ENG1014 · Engineering numerical analysis

Overview

This unit introduces engineering computing fundamentals in the contexts of data analysis and modelling of physical systems. The Python programming language will be introduced, including techniques for importing/exporting data and communicating results from computational analysis. Programming structures such as loops, conditional statements and functions will be introduced. You will use programming to examine a range of complex engineering systems, including performing basic analysis on large data sets and modelling systems that do not exhibit analytical solutions. Problems related to these systems will be solved using various numerical methods for linear regression, root-fiding, numerical integration and numerical solutions to ordinary differential equations. The underlying assumptions, complexities and uncertainties associated with the models and numerical methods will be emphasised.

Areas of study: E3001 Bachelor of Engineering (Honours)

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 50% Final assessment: 50% This unit contains threshold hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.

Requisites

Learning outcomes

1. Identify appropriate programming structures and functions to solve simple computational tasks.
2. Discern mathematical and computational information in prose descriptions of diverse engineering problems, and incorporate this into computed solutions.
3. Select and use appropriate programming structures and functions to solve engineering problems.
4. Select and use appropriate text or graphical formats to communicate the solutions to engineering problems.
5. Describe uncertainties and errors associated with numerical models and methods and their implications on the computed results.

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

Student experience, not official rules. Nothing here changes what the Handbook says.