CBE 2345
Transcript Abbreviation:
Comp Methods
Course Description:
Chemical engineering problems are often described by a complex set of mathematical equations that cannot be solved analytically. The main goal of this course is to introduce students to a variety of computational methods/algorithms to develop solutions to such challenging problems as well as how to implement these solutions on a computer using a relevant programming environment (e.g., Python)
Course Levels:
Undergraduate (1000-5000 level)
Designation:
Required
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
CBE 2200
Electronically Enforced:
Yes
Exclusions
(N/A)
Course Goals / Objectives:
Understand and apply the basics of scientific computing.
Learn how to numerically setup and solve linear and nonlinear systems of equations as well as characterize potential errors in the identified solutions
Learn how to define, interpret, and solve constrained optimization problems using state-of-the-art software tools.
Learn fundamental concepts related to the characterization and solution methods for ordinary differential equations as a framework for modeling dynamic systems.
Learn the core concepts for constructing and interpreting data-driven models.
Check if concurrence sought:
Yes
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Programming / Scientific Computing | 5 | 0 | 0 | 0 |
| Linear Systems | 6 | 0 | 0 | 0 |
| Nonlinear Systems | 4 | 0 | 0 | 0 |
| Differentiation, Interpolation, and Integration | 5 | 0 | 0 | 0 |
| Numerical Optimization | 7 | 0 | 0 | 0 |
| Initial Value Problems | 6 | 0 | 0 | 0 |
| Boundary Value Problems | 3 | 0 | 0 | 0 |
| Statistics, Regression, and Machine Learning | 4 | 0 | 0 | 0 |
| Total | 40 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components
(N/A)
Grade Roster Component
(N/A)
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Exams | 50% |
| Group Project | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Numerical Methods for Engineers | SC Chapra and RP Canale | 2021 |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Some contribution (1-2 hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Substantial contribution (3-6 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 4 | Some contribution (1-2 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 5 | Substantial contribution (3-6 hours) | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| 6 | Significant contribution (7+ hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Significant contribution (7+ hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info:
Data Analysis - Quantitative
1.1A Explain basic concepts of statistics and probability1.2A Apply methods needed to analyze and critically evaluate statistical arguments1.3A Recognize the importance of statistical ideas
Technology
1.2 Recognize how technologies emerge and change
Attachments
(N/A)
Additional Notes or Comments
(N/A)