An attempt to minimize interference drag in a wing-fuselage junction by means of inserting a fillet is presented in this thesis. The case of a low-wing com- mercial transport aicraft at cruise conditions is examined. Due to the highly three dimensional behaviour of the flow field around the junction, a thin-layer Navier-Stokes code was implemented to estimate the drag forces at the junc- tion. Carefully selected design variable combinations based on-the theory of Design of Experiments constituted the initial group of feasible cases for which the flow solver had to be run. The drag values of these feasible cases were then used to create a second order response surface which could predict with rea- sonable accuracy the interference drag given the value of the design variables within the feasible region. A further optimization isolated the minimum in- terference drag combination of design variable values within the design space. The minimurn interference drag combination of design variable values was eval- uated numerically by the flow solver. The prediction of the response surface and the numerical value obtained by the flow solver for the interference drag of the optimal wing-fuselage combination differed by less than five percent. To demonstrate the ability of the method to be used in an interdisciplinary analysis and optimization program, a landing gear design module is included which provides volume constraints on the fillet geometry during the fillet sur- face definition phase. The Navier Stokes flow analyses were performed on the Cranfield Cray su- percomputer. Each analysis required between eight to twelve CPU hours, and the total CPU time required for the optimization of the six variable model described in the thesis required thirty Navier Stokes runs implementing the Design of Experimens and Surface Response Methodology implementation. For comparison, a typical optimization implementing a classical conjugate di- rections optimizer with no derivative information available would probably require more than forty iterations. Both the optimization and the flow solver results are discussed and some recommendations for improving the efficiency of the code and for further ap- plications of the method are given.

Small unmanned aircraft and electric vertical takeoff and landing (eVTOL) aircraft have recently emerged as vehicles able to perform new missions and stimulate future air transportation methods. This dissertation presents several system identification research advancements for these modern aircraft configurations enabling accurate mathematical model development for flight dynamics simulations based on wind-tunnel and flight-test data. The first part of the dissertation focuses on advances in flight-test system identification methods using small, fixed-wing, remotely-piloted, electric, propeller-driven aircraft. A generalized approach for flight dynamics model development for small fixed-wing aircraft from flight data is described and is followed by presentation of novel flight-test system identification applications, including: aero-propulsive model development for propeller aircraft and nonlinear dynamic model identification without mass properties. The second part of the dissertation builds on established fixed-wing and rotary-wing aircraft system identification methods to develop modeling strategies for transitioning, distributed propulsion, eVTOL aircraft. Novel wind-tunnel experiment designs and aero-propulsive modeling approaches are developed using a subscale, tandem tilt-wing, eVTOL aircraft, leveraging design of experiments and response surface methodology techniques. Additionally, a method applying orthogonal phase-optimized multisine input excitations to aircraft control effectors in wind-tunnel testing is developed to improve test efficiency and identified model utility. Finally, the culmination of this dissertation is synthesis of the techniques described throughout the document to form a flight-test system identification approach for eVTOL aircraft that is demonstrated using a high-fidelity flight dynamics simulation. The research findings highlighted throughout the dissertation constitute substantial progress in efficient empirical aircraft modeling strategies that are applicable to many current and future aeronautical vehicles enabling accurate flight simulation development, which can subsequently be used to foster advancement in many other pertinent technology areas. / Doctor of Philosophy / Small, electric-powered airplanes flown without an onboard pilot, as well as novel electric aircraft configurations with many propellers that operate at a wide range of speeds, referred to as electric vertical takeoff and landing (eVTOL) aircraft, have recently emerged as aeronautical vehicles able to perform new tasks for future airborne transportation methods. This dissertation presents several mathematical modeling research advancements for these modern aircraft that foster accurate description and prediction of their motion in flight. The mathematical models are developed from data collected in wind-tunnel tests that force air over a vehicle to simulate the aerodynamic forces in flight, as well as from data collected while flying the aircraft. The first part of the dissertation focuses on advances in mathematical modeling approaches using flight data collected from small traditional airplane configurations that are controlled by a pilot operating the vehicle from the ground. A generalized approach for mathematical model development for small airplanes from flight data is described and is followed by presentation of novel modeling applications, including: characterization of the coupled airframe and propulsion aerodynamics and model development when vehicle mass properties are not known. The second part of the dissertation builds on established airplane, helicopter, and multirotor mathematical modeling methods to develop strategies for characterization of the flight motion of eVTOL aircraft. Innovative data collection and modeling approaches using wind-tunnel testing are developed and applied to a subscale eVTOL aircraft with two tilting wings. Statistically rigorous experimentation strategies are employed to allow the effects of many individual controls and their interactions to be simultaneously distinguished while also allowing expeditious test execution and enhancement of the mathematical model prediction capability. Finally, techniques highlighted throughout the dissertation are combined to form a mathematical modeling approach for eVTOL aircraft using flight data, which is demonstrated using a realistic flight simulation. The research findings described throughout the dissertation constitute substantial progress in efficient aircraft modeling strategies that are applicable to many current and future vehicles enabling accurate flight simulator development, which can subsequently be used for many research applications.

In this thesis, the authors investigate how machine learning can be utilized for speeding up the design optimization process of gas turbines. The Finite Element Analysis (FEA) steps of the design process are examined if they can be replaced with machine learning algorithms. The study is done using a component with given constraints that are provided by Siemens Energy AB. With this component, two approaches to using machine learning are tested. One utilizes design parameters, i.e. raw floating-point numbers, such as the height and width. The other technique uses a high dimensional mesh as input. It is concluded that using design parameters with surrogate models is a viable way of performing design optimization while mesh input is currently not. Results from using different amount of data samples are presented and evaluated.