CBE 5790
Transcript Abbreviation:
Model & Simulation
Course Description:
Application of chemical and biomolecular engineering principles to construct mathematical models of processes and perform simulations.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
3.00
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
7 weeks (autumn or spring)
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Jr, Sr, or Grad standing in CBE.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 790.
Course Goals / Objectives:
Become familiar with the basic concepts of modeling and simulation
Master programming fundamentals: flow control, loops, conditionals, functions, and subfunctions, input and output variables, working with numeric and character variables
Learn how to derive the appropriate set of differential and/or algebraic equations to be solved for a particular problem of interest, and how to write a program in MATLAB to obtain a solution
Understand modeling and simulation strategies for systems involving continuous and discrete variables
Understand modeling and simulation strategies for systems involving deterministic and stochastic phenomena
Demonstrate ability to write programs to model a wide variety of processes and phenomena relevant to chemical and biomolecular engineering
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Overview of modeling and simulation; physical and mathematical models, phenomenological vs. mechanistic, continuous vs. discrete, deterministic vs. stochastic, algorithms | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Algorithms; programming fundamentals: flow control using loops and conditionals, nesting, writing efficient MATLAB code | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Primary functions, subfunctions, nested functions, variable scope, recursion, creating functions that take multiple inputs and return multiple outputs, troubleshooting and code debugging | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Working with string (character) data types; character arrays, structure variables, cell arrays | 4.0 | 0.0 | 0 | 0.0 | 0 |
| MATLAB graphics, working with handle graphics, differential equation solvers, solving systems of ODEs, stiff ODEs | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Solving systems of linear algebraic equations and systems of nonlinear algebraic equations; application to modeling fluid behavior using thermodynamic equations of state | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Solving systems containing both differential and algebraic equations (DAEs); simulation examples: predator-prey, disease epidemics, chemical reactor design | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Stochastic simulation of chemical reactions, the Gillespie algorithm, application of this approach to model virus reproduction in a cell | 4.0 | 0.0 | 0 | 0.0 | 0 |
| More examples of stochastic-discrete simulations: computing probabilities, modeling evolution, sequence analysis, replication, "mutation", and selection | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Introduction to game theory and Prisoner's Dilemma, class PD tournament, Monte Carlo simulations | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Graphical (visual) simulations: random walk simulations in2D, relation to polymers and proteins, boundary conditions, cellular automata | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Introduction to molecular simulation; intermolecular potential functions and energy minimizations | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 43 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Surveys | 20% |
| Project | 15% |
| Midterm Exam | 20% |
| Final Exam | 20% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: