Outline

Overview

This course is designed to provide students with the necessary tools for mathematically modelling and solving problems in engineering. Engineering methods will be considered for a range of canonical problems including; Conduction heat transfer in one and two dimensions, vibration, stress and deflection analysis, convection and stability problems. The focus will be on real problems, deriving analytical solutions via separation of variables; Fourier series and Fourier transforms; Laplace transforms; scaling and solving numerically using finite differences, finite element and finite volume approaches.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	{(MATH1X61 or MATH1971) or [(MATH1X21 or MATH1931) and MATH1X02]} and [(MATH1X62 or MATH1972) or (MATH1X23 or MATH1933)] and (ENGG1801 or ENGG1810 or INFO1X10 or DATA1X02)
Corequisites ?	None
Prohibitions ?	BMET2960
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Xiaofeng Wu, xiaofeng.wu@sydney.edu.au
Lecturer(s)	Xiaofeng Wu, xiaofeng.wu@sydney.edu.au
Lecturer(s)	Andre Kyme, andre.kyme@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Exam Written exam in-person Note: Minimum 40% required in exam to pass the course	30%	Formal exam period	2 hours
Outcomes assessed:
Small test	Mid-Semester Quiz 1 In-class test	10%	Week 04 Due date: 20 Mar 2025 at 12:55	50 mins
Outcomes assessed:
Online task	Early Feedback Task Online Quiz #earlyfeedbacktask	1%	Week 04 Due date: 18 Mar 2025 at 09:00	Unlimited attempts until deadline
Outcomes assessed:
Assignment	Assignment 1 Individual assignment based on analytical and computational skills.	15%	Week 06 Due date: 04 Apr 2025 at 23:59	Approx. 10 pages written calc/discussion
Outcomes assessed:
Small test	Mid-Semester Quiz 2 In-class test	10%	Week 10 Due date: 08 May 2025 at 12:55	50 mins
Outcomes assessed:
Assignment	Assignment 2 Individual assignment based on analytical and computational skills.	15%	Week 12 Due date: 23 May 2025 at 23:59	Approx. 10 pages written calc/discussion
Outcomes assessed:
Online task	Weekly pre-work Online assessment	4%	Weekly	n/a
Outcomes assessed:
Online task	Tutorial assessment Computational task submitted via Matlab Grader (wks 2-13, due Tue 9 am) NOTE: Matlab Grader submissions are only considered if the student attended the corresponding weekly tutorial.	15%	Weekly	n/a
Outcomes assessed:
= hurdle task ? = AI allowed ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Assignment 1 (15%): Analytical and numerical solution of the heat equation. Generative AI use for this task is only permitted as per the AMME2000/BMET2960 Generative AI policy available on Canvas.
Assignment 2 (15%): Analytical and numerical solution of the heat and/or wave and/or Laplace equations. Generative AI use for this task is only permitted as per the AMME2000/BMET2960 Generative AI policy available on Canvas.
Quiz 1 (10%): Material in Sections 1 and 2 of the Lecture Slides. Quiz 1 is strictly in-person, on-campus.
Quiz 2 (10%): Analytical solutions to the heat, wave, Laplace equations, integrals and transforms. Quiz 2 is strictly in-person, on-campus.
Weekly pre-lecture quizzes (5% - includes 1% for the Early Feedback Task in Week 4): A short weekly online quiz based on the pre-lecture work for the week, to be completed prior to the lectures that week. Students have unlimited attempts up until the deadline each week. Students may use Generative AI to help them complete this task.
Tutorial assessment (15%): The MATLAB Grader tutorial assessments follow the tutorials and must be completed online by 9 am Tuesday of the following week. The task associated with the Week 1 tutorial is not assessed. Each of the 11 exercises from Weeks 2-12 is scored as 1, 0 (or partial) and the best 10 scores are used to compute the final score (/15%). A student successfully completing 10 or 11 of the tutorial exercises from Week 2 onwards will gain the full 15%. Generative AI use for this task is only permitted as per the AMME2000/BMET2960 Generative AI policy available on Canvas. Note that a student's MATLAB Grader submission for a given week will only be considered for marking if they attended the relevant tutorial in person.
Final exam (30%): 2-hour exam. A minimum mark of 40% is required in the Final Exam to pass the course.

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) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% per day late for assignments

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.

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 2023 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 2023. 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
Multiple weeks	6 hours of independent study required per week to ensure that the student is up to speed with lecture materials and completing tutorial work	Independent study (78 hr)
Week 01	1. Introduction to the UoS; 2. Introduction to numerical methods; 3. Discretisation; 4. Interpolation; 5. Least squares; 6. Cubic Splines; 7. Taylor series; 8. Finite differences	Lecture and tutorial (4 hr)
Week 02	1. What is a PDE?; 2. Generic PDE introduction inc. classification; 3. Derivation of the heat diffusion equation; 4. Exact solution of the heat diffusion equation (Fourier series); 5. Solution of heat equation via separation of variables; 6. Heat equation with non-homogeneous boundary conditions	Lecture and tutorial (4 hr)
Week 03	1. Initial value problems, boundary value problems, initial conditions, boundary conditions, well posed problems; 2. Accuracy, stability, consistency; 3. Linear algebra;	Lecture and tutorial (4 hr)
Week 04	Forward time centred space solution of the heat diffusion equation.	Lecture and tutorial (4 hr)
Week 05	1. Heat equation with more complex initial and boundary conditions; 2. Introduction to and derivation of the wave equation; 3. Classification of wave-like equations; 4. Approximate solution using Fourier series	Lecture and tutorial (4 hr)
Week 06	1. Wave equation with complex initial conditions; 2. Numerical solution of the wave equation.	Lecture and tutorial (4 hr)
Week 07	1. Introduction and derivation of the Laplace and Poisson equation; 2. Applications; 3. Exact solution based on Fourier series. 4. Numerical discretization of the 2D Laplace equation; 5. Solution using iterative methods;	Lecture and tutorial (4 hr)
Week 08	1. Revision; 2. Fourier integrals and transforms;	Lecture and tutorial (4 hr)
Week 09	1. Fourier integral solutions to infinite problems; 2. Introduction to the FFT and signal processing.	Lecture and tutorial (4 hr)
Week 10	1. Introduction to the Laplace transform.	Lecture and tutorial (4 hr)
Week 11	1. Solution of the semi-infinite wave equation using Laplace transforms; 2. Laplace transform solution to the heat equation.	Lecture and tutorial (4 hr)
Week 12	1. Introduction to finite element analysis and applications; 2. Examples.	Lecture and tutorial (4 hr)
Week 13	Revision	Lecture (4 hr)

Attendance and class requirements

Attendance at lectures is each student's responsibility - marks are not allocated for lecture attendance. However, given how important tutorials are for understanding and applying the course material and helping with assignments, in-person attendance at the weekly tutorial is a requirement in order for the corresponding weekly MATLAB Grader submission to be considered. Statistics from previous years show a strong correlation between tutorial non-attendance and performing poorly/failing the course. Linking tutorial attendance to eligibility of MATLAB Grader submissions is therefore designed to achieve best learning.

All lectures are recorded and available for viewing after the lecture. No tutorials are recorded.

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.

Required readings

The recommended text for this UoS is:

Advanced Engineering Mathematics, E. Kreyszig, 10th Edition, Wiley, 2011.
(Optional for finite element analysis) Spectral/hp Element Methods for CFD, G. Karniadakis & S. J. Sherwin, 2nd Edition, OUP Oxford, 2005.

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. understand and apply the physical relations and mathematical modelling of fundamental problems in engineering structures, fluid mechanics and heat and mass transfer.
LO2. creatively solve assignment problems, which focus on real-life engineering challenges
LO3. have developed proficiency in a structured approach to engineering problem identification, modelling and solution; develop proficiency in translating a written problem into a set of algorithmic steps, and then into computer code to obtain a solution

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.

Several course adjustments will be made in 2025 based on constructive feedback from students and course evaluation by the teaching team. These adjustments include: adjustment of the assessment profile to increase the weighting of the MATLAB Grader (tutorial) tasks to 15%; minimum 40% requirement for the Final Exam; simplified finite element analysis component in Week 12. In-person tutorial attendance is a requirement for a student's tutorial assessment (MATLAB Grader submission) to be considered.

Additional information

AMME2000/BMET2960/BMET9960 has a Generative AI Policy outlining how these technologies can and cannot be used in these courses. Students must familiarise themselves with this policy and abide by it for all assessment tasks. Each of the Assignments requires submission of a Generative AI Use Statement, regardless of whether these technologies were used or not. Full details are available on our Canvas site.

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

Early feedback task

Assessment summary

Assessment criteria

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

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

AMME2000: Engineering Analysis

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

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

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect