(97f) Scientific Computing for Chemical Engineers in Python

One of the most important “tools for modern engineering practice” is the ability to write computer programs. It is increasingly essential that our students learn how to code, both for their potential for future chemical engineering practice or to give them the skills they need to pursue a job in developing software.

In my required junior-level course, students learn numerical methods by applying them to chemical engineering problems in material and energy balances, thermodynamics, fluid flow, heat transfer, separations, and chemical reactor analysis. Topics include floating-point calculations and round-off error, algorithms and convergence, finding roots by bisection or Newton’s method, exact curve fitting, interpolation, extrapolation, numerical integration and differentiation, numerical solution of initial value problems, stiffness, matrices and determinants, matrix properties, special matrices, methods of solution for systems of linear equations using matrices, eigenvalues, eigenvectors, solving systems of non-linear equations, and their applications to unit operations.

The course was previously taught in Matlab but is now in Python for the Spring 2019 semester. I will discuss the content of the course, some example problems, the difficulties in transitioning the course from one language to another, and preliminary results on year-over-year student performance.