Multidisciplinary Design Optimization (MDO) is an area in engineering design which has been growing rapidly in terms of applications in the last few decades, aircraft design being no exception to that. The application of MDO to aircraft and more specifically, wing design, presents many challenges, since disciplines like aerodynamics and structures have to be combined and interact. The level to which this interaction is implemented depends only on how much one is willing to pay in terms of computational cost. The objective of the current work is therefore to develop a simplified MDO tool, suitable for the preliminary design of aircraft wings. At the same time, versatility in the definition of optimization problems (in terms of design variables, constraints and objective function) is given great attention. At the same time, modularity will ensure that this framework is upgradeable with higher-fidelity and/or more capable modules. The disciplines that were chosen for interaction were aerodynamics and structures/ aeroelasticity, though more data can be extracted from their results in order to perform other types of analyses. The aerodynamics module employs a Vortex Lattice code developed specifically for the current implementation of the tool. The structural module is based on Equivalent Plate model theory. The fluid structure interaction is simply one-way, wherein the aerodynamics loads are passed on to the structural analyzer for computation of the static deformation. Semi-empirical relations are then used to estimate the flutter speed. The optimizer, which controls the activity of the other modules, makes use of a gradient based algorithm (Sequential Quadratic Programming) to search for a local minimum of a user defined objective function. Among the myriad of MDO strategies available, two are chosen to exemplify the modularity of the tool developed: Multidiscipline Feasible (MDF) and Sequential Optimization (SO), and their results are compared. Several case studies are analyzed to cover a broad spectrum of the capabilities of the framework. Because user interaction is of prime concern in design optimization, a graphical interface (GUI) of the tool is presented. Its advantages in terms of the set up of optimization problems and post-processing of results are made clear. In conclusion, some topics for future work regarding the expansion and improvement of the features of the application are noted.

L'objectif de cette thèse est de mettre en place une méthodologie et les outils associés en vue d'harmoniser le processus de construction des modèles de performances et de qualités de vol. Pour ce faire, des techniques de réduction de modèles ont été élaborées afin de satisfaire des objectifs industriels contradictoires de taille mémoire, de précision et de temps de calcul. Après avoir établi une méthodologie de construction de modèles réduits et effectué un état de l'art critique, les Réseaux de Neurones et le High Dimensional Model Representation ont été choisis, puis adaptés et validés sur des fonctions de petite dimension. Pour traiter les problèmes de dimension supérieure, une méthode de réduction basée sur la sélection optimale de sous-modèles réduits a été développée, qui permet de satisfaire les exigences de rapidité, de précision et de taille mémoire. L'efficacité de cette méthode a finalement été démontrée sur un modèle de performances des avions destiné à être embarqué. / The objective of this thesis is to provide a methodology and the associated tools in order to standardize the building process of performance and handling quality models. This typically leads to elaborate surrogate models in order to satisfy industrial contrasting objectives of memory size, accuracy and computation time. After listing the different steps of a construction of surrogates methodology and realizing a critical state of the art, Neural Networks and High Dimensional Model Representation methods have been selected and validated on low dimension functions. For functions of higher dimension, a reduction method based on the optimal selection of submodel surrogates has been developed which allows to satisfy the requirements on accuracy, computation time and memory size. The efficiency of this method has been demonstrated on an aircraft performance model which will be embedded into the avionic systems.

La première année de thèse a permis de mettre en avant deux aspects concernant la problématique de gestion de l’énergie, à savoir le contrôle court terme et le contrôle long terme de l’énergie respectivement. La première problématique a été étudiée pendant la deuxième année de thèse et a débouché sur la proposition d’une architecture de contrôle multi-actionneurs utilisant les moteurs et les aérofreins dans l’objectif d’augmenter l’autorité de contrôle de l’énergie de l’avion. La seconde problématique a été étudiée pendant la troisième année et a débuté par une étude préliminaire reposant sur le calcul d’une séquence optimale de commandes des becs/volets et train d’atterrissage permettant d’amener l’avion à un certain niveau d’énergie en approche tout en minimisant l’utilisation des moteurs et des aérofreins. Par la suite, l’étude a été étendue afin de prendre en compte la régulation des moteurs, l’utilisation des aérofreins et la modification de la trajectoire verticale. Finalement,une solution basée sur un calcul d’optimisation a été développée puis intégrée au sein d’un simulateur de bureau temps-réel, testée avec une interface homme machine adéquat et pour finir présentée à des pilotes d’essais pour validation. / The first year of thesis allowed to foreground two aspects of the energy management issue,namely the short term control and the long term control, respectively. The first issue was studied during the second year and ended with the proposition of a solution mixing both the airbrakes and engines. The second and last issue started with a preliminary study which consisted of computing an optimal slat/flap command sequence bringing the aircraft to theright energy level in approach while minimizing the use of engines and airbrakes. Then, this study was extended in order to take into account the regulation of engines and airbrakes aswell as vertical trajectory modification. Finally, this optimization-based solution has been integrated within an accurate real-time desktop simulator, tested with a human-machine interface, and then presented to flight test pilots for validation.

Letecká doprava jako všechny ostatní dopravy podílí na produkci emisí skleníkových plynů, což je hlavní důvod změn klimatu. Disertační práce je zaměřena na možnost využití alternativního zdroje energie (paliva, motor) v letectví, aby se snížily emise produkované letadel. Oblast,na která již pracuje je všeobecné letectví, zejména letadel kategorie LSA a VLA. Tři možnosti, alternativní zdroj energie, budou diskutovány. První používá LPG palivo, další je elektrické motory, a poslední přidání katalyzátoru a výfukového systému. U každého z nich bude uvedeno výhody a nevýhody, hlavní změnu pohon letadla nebo výfukového systému a různé výkonnosti letadla v důsledku těchto změn.

<p> This work focuses on solving the general optimal control problems with smart-learning-enabled and theory-supported optimal control (SET-OC) approaches. The proposed SET-OC includes two main directions. Firstly, according to the basic idea of the direct method, the smart-learning-enabled iterative optimization algorithm (SEIOA) is proposed for solving discrete optimal control problems. Via discretization and reformulation, the optimal control problem is converted into a general quadratically constrained quadratic programming (QCQP) problem. Then, the SEIOA is applied to solving QCQPs. To be specific, first, a structure-exploiting decomposition scheme is introduced to reduce the complexity of the original problem. Next, an iterative search, combined with an intersection-cutting plane, is developed to achieve global convergence. Furthermore, considering the implicit relationship between the algorithmic parameters and the convergence rate of the iterative search, deep learning is applied to design the algorithmic parameters from an appropriate amount of training data to improve convergence property. To demonstrate the effectiveness and improved computational performance of the proposed SEIOA, the developed algorithms have been implemented in extensive real-world application problems, including unmanned aerial vehicle path planning problems and general QCQP problems. According to the theoretical analysis of global convergence and the simulation results, the efficiency, robustness, and improved convergence rate of the optimization framework compared to the state-of-the-art optimization methods for solving general QCQP problems are analyzed and verified. Secondly, the onboard learning-based optimal control method (L-OCM) is proposed to solve the optimal control problems. Supported by the optimal control theory, the necessary conditions of optimality for optimal control of the optimal control problem can be derived, which leads to two two-point-boundary-value-problems (TPBVPs). Then, critical parameters are identified to approximate the complete solutions of the TPBVPs. To find the implicit relationship between the initial states and these critical parameters, deep neural networks are constructed to learn the values of these critical parameters in real-time with training data obtained from the offline solutions.  To demonstrate the effectiveness and improved computational performance of the proposed L-OCM approaches, the developed algorithms have been implemented in extensive real-world application problems, including two-dimensional human-Mars entry, powered-descent, landing guidance problems, and fuel-optimal powered descent guidance (PDG) problems. In addition, considering there is no thorough analysis of the properties of the optimal control profile for PDG when considering the state constraints, a rigid theoretical analysis of the fuel-optimal PDG problem with state constraints is further provided. According to the theoretical analysis and simulation results, the optimality, robustness, and real-time performance of the proposed L-OCM are analyzed and verified, which indicates the potential for onboard implementation. </p>

<p>Cyber-physical systems (CPS) are a class of complicated systems integrating cyber components with physical components. Although such a cyber-physical interaction improves the system performance and intelligence, it increases the system complexity and makes the system vulnerable to various types of faults, failures, and cyber-attacks. To assure the security and improve the resiliency of CPS, it is found that the hybrid system model can be a powerful tool in the domain of fault detection and isolation, cyber-attack diagnosis and containment, as well as resilient control and reconfiguration. Several problems are concerned in this dissertation. For situational awareness, \textit{mode discernibility}, which stands for whether the discrete state of a hybrid system can be correctly identified, is characterized and discussed with potential applications to monitoring system design. For CPS vulnerability analysis, the problem of stealthy attack design for systems with switching structures is investigated, which is motivated by the recent literature. To further understand and remedy for the vulnerabilities, the detectability and identifiability for severe cyber-attacks are defined and characterized, which are followed by the discussions on the methodologies for cyber-attack detection and identification. Last but not least, based on the understanding of identifiability, a framework of resilient control design is proposed to mitigate the impact of cyber-attacks, which can be generalized in future to account for additional design criteria.</p>

<p>The use of lower airspace for air taxi and cargo applications opens up exciting prospects for futuristic Unmanned Aircraft Systems (UAS). However, ensuring the safety and security of these UAS within densely populated urban areas presents significant challenges. Most modern aircraft systems, whether unmanned or otherwise, rely on the Global Navigation Satellite System (GNSS) as a primary sensor for navigation. From satellite navigations point of view, the dense urban environment compromises positioning accuracy due to signal interference, multipath effects, etc. Furthermore, civilian GNSS receivers are susceptible to spoofing attacks since they lack encryption capabilities. Therefore, in this thesis, we focus on examining the safety and cybersecurity assurance of UAS in dense urban environments, from both theoretical and experimental perspectives. </p> <p>To facilitate the verification and validation of the UAS, the first part of the thesis focuses on the development of a realistic GNSS sensor emulation using a Gazebo plugin. This plugin is designed to replicate the complex behavior of the GNSS sensor in urban settings, such as multipath reflections, signal blockages, etc. By leveraging the 3D models of the urban environments and the ray-tracing algorithm, the plugin predicts the spatial and temporal patterns of GNSS signals in densely populated urban environments. The efficacy of the plugin is demonstrated for various scenarios including routing, path planning, and UAS cybersecurity. </p> <p>Subsequently, a robust state estimation algorithm for dynamical systems whose states can be represented by Lie Groups (e.g., rigid body motion) is presented. Lie groups provide powerful tools to analyze the complex behavior of non-linear dynamical systems by leveraging their geometrical properties. The algorithm is designed for time-varying uncertainties in both the state dynamics and the measurements using the log-linear property of the Lie groups. When unknown disturbances are present (such as GNSS spoofing, and multipath effects), the log-linearization of the non-linear estimation error dynamics results in a non-linear evolution of the linear error dynamics. The sufficient conditions under which this non-linear evolution of estimation error is bounded are derived, and Lyapunov stability theory is employed to design a robust filter in the presence of an unknown-but-bounded disturbance. </p>

<p>  </p> <p>With rising concerns over commercial aviation’s contribution to global carbon emissions, there exists a tremendous pressure on the aviation industry to find advanced technological solutions to reduce its share of CO2 emissions. Single-aisle (or narrowbody) aircraft are the biggest contributors to CO2 emissions by number of operations, insisting a need to reduce / eliminate their aircraft-level fuel consumption as soon as possible. A potential solution for this is to operate fully-electric single-aisle aircraft; however, the limitations of the current (and predicted future) battery technology is forcing the industry to explore hybrid-electric aircraft as a possible mid-term solution.</p> <p>Modeling hybrid-electric aircraft comes with its own challenges due to the presence of two different propulsion sources – gas turbine engines (powered by Jet-A fuel) and electric motors (powered by batteries). Since traditional sizing approaches and legacy sizing tools do not seem to work well for hybrid-electric aircraft, this work presents a “flight-mechanics-based” conceptual sizing tool for hybrid-electric aircraft, set up as a Multidisciplinary Design Optimization (MDO) toolbox. Some of the key features of the sizing tool include concurrently sizing the electric motors and downsizing the gas turbine engines while meeting the one-engine-inoperative (OEI) and top-of-climb constraints, and re-sizing the fuselage to account for the volumetric constraints associated with required batteries.</p> <p>Current work considers a parallel hybrid-electric single-aisle aircraft with a 900 nmi design range, with electric power augmentation (with electric motors operating at full throttle) available only for the takeoff and climb segments when sizing the aircraft. Four hybrid-electric propulsion technology cases are considered, and the resulting hybrid-electric aircraft show 15.0% to 22.5% reduction in fuel burn compared to a Boeing 737-800 aircraft.</p> <p>Another challenge with modeling hybrid-electric aircraft is determining their off-design performance characteristics (considering a different payload or mission range, or both). This work presents an energy management tool – set up as a nonlinear programming optimization problem – to minimize the fuel burn for a payload-range combination by identifying the optimal combination of throttle settings for the gas turbine engines and the electric motors during takeoff, climb, and cruise, along with identifying an optimal flight path. The energy management tool enables fuel savings of at least of 2%, with actual savings ranging from 142.1 lbs to 276.1 lbs per trip for a sample route (LGA–ORD) at a 80% load factor.</p> <p>Although the hybrid-electric aircraft sizing and performance analysis studies show encouraging results about the potential reduction in carbon emissions at an aircraft level, the future fleet-level carbon emissions are not expected to reduce proportionally to these aircraft level emission reductions. This work predicts the fleet-level environmental impacts of future single-aisle parallel hybrid-electric aircraft by modeling the behavior of a profit-seeking airline (with a mixture of conventional all Jet-A fuel burning and hybrid electric aircraft in its fleet) using the Fleet-Level Environmental Evaluation Tool (FLEET). FLEET’s model-based predictions rely upon historically-based information about US-touching airline routes and passenger demand served by US flag-carrier airlines from the Bureau of Transportation Statistics to initiate model-based predictions of future demand, aircraft fleet mix, and aircraft operations. Using the aircraft performance coefficients from the energy management tool to represent the behavior of a single-aisle parallel hybrid-electric aircraft, the FLEET simulation predicts the changes in the fleet-wide carbon emissions due to the introduction of this new aircraft in an airline fleet in the year 2035. By 2055, FLEET results predict that the fleet-wide CO2 emissions with hybrid-electric aircraft in the fleet mix are at least 1.2% lower than the fleet-wide CO2 emissions of a conventional (all Jet-A fuel burning) aircraft-only airline. The rather limited reduction in emissions is an attribute of the reduced range capability and higher operating cost of the hybrid-electric aircraft (relative to a conventional aircraft of similar size). This causes the airline to change the usage, acquisition and retirement of its conventional aircraft when hybrid-electric aircraft are available; this is most notable to serve passenger demand on certain predominantly single-aisle service routes that cannot be flown by the future single-aisle hybrid-electric aircraft. </p>

<p dir="ltr">I conducted an in-depth analysis of the frequency and severity of external load accidents involving Chinook helicopters over a period of 30 years. The literature review encompassed General Aviation (GA) and ground-based safety organizations, while the data analysis predominantly relied on secondary data from the Army Combat Readiness Center (ACRC). In conducting this study, I aimed to identify key trends, causes, and effects of these accidents, particularly emphasizing material failures, human errors, and the substantial impact of rotor downwash as horizontal wind velocities in proximity to the ground. The study's goal was to improve safety and operational efficiency in Chinook external load operations by identifying frequency and severity of accidents over a 30-year period. The hope was that this would provide valuable insights for improvements in risk mitigation techniques.</p><p dir="ltr">By using an exploratory secondary data analysis of both publicly available U.S. Army accidents and accident data provided by the U.S. ACRC, I found that Chinook rotor downwash, which manifests as horizontal wind velocity when in close proximity to the ground, is the most significant and underreported factor. Based on the findings of this research, I recommend improved classification and documentation of such accidents. The findings highlighted the urgency of updating training and operational procedures to effectively address the unique challenges posed by rotor downwash and high gross weights in proximity to the ground, typical of Chinook external load Pickup and Landing Zone (PZ/LZ) operations. Implementing these recommendations is expected to enhance safety measures in both training and practical operations, ultimately reducing future accidents and improving safety standards in the aviation industry.</p>

<p dir="ltr">In the context of a Human-in-the-Loop (HITL) system, the accuracy of reachability analysis plays a significant role in ensuring the safety and reliability of HITL systems. In addition, one can avoid unnecessary conservativeness by explicitly considering human control behavior compared to those methods that rely on the system dynamics alone. One possible approach is to use a Gaussian Mixture Model (GMM) to encode human control behavior using the Expectation-Maximization (EM) algorithm. However, relatively few works consider the admissible control input ranges due to physical limitations when modeling human control behavior. This could make the following reachability analysis overestimate the system's capability, thereby affecting the performance of the HITL system. To address this issue, this work presents a constrained stochastic reachability analysis algorithm that can explicitly account for the admissible control input ranges. By confining the ellipsoidal confidence region of each Gaussian component using Sequential Quadratic Programming (SQP), we probabilistically constrain the GMM as well as the corresponding stochastic reachable sets. A comprehensive mathematical analysis of how the constrained GMM can affect the stochastic reachable sets is provided in this work. Finally, the proposed stochastic reachability analysis algorithm is validated via an illustrative numerical example.</p>