Outline

Overview

Objectives: To provide students with the necessary skills to use commercial Computational Fluid Dynamics packages and to carry out research in the area of Computational Fluid Dynamics. Expected outcomes: Students will have a good understanding of the basic theory of Computational Fluid Dynamics, including discretisation, accuracy and stability. They will be capable of writing a simple solver and using a sophisticated commercial CFD package. Syllabus summary: A course of lectures, tutorials and laboratories designed to provide the student with the necessary tools for using a sophisticated commercial CFD package. A set of laboratory tasks will take the student through a series of increasingly complex flow simulations, requiring an understanding of the basic theory of computational fluid dynamics (CFD). The laboratory tasks will be complemented by a series of lectures in which the basic theory is covered, including: governing equations; finite difference methods, accuracy and stability for the advection/diffusion equation; direct and iterative solution techniques; solution of the full Navier-Stokes equations; turbulent flow; Cartesian tensors; turbulence models.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	(AMME2000 and MECH3261) or (AMME2000 and AERO3260) or ENGG5202 or MECH8261
Corequisites ?	None
Prohibitions ?	AMME8202
Assumed knowledge ?	Partial differential equations; Finite difference methods; Taylor series; Basic fluid mechanics including pressure, velocity, boundary layers, separated and recirculating flows. Basic computer programming skills;
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Steven Armfield, steven.armfield@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Contribution	Lab attendance/completion Lab attendance/completion	8%	Multiple weeks	Each Lab 2 hours	AI allowed
Outcomes assessed:
Written work	Assignment 1 Assignment	15%	Week 05 Due date: 27 Mar 2026 at 23:59 Closing date: 10 Apr 2026	20 pages maximum	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Quiz 1 Quiz	10%	Week 06 Due date: 31 Mar 2026 at 17:00 Closing date: 31 Mar 2026	1 hour	AI prohibited
Outcomes assessed:
Written work	Lab report 1 Lab report	4%	Week 07 Due date: 17 Apr 2026 at 23:59 Closing date: 01 May 2026	10 pages maximum	AI allowed
Outcomes assessed:
Written work	Lab report 2 Lab report	4%	Week 09 Due date: 01 May 2026 at 23:59 Closing date: 15 May 2026	10 pages maximum	AI allowed
Outcomes assessed:
Written work	Assignment 2 Assignment	15%	Week 10 Due date: 08 May 2026 at 23:59 Closing date: 22 May 2026	20 pages maximum	AI allowed
Outcomes assessed:
Written work	Lab report 3 Lab report	4%	Week 11 Due date: 15 May 2026 at 23:59 Closing date: 29 May 2026	10 pages maximum	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Quiz 2 Quiz	10%	Week 12 Due date: 19 May 2026 at 17:00 Closing date: 19 May 2026	1 hour	AI prohibited
Outcomes assessed:
Written work	Major project Report	30%	Week 13 Due date: 29 May 2026 at 23:59 Closing date: 12 Jun 2026	25 pages maximum	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Assignments: Two assignments based on developing computer programs using MATLAB which are then used to solve fluid dynamics problems.
Lab reports: Three lab reports based on fluid dynamics simulations performed using a commercial software package (ANSYS).
Quizzes: Two quizzes based on computational fluid dynamics theory presented in lectures.
Major project: Group project based on theoretical analysis and numerical simulation of a complex fluid problem.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Explicit finite difference discretisation of diffusion equation; 2. Solution approach; 3. Navier-Stokes equations	Lecture (1 hr)
Week 02	1. Inversion; 2. Implicit finite difference discretisation of diffusion equation	Lecture (1 hr)
Week 02	1. Inversion; 2. Implicit finite difference discretisation of diffusion equation	Tutorial (1 hr)
Week 03	Accuracy and stability	Lecture (1 hr)
Week 03	Accuracy and stability	Tutorial (1 hr)
Week 04	Finite difference discretisation of the advection/diffusion equation	Lecture (1 hr)
Week 04	Finite difference discretisation of the advection/diffusion equation	Tutorial (1 hr)
Week 05	Accuracy stability of the advection diffusion equation	Lecture (1 hr)
Week 05	Accuracy stability of the advection diffusion equation	Tutorial (1 hr)
Week 06	1. Gauss-Seidel; 2. Alternating direction implicit; 3. Direct; 4. Jacobi	Lecture (1 hr)
Week 06	1. Gauss-Seidel; 2. Alternating direction implicit; 3. Direct; 4. Jacobi	Tutorial (1 hr)
Week 07	Finite volume method	Lecture (1 hr)
Week 07	Finite volume method	Tutorial (1 hr)
Week 08	1. Solution methods for the Navier-Stokes equations; 2. Projection; 3. MAC	Lecture (1 hr)
Week 08	1. Solution methods for the Navier-Stokes equations; 2. Projection; 3. MAC	Tutorial (1 hr)
Week 09	1. Boundary conditions for pressure; 2. Boundary conditions for velocity and scalars	Lecture (1 hr)
Week 09	1. Boundary conditions for pressure; 2. Boundary conditions for velocity and scalars	Tutorial (1 hr)
Week 10	1. Direct simulation; 2. Turbulent flow	Lecture (1 hr)
Week 10	1. Direct simulation; 2. Turbulent flow	Tutorial (1 hr)
Week 11	1. Mixing length; 2. Cartesian tensors; 3. Turbulence models	Lecture (1 hr)
Week 11	1. Mixing length; 2. Cartesian tensors; 3. Turbulence models	Tutorial (1 hr)
Week 12	1. Reynolds stress; 2. k-epsilon	Lecture (1 hr)
Week 12	1. Reynolds stress; 2. k-epsilon	Tutorial (1 hr)
Week 13	Large eddy simulation	Lecture (1 hr)
Week 13	Large eddy simulation	Tutorial (1 hr)

Attendance and class requirements

All lab classes must be attended and completed

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. write a consulting report
LO2. plan and manage a major group project
LO3. assess fluid mechanics problems commonly encountered in industrial and environmental settings, construct and apply computational models, determine critical control parameters and relate them to desired outcomes and write reports
LO4. use a state of the art commercial computational fluid dynamics package
LO5. write a basic Navier-Stokes solver and to assess the stability, accuracy and convergence of Navier-Stokes solvers.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Overall the the unit was received positively with only one comment requesting a slower pace in the lectures. Next year I will look at dropping some of the earlier material already covered in previous courses which will allow a slower pace for the more advanced material.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

AMME5202: Computational Fluid Dynamics

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links