep Q-Network (DQN) and the Advantage Actor Critic (A2C) method. State-action value functions are approximated using Artificial Neural Networks (ANN) and trained according to RL principles. Training of the RL agent architecture occurs under fixed or random initial environment conditions. A large database of training tests has been collected. Trained agents show promising performance in achieving extended coverage of the target. Policy learning is demonstrated by displaying that RL agents, at minimum, have higher mapping performance than agents that behave randomly. Furthermore, RL agent may learn to maneuver the spacecraft to control target lighting conditions as a function of the Sun location. This work, therefore, preliminary demonstrates the applicability of RL to autonomous imaging of an uncooperative space object, thus setting a baseline for future works.

Aircraft operators maintenance decisions supporting method

A key element of exploitation processes constitutes maintenance operations and tasks. While being conducted in the proper way, they have a crucial effect on achieving the assumed by aircraft designer and operator goals. Properly conducted maintenance operations allow to meet all the technical objects readiness requirements as well as to achieve desired acceptable risk level. Maintenance system effectiveness might be generally a crucial task for company or entity responsible for the maintenance. In this context, particularly relevant become technical object maintenance procedures and tasks developed by their manufacturers. Experience of the article authors quite early shows the need of the mainte-nance programmes modification. Aircraft manufacturers usually are not so eager to develop and implement mainte-nance programme modifications. Presented situation is very much the case in aviation transport. This was the reason why authors of this article decided to prepare and develop this elaboration which might constitute the assistance and supports complex technical objects users in maintenance decision. The main purpose of this article is to present maintenance decisions’ supporting method for the aircraft operators. This article provides guidelines which include a description of risk in the context of aviation maintenance and introduction of some methodologies, tools and criteria that support identification, analysis and evaluation of risk. Authors included idea, how the aircraft preventive maintenance could be used to mitigate aircraft failure risk during flight operations. It also shows how to adopt and develop effective maintenance program using tools for adequate risk analysis, optimal interval assignments, and selection of the most effective maintenance task. Authors presented methodology and de-scribed steps of the logic diagram analysis for the aircraft systems and their components, in order to manage and adopt aircraft maintenance program to fulfil aircraft airworthiness requirements and operational availability. The whole methodology was described on the basis of the F 16 aircraft maintenance system and with reference to the maintenance data. This article might also constitute an introduction to the aircraft maintenance programme development method.

EFFECTS OF REWARD PRACTICES ON EMPLOYEE JOB SATISFACTION OF KENYA AIRWAYS, NAIROBI CITY COUNTY, KENYA

The human resource department plays a vital role in assuring that the employees are satisfied with the job that they do every day. Research has shown that satisfied workers are more productive and committed to their teams. Recently, Kenya Airways have faced numerous challenges resulting from a reduced rate of productivity for their workers and high turnover for its technical staff as a result of an increased level of dissatisfaction ensuing from poor pay, slow career progression, and hiring of expatriates. The main objective of this study was to investigate the effects of reward practices on employee job satisfaction at Kenya Airways in Nairobi County, Kenya. This study was anchored on two theories that include Abraham Maslow’s hierarchy of needs theory, and Expectancy theory. The study used descriptive research design to gather data on different subjects based on the research problem. Kenya Airways has 1501 staff; hence the study focused on a sample size of 150 employees from flight operations, HR, finance, operations, and commercial departments representing 10% of the workers’ population. The research collected both primary and secondary data to ensure that adequate data were gathered for analysis. A pilot study was carried on the instruments for collecting data to ensure the vital components of the main study are feasible. The testing of the instrument was carried out to guarantee reliability and validity. Additionally, the study adopted Cronbach’s Alpha to measure reliability and validity. Statistical Package for Social Sciences (SPSS) V22 was utilized for the interpretation and analysis of data. Multiple regression analysis was achieved using SPSS to determine the association of the variables used in the study. The findings showed the correlation study to determine the relationship between reward practices was positively correlated with job satisfaction (r = 0.751, ρ<0.05) Showing that reward practices had a positive correlation with job satisfaction. Based on the findings of this study, Reward was the most significant variable that affect job satisfaction with a beta value of (β2 = 0.847). Looking at the p values of all the variables, it had (p < 0.0). The research study concluded reward practices significantly and positively affects employee job satisfaction of Kenya airways. The study recommended that the management should focus to offer pay and benefits equal to competitors to motivate workers to remain dedicated to the company and reduce the high turnover rate among the technocrats. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0889/a.php" alt="Hit counter" /></p>