Computational Modelling of Mechanical Systems
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Prerequisites
NEF1201 - Engineering Mathematics 2; and
NEF1003 - Engineering Dynamics; or
NEF1102 - Engineering Physics 1
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
In this unit, you will discover how computational modelling transforms the way engineers design, test, and optimise mechanical systems. You will build and validate numerical models that reveal how structures and machines behave under real-world conditions, applying statistical, frequency-domain, and simulation techniques to solve complex engineering challenges.
By integrating data analysis, programming, and sustainability principles, you will learn how digital tools can enhance performance prediction, reduce material waste, and accelerate innovation. Through practical modelling projects and simulations, you will develop the confidence to translate data into meaningful design insights and communicate results that drive smarter, more sustainable engineering decisions.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Develop and apply computer-based models to solve complex mechanical engineering problems across varied contexts;
- Evaluate statistical and computational analyses to quantify risk and evaluate sustainability impacts in engineering processes;
- Apply frequency-domain analysis to characterise and interpret mechanical system responses from synthesised and measured data;
- Solve ordinary differential equations numerically to evaluate and validate mechanical models for design and testing; and
- Communicate and interpret modelling results through clear, evidence-based reports and digital presentations.
Assessment
For Melbourne campuses
Assessment type: Exercise | Grade: 15%
In-class coding and debugging challenge (Individual) (45 mins)
Assessment type: Portfolio | Grade: 35%
Structured portfolio of weekly computational modelling and data-analysis tasks (Individual) (2000 words)
Assessment type: Test | Grade: 50%
Problem-solving task applying modelling, validation, and interpretation principles (Individual) (90 mins)
Exercise: This secure in-class assessment ensures authenticity by requiring students to apply programming and debugging skills to unseen problems under supervised conditions. Real-time reasoning and unpredictable coding challenges verify student understanding.
Portfolio: Structured weekly portfolio of computational modelling and analysis tasks with progressive feedback and validation discussions. Students demonstrate the ability to develop, test, and refine models while explaining results through annotated code, outputs, and reflections.
Test: This secure invigilated test ensures authenticity by requiring students to solve unseen analytical and computational problems using modelling and validation principles. Unpredictable problem sets confirm individual capability and independent understanding.
Required reading
Required readings will be made available on VU Collaborate.
As part of a course
This unit is studied as part of the following course(s):