Ground Operations Research Articles

Airborne soil-derived dust hazards in aviation

Abstract Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating condition due to corrosion and abrasion on the aircraft surfaces, and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs, and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skillset, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders, and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

The Impact of Climate Change on Future Ground Operations

The information about battlefield environment changes and operational impacts on Korean Peninsula due to climate change is essential for national defense. In this study, the future impact of four ground operations was analyzed by using the national climate change standard scenario based on the IPCC 6<sup>th</sup> report. As a result, it was analyzed that the number of operational-limited days for ambush and airlift operations would decrease, making the operational environment favorable. However, the operational environment unfavorable as the number of operational-limited days for crossing and reconnaissance operations increase, but the number is not large so much.

Predictability of Flight Arrival Times Using Bidirectional Long Short-Term Memory Recurrent Neural Network

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations.

The Role of Micro Propulsion in Enabling Autonomous Operations of Nanosatellites

Micro-Electro-Mechanical Systems (MEMS)-based propulsion devices have significant potential for providing high specific power. The application of Micro Power Generation technology to space propulsion systems can enhance orbit and station-keeping capabilities, potentially at lower costs. New-generation spacecraft and satellites are becoming smaller and require micro-propulsion systems for all aspects of their operations. The development of micro-propulsion systems with associated combustors for micro-electricity generation, utilizing liquid fuel, presents major challenges in fuel injection, mixing, combustion, and chemical reactor systems. A micro-spacecraft with high-accuracy station-keeping and attitude control capabilities needs to have low mass and deliver small impulses. Each nanosatellite will weigh a maximum of 10 kg, including the propellant mass. Provisions for orbital manoeuvres, attitude control, multiple sensors, and instruments, along with full autonomy, will result in a highly capable miniaturized satellite. All onboard electronics will endure a total radiation dose of 100 k rads over a two-year mission lifetime. Nanosatellites designed for in-situ measurements will be spin-stabilized and equipped with a complement of particles and fields instruments. Nanosatellites intended for remote measurements will be three-axis stabilized and equipped with imaging and radio wave instruments. Key technologies under development include: advanced, miniaturized chemical propulsion systems; miniaturized sensors; highly integrated, compact electronics; autonomous onboard and ground operations; miniaturized onboard orbit determination methods; onboard RF communications capable of transmitting data to Earth from significant distances; lightweight and efficient solar array panels; lightweight, high-output battery cells; a miniaturized heat transport system; lightweight yet strong composite materials for nano-satellite and deployer-ship structures; and simple, reusable ground systems.

Resilience assessment of airport aircraft area network operations under thunderstorm weather

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.

Onboard guidance and control algorithms for increased autonomous aerobraking capabilities at Mars

Aerobraking represents a valuable option for interplanetary missions thanks to its capability of reducing the orbital semi-major axis through multiple atmospheric passages, thus with limited fuel consumption. However, due to structural and thermal loads the spacecraft undergoes, mission risks and ground operations costs increase. This study aims to tackle this aerobraking drawback by enhancing its autonomy. The main activities required for aerobraking execution, including atmospheric prediction, orbital state estimation, and orbit control, are tailored for onboard execution. Regarding orbit control, two different methodologies, differentiated by working principle and prediction horizon breadth, are investigated. The first plans orbit control manoeuvres based on the prediction of the atmospheric conditions for a single upcoming pericentre, while the second schedules and optimizes the manoeuvres considering the recent atmospheric conditions experienced by the spacecraft and the altitude trend of multiple upcoming pericentres. The whole operational concept is tested within a simulated environment, with the primary objective of accurately reproducing the intense atmospheric variations of the Martian atmosphere, which represents the most challenging aspect during aerobraking execution. The numerical testing, involving the simulation of different aerobraking regimes under varied conditions, shows that the natural dynamics of the orbital motion can be exploited to minimize both fuel consumption for orbit control manoeuvres and spacecraft load variations, thus enabling more efficient aerobraking and more stable flight conditions, even with the limited predictive capabilities that characterize onboard resources. Both control logics permit the aerobraking execution without exceeding the maximum allowed thermal limits, hence showing their effectiveness. Furthermore, a Monte Carlo analysis conducted on a complete aerobraking simulation reveals a 25.5% reduction in propellant consumption and a 9.4% reduction in heat rate variability when the orbit control decision process uses information relative to multiple future pericentres.

THE AIRCRAFT ENGINE PROPELLER MAIN CHARACTERISTICS DETERMINATION IMPROVED METHOD

An improved method of the fixed and variable pitch propeller aerodynamic characteristics determining that based on the vortex theory is presented. It is shown that the design of propellers has it`s peculiarities, it does not take into account a number of factors that affect the propeller operation to one or another degree. This actualized the special purpose unmanned aerial vehicles engines propellers aerodynamic characteristics calculating methodology improvement. Since the use of the propeller profiles performances in the design requires the air compressibility correction introduction already after the selection of the propeller, that complicates the design, therefore the mathematical model of the variable-pitch propeller working process has been improved based on the profile model with the correct consideration of the Mach and Reynolds numbers influence on the aerodynamic characteristics of the air screw. This advantage of the developed airfoil model allows the design and verification of a subsonic propeller for ground and flight operations. Methods of design and verification calculations of propeller blades are presented. Verification of the improved methodology was carried out by comparing calculated data with known experimental data. The reliability of the improved aircraft engine propeller main performances determination methodology is substantiated by the correct application of the numerical and experimental methods of aerodynamics, the consistency of theoretical provisions with experimental data. The satisfactory coincidence of the calculated data obtained by the improved method of the engine propeller main performances determination allowed us to draw a conclusion concerning its efficiency. The improved technique for the main performances of an aircraft engine propeller determination does not require knowledge on the propeller profiles aerodynamic characteristics from which the blade is drawn, since these characteristics are calculated based on known geometric data, taking into account the Mach and Reynolds numbers. This allows you to design and build a subsonic propeller of both fixed pitch and variable pitch.

Effects of the droplet size and engine size on two-phase kerosene/air rotating detonation engines in flight operation conditions

The numerical and experimental research of rotating detonation engines is usually conducted in ground environmental conditions. Although many breakthroughs have been made, there are still deficiencies in understanding engines operating in real flight conditions. In order to reveal the effect of engine size and initial kerosene droplet size on the rotating detonation engines in flight conditions, the Eulerian-Lagrangian model is adopted, and a series of two-phase kerosene/air rotating detonation cases are simulated in the conditions of Mach 5 and 24 km altitude. When 5 μm and 10 μm droplets are adopted, the RDE behaves like gaseous rotating detonations with clear cellular structures. When the 20 μm and 30 μm droplets are adopted, the rotating detonation waves tend to be divided into two layers and form the λ-shaped shock structure. The results indicate that droplet size and engine size influence detonation wave propagation and flow field mainly through droplet heat absorption and evaporation height. Increasing the engine sizes can promote the two-phase rotating detonation and broaden the initial diameter range capable of obtaining rotating detonation waves. However, as droplet size increases, the fresh mixture layer becomes stratified, with an upper layer predominantly comprising fuel vapor and a lower layer of fuel droplets, which results in an λ-shaped shock structure at the detonation front. The flight operation condition brings in differences in features such as detonation wave height and velocity deficit. The detonation height in flight conditions is higher than that in ground conditions. The most velocity deficit in flight conditions observed in our study is below 15 %, which is lower than the results (22.5 %) in ground operating conditions. These results indicate that the optimal design of the rotating detonation engine must consider the real operation conditions and the effect of engine sizes and droplet sizes.

Quantitative analysis of temporal stability and instrument performance during field experiments of an airborne QCLAS via Allan–Werle-plots

Allan–Werle-plots are an established tool in infrared absorption spectroscopy to quantify temporal stability, maximum integration time and best achievable precision of a measurement instrument. In field measurements aboard a moving platform, however, long integration times reduce time resolution and smooth atmospheric variability. A high accuracy and time resolution are necessary as well as an appropriate estimate of the measurement uncertainty. In this study, Allan-Werle-plots of calibration gas measurements are studied to analyze the temporal characteristics of a Quantum Cascade Laser Absorption Spectrometer (QCLAS) instrument for airborne operation. Via least-squares fitting the individual noise contributions can be quantified and different dominant regimes can be identified. Through simulation of data according to the characteristics from the Allan-Werle-plot, the effects of selected intervals between in-flight calibrations can be analyzed. An interval of 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\,$$\\end{document}min is found sufficient for successful drift correction during ground operation. The linear interpolation of the sensitivity increases the accuracy and lowers the measurement uncertainty from 1.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.1\\,\\%$$\\end{document} to 0.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.2\\,\\%$$\\end{document}. Airborne operation yields similar results during segments of stable flight but suffers from additional flicker and sinusoidal contributions. Simulations verify an appropriate interval of 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\,$$\\end{document}min in airborne operation. The expected airborne measurement uncertainty is 2.45 ppbv.

Flight verification of cooling self-sustaining high-temperature superconducting motor

The global shift towards sustainable development and technological advancements has propelled the energy transition trend. Recognizing the substantial environmental impact of conventional commercial airplanes, there is a growing urgency to develop a sophisticated superconducting motor system for commercial aviation. The advent of high-temperature superconducting motors presents a transformative leap, offering significant advantages in power density and efficiency when compared to traditional motors. To validate the issues that future liquid-hydrogen superconducting electric airplanes may encounter, a kilowatt-class aerospace high-temperature superconducting motor is designed. Based on the requirements of airborne applications, critical parameters such as electromagnetic characteristics, operating characteristics, and AC losses have been analyzed. Furthermore, extensive research and testing have been conducted on the superconducting motor magnet, leading to the successful assembly of a prototype. The superconducting motor has a rated output power of 2.7 kW and a rated speed of 5000 rpm. Rigorous ground operation performance tests have also been conducted to ensure the feasibility and reliability of the motor in practical applications. Benefiting from the topological structure design, the superconducting motor has an excellent sealing performance at low temperatures. The superconducting motor can maintain low temperature and high vacuum for a long time, when the vacuum pump is removed and the liquid nitrogen inlet is closed after the motor is completely cooled. The culmination of these endeavors is the realization of a successful flight validation of an unmanned aerial vehicle equipped with a high-temperature superconducting motor, demonstrating a sustained flight of nearly one hour.

An Adaptive Similar Scenario Matching Method for Predicting Aircraft Taxiing Time

Accurate prediction of taxiing time is important in ensuring efficient and safe operations on the airport surface. It helps improve ground operation efficiency, reduce fuel waste, and improve carbon emissions at the airport. In actual operations, taxiing time is influenced by various factors, including a large number of categorical features. However, few previous studies have focused on selecting such features. Additionally, traditional taxiing time prediction methods are often black-box models that only provide a single prediction result; they fail to provide effective practical references for controllers. Therefore, this paper analyses the features that affect taxiing time from different data types and forms a taxi feature set consisting of nine key features. We also propose a taxiing time prediction method based on adaptive scenario matching rules. This process classifies the scenarios into multiple typical historical scenario sets and adaptively matches the current target scenario to a typical scenario set based on quantified rules. Then, based on the matching results, a pre-trained model obtained from the corresponding scenario set is used to predict the taxiing time of an aircraft in the target scenario, aiming to mitigate the impact of data heterogeneity on prediction results. Experimental results show that compared to baseline methods, the mean absolute error and root mean square error of the proposed method decreased by 4.8% and 12.6%, respectively. This method significantly reduces the fluctuations in results caused by sample heterogeneity and enhances controllers’ acceptance of prediction results from the model. It can be used to further improve auxiliary decision making systems and enhance the precise control capabilities of airport surface operations.

Evaluation of the effectiveness of methodological approaches to hierarchical intelligent control of ground-air Ad-Hoc communication network

The article shows the features of intelligent management processes of a new type of ground-air communication networks that are rapidly being implemented. The dynamic nature of the conditions of operation and behavior of communication nodes, both ground and air, causes a rapid increase in the volume of service information necessary to ensure continuous and adaptive management in real time. One of the ways to solve this problem is the redistribution of management tasks at different stages of the management cycle, which is classically divided into the stages of planning, deployment and operational management. On the one hand, the increase in entropy at the planning stage complicates this process, but this approach will increase the probability of making "correct" management decisions from the standpoint of quality management (network metrics). The work investigates a new architecture of a hierarchical intelligent ground-air communication network control system based on the model-free Reinforcement learning algorithm as a Q-learning network agent and FA-OSELM online sequential extreme machine learning algorithms as node-level agents. The IСS model is presented, its adequacy is checked, and the process of its learning at the planning stage on various mobility models is shown. An important feature of the IСS training process is the application of the developed mobility model, disclosed in the article, which describes the interaction processes of communication nodes at a deeper level. The work conducted a study of the representativeness of the training sample, obtained using the developed mobility model, that was carried out relative to the existing ones, and it was determined that despite the smaller volume of the population of the initial data, it was possible to ensure a better quality of resource management.

How can a balanced ecosystem of technology, collaboration and innovation build greener airports? The case study of Groningen Airport Eelde

This paper discusses Groningen Airport Eelde’s (GRQ) transition to sustainable aviation, emphasising its commitment to minimising environmental impact while enhancing regional connectivity. It details GRQ’s initiatives, collaborations and future plans, showcasing how the airport leverages its unique position to spearhead the adoption of sustainable technologies, particularly in hydrogen. Through collaboration, innovation and strategic partnerships, GRQ aims to lead the way towards a more sustainable aviation future. GRQ’s commitment to minimising environmental impacts aligns with community interests, maintaining local trust and government support. By 2030, GRQ aims for a fully emission-free ground operation. The airport has successfully implemented a 22MW airside solar park and is now pioneering a comprehensive hydrogen ecosystem, aiming to become Europe’s first Hydrogen Valley Airport. Despite challenges in funding and legislation, GRQ advises other airports to build consortia, connect with regional partners and prioritise sustainability.

Aircraft Taxi Path Optimization Considering Environmental Impacts Based on a Bilevel Spatial–Temporal Optimization Model

Aircraft taxiing emissions are the main source of carbon dioxide and other pollutant gas emissions during airport ground operations. It is crucial to optimize aircraft taxiing from both spatial and temporal perspectives to improve airport operation efficiency and reduce aviation emissions. In this paper, a bilevel spatial and temporal optimization model of aircraft taxiing is constructed. The upper-level model optimizes the aircraft taxiing path, and the lower-level model optimizes the taxiing start time of the aircraft. By the iterative optimization of the upper- and lower-level interactions, the aviation fuel consumption, flight waiting time, and number of taxiing conflicts are reduced. To improve the calculation accuracy, the depth-first search algorithm is utilized to generate the set of available paths for aircraft during the model solution process, and a model solution method based on the genetic algorithm is constructed. Simulation experiments using Tianjin Binhai International Airport as the research object show that adopting the waiting taxiing strategy can effectively avoid taxiing conflicts and reduce aviation fuel consumption by 753.18 kg and 188.84 kg compared to the available path sets generated using Dijkstra’s algorithm and those created manually based on experience, respectively. Conversely, adopting an immediate taxi-out strategy caused 54 taxiing conflicts and increased aviation fuel consumption by 49.44 kg. These results can provide safe and environmentally friendly taxiing strategies for the sustainable development of the air transportation industry.

Towards Environmentally Sustainable Aviation: A Review on Operational Optimization

In recent years, the rapid growth of air traffic has intensified pressure on the air transport system, leading to congestion problems in airports and airspace. The projected increase in demand exacerbates these issues, necessitating immediate attention. Additionally, there is a growing concern regarding the environmental impact of the aviation sector. To tackle these challenges, the adoption of advanced methods and technologies shows promise in expanding current airspace capacity and improving its management. This paper presents an overview of sustainable aviation, drawing on publications from academia and industry. The emphasis is on optimizing both flight and ground operations. Specifically, the review delves into recent advancements in airline operations, airport operations, flight operations, and disruption management, analyzing their respective research objectives, problem formulations, methodologies, and computational experiments. Furthermore, the review identifies emerging trends, prevailing obstacles, and potential directions for future research.

From acute food insecurity to famine: how the 2023/2024 war on Gaza has dramatically set back sustainable development goal 2 to end hunger

The widespread destruction and the devastating humanitarian toll caused by the ongoing war on Gaza have transformed this besieged Strip into a place of death and despair. This review will explore the implications of this war for food security, focusing on Sustainable Development Goal (SDG) 2, which seeks to fight malnutrition and food insecurity and achieve zero hunger by 2030. This work is based on a review of grey literature, such as reports from government and non-governmental agencies, as well as recent scientific journal publications. Our results show that the ongoing war on Gaza has exacerbated the already acute food insecurity that Gazans have been struggling with since the blockade was imposed in 2007. Restless bombardment and ground operations have damaged or even razed agricultural land and all food production infrastructure (such as bakeries, mills, and food processing facilities), destroying Gaza’s food system. Facing catastrophic levels of hunger, some families, especially in northern Gaza have recently been resorting to eating animal feed and weeds to survive. With the starvation of civilians being used as a method of warfare, many experts and human rights organizations argue that Gaza is now the world’s worst hunger crisis and its population is on the verge of famine, if not already there. Moreover, this unprecedented humanitarian crisis in Gaza could have significant consequences on global food security in its six pillars, jeopardizing the implementation of SDG 2. While international organizations are making efforts to mitigate the catastrophic food shortage and famine, more comprehensive and sustainable solutions are needed to address the root causes of food insecurity in Gaza and ensure that all residents have access to an adequate and nutritious diet.

Suppression of surficial maghaemite magnetic noise using an Unmanned Aerial Vehicle deployed magnetometer

Data from two areas in the Tennant Creek mineral field that were recently flown with a magnetometer towed below an Unmanned Aerial Vehicle (UAV or “drone”) using a sensor height of 15–20 m above ground are compared to older datasets acquired on the ground and in the air. The ground data are shown to be severely affected by surficial maghaemite, creating significant noise degradation of the ground data. Conversely, regional data flown at a height of 60 m above ground level only produces very subtle anomalies over the targeted features that could be easily missed by interpreters. Low-level, high-resolution UAV data shows significant improvement in signal-to-noise equivalent to upward continuation: filtering out the surficial signals while retaining excellent detectability of small subsurface ironstone targets, and significant gains in surveying efficiency over both ground and airborne operations.

Analysis of the shimmy in the landing gear of a general-purpose helicopter for different payloads

Landing gears, which are designed to absorb impact energy in order to provide support to the aircraft during landing, take-off and ground operations, are important components of the structure. Shimmy, which is one of the causes of aircraft failures, is a vibration that occurs as a result of the interaction of tire and landing gear dynamics and can cause an accident. In this study, the shimmy behavior of a helicopter used for civil, military and air ambulance purposes are investigated at different load and taxiing velocities. A landing gear single degree of freedom mathematical model is obtained and a Von Schlippe stressed spring tire model is included. The eigenvalues of the equations of motion are analyzed and stability maps of the system for different operating conditions are obtained. Runge-Kutta method (MATLAB ode45) is used to solve the equations of motion and a viscous damper is proposed. To obtain the damping, versions of the non-Newtonian silicone oil in the damper with different kinematic viscosities were evaluated. Apparent viscosities were obtained using the rheological values of the silicone oil and the analysis was repeated. As a result of the shimmy analysis at the initial velocities and maximum loading values required for the helicopter to taxiing, it was seen that damping was achieved in all conditions by using versions of the silicone oil with higher kinematic viscosities.

Energy of Fluid (EOF) Simulation of Large-Scale Densification and Solidification of Hydrogen

The current research develops a finite volume-based Computation Fluid Dynamic (CFD) model utilizing an Energy of Fluid (EOF) approach to generate a simulation of the densification of liquid hydrogen (LH2) in the Integrated Refrigeration and Storage (IRAS) experimental tank for the Ground Operations Demonstration Unit for Liquid Hydrogen Project (GODU-LH2) at the Kennedy Space Center (KSC). The computational code will integrate a commercial software pressure-based model with User Defined Functions (UDF) generated and added to the model. The modified model will solve the energy equation in terms of internal energy and provide temperature and pressure calculations for a given tank geometry. This method will decrease computational time when compared to the built-in commercially available Enthalpy solvers. Currently the research focused on utilizing User Defined Scalars (UDS) compared to the enthalpy model and a lumped node analysis to show the written UDS is as accurate as the built-in solvers, and the models will be verified using known solutions for solidification and validated with the experimental results from the Cryogenics Test Laboratory (CTL) at KSC for the IRAS densification process.