MSE Program Overview...MSE Program Requirements Minimum of 30 credit hours At least 5 courses in...

MSE Program Overview

Fall 2020

Basic Academic Structure

The Aero Department is subdivided into groups:

⮚Autonomous systems and control

⮚Aerodynamics & Propulsion (A&P)

⮚Structures and Materials (SM)

⮚Computation

⮚Space systems

Research activities and projects are not limited to these areas

⮚Several “multidisciplinary” activities

⮚Cut across different groups

Graduate Program is built around individual needs/interests⮚Graduate students put together a course work plan

MSE Program Requirements

⮚ Minimum of 30 credit hours

⮚ At least 5 courses in aerospace engineering at 500-level or higher (B or better grade)

⮚ Up to 6 credits (two courses) of AE590 Directed Study

⮚ AE585 Aerospace Seminar (1 credit) expected at least once (max 3 credits)

⮚ Two approved1 mathematics courses (B or better grade)

⮚ Maximum of four credit hours of non-technical courses in an approved subject area at the 500 level or higher. Approved subject areas include: Business, Entrepreneurship, and English (including ELI). Non-technical courses not in these areas can be petitioned to the graduate chair for approval.

1https://aero.engin.umich.edu/academics/courses/graduate-courses/#mathreqs

Overview of Expected Academic Performance

⮚Full time student: 9 credit hours per semester

⮚Pay attention to your academic performance

⮚Consider research activity through AE 590 Directed Studies

Academic Advising

⮚You all have been assigned to an Academic Adviser (AA)

⮚The AA can help you with your course selection. However…

⮚You are responsible for defining your program of study

⮚If you want to change advisers, please contact Denise Phelps

⮚The Grad Committee members are here to help:

⮚ Prof. Karthik Duraisamy – Aerodynamics & Propulsion (Gas Dynamics)

⮚ Prof. Ilya Kolmanovsky – Flight Dynamics & Control

⮚ Prof. Veera Sundararaghavan – Structures & Materials

⮚ Prof. Jean-Baptiste Jeannin - Computation

Complement your degree with business, leadership, and innovation skills

Stand out to employers and recruiters

● Coursesincluding Innovation Careers, Project Management & Consulting, Intellectual Property Strategy, Funding & Ownership, Interpersonal Skills, and more

● Certificate in Innovation & Entrepreneurship12-credit program open to all master’s and PhD students

● NSF I-Corps Explore commercialization potential of technology

WEB: cfe.umich.eduEMAIL: [email protected]

Center for Entrepreneurship

mailto:[email protected]

This is an exciting time! There are a lot of things to do and to learn —much more than you can fit in your schedule.

Enjoy it!

Structures and MaterialsDepartment of Aerospace Engineering

The University of Michigan

Structural and Materials Faculty

Carlos Cesnik Peretz Friedmann Nakhiah GoulbourneDan Inman Joaquim Martins

John Shaw Veera Sundararaghavan Henry Sodano Peter Washabaugh Anthony Waas

Daniel Inman, PhD Michigan State University• Smart materials and structures as applied to morphing aircraft, energy

harvesting, structural health monitoring and clearance control in jet engines

• Gust alleviation in UAVs• Cable harnessed satellites• Wind turbine blade monitoring

Carlos Cesnik, PhD Georgia Institute of Technology • Active aeroelastic structures• Computational aeroelasticity• Structural health monitoring: guided-wave modeling, transducer design,

signal processing

Structural and Materials Faculty

Peretz Friedmann, DSc Massachusetts Institute of Technology• Rotary and fixed wing computational aeroelasticity• Vibration and noise reduction in helicopters• Hypersonic vehicle aerothermoelasticity• Multidisciplinary optimization• Turbomachinery aeroelasticity

Nakhiah Goulbourne, PhD Pennsylvania State University • Mechanics of electroactive polymers• Constitutive behavior of soft materials• Bio-inspired skins and membranes• High strain rate response of polymers and composites

Structures and Materials Faculty continued

Joaquim Martins, PhD Stanford University• MDO methodologies to the design of aircraft configurations• Focus on high-fidelity simulations that take advantage of high-performance parallel computing

John Shaw, PhD University of Texas at Austin • Mechanics of adaptive materials and structures• Instabilities and thermomechanical behavior of solids and experimental mechanics


Henry Sodano, PhD Virginia Tech• Composites, multifunctional material, and self-healing polymers• Nanocomposites and nanotechnology• Interfaces and MEMS/NEMS sensors• Energy harvesting• Vibration Control

Veera Sundararaghavan, PhD Cornell University • Integrated computational materials engineering• Materials-by-design and materials informatics• Computational mechanics and atomistic simulations• Crystal plasticity


Peter Washabaugh, PhD California Institute of Technology• Experimental solid mechanics• Fracture mechanics• Instrumentation• Non-destructive testing• Optimization


Anthony Waas, PhD California Institute of Technology• Additive manufacturing • Structural integrity and damage tolerance of composites. • Mechanics of textile composites• Ceramic composites for high-temperature applications

Major Research Areas

Fixed and Rotary Wing Aeroelasticity, Aeromechanics

Cesnik, Friedmann, Martins

Smart Materials and Structures

Goulbourne, Inman, Shaw

Composite Materials

Sodano, Sundararaghavan, Waas

Multidisciplinary Design Optimization

Friedmann, Martins

Structures and Materials CoursesCurriculum can be tailored to student’s interests

AE 513 Solids and Structures I

AE 518 Elastic Stability

AE 543 Structural Dynamics

AE 510 Finite Elements I

AE 514 Solids and Structures II

AE 516 Composite Structures

AE 544 Aeroelasticity

AE 545 Aeromechanics of Rotary Wing Vehicles

Other Aero Courses

AE 511 Finite Elements II

AE 523 CFD I (GD)

AE 540 Intermediate Dynamics

AE 579 Control of Fluids and Structures

AE 618 Advanced Stability

AE 714 Atomistic Modeling

AE 588 Multidisciplinary Design Optimization

AE 714 Multifunctional Materials and Structures

Courses Outside Aero

ME 512 Theory of Elasticity, ME 516 Thin Films and Fracture, ME 517 Mechanics of Polymers, ME 519 Plastic Theory

Aerospace ComputingDepartment of Aerospace Engineering


Computation Faculty

Karthik DuraisamyElla AtkinsChris FidkowskiDennis Bernstein

Veera Sundararaghavan Venkat Raman

Jean-Baptiste JeanninAlex Gorodetsky

Joaquim Martins

Vasileios Tzoumas

Computation : Major Research Areas

Autonomous air vehicles

➢Atkins, Jeannin, Gorodetsky, Tzoumas

Multi-scale modeling

➢Sundararaghavan, Duraisamy, Raman

Aerospace information systems

➢Atkins, Jeannin

Computational science

➢Duraisamy, Gorodetsky, Fidkowski, Raman, Sundararaghavan

Data-driven Scientific computing

➢ Duraisamy, Raman, Gorodetsky

Optimization

➢Martins, Tzoumas, Gorodetsky

Aerospace Information Systems

Enable aerospace systems to reason

about goals and motions for safe,

intelligent, and collaborative operation

alone and with human/robotic companios

Response to dangerous unanticipated

events

Adaptive Guidance and Flight Planning:

Enabling autonomous or semi-

autonomous safe landing following in-

flight failures and damage

UAV Sensor/Software Testbeds

Flying Fish UAV Autonomous aerospace systems laboratory

Autonomous Exploration Rover

Formal Verification and Aircraft Collision Avoidance

Formal Verification…

• We prove strong mathematical properties on

critical, typically embedded, software

• Example: We formally verified (and fixed bugs) in

the ACAS X Collision Avoidance System, the

successor of TCAS

… applied to Hybrid Systems

• Aircraft software interacts with physics: we want to

guarantee physical properties (e.g., no collision)

• Model is a hybrid systems: discrete transitions

(software steps), continuous dynamics (physics)

safe

CL1500

CL1500

Uncertainty quantification & data-driven physics

If we run a simulation, can

we characterize sources of error and uncertainty?

Numerical errors

Modeling errors

Randomness in the system

How can we develop effective predictive models using

real world data?

Inverse problems

Machine Learning

Reduced order modeling

Centers established

Center for data-driven computational physics

AFOSR/AFRL Center of Excellence in Rocket

Combustor Dynamics

High Performance Computing

Advanced Research Computing (ARC) administers almost 25,000 CPUs in a Linux-based cluster

Clusters of nodes of different types

for faculty and grad student research

Over 400 TB of high speed scratch space

Over 100 commercial and open source

applications and libraries

Free training several times per year

Free support to help you get started

and answer questions

Other architectures such as GPUs also available

Computations Courses

Curriculum can be tailored to student’s interests

AE 550 Linear Systems

AE 552 Aerospace Information Systems

AE 740 Statistical Inference and Learning

AE 729 Data-driven and Reduced complexity Modeling

AE 729 Statistical Methods for Aerospace Engineering

AE 566 Data Analysis and System Identification

AE 523 Computational Fluid Dynamics I

AE 623 Computational Fluid Dynamics II

AE 588 Multidisciplinary Design and Optimization

EECS 563 Hybrid Systems

EECS 587 Parallel Computing

EECS 590 Advanced Programming Languages;

EECS 505 Computational Data science & Machine Learning

NERS 590 Methods & Practice of Scientific Computing

EECS 591 Distributed Systems

EECS 542 Computer Vision

EECS 545 Machine Learning

Autonomous Systems and ControlDepartment of Aerospace Engineering


Autonomous Systems and Control Faculty

James CutlerElla Atkins Anouck GirardDennis Bernstein

Ilya Kolmanovsky Dimitra PanagouJean-Baptiste JeanninAlex Gorodetsky Vasileios Tzoumas


Ella Atkins, PhD University of Michigan • Autonomy research in aviation • Augmenting onboard decision systems and supporting closer astronaut-robot collaboration

• Use models and algorithms from the control systems and computer science communities to best solve key Aerospace challenges

Dennis S. Bernstein, PhD University of Michigan • Theory and application of nonlinear system identification• Large-scale state estimation for data assimilation, and adaptive

control

James W. Cutler, PhD Stanford • Space systems• Communication• Robust computing infrastructure• Remote sensing (emphasis on magnetometers)

Anouck Girard, PhD UC Berkeley • Nonlinear systems• Hybrid systems• Embedded systems• Cooperative control and unmanned vehicles


Alex Gorodetsky, PhD Massachusetts Institute of Technology• Uncertainty quantification and statistical learning• Decision making under uncertainty for dynamical systems• Computational approaches for large-scale learning and approximation

Jean-Baptiste Jeannin, PhD Cornell University • Formal verification of cyber-physical systems• Aerospace software systems• Logics and semantics of programming languages• Programming with coinductive types• Software security


Ilya Kolmanovsky, PhD University of Michigan • Control of systems with state and control constraints• Model Predictive Control • Control applications in aerospace and automotive systems• Modeling and control of engines and propulsion systems

Dimitra Panagou, PhD National Technical University of Athens, Greece • Nonlinear systems, multi-agent systems, decentralized/distributed

systems, etc.• Set-theoretic methods in control, motion, and path planning with

applications in unmanned aerial systems• Robotic networks and autonomous multi-vehicle systems


Vasileios Tzoumas, PhD University of Pennsylvania(starting January 2021)• Learning for Control• Robotic Perception• Combinatorial and Distributed Optimization• Adaptive, Self-Reconfigurable Systems


Autonomous Systems and Control Major Research Areas

Autonomous air vehicles

Atkins, Girard, Gorodetsky, Panagou, Tzoumas

Aerospace information systems

Atkins, Gorodetsky, Jeannin, Tzoumas

Adaptive control for aerospace applications

Bernstein

Spacecraft dynamics, control, and systems engineering

Bernstein, Cutler, Kolmanovsky

Control of constrained and propulsion systems

Kolmanovsky

Flight Dynamics and Controls CoursesCurriculum can be tailored to student’s interests | Color Coding: FALL 2020 - WINTER 2021 - TBD

AE 540 Intermediate Dynamics

AE 550 Linear Systems

AE 552 Aerospace Information Systems

AE 548 Astrodynamics

AE 551 Nonlinear Systems and Control

AE 573 Spacecraft Dynamics and Control

AE 575 Flight and Trajectory Optimization

AE 584 Avionics, Navigation and Guidance of Aerospace Vehicles

Other Aero Courses

AE 566 Data Analysis and System Identification

AE 580 Linear Feedback Control Systems

AE 740 Model Predictive Control

AE 740 Inference, Estimation, and Learning

AE 579 Control of Structures and Fluids

AE 572 Dynamics and Control of Aircraft

Courses Outside Aero

MATH 558 Applied Nonlinear Dynamics, MATH 658 Nonlinear Dynamics, Geometric Mechanics and Control, EECS 461 Embedded Control, EECS 501 Probability and Random Processes, EECS 545 Machine Learning, EECS 558 Stochastic Control, EECS 600 Function Space Methods in Systems Theory, EECS 566 Discrete Event Systems, EECS 662 Advanced Nonlinear Control, NA 531 Adaptive Control, ROB 501 Math for Robotics, ROB 550 Robotic Sys Lab, AE 558 Applied Nonlinear Dynamics, ME 561 Design of Digital Control Systems

U-M Controls Group

College of Engineering Controls Group

aero.engin.umich.edu/research/control

College of Engineering Control Seminar Series

Time: 3:30 – 4:30 PMDay: FridaysPlace: 1500 EECS Bldg

Space SystemsDepartment of Aerospace Engineering


Space Systems Faculty

James Cutler Benjamin JornsTamas Gombosi

James W. Cutler, PhD Stanford • Space systems, CubeSats• Communication• Robust computing infrastructure• Remote sensing (emphasis on magnetometers)

Tamas Gombosi, PhD Lóraánd Eötvös University, Budapest(CLaSP)• Space plasma phyics• Predictive Global Space Weather Simulation Framework• Physics of the Space Environment of Planets


Benjamin Jorns, PhD Princeton• Electric Propulsion Systems• High-power Hall Thrusters• Low Temperature Plasmas• New Forms of Space Propulsion


Space Systems CoursesCurriculum can be tailored to student’s interests

AE 548 Astrodynamics

AE 549 Orbital Analysis and Determination

AE 573 Dynamics and Control of Spacecraft

AE 581 Space System Management

AE 582 Spacecraft Technology

AE 583 Management of Space Systems Design

AE 597 Fundamentals of Space Plasma Physics

Courses Outside Aero: Many courses in CLaSP (Climate and Space Sciences and Engineering), Astronomy, Physics

Aerodynamics & Propulsion*Department of Aerospace Engineering


*Gas Dynamics

Faculty

Luis Bernal Jim Driscoll Chris Fidkowski

Alec Gallimore Mirko Gamba

Karthik Duraisamy

Ken Powell Phil RoeVenkat Raman

Ben Jorns

Major Research AreasFluid Dynamics/Aerodynamics

Fidkowski, Gamba, Duraisamy

Combustion/Chemical Propulsion

Driscoll, Gamba, Raman

Computational Fluid Dynamics

Fidkowski, Duraisamy, Powell, Roe, Raman

Electric Propulsion/Plasmas

Jorns, Gallimore, Powell

Hypersonics

Driscoll, Gamba, Roe

Computational Fluid Dynamics

Algorithm development and

numerical simulations for a

variety of physical problems:

Aerodynamics

Space plasma physics

Aeroacoustics

Combustion

Hypersonic

aerothermodynamics

Space propulsion

Electric Propulsion and Plasma Physics

High-Power Plasma Propulsion

Nested-Channel Hall Effect Thruster (UM)

Develop 80-kW-class NHT (X3-80)

Investigate channel coupling phenomena

Computational modeling

Time-Resolved Plasma Diagnostics

Probe-Based Diagnostics

Develop 1-μs temporal resolution

Cavity Ring-Down Spectroscopy

Optical Diagnostics, LIF

Electric Propulsion and Plasma Physics cont’d

Plasma/Materials Interaction

Plasma-Wall Interactions

Develop plasma cells with low-density plasma (thick sheath)

e-gun to stimulate secondary electron emission (SEE)

LIF and probes to characterize sheath/bulk plasma

Ion sputtering of thruster walls

Modeling and Simulation

Fundamental plasma physics (sheaths)

Electric propulsion thrusters (Hall, ion, etc.)

Spacecraft thruster plumes

Michigan/AFRL Center of Excellence in Electric Propulsion (MACEEP)

11

Experimental Facilities

The University of Michigan - Plasmadynamics and Electric Propulsion Laboratory (PEPL)

A critical requirement for the proposed capability is to have a vacuum facility of high pumping speed and sufficient

volume to minimize facility effects, which of course becomes more important and difficult to achieve as thruster

size and power continue to increase. Much of the proposed Center experimental work will take place in PEPL's 6-

m-diameter by 9-m-long Large Vacuum Test Facility (LVTF). The LVTF (Fig. 4) underwent a facility upgrade in

1998 with Air Force funding wherein four CVI-TM1200 internal cryopumps were installed to replace the oil

diffusion pumps previously used. The four cryopumps give the LVTF a measured xenon pumping speed of 140,000

l/s. An Air Force DURIP grant was used to add three TM1200 cryopumps to the LVTF in August of 2000. This

enables the pumping speed of the facility to reach 245,000 l/s on xenon. The base pressure of the LVTF is less than

2x10-7

torr. The LVTF can maintain a pressure within the 10-6

torr range during the operation of 10-kW-class HETs.

A louvered 1.8 m by 1.8 m graphite beam dump is located on the center of the endcap downstream of the thruster to

minimize deposition of back-sputtered material from the bare tank wall. The thruster is always operated at least 4 m

from the beam dump. PEPL operates two smaller vacuum chambers (approximately 2 x 3 m) and has a significant

suite of probe, sensor, optical/laser, and microwave diagnostics. The MPPC will be operated in one of these

chambers.

The University of Michigan - Electron Microbeam Analysis Laboratory (EMAL)

The Electron Microbeam Analysis Laboratory is a university-wide user facility for the microstructural and

microchemical characterization of materials. The laboratory was originally established in 1978 with the goals of

providing and maintaining state-of-the-art equipment for use by the university research community. The main

instrument at EMAL includes:

Figure 3: Multi-Cathode Discharge Chamber (MCDC). The MCDC, which has probe and optical access

throughout chamber, will serve as the test article for this effort.

Michigan/AFRL Center of Excellence in Electric Propulsion (MACEEP)

9

depending on operating condition, the surface potential due to secondary processes can actually change sign, SEE

affects thruster operation in a number of ways including altering plume divergence, and thus thruster performance,

discharge channel wall thermal loading, and life. This effort therefore aims to elucidate these processes through

intensive diagnostics of near-wall conditions in a Multi-Pole Plasma Chamber (MPPC) test cell so that a

phenomenological model can be constructed that should ultimately lead to a computational model and thruster

validation experiments demonstrating an understanding of SEE impact on thruster operation.

Brief Overview

In order to study this problem, a two-pronged approach will be taken. The EEDF of secondary electrons induced by

electron bombardment of a ceramic target in an ultra-high vacuum facility will be determined. Charge equilibrium

will be stabilized by the use of an ion gun, which will be used concurrently with the electron beam gun. In this

manner, arbitrary charge distributions and associated electric fields will be attainable. This approach allows for

charging effects to be controlled. A Kelvin probe will be used to measure the deposited charge and charging

potential of a ceramic substrate by simply rotating the target into the range of the Kelvin probe. The ultrahigh

vacuum will allow for elucidation of exclusively secondary electron effects and their dependence on gas coverage,

surface morphology, and insulator type.

An illustration of the MPPC source as shown in Figure 2 will be developed from an existing ion thruster discharge

chamber. The chamber will be fitted with an electron gun to induce secondary emission of ceramic samples

immersed in a tenuous discharge plasma, which will be created by a low-flow hollow cathode. One can tease out the

effects of SEE by simply comparing experimental results with the electron gun dormant and in operation. The actual

basis for the MPPC will be the Multi-Cathode Discharge Chamber (MCDC), which is an ion thruster discharge

chamber developed at NASA Glenn Research Center and Michigan in support of NASA’s Jupiter Icy Moons

Orbiter mission (JIMO). The MCDC was a prototype chamber to test the notion of operating two or more hollow

cathodes sequentially to achieve the desired thruster life. This project was a risk-reduction effort for the microwave

ionization source baselined for this thruster.

A number of diagnostics will be ultimately used to elucidate the effect of secondary emission on local plasma

properties. These diagnostics include:

· LIF measurements in the sheath and pre-sheath to infer

the potential distribution (ion velocity) with and without

SEE;

· Emissive and electrostatic probes to infer changes to bulk

plasma properties;

· Flush-mounted wall probes to measure the EEDF at the

surface and infer bulk plasma properties of the sheath;

· Novel optical emission measurement to assess EEDF

cooling and the evolution of the tail of the distribution

function with SEE‡;

· A novel emissive/gridded and pinhole probe to interrogate

thick sheaths to measure the EEDF;

· Kelvin probe measurements of the charge distribution on

the sample surface;

· Electron energy analyzer to assess primary electron

impact energies;

· Surface roughness characterization and composition of

samples before and after plasma exposure; and

· Variable transverse magnetic field to alter magnetic field angle of incidence.

With this cadre of diagnostics, the evolution of the sheath at the ceramic can be characterized as a function of

primary electron flux, angle, stray magnetic field, and energy. Variation in sheath thickness can also be measured

directly. The MPPC will be designed for ease of use at Michigan (Professor Foster’s and Gallimore’s labs), at

‡ A technique developed at Wisconsin by Professor Foster’s former postdoc advisor.

Figure 2: Schematic representation of the

test apparatus.

Hypersonic Vehicles

Air-Breathing Vehicles

High speed aerodynamics

Shock-boundary interactions

Plasma vehicle control

Communications blackout

Re-entry Capsules

Aerothermodynamics

Propulsive Decelerators

Reaction Control Systems

Thermal Protection Systems

Turbulence and Fluid MechanicsDual-Plane Stereo PIV

(DSPIV) Measurements

Supersonic (M = 3)

Turbulence ExperimentsMicro Vehicle

Aerodynamics

Aerodynamics & Propulsion Courses

Curriculum can be tailored to student’s interests | COLOR CODING: FALL - WINTER - TBD

➢ Aero 520 Compressible Flow I

➢ Aero 522 Viscous Flow

➢ Aero 532 Molecular Gas Dynamics

➢ Aero 523 CFD I

➢ Aero 525 Turbulent Flow I

➢ Aero 533 Combustion I

➢ NERS 571 Intermediate Plasma Physics I

➢ Aero 579 Fluid/Structure Control

➢ Aero 521 Experimental Methods

➢ Aero 544 Aeroelasticity (S&M)

➢ Aero 588 Multidisciplinary Design Optimization

➢ Aero 524 Aerodynamics II

➢ Aero 526 Hypersonics

➢ Aero 527 Unsteady Aero and Acoustics

➢ Aero 530 Gas Turbine Propulsion

➢ Aero 535 Rocket Propulsion

➢ Aero 536 Electric Propulsion

➢ Aero 627 Advanced Gasdynamics

➢ Aero 623 Advanced CFD

➢ Aero 625 Advanced Turbulent Flow

➢ Aero 597 Space Plasma Physics

➢ Aero 633 Advanced Combustion

➢ Aero 729 Large Eddy Simulations

➢ Aero 729 Automotive Aerodynamics

Questions & Answers

aero.engin.umich.edu/academics/graduate/mse/

Thank you for attending the Aerospace Engineering Graduate Orientation!

Thank you.aero.engin.umich.edu

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