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 ?	None
Corequisites ?	None
Prohibitions ?	AMME5202
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	No

Teaching staff

Coordinator	Steven Armfield, steven.armfield@sydney.edu.au
Lecturer(s)	Steven Armfield, steven.armfield@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Lab attendance/completion Lab attendance/completion	8%	Multiple weeks	n/a
Outcomes assessed:
Tutorial quiz	Quiz 1 Quiz	10%	Week 06	n/a
Outcomes assessed:
Assignment	Lab report 1 Lab report	4%	Week 06	n/a
Outcomes assessed:
Assignment	Assignment 1 Assignment	15%	Week 07	n/a
Outcomes assessed:
Assignment	Lab report 2 Lab report	4%	Week 09	n/a
Outcomes assessed:
Assignment	Assignment 2 Assignment	15%	Week 10	n/a
Outcomes assessed:
Assignment	Lab report 3 Lab report	4%	Week 11	n/a
Outcomes assessed:
Tutorial quiz	Quiz 2 Quiz	10%	Week 12	n/a
Outcomes assessed:
Assignment	Major project Report	30%	Week 13	n/a
Outcomes assessed:
= 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

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.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

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

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.

This should be a clone of AMME5202, entered by hand owing to brain dead nature of the system.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

AMME8202: Computational Fluid Dynamics

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect