Courses

Systems Simulation

Simulation is a process of designing and creating computerized models of real or proposed systems for the purpose of conduction numerical experiments to understand and analyze the behavior of the system for a given set of conditions. The course introduces various simulation modeling techniques, using Arena simulation software.

Advanced Facilities Design

Study of the theoretical and applied aspects of facility logistics. Topics include location, layout, material handling systems, storage and warehouse systems, and cellular systems.

Big Data Risk Analytics for Engineering Management and Public Policy

In this course, I will first cover the foundational principles in risk analysis and will then delve into methods used for developing quantitative risk analytics. Big data risk analytics leverages computational statistics and data mining to build predictive models based on large data sets to draw insights for engineering management and public policy application areas. As data becomes more prevalent across many different areas of importance in engineering, policy analysis, and management, predictive risk analytics is emerging as an increasingly important topic. This course assumes a working knowledge of probability and statistics and builds from this to introduce modern supervised and unsupervised learning techniques. Besides covering the foundations in risk analysis as well as the key principles for developing predictive risk models with high generalization performance, the course will also cover a range of semi- and non-parametric algorithms (e.g., generalized additive models, tree-based models, neural nets, support vector machines), meta-algorithms (e.g., boosting and bagging) as well as widely used unsupervised learning techniques such as PCA and clustering.

Human Factors of Medical Devices

This course focuses on industry needs and expectations for human factors engineers in the medical device application area. This course is to cover human factors topics, learn human factors assessment techniques, and apply these knowledge and methods to fulfil regulatory requirements (focusing on HE75 and FDA human factors guidance.

Technical Project Management

This course may not be allowed on a master's plan of study if GRAD 59000-Program Management is also taken. Please consult your Graduate Program Office. Whether an organization is developing a new product, redesigning an existing process, or planning an event, project management is necessary to ensure that the project team fulfills its budget, schedule, and organizational requirements. This course will focus on all aspects of project management from project selection through project termination. The material covered will include both quantitative techniques (such as resource scheduling and project crashing) and management issues (such as team dynamics and communication). The course is designed for students with no previous project management experience.

Linear Algebra with Applications

Basic vector space theory; linear transformations; topics in matrix theory such as QR and LU factorization, eigenvalues, and quadratic forms.

Advanced Mathematics for Engineers and Physicists I

Linear algebra, systems of differential equations, stability, Laplace transforms, Fourier series, Fourier transforms, partial differential equations.

Advanced Mathematics for Engineers and Physicists II

Vector calculus: line integrals, surface integrals, divergence and Stokes theorems, Complex variables: Cauchy theory, power series, residues, conformal mappings, potential theory.

Advanced Thermodynamics

Fundamental laws of thermodynamics and their application to thermal systems; second-law analysis, and the concept of exergy and its usefulness in optimizing thermal systems; introduction to chemical thermodynamics, and phase and chemical equilibrium; thermodynamics of combustion systems, heat transfer associated with combustion reactions, and equilibrium composition of the products of combustion.

Statistical Thermodynamics

The molecular interpretation of thermodynamic equilibrium. Development of the partition function. Introduction to quantum mechanics and molecular spectroscopy. The Maxwell-Boltzmann formulation of statistical mechanics and applications to ideal gases, solids, radiation, and laser diagnostics. The Gibbs formulation of statistical mechanics and application to real gases. Kinetic theory and applications to transport properties and chemical kinetics.

Intermediate Heat Transfer

Heat and mass transfer by diffusion in one-dimensional, two-dimensional, transient, periodic, and phase change systems. Convective heat transfer for external and internal flows. Similarity and integral solution methods. Heat, mass, and momentum analogies. Turbulence. Buoyancy driven flows. Convection with phase change. Radiation exchange between surfaces and radiation transfer in absorbing-emitting medial. Multimode heat transfer problems.

Intermediate Fluid Mechanics

This course will begin with basic physical concepts related to fluid dynamics and an introduction to some mathematical tools to facilitate later fluid dynamic analysis. We will then discuss the kinematics of fluid motion. Then we will derive the basic laws of fluid dynamics which include the conservation of mass, momentum, and energy equations ending up with the Navier-Stokes equations. We will then apply these equations to solve a number of classical fluid flows. A discussion of potential flow and vorticity dynamics follows. Then we will explore boundary layers, stability, transition, and turbulence.

Gas Dynamics

One-dimensional compressible flows including basic concepts; isentropic flow; normal and oblique shock waves; flows with heat transfer (Rayleigh line), friction (Fanno line), and mass addition; simple waves; small perturbation theory for linearized, steady flows; method of characteristics for two-dimensional, steady flow and one-dimensional, unsteady flow.

Heat Transfer in Electronic Systems

Traditional and innovative methods for heat dissipation from electronic systems, and assessment of these methods over a range of applications and scales, will be covered. Special emphasis is given to industry applications with guest lectures to be delivered by experts to discuss thermal management trends.

Engineering Acoustics

The basic concepts of wave propagation in the context of mechanical vibration of simple systems. The fundamental assumptions of linear acoustics through the derivation of the wave equation and its simple solutions in plane and spherical forms. Plane wave transmission through barriers. Issues related to modeling and describing acoustical sources. The fundamental mechanisms of sound generation are emphasized, as is the directionality associated with various source types. Sound propagation in ducts, and the concepts of muffler design. Sound propagation in rooms, especially with respect to the effect of sound absorbing treatments on steady-state and transient sound in rooms.

Analysis of Thermal Systems

This course covers the philosophy, theory, and applications of the analysis, modeling and optimization of thermal systems. More specifically, vapor compression, absorption, advanced heat pumping and refrigeration cycles, and not-in-kind cooling technologies are studied in detail. Students combine the use of thermodynamics, heat transfer, fluid mechanics, and numerical methods to develop and apply mathematical models for the analysis and optimization of specific cycles and their equipment for real applications

Combustion

Physical and chemical aspects of basic combustion phenomena. Classification of flames. Measurement of laminar flame speed. Factors influencing burning velocity. Theory of flame propagation. Flammability. Chemical aspects. Chemical equilibrium. Chain reactions. Calculations and measurement of flame temperature. Diffusion flames. Fuels. Atomization and evaporation of liquid fuels. Theories of ignition, stability, and combustion efficiency.

Turbomachinery 2

Introduction to Scientific Machine Learning

This course introduces data science to engineers with no prior knowledge. Throughout the course, the instructor follows a probabilistic perspective that highlights the first principles behind the presented methods with the ultimate goal of teaching the student how to create and fit their own models.

Engineering Design: A Decision-Based Perspective

Engineering design as a decision-making process; Multi-criteria decision making in design under uncertainty; Group decision making in design processes; Sequential decision making; Model-based and data-driven decision making; Heuristics and biases in design decision making. Applications to engineering design including estimation of customer preferences, simulation-based design, and sustainable design.