The study is conducted to determine practical modelling and simulation techniques to perform dynamic stability and performance analysis on a 3 Degrees of freedom aircraft model using a Modelica-based commercial tool called Modelon Impact. This study is based on a conceptual aircraft model where in-depth details about the aircraft configuration are unknown and the aim is to determine a suitable model that can capture the longitudinal dynamics and aerodynamic constraints of the aircraft during the conceptual design phase. Requirements include short execution time, easy model development, and minimal data requirements. Therefore, this thesis aims at developing plant and control architectures in  Modelon Impact which can be utilized for the rapid development of aircraft concepts with adequate fidelity in a longitudinal mission-based tracking environment. In a conceptual aircraft design environment, to identify a suitable methodology that mitigates the limitations of a traditional feedback controller, two methodologies are considered for comparison: Sequential DAE resolution (SDR) and Dynamic inversion (DI) control which is discussed from an object-oriented aircraft model. The advantages and shortcomings of each of the models discussed above are compared by conducting several experiments in increasing order of longitudinal mission complexity, and the most appropriate model among the two for a conceptual stage of aircraft design development is ascertained. The two methodologies discussed are compared for their level of complexity, code structure, readability, and ease of usability.

<p>The main accomplishments of this research are </p> <p>(1) developed a high fidelity computational methodology based on large eddy simulation to capture lean blowout (LBO) behaviors of different fuels; </p> <p>(2) developed fundamental insights into the combustion processes leading to the flame blowout and fuel composition effects on the lean blowout limits; </p> <p>(3) developed artificial intelligence-based models for early detection of the onset of the lean blowout in a realistic complex combustor. </p> <p>The methodologies are demonstrated by performing the lean blowout (LBO) calculations and statistical analysis for a conventional (A-2) and an alternative bio-jet fuel (C-1).</p> <p>High-performance computing methodology is developed based on the large eddy simulation (LES) turbulence models, detailed chemistry and flamelet based combustion models. This methodology is employed for predicting the combustion characteristics of the conventional fuels and bio-derived alternative jet fuels in a realistic gas turbine engine. The uniqueness of this methodology is the inclusion of as-it-is combustor hardware details such as complex hybrid-airblast fuel injector, thousands of tiny effusion holes, primary and secondary dilution holes on the liners, and the use of highly automated on the fly meshing with adaptive mesh refinement. The flow split and mesh sensitivity study are performed under non-reacting conditions. The reacting LES simulations are performed with two combustion models (finite rate chemistry and flamelet generated manifold models) and four different chemical kinetic mechanisms. The reacting spray characteristics and flame shape are compared with the experiment at the near lean blowout stable condition for both the combustion models. The LES simulations are performed by a gradual reduction in the fuel flow rate in a stepwise manner until a lean blowout is reached. The computational methodology has predicted the fuel sensitivity to lean blowout accurately with correct trends between the conventional and alternative bio-jet fuels. The flamelet generated manifold (FGM) model showed 60% reduction in the computational time compared to the finite rate chemistry model. </p> <p>The statistical analyses of the results from the high fidelity LES simulations are performed to gain fundamental insights into the LBO process and identify the key markers to predict the incipient LBO condition in swirl-stabilized spray combustion. The bio-jet fuel (C-1) exhibits significantly larger CH₂O concentrations in the fuel-rich regions compared to the conventional petroleum fuel (A-2) at the same equivalence ratio. It is observed from the analysis that the concentration of formaldehyde increases significantly in the primary zone indicating partial oxidation as we approach the LBO limit. The analysis also showed that the temperature of the recirculating hot gases is also an important parameter for maintaining a stable flame. If this temperature falls below a certain threshold value for a given fuel, the evaporation rates and heat release rated decreases significantly and consequently leading to the global extinction phenomena called lean blowout. The present study established the minimum recirculating gas temperature needed to maintain a stable flame for the A-2 and C-1 fuels. </p> The artificial intelligence (AI) models are developed based on high fidelity LES data for early identification of the incipient LBO condition in a realistic gas turbine combustor under engine relevant conditions. The first approach is based on the sensor-based monitoring at the optimal probe locations within a realistic gas turbine engine combustor for quantities of interest using the Support Vector Machine (SVM). Optimal sensor locations are found to be in the flame root region and were effective in detecting the onset of LBO ~20ms ahead of the event. The second approach is based on the spatiotemporal features in the primary zone of the combustor. A convolutional autoencoder is trained for feature extraction from the mass fraction of the OH ( data for all time-steps resulting in significant dimensionality reduction. The extracted features along with the ground truth labels are used to train the support vector machine (SVM) model for binary classification. The LBO indicator is defined as the output of the SVM model, 1 for unstable and 0 for stable. The LBO indicator stabilized to the value of 1 approximately 30 ms before complete blowout.

<p dir="ltr">In this dissertation, we explore the design and control of complex advance systems at various scales with a focus on UAVs in urban environment. In Chapter 2, we introduce a standard for defining routing restrictions based on potential field and propose an air traffic management infrastructure that takes advantage of it. The infrastructure would facilitate the deployment of a collaborative platform, enabling independent parties involved in aerial travel to operate within the same airspace. Chapter 3 introduces a path planning framework tailored for restrictive routing within potential fields. At its core, it decomposes a potential field into multi-scale cells with an estimated risk for restriction violation and utilizes graph-based path planning algorithms to generate routes that are demonstratively safe. Chapter 4 formalizes a new class of neural controllers and an accompanying architecture that use Pontryagin's maximum/minimum principle to generate future state prediction of a system, and the corresponding optimal control needed to drive it to variable online reference state. The proposed model allows for adjustment of the transient characteristics of the system and control. The work sets the stage for the development of advanced flight algorithms for UAVs.</p>