Engineering computational methods

Computational and Experimental Methods

Engineering computational methods is a 1st semester course held at University of Leeds, England.


1. semester - University of Leeds, UK


Daniel Ruprecht

Course Points (ECTS)


Contact Information

University of Leeds, UK

Course Overview

Module objectives

On successful completion of this module, students should understand the basic concepts of computational and experimental methods. In order to fulfil this goal, the module is divided into three sections. The first enables the student to select a suitable numerical algorithm and develop a computer code in Matlab implementing it. The second section concentrates on achieving a basic understanding of a variety of traditional and advanced laser diagnostic techniques for the measurement of parameters that include pressure, temperature, velocity, species concentration, stress and vibration. The third section addresses computerised data acquisition and signal processing techniques and, therefore, provides the link between sections one and two.

Outline syllabus

  • The role of approximations in engineering computation, introduction to Matlab and programming in Matlab M code: data types, assignments, constants, logical expressions, decisions and loops.
  • Numerical solution of ordinary differential equations using Matlab functions and via numerical programming methods.
  • Classification of partial differential equations into parabolic, elliptic and hyperbolic type.
  • Numerical methods for solving engineering examples of parabolic, elliptic and hyperbolic equations, signal processing using Matlab.
  • Transducers and optical techniques; temperature, pressure, velocity, species concentration, vibration, stress and strain.
  • Lasers and laser diagnostics.
  • Automotive and energy applications.
  • Computer-based data acquisition: analogue interfacing basics, PC data acquisition boards, analogue to digital and digital to analogue conversion, digital i/o and counters/timers.
  • Frequency analysis: frequency content of signals, Fourier series, Fourier transform and frequency spectrum, discrete Fourier transform, sample rate and aliasing.
  • Digital filtering: transform function, first and second order, Bode plots digital filters, difference equation, discretising continuous-time filters.

Monitoring of student progress:

  • Continuous assessment in computing practical classes.
  • Coursework (experimental methods, assignment, computing assignment 50%)