This is a graduate-level course to cover the advanced topic of flight dynamics: its modelling, control, and simulation. The purpose is to develop a comprehensive understanding and systematic development of (fixed-wing) aircraft dynamics (vs. fundamental understanding and specific treatment at the undergraduate level), through mathematical modelling, control systems design and analysis, as well as dynamic behaviour visualization through computational simulations. Advanced topics of current leading-edge research progress in flight dynamics and control are also introduced.

Introductory course. Topics include: mathematical models of man/machine systems, experimental results, examples, linear modelling, stability, nonlinear modelling, optimal control model, control tasks and applications, flight simulation techniques.

Unpiloted aircraft, known as UAVs, drones or aerial robots, are very quickly becoming a major sector of the aerospace industry. They are increasingly used in aerial photography, inspection of infrastructure, delivery of small packages, and other applications requiring inexpensive and flexible flight. The basic physical, scientific and engineering principles necessary to design a remote-controlled fixed-wing or quad-rotor UAV are explained in this course. These include aerodynamics, propulsion, structures and control. A key part of this course will be a group project to create a detailed design of a UAV that is capable of performing a specific function.

This course is the second part of the CARRE core courses, following AER1216H, which covers the fundamental principles related to UAV design: structures, aerodynamics and control. AER1216H is the prerequisite of this course, unless approved by the instructor. In AER1217H, the focus is placed on the development of unmanned aerial systems (UAS), with the theme of autonomy in navigation and control, as well as flight performance analysis and evaluation. The course curriculum will be delivered in both lectures and development projects, including flight tests. The contents include: quadrotor or fixed-wing UAV dynamics and control; sensing and estimation for UAVs; navigation and path planning; instrumentation and sensor payloads; computer vision. A development project will be given to students who will use the UAV platform to design an autonomous system to accomplish a specific flying mission, to be demonstrated by flight experiments.

Introductory discussion of significant length dimensions; different flow regimes, continuum, transition, collision-free; and a brief history of gas kinetic theory. Equilibrium kinetic theory; the article distribution function; Maxell-Boltzmann distribution. Collision dynamics; collision frequency and mean free path. Elementary transport theory, transport coefficients, mean free path method. Boltzmann equation; derivation, Boltzmann H-theorem, collision operators. Generalized transport theory; Maxwell's equations of change; approximate solution techniques, Chapman -Ensog perturbative and Grad series expansion methods, moment closures; derivation of the Euler and Navier-Stokes equations, higher-order closures. Free molecular aerodynamics. Shock waves.

This course is intended to be a first graduate-level course in fluid mechanics, and assumes that students have had at least one introductory fluid mechanics course at the undergraduate level. The course starts with a review of vectors, tensors, and related theorems; flow kinematics; derivations of the differential forms of the governing equations of fluid motion. Then the following subjects are covered: exact solutions (solutions with parallel boundaries, solutions with circular symmetry, pulsating flows, stagnation-point flows, etc.); special forms of governing equations (Kelvin's theorem, vorticity transport theorem, equations for inviscid flow (Euler); and boundary layer theory (boundary layer equations, boundary layer on a flat plate: Blasius solution, approximate solutions, effect of pressure gradient, separation, perturbation techniques, stability of boundary layers, etc.)

This course covers the fundamentals of aeracoustics as it applies to general and commercial aviation. The essentials of linear acoustics are presented and related to fluid motion to arrive at fundational theories of aeroacoustics, including Lighthill's acoustics analogy, the Ffowcs-Williams-Hawkings equation and Goldstein's equation. The concepts are applied to flows at low Mach numbers, with specific applications sound generation by turbulent flows as well as leading and trailing edge noise. The course will also cover a number of topics related to experimental methods relevant to aeroacoustics. This will include the basics of aeroacoustic test facilities, instrumentation and signal processing. The course is meant for graduate students with strong backgrounds in fluid dynamics but that may lack knowledge of acoustics.

This course provides the students who are intending a career in combustion/reacting flows, fluid mechanics or propulsion an opportunity to do an in-depth study of some of the current academic research areas with implications of practical importance. It will also be suitable for graduate students who have a good background in essentials of their research area, but need a specialized course to cover material not available in other graduate courses. The intention is not to replace or to overlap with the literature review of the students theses work. The course will cover 3 to 4 topics from the following: non-intrusive experimental; techniques in isothermal and reacting flows; activation energy asymptotics; high-speed combustion; metal combustion in propulsion; thermo-acoustics in propulsion systems; soot formation and oxidation kinetics; theory of partially premixed turbulent combustion; synthesis of nano-materials by combustionhigh-pressure combustion. Topic selection will depend on the interests of the students taking the course. Similar topics will be added as needed.

This course covers the fundamentals of aeracoustics as it applies to general and commercial aviation. The essentials of linear acoustics are presented and related to fluid motion to arrive at fundational theories of aeroacoustics, including Lighthill's acoustics analogy, the Ffowcs-Williams-Hawkings equation and Goldstein's equation. The concepts are applied to flows at low Mach numbers, with specific applications sound generation by turbulent flows as well as leading and trailing edge noise. The course will also cover a number of topics related to experimental methods relevant to aeroacoustics. This will include the basics of aeroacoustic test facilities, instrumentation and signal processing. The course is meant for graduate students with strong backgrounds in fluid dynamics but that may lack knowledge of acoustics.

This course presents the fundamental aspects of modern flow control. The framework of the course will be cast starting with a brief review of the development of flow control from its birth at the turn of the 20th century to current state of the art techniques and methodologies. The key concepts, fundamental to modern flow control, will thus be extracted and categorized throughout the course; including topics such as flow instabilities; dynamic and closed-loop control; actuators and sensors; modeling and simulations.

This course presents an overview of numerical modelling techniques for the prediction of turbulent flows. The emphasis is on the capabilities and limitations of engineering approaches commonly used in computational fluid dynamics (CFD) for the simulation of turbulence. Topics include: Introduction to turbulent flows; definition of turbulence; features of turbulent flows; requirements for and history of turbulence modelling. Conservation equations for turbulent flows; Reynolds and Favre averaging; velocity correlations, Reynolds-averaged Navier-Stokes equations (RANS); Reynolds stress equations; effects of compressibility. Algebraic models; eddy viscosity and mixing length hypothesis; Cebeci-Smith and Baldwin-Lomax models. Scalar field evolution models; turbulence energy equation; one- and two-equation models; wall functions; low-Reynolds-number effects. Second-order closure models; full Reynolds-stress and algebraic Reynolds stress models. Large-Eddy Simulation (LES) techniques. Direct Numerical Simulation (DNS) Methods.

The following topics are covered: method of characteristics for solving hyperbolic conversation laws; characteristics of rarefaction, compression and shock waves; reflection, collision, refraction and overtaking of shock waves, expansion waves and contact surfaces; Riemann problem and solution; shock waves interacting with an area enlargement and reduction; shock-tube problem.

This course will cover topics relating to the impact of aircraft on the environment, including noise, local and global emissions, and lifecycle analysis. Students will be exposed to means of quantitative assessment of the impact of aviation noise and emissions as well as metrics for assessing global climate effects. Current and future technologies for mitigating environmental problems will be covered.

This course presents the fundamentals of numerical methods for inviscid and viscous flows. The following topics are covered: finite-difference and finite-volume approximations, structured and unstructured grids, the semidiscrete approach to the solution of partial differential equations, time-marching methods for ordinary differential equations, stability of linear systems, approximate factorization, flux-vector splitting, boundary conditions, relaxation methods, and multigrid.

The course first concentrates on the algorithmic details of two specific codes for solving the compressible Navier-Stokes equations, ARC2D and FLOMG. Topics include generalized curvilinear coordinates, approximate factorization, artificial dissipation, boundary conditions, and various convergence acceleration techniques, including multigrid. This is followed by the following topics: flux-difference splitting and high-resolution upwind schemes, including total variation diminishing schemes.

Introduction to upwind finite-volume methods widely used in computational fluids dynamics (CFD) for the solution of high-speed inviscid and viscous compressible flows. Topics include: Brief review of conservation equations for compressible flows; Euler equations; Navier-Stokes equations; one- and two-dimensional forms; model equations. Mathematical properties of the Euler equations; primitive and conserved solution variables; eigensystem analysis; compatibility conditions; characteristic variables, Rankine-Hugoniot conditions and Riemann invariants; Riemann problem and exact solution. Godunov's method; hyperbolic flux evaluation and numerical flux functions; solution monotonicity; Godunov's theorem. Approximate Riemann solvers; Roe's method. Higher-order Godunov-type schemes; semi-discrete form; solution reconstruction including least-squares and Green-Gauss methods; slope limiting. Extension to multi-dimensional flows. Elliptic flux evaluation for viscous flows; diamond-path and average-gradient stencils; discrete-maximum principle. High-order methods; essentially non-oscillatory (ENO) schemes.

This course is aimed to provide an overview of the fundamental physical processes in large Reynolds number turbulent flows. Topics include review of tensors, probabilistic tools, and conservation laws. Free shear flows: turbulent kinetic energy transport and dissipation. Scales of turbulent motion: Kolmogorov hypothesis, structure functions, Kármán-Howarth equation, 4/5th law, Fourier modes, Kolmogorov-Obukhov spectrum, intermittency, and refined similarity hypothesis. Turbulent mixing: scalar transport and dissipation. Alignments of vorticity, scalar gradient, and strain rates. Diagnostics in turbulent flows.

This course will provide instruction in three areas crucial to aerospace structural design: fiber composite materials, thin walled structures, and finite element methods. All three will be taught in a manner such that their interrelation is made clear. The course will begin with a composite materials, their mechanics and application. General theories of shells and thin walled structures, which are essential to aircraft design, will next be discussed. Finally, finite element methods of use in modelling aircraft structures and composites will be described. No specific background in any of these three topics is required, but a good knowledge of solid and structural mechanics will be assumed.

This course focuses on materials used under extreme conditions for aerospace applications, mainly high-temperature materials (e.g., Ni-based superalloys), coating systems (especially thermal and environmental barrier coating systems), and lightweight materials (e.g., Al, Ti, and composites). These material systems for extreme conditions are compared to standard materials, which are already familiar to the students. The focus will be on materials for turbines, (reusable) rocket engines, and structural components for aerospace structures. For these applications, material selection is discussed, manufacturing routines are highlighted, and the understanding of fundamental material behaviour is deepened. In detail, creep mechanisms, diffusion, oxidation, high-temperature corrosion, failure mechanisms, and thermal stability of the microstructure are covered in this course.

This course focuses on the properties, design, and manufacturing of metamaterials in the context of aerospace structures. Metamaterials (also called architectured materials or materials-by-design) are materials with carefully designed meso- and micro-structures to achieve macroscopic properties which are not typically observed in conventional engineering materials. Therefore, the geometry of metamaterials directly influences their properties, rather than their compositions, as found, for example, in typical alloy systems.

Metamaterials are often characterized by a spatial symmetry. The most well-known category of metamaterials are truss structures in bridge and tower structures in civil engineering. Advances in additive manufacturing enabled the design and manufacturing of these truss networks on the meso-and micro-scale. They combine desirable mechanical properties, e.g., high stiffness, high strength, and high fracture toughness, while still maintaining a low density. This unique combination of mechanical properties creates highly sought-after materials for aerospace applications, such as stiffening components in reusable rockets, high-toughness aircraft fuselages, or zero thermal expansion structures in satellites. Other classes of metamaterials will be briefly explored in this course, which combine beneficial mechanical properties with, for example, the capability of manipulating electromagnetic waves (blocking wave-propagation, embedding sensors, or tailoring the sound propagation). Finally, novel classes of metamaterials will be discussed in this course, e.g., active materials and design for self-assembly.

Topology optimization is a relatively new method for the computational design of structures that enables optimal structural design beyond traditional size and shape optimization. Specifically, topology optimization identifies where to put material and where to put holes within the design domain. This course will examine the background to topology optimization, the theory and algorithms necessary to build a topology optimization code, and the two main approaches to topology optimization. At the conclusion of the course, students will be able to program a basic topology optimization code and use a common commercial software package.

This is an introductory graduate-level course on computational optimization and it is assumed that students have had undergraduate level training in multivariable calculus, linear algebra, and MATLAB programming. The topics to be covered in this course include: formulation of optimization problems, non-gradient and stochastic search techniques, gradient-based optimization algorithms for unconstrained and constrained problems, numerical methods for sensitivity analysis, surrogate modeling, surrogate-assisted optimization frameworks, applications of optimization algorithms to design, parameter estimation and control.

This is an introductory graduate-level course on uncertainty quantification and it is assumed that students have had undergraduate level training in statistics, linear algebra, and numerical methods for partial differential equations. The topics to be covered include: verification and validation of computational models, construction of probabilistic uncertainty models, Monte Carlo and Quasi-Monte Carlo simulation methods, importance sampling and variance reduction techniques, sparse quadrature schemes, perturbation methods, polynomial chaos expansions, stochastic Galerkin projection schemes, and an introduction to robust design optimization.

This course introduces variational formulations and associated finite element methods for partial differential equations in continuum mechanics, including both elliptic and hyperbolic equations. An equal emphasis is placed on mathematical theory and practical implementation. Theoretical topics include discussions of well-posedness, optimality, and a priori and a posteriori error estimates. Practical topics include implementation of finite elements, matrix and vector assembly, adaptive mesh refinement, and Krylov-subspace linear solvers.

Advanced topics in spacecraft dynamics and control. Course includes a project. Topics include input-output stability analysis and Lyapunov stability analysis with applications to spacecraft attitude control; feedforward, feedback, and adaptive controller design. Quaternion feedback. Linear state-space analysis and observer-based compensator design. Flexible spacecraft dynamics: equations of motion, spatial discretization, modal equations, constrained and unconstrained modes. Flexible spacecraft control: spillover, controller discretization, LQG, H-infinity, and positive real design.

This is a seminar course designed to introduce students to the fundamentals of multibody dynamics with particular emphasis on the dynamics of robotic systems. Each student, in consultation with the course coordinator, will be required to select two topics in the area, investigate them thoroughly and present a seminar on each to the other members of the class. Students may choose topics well-treated in the mechanical literature or ones which are more research-oriented, perhaps requiring some original input on the part of the student.

This course introduces the fundamentals of state estimation for aerospace vehicles. Knowing the state (e.g., position, orientation, velocity) of a vehicle is a basic problem faced by both manned and autonomous systems. State estimation is relevant to aircraft, satellites, rockets, landers, and rovers. This course teaches some of the classic techniques used in estimation including least squares and Kalman filtering. It also examines some cutting edge techniques for nonlinear systems including unscented Kalman filtering and particle filtering. Emphasis is placed on the ability to carry out state estimation for vehicles in three-dimensional space, which is complicated by vehicle attitude and often handled incorrectly. Students will have a chance to work with datasets from real sensors in assignments and will apply the principles of the course to a project of their choosing.

This course presents the fundamentals of robotic perception based on a foundation of probability, statistics and information theory. Common sensor types and their probabilistic modeling are surveyed, including computer vision, Lidar, radar, GNSS/INS and odometry. Methods for feature extraction, description and matching, direct photometric and point cloud registration, outlier rejection are presented in the context of a robotic localization and mapping front end. Object detection and tracking, semantic segmentation and prior maps are fused to form a complete perceptual view of dynamic environments for a wide range of robotic applications.

A rigorous mathematical study of the motion planning problem for aerial, ground, and mobile manipulator robot platforms and for multi-robot systems. Geometric representations and the robot configuration space. Sampling-based motion planning. Feedback motion planning in continuous spaces. Planning under sensor uncertainty and with differential constraints.Course project involving the implementation of modern planning algorithms in simulation and (potentially) on a real mobile manipulator.

This course presents optimal, adaptive, and learning control principles from the perspective of robotics applications. Working from the Hamilton-Jacobi-Bellman formulation, optimal control methods for aerial and ground robots are developed. Real world challenges such as disturbances, state estimation errors and model errors are addressed and adaptive and reinforcement learning approaches are derived to address these challenges. Course project involves simulated control of an aerial vehicle, with aerodynamic models and wind disturbances.