ChE 541 Syllabus


LSB 2004


Monday, Wednesday, Friday: 10:00-10:50 AM


ChEn 376 (heat transfer), Math 303


The course website is


David O. Lignell
330T Engineering Building
Office hours (general): T, W, Th 4:00-5:00 pm



This course provides an introduction to the principles and applications of numerical methods as applied to typical engineering problems. The field of numerical methods is very broad and includes more material than can be covered in one semester. Chemical engineering deals extensively with transient and/or spatially developing processes as described by differential equations. The course will emphasize the practical numerical solution of differential equations including initial and boundary value ordinary differential equations, as well as partial differential equations. Solution of linear and nonlinear systems of equations, integration, interpolation, and theoretical analysis of numerical methods will be covered.

Solution of problems with numerical methods implies computer programming. Students are expected to have some familiarity with programing. Any language may be used. However, Python is preferred. Matlab or Julia are also acceptable. In some cases, Python and Matlab templates will be provided, and if a student uses a different language, the student will need to port the template over to the chosen language. Python, Matlab, and Julia are available on all operating systems, and include a wide array of built-in functionality, solvers, and visualization capabilities. Python and Julia are free. The Jupyter Notebook or Jupyter Lab are the recommended Python interfaces.


The textbook for this class is Numerical Methods for Engineers and Scientists by Joe. D. Hoffman, 2nd edition, CRC Press, 2001. There are many other good books, including the following

  • Numerical Methods by Germund Dahlquist and Ake Bjorck. Dover Books on Mathematics, 2003.
  • Numerical Recipes by Press et al., 3rd edition, Cambridge University Press, 2007.

Other references are listed on the course webpage.


  • Homework will be turned in on Learning Suite as a single file.
  • Grades will be posted on Learning Suite.
  • All students must complete their own assignment reflecting their own work, but group work is allowed.
  • You may not refer to any solutions from previous semesters.
  • There will be around ten assignments.
  • Assignments are due at 10:00 pm on the due date.
    • Submissions after 11:00 pm will be accepted up to one week late for half credit.


A final exam will be administered remotely as announced later in the semester. A passing score is required on the final in order to pass the class.


Grades for the course will be based on the following distribution:

  • Homework/Quizzes: 40%
  • Midterms/Projects: 40%
  • Final exam: 20%

Grade scale: A ≥ 95%; A- ≥ 90%; B+ ≥ 87%; B ≥ 83%; B- ≥ 80%; etc.

Learning outcomes

  • Students will apply computational techniques to solve a wide range of numerical problems arising in engineering.
  • Students will become proficient in numerical computer programming.
  • Students will learn theory, algorithms, implementation, and analysis of output for numerical methods such as the following:
    • round off and truncation errors,
    • stability and convergence criteria,
    • direct and iterative solution of linear systems of equations,
    • solution of nonlinear algebraic systems of equations,
    • solution linear and nonlinear systems of initial and boundary value ordinary differential equations,
    • classification and characteristics of partial differential equations,
    • solution of elliptic, parabolic, and hyperbolic partial differential equations using finite difference and finite volume methods,
    • numerical integration and interpolation of functions.

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