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ItemRunway safety improvements through a data driven approach for risk flight prediction and simulation(Georgia Institute of Technology, 2022-12-19) Lee, HyunkiRunway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.
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ItemA Framework for Integrating Advanced Air Mobility Vehicle Development, Safety and Certification(Georgia Institute of Technology, 2022-04-28) Markov, AlexanderAs urbanization continues to grow world wide, cities are experiencing challenges dealing with the increases in pollution, congestion, and availability of public transportation. A new market in aviation, Advanced Air Mobility, has emerged to address these challenges by engineering novel aircraft that are all electric and meant to transport people within and between cities quickly and efficiently. The scale of this market and the associated operations means that vehicles will need to fly with increased autonomy. The lack of highly trained and skilled pilots, along with the increased work load for novel aircraft makes piloted aircraft infeasible at the scale intended or Advanced Air Mobility. While a variety of concepts have been created to meet the performance needs of such operations, the safety and certification requirements of these aircraft remain unclear. The paradigm shift from conventional aircraft to novel, highly integrated, and autonomous aircraft presents many challenges which motivate this work. An emphasis is placed on the safety assessment and the gaps between current regulations and the needs for Advanced Air Mobility. The research objective of this work is to develop a framework for the development and safety assessment of autonomous Advanced Air Mobility aircraft by first examining the existing methods, techniques, and regulations. In doing so, several gaps are identified pertaining to the hazard analysis, reliability analysis of Integrated Modular Avionics systems, and the inclusion of a Run-Time Assurance architecture for vehicle control. An improved hazard analysis approach is developed to capture functional failures as well as systematic areas that can lead to unsafe system behavior. The Systems-Theoretic Process Analysis is supplemented to the Continuous Functional Hazard Assessment so that system behavior and component interactions can be captured. Unsafe system and component actions are identified and used to develop loss scenarios which provide context to the specific conditions that lead to loss of critical vehicle functionality. This information is traced back to identified hazards and used to establish constraints to mitigate unsafe behavior. The Functional Hazard Assessment is then applied to applicable scenarios to provide severity and risk information so that quantitative metrics can be used in additional to qualitative ones. The improved approach develops requirements and determines component and system constraints so that requirements can be refined. It also develops a control structure of the system and assigns traceable items at each step to track how unsafe actions, losses, hazards, and constraints are linked. To improve the reliability modeling of complex modular avionics systems utilizing Multi-Core Processing, a Dynamic Bayesian Network modeling method is developed. This method first utilizes the existing methods defined in ARP 4761 for reliability analysis, namely the Fault Tree Analysis. A mapping is identified for converting fault trees to Bayesian networks, before a Dynamic Bayesian Network is developed by defining how component reliability changes with time. The capability to model reliability of these kinds of systems overtime alone is useful for developing and evaluating maintenance schedules. Additionally, it can handle degradable and repairable components and has the capability to infer failure probabilities using observed evidence. This is useful for identifying weak areas of the system that may be the most likely to cause an overall system failure. A secondary capability is the modeling of uncertainty and the reliability impacts of Multi-Core Processing factors. Subject Matter Expert input and test data can be used to develop conditional dependencies between factors like Worst-Case Execution time, complexity, and partitioning of multi-core systems and their impact on the reliability of the Real-Time Operating System. The added safety challenges of interference and system complexity can be modeled earlier in the design process and can quickly be updated as more information becomes available. Finally, the safe inclusion of autonomy is addressed. To do so, a Simplex architecture is chosen for the development and testing of complex controllers. These controllers are non0deterministic in nature and would otherwise not be certifiable as a result. The Simplex architecture uses an assured back up controller that is triggered when a monitor senses that some predefined safety threshold is breached and gives control back once the system is back to nominal operations. This architecture enables the use of complex control and functionality while also enabling the overall system to be certified. A model predictive control algorithm is developed using a recursive neural network and a receding horizon control scheme that allows a simple system to be controlled quickly and accurately. A PID controller is used as the assured back up controller and the monitoring and triggering capability is demonstrated. The architecture successfully triggers the back up when a threshold is exceeded and hands control back over to the complex controller when the system is brought back to nominal conditions. The main contribution of this dissertation is the development of a modified development assurance and safety management framework that is applicable to Advanced Air Mobility aircraft. The modifications made are specifically targeted at the challenges of applying the existing framework to novel, integrated, complex, and autonomous aircraft. This supports the objective of this research and provides guidance for how existing well understood and trusted methods can be modified for novel applications.
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ItemShip and Naval Technology Trades-Offs for Science And Technology Investment Purposes(Georgia Institute of Technology, 2022-01-14) Gradini, RaffaeleLong-term naval planning has always been a challenge, but in recent years the difficulty has increased. The degradation of the security environment is leading toward a more volatile, uncertain, complex, and ambiguous world, heavily affecting the quality of predictions needed in long-term defense technology investments. This work tackles the problem from the perspective of the maritime domain, with a new approach stemming from the state-of-the-art in the defense investment field. Moving away from classic methodologies that rely on well-defined assumptions, it is possible to find investment processes that are broad enough, yet concrete, to support decision making in naval technology trades for science and technology purposes. In fulfilling this objective, this work is divided in two main areas: identifying technological gaps in the security scenario and providing robust technology investment strategies to cover those gaps. The core of the first part is the capability of decomposing maritime assets using modern taxonomies, to map the impact of different technologies on ships. Once technologies are mapped, they can be traded inside assets, and assets inside fleets to quantitatively evaluate the overall fleet robustness. The first deliverable achieved through this process is called Vulnerable Scenarios, a list of possible conflict scenarios in which a tested fleet would consistently fail. The second deliverable is called Robust Strategies and is made of different technological investments to allow the studied fleet in succeeding the discovered Vulnerable Scenario. To find the first deliverable a large set of scenarios were simulated. The results of this simulation were analyzed using the Patient Rule Induction Method to isolate, among the large set of relevant cases, a subgroup of Vulnerable Scenarios. These were identified by highlight commonalities on shared parameters and variables. Once the Vulnerable Scenarios were discovered, an ad-hoc adaptive response system using a “signpost and trigger” mechanism was used to identify different technologies on the ships studied that could enhance the overall robustness of the fleet. In identifying these technologies, the adaptive system was supported by different taxonomies in performing the different technological trades that allowed the algorithm to find Robust technology Strategies. The methodology was completed by a ranking system that was designed to firstly check all the Robust Strategies in all the scenarios of interest, and then to compare them against ranking metrics defined by decision makers. To test the created methodology, several experiments were conducted across two use cases. The first use case, which involved an anti-submarine warfare (ASW) mission, was used to demonstrate the individual pieces employed in the creation of the methodology. The second use case, involving a large operation made of several tasks, was used to test the overall methodology as one. Both use cases were designed on the same original scenario created in collaboration with former generals and admirals of the US Air Force and the Italian Navy. The primary results of this experiments show that once Vulnerable Scenarios are discovered, it is possible to employ an iterative algorithm that recursively infuse new technologies into the fleet. This process is repeated until Robust Technology Strategies that can support the fleet are selected. The missions designed demonstrated the presence of gaps which had to be covered via technology investment showing how planners will have to account for new technologies to be able to succeed in future challenges. The methodology created in this thesis provided an innovative way of enhancing the screening of maritime scenarios, reducing the leading time for investment decisions on naval technologies. In conclusion, the work done in this thesis helps in advancing the state of the art of methodologies used by planners when looking for Vulnerable Scenarios and for new technologies to invest on. Therefore, this thesis demonstrates that by employing the proposed methodology, Vulnerable Scenarios and relevant technologies can be identified in less time than by employing current methods. These efforts will support planners and decision makers in reacting faster to new emerging threats in unforeseen naval scenarios and, will enable them to identify in a rapid fashion in which areas more investments are needed.
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ItemA DATA-DRIVEN METHODOLOGY TO ANALYZE AIR TRAFFIC MANAGEMENT SYSTEM OPERATIONS WITHIN THE TERMINAL AIRSPACE(Georgia Institute of Technology, 2021-12-10) Corrado, Samantha JaneAir Traffic Management (ATM) systems are the systems responsible for managing the operations of all aircraft within an airspace. In the past two decades, global modernization efforts have been underway to increase ATM system capacity and efficiency, while maintaining safety. Gaining a comprehensive understanding of both flight-level and airspace-level operations enables ATM system operators, planners, and decision-makers to make better-informed and more robust decisions related to the implementation of future operational concepts. The increased availability of operational data, including widely-accessible ADS-B trajectory data, and advances in modern machine learning techniques provide the basis for offline data-driven methods to be applied to analyze ATM system operations. Further, analysis of ATM system operations of arriving aircraft within the terminal airspace has the highest potential to impact safety, capacity, and efficiency levels due to the highest rate of accidents and incidents occurring during the arrival flight phases. Therefore, motivating this research is the question of how offline data-driven methods may be applied to ADS-B trajectory data to analyze ATM system operations at both the flight and airspace levels for arriving aircraft within the terminal airspace to extract novel insights relevant to ATM system operators, planners, and decision-makers. An offline data-driven methodology to analyze ATM system operations is proposed involving the following three steps: (i) Air Traffic Flow Identification, (ii) Anomaly Detection, and (iii) Airspace-Level Analysis. The proposed methodology is implemented considering ADS-B trajectory data that was extracted, cleaned, processed, and augmented for aircraft arriving at San Francisco International Airport (KSFO) during the full year of 2019 as well as the corresponding extracted and processed ASOS weather data. The Air Traffic Flow Identification step contributes a method to more reliably identify air traffic flows for arriving aircraft trajectories through a novel implementation of the HDBSCAN clustering algorithm with a weighted Euclidean distance function. The Anomaly Detection step contributes the novel distinction between spatial and energy anomalies in ADS-B trajectory data and provides key insights into the relationship between the two types of anomalies. Spatial anomalies are detected leveraging the aforementioned air traffic flow identification method, whereas energy anomalies are detected leveraging the DBSCAN clustering algorithm. Finally, the Airspace-Level Analysis step contributes a novel method to identify operational patterns and characterize operational states of aircraft arriving within the terminal airspace during specified time intervals leveraging the UMAP dimensionality reduction technique and DBSCAN clustering algorithm. Additionally, the ability to predict, in advance, a time interval’s operational pattern using metrics derived from the ASOS weather data as input and training a gradient-boosted decision tree (XGBoost) algorithm is provided.
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ItemAerodynamics and aeroacoustic sources of a coaxial rotor(Georgia Institute of Technology, 2018-04-10) Schatzman, Natasha LydiaVehicles with coaxial, contra-rotating rotor systems (CACR) are being considered for a range of applications, including those requiring high speed and operations in urban environments. Community and environmental noise impact is likely to be a concern in these applications. Design parameters are identified that effect the fundamental aerodynamics and fluid dynamic features of a CACR in hover, vertical, and edgewise flight. Particular attention is paid to those features affecting thickness, loading, blade vortex interaction (BVI), and high speed impulsive (HSI) noise. Understanding the fluid dynamic features is a precursor to studying the aeroacoustics of a coaxial rotor. Rotor performance was computed initially using Navier-Stokes solver with prescribed blade section aerodynamic properties, the results validated against generic experimental test cases. The fluid dynamics of blade interactions was simplified and broken into a 2-D blade crossing problem, with crossing locations and velocity fields from the rotor results. Two trains of 8 airfoils passing were simulated to understand the effects due to shed vorticity. The airfoils are displaced vertically by a distance equivalent to the typical spacing between the upper and lower rotors of a coaxial system. A 2D potential flow code and 2D OVERFLOW compressible-flow Navier-Stokes solver were used to investigate the complex coaxial rotor system flow field. One challenge of analyzing the CACR is the difficulty in envisioning all the possible interactions and their possible locations as flight conditions and rotor designs change. A calculation tool has been developed to identify time and location of blade overlap. The tool was then integrated with a wake aerodynamics model to identify locations and instances of upper rotor tip vortex interaction with a lower rotor blade. This tool enables rapid identification of different types of BVI based on relative rotor orientation. Specific aerodynamic phenomena that occur for each noise source relevant to CACR are presented, along with computational tools to predict these occurrences.
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ItemA framework to enable rotorcraft maintenance free operating periods(Georgia Institute of Technology, 2018-04-05) Bellocchio, Andrew T.The British Ultra-Reliable Aircraft Pilot Program of the late 1990s introduced the sustainment concept of a Maintenance Free Operating Period (MFOP) where aircraft become fault tolerant, highly reliable systems that minimizes disruptive failures and maintenance for an extended period of operations. After the MFOP, a single Maintenance Recovery Period (MRP) consolidates repair of accrued faults and inspections to restore an aircraft’s reliability for the next MFOP cycle. The U.S. Department of Defense recently adopted MFOP as a maintenance strategy for the next generation of rotorcraft named Future Vertical Lift. The U.S. military desires the assurance of uninterrupted flight operations that an MFOP strategy provides to enable an expeditionary force. This work develops a framework to balance downtime, dependability, and maintainability of an MFOP rotorcraft. It begins with the hypothesis that metrics using the mean are insufficient in a MFOP strategy and that metrics that include the time history of failure are as important as the rate of failure. It will utilize a Discrete Event Simulation to model the MFOP, MRP, and the success rate as operational metrics. The work will identify which subsystem(s) limit the MFOP of an aircraft and which components drive MRP higher. It will explore a framework to build policies for availability and success rate where preventive component renewals occur at discrete multiples of the MFOP. Finally, it will test the hypothesis that an operator has some control over the MFOP to meet changing operational demands by adapting the MRP through an aggressive lifing policy.
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ItemA framework for the optimization of doctrine and systems in Army Air Defense units using predictive models of stochastic computer simulations(Georgia Institute of Technology, 2017-04-05) Wade, Brian M.This thesis presents a new methodology that can be used to address large-scale raids made up of different types of Theater Ballistic Missiles (TBMs) and Cruise Missiles (CMs) that attempt to overwhelm the Air Defense Artillery (ADA) systems at a particular location. The primary focus will be on how existing ADA systems can adjust their tactics in order to minimize the damage caused by threats that are not shot down and impact friendly forces. Nearly all the literature to date optimizes systems and tactics to reduce the number of leakers — threats not shot down — that impact the ground. However, simply counting the number of leakers does not adequately describe the effects to friendly forces. Instead, the first part of this thesis combines existing methods for external ballistics, concrete penetration, explosive cratering, and weapon blast and fragmentation damage in order to create an integrated program that can describe the damage to an airfield runway, infrastructure, and parked aircraft. The second part of this thesis focuses on modeling the ADA missile engagements using an accredited Department of Defense ADA simulation model called the Extended Air Defense Simulation (EADSIM). Both the airfield damage model and ADA simulation have run times ranging from minutes to hours. They are also stochastic; so a large number of runs are required for each input vector in order to properly understand the output range. In order to reduce the computation time to allow for later optimization, the methods of design of experiments and machine learning were used to create fast running models that predict the outputs of these simulations. The final part of this work uses these prediction algorithms to first optimize the TBM and CM fire plan, then optimize the ADA defense tactics, and finally optimize the ADA defense tactics with a new interceptor missile system.
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ItemA methodology to achieve microscopic/macroscopic configuration tradeoffs in cooperative multi-robot systems design(Georgia Institute of Technology, 2017-04-04) Durand, Jean Guillaume Dominique SebastienThe exponential growth experienced by the robotics sector over the past decade has fostered the proliferation of new architectures. Optimized for specific missions, these platforms are in most cases limited by their embarked computational power and a lack of full situational awareness. More robust, flexible, scalable, and inspired by nature, group robotics represent an interesting approach to overcome some limitations of these single agents and take advantage of the heterogeneity of the current robotics fleet. Their essence lies in accomplishing more complex synergistic behaviors through diversity, simple rules, and local interactions. However, the design of robotic groups is complex as decision-makers have to optimize the group operation as well as the performance of each individual unit, for the group performance. In particular, key questions arise to know whether resources should be allocated to the characteristics of the group, or to the individual capabilities of its agents in order to meet the established requirements. Current methods of swarm engineering tend to perform sequential optimization of the microscopic level (the agents) and then the macroscopic level (the group), which results in suboptimal architectures. In this context, efficiently comparing two different groups or quantifying the superiority of a group versus a single-robot design may prove impossible. Same goes of the determination of an optimal architecture for a given mission. With a special emphasis on aerial vehicles, the present research proposes to establish a methodology to achieve microscopic/macroscopic configuration tradeoffs in the design of cooperative multi-robot systems. The resulting product is the MASDeM: Multi-Agent Systems Design Methodology. A novel multi-level multi-architecture morphological approach is first introduced to facilitate design space exploration, and a mesoscopic level simulation-based design method is used to bridge the gap between microscopic and macroscopic levels. Using these first blocks, an innovative optimization technique is suggested based on two interconnected loops which differs from the classical sequential approach presently used by the research community. Results of this research show that simultaneous optimization can have clear benefits if applied to the design of multi-robot systems and on particular cases, average improvements of 16 percent were observed on the main performance metric. The proposed optimizer proves to be a key enabler for fully heterogeneous swarms, a capability which is not possible in the current paradigm. Moreover, the optimization algorithm was efficiently designed and exhibits a speedup of at least 50 percent when compared to current techniques. Finally, the exploration of the design space is effectively carried out with a combination of morphological reduction, morphological tree representation, and mesoscopic modeling. Indeed, applied to multi-robot systems, such models prove being several times faster than usual simulation approaches while remaining in the same range of accuracy.
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ItemA new rotorcraft design framework based on reliability and cost(Georgia Institute of Technology, 2016-07-26) Scott, Robert C.Helicopters provide essential services in civil and military applications due to their multirole capability and operational flexibility, but the combination of the disparate performance conditions of vertical and cruising flight presents a major compromise of aerodynamic and structural efficiency. In reviewing the historical trends of helicopter design and performance, it is apparent that the same compromise of design conditions which results in rotorcraft performance challenges also affects reliability and cost through vibration and fatigue among many possible factors. Although many technological approaches and design features have been proposed and researched as means of mitigating the rotorcraft affordability deficit, the assessment of their effects on the design, performance, and life-cycle cost of the aircraft has previously been limited to a manual adjustment of legacy trends in models based on regression of historical design trends. A new approach to the conceptual design of rotorcraft is presented which incorporates cost and reliability assessment methods to address the price premium historically associated with vertical flight. The methodology provides a new analytical capability that is general enough to operate as a tool for the conceptual design stage, but also specific enough to estimate the life-cycle effect of any RAM-related design technology which can be quantified in terms of weight, power, and reliability improvement. The framework combines aspects of multiple design, cost, and reliability models – some newly developed and some surveyed from literature. The key feature distinguishing the framework from legacy design and assessment methods is its ability to use reliability as a design input in addition to the flight conditions and missions used as sizing points for the aircraft. The methodology is first tested against a reference example of reliability-focused technology insertion into a legacy rotorcraft platform. Once the approach is validated, the framework is applied to an example problem consisting of a technology portfolio and a set of advanced rotorcraft configurations and performance conditions representative of capabilities desired in near-future joint service, multirole rotorcraft. The framework sizes the different rotorcraft configurations for both a baseline set of assumptions and a tradespace survey of reliability investment to search for an optimum design point corresponding to the level of technology insertion which results in the lowest life-cycle cost or highest value depending on the assumptions used. The study concludes with a discussion of the results of the reliability trade study and their possible implications for the development and acquisition of future rotorcraft.
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ItemRapid determination of mass and stiffness distribution on primary skin-stiffener structures(Georgia Institute of Technology, 2016-07-20) Noevere, August T.In modern conceptual/preliminary design of aerospace vehicles it is common for a large number of concepts and configurations to be rapidly explored. For each configuration, the structures discipline is responsible for determining an internal structural arrangement and detailed component design that minimizes mass while supporting external loads and other requirements. The proposed research presents a methodology suited for rapid design of structures which is capable of optimizing mass while easily meeting these requirements. Specifically, the methodology focuses on the stiffened panel optimization problem for metallic and composites. A change of variables is performed to allow accurate linearization of the design space, thereby greatly increasing optimization efficiency. The stiffened panel design space is recast in terms of equivalent smeared stiffness, using terms from the [ABD] stiffness matrix. This reformulation is enabled by the use of response surface equations to map the panel failure criteria (such as material failure, local buckling, etc.) to be a function of stiffness terms only. The resulting linear design space can be quickly optimized with the Simplex Algorithm. Thus, the approach is able to perform physics-based panel optimization with a level of efficiency appropriate for conceptual design studies. This approach is validated for a metallic and composite I-stiffened panel, as well as a composite laminate. Additionally, the methodology is demonstrated to couple well with the FEM-based design environment of a wing box for both metallic and composite construction. Overall, the methodology was shown to provide significant improvement in stiffened panel optimization efficiency over traditional tools while retaining accuracy within 10% of those tools.