AE 598

Fall 2019 All Classes

All Classes

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in aerospace engineering intended to augment existing formal courses. Topics and prerequisites vary for each section. See Class Schedule or departmental course information for both.

May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

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AE 598 class schedule data for fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72135	Lecture-Discussion	CIF	10:00AM -11:20AM	TR	410C1 Engineering Hall	Goza, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: CFD for Incompressible Flow Section Info: This course will focus on finite volume methods for incompressible flows. The key topics of the course will be i) spatial discretization of the governing equations (which will include ideas such as the staggered-grid formulation, the adherence of discrete operators to discrete conservation laws, and the mimetic nature of certain operators), ii) time stepping applied within the differential-algebraic framework that characterizes the incompressible Navier-Stokes equations (which will include fractional-step approaches and their representation as a projection procedure), and iii) treatment of boundary conditions in 2D. Time permitting, these ideas will be combined within an immersed boundary setting to enable treatment of immersed bodies.
69433	Online	ONL	ARRANGED	n.a.	n.a.	Panesi, M	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Uncertainty Quantification Onl Section Info: Title: Simulation prediction with quantified uncertainty Advances in computational techniques and resources have made predictive simulations an indispensable tool across engineering and science, with integration of continually more physical models into simulation tools to represent increasingly complex phenomena. This increases the challenge of both validating and quantifying the predictive uncertainty of such simulations. For true predictions there is no corresponding experimental data to check against, so quantification of predictive uncertainty increases their utility and can target pacing sources of uncertainty for reduction. This course will introduce technique for quantifying the uncertainty of simulation predictions. After the predictive science challenge is introduced and motivated with examples, we will: review basic statistical tools and distributions; discuss probability as measures of belief; examine the strengths, limitations, and design of experiments for calibration and validation; introduce quantitative model selection and hypothesis testing for the design and evaluation of physical models; and present methods to propagate known uncertainties through a predictive simulation to the quantity of interest. Inverse adjoint-based sensitivity methods will be discussed for use in validation and uncertainty quantification. Mechanics based examples will be used throughout for motivation. Prerequisites: experience in (1) numerical methods (minimally TAM470, AE370 or equivalent), (2) mathematics (TAM541/2 or equivalent), and (3) experience with fluid and/or solid mechanics. Restricted to online non-degree, online, MSAE, online MSME, online MSCEE and online MCS students. For more details on this course section, please see http://engineering.illinois.edu/online/courses/. Non-Degree students may enroll on a space-available basis with consent of Program Coordinator, Staci Tankersley (tank@illinois.edu). Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MCS:Computer Sci Online -UIUC, MS:Mechanical Engineerng -UIUC, MS: Aerospace Engr-Online-UIUC, NDEG:Grad Nondegree-CE-UIUC, MENG:Mech Engineering Onl-UIUC, MS:Industrial Engr Online-UIUC, MS:Env Engr CivilEngr ONL-UIUC, or NDEG:Undergrad Nondeg-CE-UIUC.
65309	Lecture-Discussion	UQ	9:00AM -9:50AM	MWF	410C1 Engineering Hall	Panesi, M	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Uncertainty Quantification Section Info: Title: Simulation prediction with quantified uncertainty Advances in computational techniques and resources have made predictive simulations an indispensable tool across engineering and science, with integration of continually more physical models into simulation tools to represent increasingly complex phenomena. This increases the challenge of both validating and quantifying the predictive uncertainty of such simulations. For true predictions there is no corresponding experimental data to check against, so quantification of predictive uncertainty increases their utility and can target pacing sources of uncertainty for reduction. This course will introduce technique for quantifying the uncertainty of simulation predictions. After the predictive science challenge is introduced and motivated with examples, we will: review basic statistical tools and distributions; discuss probability as measures of belief; examine the strengths, limitations, and design of experiments for calibration and validation; introduce quantitative model selection and hypothesis testing for the design and evaluation of physical models; and present methods to propagate known uncertainties through a predictive simulation to the quantity of interest. Inverse adjoint-based sensitivity methods will be discussed for use in validation and uncertainty quantification. Mechanics based examples will be used throughout for motivation. Prerequisites: experience in (1) numerical methods (minimally TAM470, AE370 or equivalent), (2) mathematics (TAM541/2 or equivalent), and (3) experience with fluid and/or solid mechanics.