<p dir="ltr">The use of autonomous vehicles, such as Unmanned Aerial Systems (UASs), Unmanned Ground Vehicles (UGVs), and Unmanned Surface Vessels (USVs), has globally increased in various applications. Their rising popularity and high accessibility have also increased the use of UASs in criminal or hazardous actions.</p><p dir="ltr">It is beneficial to rapidly compute possible surveillance system evasive paths to evaluate the effectiveness of a given sensor deployment scheme. To find these evasive trajectories, we assume full knowledge of the current and future state of the surveillance system. This assumption allows the defender to identify worst-case trajectories to counteract. The surveillance system path planning presented in this work can be leveraged for game theoretic sensor deployment.</p><p dir="ltr">A sensor deployment scheme determines the overall surveillance efficiency. Through redeployment after each assessment, it aims to approach an equilibrium that maximizes defense capabilities. Therefore, a method of evaluation that models mobile, directional sensors is demanded.</p><p dir="ltr">In response to this demand, this thesis explores the design of a computationally efficient path-planning algorithm for the space-time domain. The Space-Time Parallel RRT* (STP-RRT*) algorithm obtains multiple goal candidates, drawn from a uniform distribution over the time horizon. A set of parallel RRT* trees is simultaneously populated by each goal candidate. By leveraging a connect heuristic from RRT-Connect, parallel goal trees converge to an RRT* tree populated from a start point. This simultaneous tree growth structure returns a computation complexity of O(N log(N)), where N is the number of random samples.</p><p dir="ltr">Due to its low complexity, the STP-RRT* algorithm is suitable to be used as an evaluation metric that computes the cost of the infiltration path of a malicious autonomous system to assess the performance of the deployment layout. The feedback assessment can be used for the surveillance system redeployment to strengthen the vulnerability.</p><p dir="ltr">To identify potential and existing bottlenecks in the algorithm, a computation complexity analysis is conducted, and complexity reduction techniques are employed. Given that surveillance system characteristics are known, 1-dimensional and 2-dimensional environments are generated where positions and surveillance patterns of stationary and dynamic obstacles are randomly selected. In each randomized environment, the STP-RRT*, RRT*, and ST-RRT* are evaluated by comparing success rate, computation time, tree size, and normalized cost through 100-trial Monte Carlo simulations. Under the provided conditions, the proposed STP-RRT* algorithm outperforms two other algorithms with an improved mean success rate and reduced mean computation time by 10.02% and 12.88%, respectively, while maintaining a similar cost level, showing its potential application in surveillance-evasive path-planning problems for surveillance deployment evaluation. Finally, we integrate our algorithm with Nav2, an open-source navigation stack for various robotics applications, including UAV, UGV, and USV. We demonstrate its effectiveness via software-in-the-loop (SiTL) experiments utilizing open-source autopilot software.</p>

<p>In Chapter 2 an experiment has been conducted to measure the quenching distance of a premixed fuel-air mixture. Quenching distance refers to the physical limit below which combustion of a fuel and an oxidizer, even if present in sufficient proportions, cannot maintain combustion and propagate a flame. It is dependent on the physical area that is present for the flame to travel through, the temperature and pressure conditions, the thermal conductivity of the walls, and the specific fuel and oxidizer present. Applicable in a wide variety of industries from the automotive industry to the aerospace industry, the ability to control a combustion reaction and where it occurs can lead to increased safety and efficiency in devices such as injectors, mixing chambers, engine pistons, combustors, propellant turbopumps, and fuel-air heat exchangers. Currently, little to no quenching distance data exists for heavier-than-air hydrocarbons. Using a parallel ceramic plate setup with spark rods inside a pressure vessel to contain the initial combustion reaction, the quenching distances of the hydrocarbons is measured and a relationship with equivalence ratio is found. This relationship is used to construct a model to apply to heavier-than-air hydrocarbons.</p> <p>Chapter 3 focuses on an experiment designed to measure the flow rates of leaks in fuel-air heat exchangers. The ability to accurately quantify and understand these flow rates is crucial for assessing the performance and safety of such systems. Furthermore, the obtained flow rate data will be compared with a Computational Fluid Dynamics (CFD) model developed for micro-scale flows resulting from fuel leakage into a cross-flow of heated air within the heat exchanger. These flow rates provide a model of the volume and rate of fuel being injected into the air channels, aiding in the assessment of potential risks and hazards associated with the leakage. To validate the accuracy and reliability of the model developed for micro-scale flow, the measured flow rates obtained from the experimental setup are compared against the corresponding predictions of the model. By establishing a correlation between the experimental data and the model results, the validity of the model can be confirmed, ensuring its efficacy for future simulations and analyses.</p> <p>Chapter 4 details the creation and analysis of a program developed in Python and MATLAB for assessing combustion risk in microscale fuel-air heat exchanger channels. The Safety Net for Unquenched Flame Fronts (SNUFF) is designed as a design assistance tool for microscale flows of fuel and oxidizer, specifically for heat exchangers. This application helps analyze combustion risks in these microscale flow channels due to leaks or unintended flows caused by damage or manufacturing defects. SNUFF integrates REFPROP and flame simulation data with the models for quenching distance and microscale flow from previous chapters to generate sensitivity plots for various design parameters. This tool enables engineers to assess combustion risks in fuel-air channels, allowing them to design processes that accommodate manufacturing limitations in numerous microscale channel applications.</p>

<p dir="ltr">Managing transient heat loads has become more challenging with the increasing electrification of ground, air, and marine vehicles. Doing so requires novel designs of thermal management systems, or in some cases, novel retrofits of legacy TMSs to accommodate the addition of more electrified subsystems. However, design tools that are well suited for examining and optimizing the dynamic response of TMS over candidate operation or mission profiles are limited. In this thesis, a principled methodology and associated tools for the enumeration and dynamic optimization of all feasible architectures of an air cycle machine are presented. Graph-based modeling is pivotal for exploring and optimizing ACM architectures, providing a structured representation of system components and interactions. By modeling the ACM as a graph, with vertices and edges representing components and interactions, respectively, various component configurations and performance metrics can be systematically analyzed. This approach enables efficient exploration of design alternatives and consideration of dynamic boundary conditions (representing, for example, a complex mission profile) during optimization. Another unique contribution of this thesis is a novel application of a multi-state graph-based modeling approach for developing dynamic models of turbomachinery components. By representing multiple states within each control volume or component and connecting them through power flows, this approach accurately captures both first and second law dynamics, enabling the computation of dynamic entropy generation rates. A detailed case study demonstrates the optimization of ACM architectures based on entropy generation minimization and dynamic bleed air flow rate minimization. This study highlights the trade-offs between different optimization criteria and the potential for generalizing the tool to more complex thermofluid systems in thermal management applications. The results underscore the importance of entropy-based analysis in comparing the thermodynamic losses across various system architectures.</p>

<p dir="ltr">This thesis concentrates on the improvement of the quality of shock-dominated flows in supersonic inlets by controlling shock wave / boundary layer interactions (SWBLIs). SWBLI flow control has been a major issue relevant to scramjet-associated endeavors for many years. The ultimate goal of this study is to numerically investigate SWBLI flow control through the application of steady-state thermal sources --- which were defined to replicate the Joule heating effect produced by Quasi-DC electric discharges --- and compare the results with data obtained from previous experiments.</p><p dir="ltr">Numerical solutions were obtained using both a three-dimensional, unsteady Reynolds-averaged Navier-Stokes (RANS) solver with a Spalart-Allmaras (SA) Detached Eddy Simulation (DES) turbulence modeling method and also a simple three-dimensional, compressible RANS solver with a SA turbulence model. Computations employed an ideal gas thermodynamic model. The numerical code is Stanford University Unstructured (SU2), an open-source, unstructured grid, computational fluid dynamics code. The SU2 code was modified to include volumetric thermal source terms to represent the Joule heating effect of electric current flowing through the gas. The computational domain, source term configuration, and flow conditions were defined in accordance with experiments carried out at the University of Notre Dame. Mach 2 flow enters the three-dimensional test domain with a stagnation pressure of 1.7 bar. The test domain is contained by four isothermal side walls maintained at room temperature, as well as an inlet and outlet. A shock wave (SW) generator, a symmetric 10 degree wedge, is positioned on the upper surface of the test domain. The overall length of the test sections is 910 mm and inlet length of the computational domain is increased prior to the location of shock wave generator in order to allow for adequate boundary layer growth. Volumetric heating source terms were positioned on the lower surface of the test domain in the reflected SW region.</p><p dir="ltr">Experimental results show that the thermal sources create a new shock train within the duct and do not initiate significant additional pressure losses. What remains to be explored is the overall characterization of the 3D flow features and dynamics of the thermally induced SW and the effect of gas heating on total pressure losses in the test section.</p><p dir="ltr">Numerical solutions validate what is observed experimentally, and offer the ability to gather more temporally and spatially-resolved measurements to better understand and characterize shock-dominated flow control in a supersonic inlet or duct. Although thermally driven SWBLI flow control requires additional research, this study alleviates the dependency on experimentally driven data and adds insight into the nature of the complex unsteady, three-dimensional flowfield.</p>

<p dir="ltr">Methodologies for optimizing UAVs' control for varied environmental conditions have become crucial in the recent development for UAV control sector, yet they are lacking. This research focuses on the dynamism of the Gazebo simulator and PX4 Autopilot flight controller, frequently referenced in academic sectors for their versatility in generating close-to-reality digital environments. This thesis proposed an integrated simulation system that ensures realistic wind and gust interactions in the digital world and efficient data extraction by employing an industrial standard control communication protocol called MAVLink with the also the industry standard ground control software QGroundControl, using real and historical weather information from NOAA database. This study also looks into the potential of reinforcement learning, namely the DDPG algorithm, in determining optimal UAV safety distance, trajectory prediction, and mission planning under wind disruption. The overall goal is to enhance UAV stability and safety in various wind-disturbed conditions. Mainly focusing on minimizing potential collision risks in areas such as streets, valleys, tunnels, or really anywhere has winds and obstacles. The ROS network further enhanced these components, streamlining UAV response analysis in simulated conditions. This research presents a machine-learning approach to UAV flight safety and efficiency in dynamic environments by synthesizing an integrated simulation system with reinforcement learning. And the results model has a high accuracy, reaching 91%, 92%, and 97% accuracy on average in prediction of maximum shifting displacement, and left/right shifting displacement, when testing with real wind parameters from KLAF airport. </p>

<p>  </p> <p>Aircraft noise is one of the most significant environmental concerns for the aviation industry, and it adversely affects the physical and mental health of community members who are in close proximity to airports. The operations and expansion of airports and land use planning are affected because of the community’s adverse reaction to such annoyances. Aircraft operations and fleet mix information are required when airport managers and stakeholders execute the Aviation Environmental Design Tool (AEDT) to compute the noise metrics; however, these data are unavailable from over 2,000 United States non-primary General Aviation (GA) airports that lack full-time air traffic control facilities or personnel. </p> <p>This study developed a low-cost noise assessment technology for non-towered GA airports. The Automatic Dependent Surveillance-Broadcast (ADS-B) messages were obtained using an inexpensive ADS-B receiver. A barometric pressure calibration was applied to improve the aircraft operations estimation. A fleet mix database was created by linking the collected ADS-B data to an FAA-registered aircraft database containing U.S.-registered aircraft information (such as types of aircraft and engines). Specific aircraft information was obtained by filtering the International Civil Aviation Organization (ICAO) identification code from the obtained ADS-B records. A set of 20 advanced aircraft performance parameters was constructed to determine the operation mode and corresponding power setting. The corresponding noise levels were determined using the EUROCONTROL Aircraft Noise and Performance (ANP) database.</p> <p>The testing and validation results from the case study at the Purdue University Airport (ICAO Code: KLAF) demonstrated the developed low-cost approach could identify aircraft noise events, and the accuracy of modeled noise data was assessed with an average error of 4.50 dBA. Therefore, the developed approach appears to be an affordable means of monitoring aircraft noise at non-towered GA airports.  </p>

Performance models provided by aircraft manufacturers are used by aircraft operators to perform flight path simulations aiming to reduce aircraft fuel consumption. However, performance models are generic and does not account for the performance deviations of each aircraft individual. The performance deviations, particularly in terms of fuel consumption, will affect the dynamic programming of flight path simulations. This may result in a less optimal flight path and ultimately lead to higher fuel consumption than expected. In hope of reducing this risk, a collection of local performance factors were derived. These factors describe the percentual deviation between the real fuel flow and the levels predicted by the performance model, and are allocated with respect to a range of flight parameters in a data library known as the performance library. A test environment is then constructed to simulate a continuous flow of flight data, where a new performance library is derived from the flight data of every month. The local performance factors of the previous month are then updated with the current; a learning process based on the weighted average ensemble approach. Further, the local performance factors are used in conjunction with the performance model to estimate the aircraft fuel consumption during cruise. The observed average prediction error is noticeably smaller than that of an equivalent global, scalar performance factor used by airlines today. The result also reveals that the prediction accuracy and versatility of the performance library is mainly determined by its resolution - higher resolution generally offers better accuracy at a cost of requiring more flight data, whereas lower resolutions are more versatile but of lower accuracy. Finally, the performance libraries of two identical aircraft are used to trace the performance deviation between them. The weighted average of all local performance factors in the performance library of respective aircraft reveal that the average fuel consumption is roughly -1.9 % and -2.5 % lower than the estimates by the performance model, ultimately proving that it is feasible to detect overall fuel efficiency deviation between two identical aircraft. / Prestandamodeller tillhandhållna av flygplanstillverkarna används oftast av flygbolagen för att utföra flygruttsimuleringar i syfte att bespara bränsle. Dock är prestandamodellerna generiska och tillgodoräknar inte prestandaavvikelserna som förekommer hos varje flygplansindivid. Dessa prestandaavvikelser, speciellt i form av bränsleförbrukning, kommer att påverka den dynamiska programmeringen i flygruttssimulationen. Följde när flygrutter som kan leda till högre förbrukningar än de ursprungligen uppskattades. I hopp om att minimera denna risk beräknades mängder av lokala prestandafaktorer, vilka grundar på prestandamodellens avvikelse från verkliga flygdata. Dessa koefficienter allokerades sedan till ett databibliotek (prestandabibliotek) med avseende på en samling av flygparametrar. En testmiljö konstruerades i följd för att simulera ett kontinuerligt dataflöde. Vidare skapades ett prestandabibliotek för varje månadsflygdata, där de nyskapade lokala prestandafaktorerna viktas med de motsvarandeparterna i föregående månadens prestandabibliotek, vilket är en inlärningsprocessbaserad på viktad medelvärdesensemble. Prestandabiblioteket applicerades sedan över prestandamodellen och det snittliga uppskattning felet observerades vara märkbart mindre än det från en motsvarande global, skalärbaserad prestandafaktor. Resultatet antyder också på att prestandabibliotekets uppskattningsnoggrannhet och allsidighet beror huvudsakligen på dess upplösning - en hög upplösning leder generellt till ökad uppskattningsnoggrannhet med på bekostnad av mer flygdata, medan lägre upplösningar tenderar att vara mer allsidiga men med mindre uppskattningsnoggrannhet. Slutligen användes prestandabiblioteken av två identiska flygplan för att spåra prestandaavvikelser som förekommer mellan dem. Viktat medelvärde av alla prestandafaktorer i respektiveflygplanets prestandabibliotek tyder på att snittförbrukningen är ungefär 1,9 % respektive2,5 % lägre än det som uppskattades av prestandamodellen. Härmed bevisades att det är möjligt att spåra varianser i snittförbrukningen mellan två identiska flygplan.

<p dir="ltr">Cyber-physical-human systems (CPHSs) integrate human cognitive capabilities into the decision and control processes of complex dynamical systems. While artificial intelligence (AI) has shown promise in controlling such systems, it often encounters challenges such as conflict with human behavior and brittleness. Moreover, even successful AI implementations may lead to negative impacts on humans, such as the degradation of manual skills and diminished situation awareness, thereby weakening humans' ability to effectively monitor and intervene in off-nominal conditions as the final decision-makers of the systems. To address these unique challenges within CPHSs, this dissertation proposes three key approaches. First, human behavior modeling approaches are proposed to enhance understanding and prediction of human behavior from the perspective of AI. Accurate modeling enables better calibration of AI's expectations regarding human teammates' intentions and skill-levels. Second, a novel shared control approach is developed to expedite human training for complex dynamic control tasks. An assistant agent supports human novices in emulating human experts by leveraging human behavior models to gauge the human's skill-levels and provide tailored assistance to help improve one's skill. Lastly, human-autonomy teaming (HAT) design is addressed from a resource allocation perspective. A systematic computational simulation approach is proposed to optimize function and attention allocation to manage trade-offs in performance, situation awareness, workload, and other considerations. The proposed frameworks are demonstrated via examples in drone applications. Numerical and experimental results, utilizing simulation platforms and human subjects, validate the efficacy of the proposed approaches. This dissertation presents significant progress in the design and implementation of CPHSs in that it offers insights and methodologies to enhance collaborative interactions between humans and autonomous systems in complex environments.</p>