CH E 374 Computational Methods in Engineering
Winter 2018 - January 08 to April 13
Class time: Monday, Wednesday, Friday 10:00-10:50 Location: NRE 1 003
Instructor:
Joe Mmbaga, PhD, PENG
(780)492-9568
joe.mmbaga@ualberta.ca
Donadeo Innovation Ctr Engineering 12-247
Office Hours: Tue & Wed 1 to 3 PM or by email appointment
Course Description:
*3.5 (fi 8) (either term, 3-1S-0) Formulation and solution of chemical and materials engineering problems;
solution of systems of linear and nonlinear algebraic equations; numerical interpolation, differentiation and
integration; numerical solution of ordinary and partial differential equations.
Prerequisites: ENCMP 100 (or equivalent). MATH 102, 201 and 209.
TA Information:
Monir Hosseini Anvari (monir1@ualberta.ca) & Anuar Caldera (caldera@ualberta.ca)
Seminar Sections:
Section Day Time Location
SEM J1 Friday 08:00 - 08:50 ETLC E2005
SEM J2 Friday 14:00 - 14:50 ETLC E2005
Course Objectives & General Content:
The main objective of the course is to introduce and develop various computational algorithms for solving a
range of engineering problems using digital computers.
Marking Scheme:
Activity Due/Scheduled Weight
TEST1 February 9th 2018 15%
TEST2 March 16th 2018 20%
Assignments Weekly 20%
Final Exam TBD 45%
CH E 374 - Winter 2018 Page 1
The Faculty recommended grade point average for a 300 level course is 3.0. Instructors have the leeway to
deviate from this average and can assign grades based on their own scheme. All grades are approved by
the department chair (or delegate). The office of the Dean has final oversight on all grades.
Term Work
All term work solutions will be posted no later than the last day of classes. All term work will be returned to
students by the final day of classes, with the exception of major term work due in the last week of classes.
The latter will be returned by the day of the final examination or the last day of the examination period if
there is no final examination in the course as per university policy; instructors will make accommodations to
return these term work. It is the responsibility of the student to pick up all their term work at the specified
time and place. Any unreturned term work, shall be retained and then shredded six months after the
deadline for reappraisal and grade appeals. Final examinations will be kept for one year as required by
university guidelines and the Government of Alberta's Freedom of Information and Protection of Privacy Act.
Calculator Policy
Only approved non-programmable calculators are permitted in examinations. Any calculator taken into an
examination must have a sticker identifying it as an acceptable non-programmable calculator (gold sticker).
Students can purchase calculators at the University Bookstore with the stickers already affixed. Calculators
purchased elsewhere can be brought to the Dean's Office where the appropriate sticker will be affixed to the
calculator.
Text and References (Mandatory):
Computational Methods for Engineers.
Mmbaga J.P., K. Nandakumar, R.E. Hayes, M.R. Flynn
Alpha Education Press, Edmonton (2016).
Text and References (Recommended):
S.S. Rao, “Applied Numerical Methods for Engineers and Scientists”, Prentice Hill, New Jersey (2006)
Steven C. Chapra & R. P. Canale, “Numerical Methods for Engineers”, 6e, McGraw Hill, New York (2006).
Kenneth J. Beers, “Numerical Methods for Chemical Engineering: Applications with MATLAB”. Cambridge
(2007).
Jaan Kiusalaas, “Numerical Methods in Engineering with MATLAB”, Cambridge University Press (2006)
Website:
eClass
Previous Examples of Evaluative Materials:
eClass
University Policies:
Policy about course outlines can be found in Course Requirements, Evaluation Procedures and Grading of
the University Calendar.
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students
are expected to be familiar with these standards regarding academic honesty and to uphold the policies of
the University in this respect. Students are particularly urged to familiarize themselves with the provisions of
CH E 374 - Winter 2018 Page 2
the Code of Student Behaviour (online at www.governance.ualberta.ca) and avoid any behaviour which
could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation
in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the
University.
Audio or video recording, digital or otherwise, of lectures, labs, seminars or any other teaching environment
by students is allowed only with the prior written consent of the instructor or as a part of an approved
accommodation plan. Student or instructor content, digital or otherwise, created and/or used within the
context of the course is to be used solely for personal study, and is not to be used or distributed for any
other purpose without prior written consent from the content author(s).
Only those items specifically authorized by the instructor may be brought into the exam facility. The use of
unauthorized personal listening, communication, recording, photographic and/or computational devices is
strictly prohibited. Students should refrain from bringing any unauthorized electronic device into an
examination room, including cell phones, high tech watches, high tech glasses or other such devices.
Learning Outcomes:
By the end of this course, students should be able to:
1. Appreciate the concept of numerical approximation in the solution of engineering problems.
2. Describe the consequences of finite precision and the limits of the numerical methods considered.
3. Select appropriate numerical methods to apply to various types of problems in engineering in
consideration of the mathematical operations involved, accuracy requirements, and available
computational resources.
4. Relate mathematical concepts that underpin the numerical methods considered.
5. Develop and implement numerical solution algorithms applied to root finding for non-linear equations
6. Develop and implement numerical solution algorithms applied to Solving systems of linear algebraic
equations
7. Develop and implement numerical solution algorithms applied to Curve fitting and Interpolation
techniques
8. Develop and implement numerical solution algorithms applied to Numerical differentiation of data and
functions
9. Develop and implement numerical solution algorithms applied to Numerical integration of data and
functions
10. Develop and implement numerical solution algorithms applied to Solutions of ordinary differential
equations including: (i) Initial value problems (ii) Boundary value problems (iii) Systems of equations
11. Develop and implement numerical solution algorithms applied to Solution of partial differential
equations using finite difference methods Initial value problems (ii) Boundary value problems (iii)
Systems of equations
Did you know that the University of Alberta has various low-to-no-cost services to help students succeed? Visit
http://www.deanofstudents.ualberta.ca/ for information about the academic, wellness, and various other support
services available to U of A students. It's never too early or too late to seek help!
CH E 374 - Winter 2018 Page 3
DEPARTMENT OF CHEMICAL & MATERIALS ENGINEERING
CH E 374 – Computational Methods in Engineering
Course Outline
The main objective of the course is to introduce and develop various computational
algorithms for solving a range of engineering problems using digital computers.
1. Introduction
Course organization, objectives.
Mathematical modeling and engineering problem solving.
Software tools: matlab
2. Solution of a single nonlinear algebraic equation
Graphical method and the incremental search.
Bisection method.
Regula falsi method.
Newton’s method, Secant method and Muller’s method.
Fixed point iteration.
3. Solving systems of linear and nonlinear algebraic equations
Direct methods - elimination, LU decomposition, tridiagonal systems.
Iterative methods- Gauss Seidel, Jacobi, Successive relaxation
Systems of nonlinear equations.
4. Numerical interpolation & curve fitting
Curve fitting.
Regression analysis
Interpolation : Lagrange and Newtons method of divided differences
5. Numerical integration and differentiation
Differentiation formulas.
Trapezoidal rule, Simpson’s rule.
Gaussian quadrature. Splines.
6. Numerical solution of ordinary differential equations
Initial Value Problems (IVP) - Runge-Kutta methods, multistep methods.
Systems of equations.
Boundary Value Problems (BVP) - Shooting method, difference methods.
7. Numerical solution of partial differential equations
Classification of partial differential equations.
Difference methods for elliptic equations.
Difference methods for parabolic equations.
CH E 374 - Winter 2018 Page 4
