Dr Travis Mitchell
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Mech & Mine Engineering School
This course provides an introduction to the development and application of numerical methods to resolve common challenges faced in mechanical engineering. It builds an important foundation of knowledge in programming, numerical algorithms, and simulation techniques that is essential for modern day engineers. The course starts with numerical approaches for interpolation, differentiation and quadrature before introducing direct and iterative approaches to solve linear systems. Solution techniques for ordinary differential equations (ODEs) and non-linear equations are formulated and techniques for data fitting and optimisation (inc. least-squares approaches) are developed and applied to engineering problems. The final module of the course formulates computational techniques to solve partial differential equations (parabolic, hyperbolic, and elliptic) with numerous applications involving heat transfer, convection-diffusion, and wave propagation. Completion of this course will provide students with the necessary skills to develop computational approaches to address a wide range of engineering problems.
Course requirements
Assumed background
First-year mathematics; basic mechanics; Python programming.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(ENGG1001 or CSSE1001) and (MATH1051 or MATH1071)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
(MATH1052 or MATH1072)
Course staff
Course coordinator
Lecturer
Dr Travis Mitchell
Dr Nick Gibbons
Professor Vincent Wheatley
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Content delivery in MECH2700 is via a 2 hour Lecture. The 3rd hour of content per week is a Contact where we work through the complete engineering problem solving process using numerical methods. You will put your learning into practice each week with a two hour Computer (or Bring Your Own Device) Laboratory Session. You only need to attend one laboratory (I01..I07) session per week and must enrol in a particular session. Please note that these lab sessions will start in Week 2.
ﾠ
Aims and outcomes
This course aims to continue the development of a student's capability in the mathematical formulation of engineering systems and problems, the application of computers to the numerical analysis of these mathematical models, and the interpretation and reporting of that analysis.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Formulate mathematical models of systems and problems encountered in engineering practice and theory.
LO2.
Construct mathematical models from approximate descriptions of systems and problems encountered in engineering practice and theory.
LO3.
Select or develop numerical methods to analyse the behaviour of mathematical models for engineering systems and problems.
LO4.
Select or develop numerical methods to effectively analyse engineering data.
LO5.
Compose computer programs to implement numerical methods and understand basic software development, testing and maintenance practices in a Python environment.
LO6.
Interpret the results of a numerical analysis in terms of the behaviour of the physical system or problem that is being studied.
LO7.
Evaluate the performance of a numerical analysis in terms of the theoretical accuracy, convergence, sensitivity to inputs and computational efficiency.
LO8.
Formally and succinctly report on the results of a numerical analysis in written and graphical format.
LO9.
Understand the language and notation used in the numerical analysis literature and be able to apply new approaches to study mathematical models.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Tutorial/ Problem Set | Weekly Applied Classes | 10% |
Due weekly at 4 pm Friday from Week 2 |
| Computer Code, Paper/ Report/ Annotation | Assignment 1 | 15% |
12/09/2025 4:00 pm |
| Computer Code, Paper/ Report/ Annotation | Assignment 2 | 15% |
31/10/2025 4:00 pm |
| Examination |
End of Semester Examination
|
60% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Weekly Applied Classes
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 10%
- Due date
Due weekly at 4 pm Friday from Week 2
Task description
Short weekly theoretical and/or coding tasks to apply the analysis techniques discussed in lectures to engineering problems. Each weekly task is due at 4pm Friday of the given week.
Please refer to Blackboard for a detailed marking criteria.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submission for the programming task will be through Gradescope, where an autograder will be applied. As such, strict naming conventions for files and functions are specified in each Exercise sheet. The theoretical task will be submitted through a multi-choice/short-form quiz on Blackboard.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
To accommodate unforeseen circustances such as illness, the best 10 of 12 submissions are used for this assessment task and timely feedback needs to be provided to students.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
The best 10 of 12 submission are used for this assessment task and timely feedback needs to be provided to students, late submissions will not be accepted.
Assignment 1
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 15%
- Due date
12/09/2025 4:00 pm
Task description
An exercise in modelling and programming that will require submission to be described on Blackboard. You are to do this exercise individually and you are welcome to ask questions in the computer lab (ICT) sessions.
Please refer to Blackboard for a detailed marking criteria.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Your assignment should be submitted as a single PDF document plus a set of Python source code files that can be run for verification. The source code should include a README file that describes how your code can be executed to produce your submitted results. A link for submission will be provided on BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Feedback is provided to students following 14 calendar days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 2
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 15%
- Due date
31/10/2025 4:00 pm
Task description
A larger exercise in modelling and programming that will require submission as to be detailed on Blackboard. You are to do this exercise in pairs (or individually) and you are welcome to ask questions in the computer lab (ICT) sessions.
If, for whatever reason, you find that your group is not functioning effectively, please contact your Course Coordinator for support.
Please refer to Blackboard for a detailed marking criteria.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Your assignment should be submitted as a single PDF document plus a set of Python source code files that can be run for verification. The source code should include a README file that describes how your code can be executed to produce your submitted results. A link for submission will be provided on BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Feedback is provided to students following 14 calendar days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
End of Semester Examination
- Hurdle
- Identity Verified
- Mode
- Written
- Category
- Examination
- Weight
- 60%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
Task description
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
Identity verified assessment (IVA)ﾠwill be through obtaining ﾠat least 45% of the available marks ﾠin the final exam. You need to pass the IVA hurdle to pass the course regardless of your final mark. Students who achieve a total mark of 50 or greater but do not pass the IVA hurdle will receive a grade of 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0.00 - 29.99 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Conditions for a Grade of 2 not satisfied. |
| 2 (Fail) | 30.00 - 44.99 |
Minimal evidence of achievement of course learning outcomes. Course grade description: MARK >= 30% (Please refer to additional information below) |
| 3 (Marginal Fail) | 45.00 - 49.99 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: MARK >= 45% and EXAM >= 40% (Please refer to additional information below) |
| 4 (Pass) | 50.00 - 64.99 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: MARK >= 50% and EXAM >= 45% (Please refer to additional information below) |
| 5 (Credit) | 65.00 - 74.99 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: MARK >= 65% and EXAM >= 60% (Please refer to additional information below) |
| 6 (Distinction) | 75.00 - 84.99 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: MARK >= 75% and EXAM >= 70% (Please refer to additional information below) |
| 7 (High Distinction) | 85.00 - 100.00 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: MARK >= 85% and EXAM >= 80% (Please refer to additional information below) |
Additional course grading information
Grading Criteria
The overall mark (MARK) will be determined as the weighted sum of grades awarded for each assessment item and used to calculate your final grade as per the criteria specified in the table above.
At the discretion of the course coordinator, final grades and marks may be scaled upwards, but not downwards.
Identity verified assessment.
Identity verified assessment (IVA)ﾠwill be through obtainingﾠ the specified grade hurdles based on the End of Semester Examination (EXAM). You need to obtain both the overall MARK and specified IVA hurdle to achieve each grade as specified in the table above.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Prior Course Attempts:
Students will not be given exemptions, or partial credit from any previous attempt of this course, for any piece of assessment. You must complete all of the learning activities and assessment items each time you take a course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Everything that you need to set up a good Python environment for engineering computation is freely available on the Internet.ﾠIf you are running a Windows system, a good system to install is the Anaconda distribution from Continuum.ﾠ If you run another system, install Python, numpy, matplotlib, Scipy, to get about the same environment. ﾠNote that you must install binary packages that are compatible with the particular version of Python that you choose.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lectures - Modelling and Numerical Methods This Learning Activity will cover the following topics: Week 1 - Course introduction. Problem solving; A numerical approach. Floating-point numbers and error. Week 2 - Interpolation (High-order polynomials, Lagrange basis functions, Chebyshev points). Good programming practices. Week 3 - Numerical differentiation (Finite difference formulas and associated errors). Week 4 - Numerical integration (Newton-Cotes formulas-Trapezoidal rule, Simpson's 1/3 rule, etc., Composite quadrature). Week 5 - Direct solutions to linear systems (Gauss-Jordan elimination, LU decomposition). Week 6 - Iterative solutions to linear systems (Jacobi, Gauss-Seidel, Relaxation methods, Least-Squares approach for over-constrained systems). Week 7 - Numerical solutions to ODEs (Euler's, Modified Euler's, Runge-Kutta, Runge-Kutta-Fehlberg, Systems of Equations). Week 8 - Solving non-linear equations (Bisection, Fixed-Point Iteration, Gradient-based methods). Week 9 - Data fitting and optimisation (Least-squares, Chebyshev polynomials, Golden search, Bracketing). Week 10 - Intro to numerical solutions of PDEs and Parabolic PDEs (Classifications, finite difference schemes, diffusion problems). Week 11 - Parabolic PDEs conti. and Hyperbolic PDEs (Boundary conditions, stability of parabolic PDE solutions, Wave equation). Week 12 - Nonlinear Hyperbolic PDEs and Elliptic PDEs (Euler equations, inviscid Burgers equation, shocks, boundary value problems, solution approaches). Week 13 - Semester review and feedback. |
Multiple weeks From Week 1 To Week 12 |
General contact hours |
Contact - Weekly interactive problem-solving Weekly workshops that begin with a physical engineering problem and work through the entire problem-solving process from defining the problem, to coding up the solution and interpreting the results, getting input from the class at every stage. |
Multiple weeks From Week 2 To Week 13 |
Applied Class |
Computer Lab Sessions To achieve the learning objectives of the course, you will need to work through weekly problem sets on solving engineering problems using numerical methods. This involves writing and executing code and interpreting the results. In order to do this at a time when applied class assistance is available, you will need to attend one of the lab sessions that will be held each week. Sessions ICT01-07 will be held in computer labs with the relevant course software. Please note ICT08-09 are designed in Bring Your Own Device rooms so students will need a device able to run Python code to make the most of these sessions. |
Additional learning activity information
1. Lectures: During lectures, key concepts will be presented together with simple examples of application. Lecture content can be used as a guide to further independent reading and study.
2. Contacts: These workshops begin with a physical engineering problem and work through the entire problem-solving process from defining the problem to coding up the solution and interpreting the results, getting input from the class at every stage.
3. Applied class exercises and assignments: Application of the principles discussed in lectures to a collection of small and large exercises. The smaller exercises and first assignment are an opportunity for independent study while the second assignment will be an opportunity for small-group work.
4. Computer Lab Sessions: These sessions give the student the opportunity to implement numerical models as part of the assigned applied class exercises. The computers in the laboratory are equipped with the required software or the required software is freely downloadable if the Bring Your Own Device option is preferred. For these sessions, Casual Academics will be present, however, please take the opportunity to try the exercises yourself before asking for help.
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.