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Unit outline_

AMME5060: Advanced Computational Engineering

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

This unit will cover advanced numerical and computational methods within an engineering context. The context will include parallel coding using MPI, computational architecture, advanced numerical methods including spectral methods, finite difference schemes and efficient linear solvers including multi-grid solvers and Krylov subspace solvers. Students will develop to skills and confidence to write their own computational software. Applications in fluid and solid mechanics will be covered.

Unit details and rules

Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Linear algebra, calculus and partial differential equations, Taylor series, the finite difference and finite element methods, numerical stability, accuracy, direct and iterative linear solvers and be able to write Matlab Scripts to solve problems using these methods

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Nicholas Williamson, nicholas.williamson@sydney.edu.au
Lecturer(s) Nicholas Williamson, nicholas.williamson@sydney.edu.au
Type Description Weight Due Length
Small continuous assessment Mini-Assignment 1
Mini-assignment, submitted in canvas and demonstrated in the laboratories
4% Week 03
Due date: 19 Aug 2022 at 14:00

Closing date: 26 Aug 2022
less than 100lines of code
Outcomes assessed: LO5 LO6
Small continuous assessment Mini-assignment 2
Mini-assignment, submitted in canvas and demonstrated in the laboratories
4% Week 04
Due date: 26 Aug 2022 at 14:00

Closing date: 02 Sep 2022
less than 100lines of code
Outcomes assessed: LO3 LO6 LO5 LO4
Assignment Assignment 1
Written report, Code and datafiles
15% Week 06
Due date: 09 Sep 2022 at 23:00

Closing date: 30 Sep 2022
100 to 200 lines of code, 4-6pg report
Outcomes assessed: LO5 LO6
Tutorial quiz Quiz 1
Written quiz with calculations, coding, analysis (in tutorial time)
15% Week 07
Due date: 16 Sep 2022 at 14:00
1hr quiz, 4-6pages of writting
Outcomes assessed: LO5 LO6
Small continuous assessment Mini-assignment 3
Mini-assignment, submitted in canvas and demonstrated in the laboratories
4% Week 10
Due date: 14 Oct 2022 at 14:00

Closing date: 21 Oct 2022
less than 100lines of code
Outcomes assessed: LO3 LO6 LO5 LO4
Tutorial quiz Quiz 2
Written quiz with calculations, coding, analysis (in tutorial time)
15% Week 12
Due date: 28 Oct 2022 at 14:00
1hr quiz, 4-6pages of writting
Outcomes assessed: LO5 LO6
Assignment group assignment Major project
A group report, computer code and oral exam
43% Week 13
Due date: 04 Nov 2022 at 02:00

Closing date: 18 Nov 2022
20pg report, ~600lines code, 20min oral
Outcomes assessed: LO1 LO2 LO3 LO4 LO6
group assignment = group assignment ?

Assessment summary

Major Project: The major project is a group project which involves developing a computational engineering solver and using this to perform some analysis. The submission will include the computer code, a report containing the analysis and a group presentation. The presentation is followed by an oral exam in the form of a panel interview. Further individual oral examination of the submission may be required. The code and report cannot be graded until the oral exam and presentation are completed.

Quiz 1,2: Both quizzes are long answer quizzes conducted during tutorial time. The handwritten solutions must be handed in at the end of the quiz. Solutions may be posted after 24hrs. These quizzes must be attended as timetabled.

Mini-assignments 1 to 3: These assessments require submission of computer code through canvas and followed by live presentation in the timetabled Friday tutorial. The grading is based on both the submitted work and the discussion with the tutor in the laboratory session. Each mini-assignment must be submitted within 7 days of the due date. Solutions to the mini-assignments will be presented in the Friday tutorial 1 week following the deadline so submission after this point is impossible. If special consideration is given the mini-assignment assessment will be replaced by a re-weighting the next quiz. In this situation, mini-assignment 1,2 would be replaced by Quiz 1 and mini-assignment 3 will be replaced by Quiz 2.

Assignment 1:This assignment involves writing computer code, testing it and writing a technical report. The submission is through canvas.

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.

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:

standard late penalties apply

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.

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.

WK Topic Learning activity Learning outcomes
Multiple weeks Independent Study to prepare for classes and to work on assessments Independent study (90 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 01 1. Introduction; 2. Compiled languages, Fortran Lecture and tutorial (4 hr) LO2 LO3 LO4 LO5 LO6
Week 02 1. High performance computing; 2. Parallel programing, MPI Lecture and tutorial (4 hr) LO2 LO3 LO4 LO5 LO6
Week 03 1. MPI; 2. Basic Linear solvers Lecture and tutorial (4 hr) LO2 LO3 LO4 LO5 LO6
Week 04 Memory Management, Advanced MPI Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 05 1. Linear Solvers 2. Multigrid solver Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 06 Krylov Space Solvers Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 Quiz, Spectral Methods Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 Spectral Methods Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 09 1. Spectral Methods; 2. Job Schedulers Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 1. OpenMP Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 11 Efficient Programs, Makefiles, Review Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 12 Quiz, Project Support Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Project Support Lecture and tutorial (4 hr) LO1 LO2

Attendance and class requirements

Attendance in the laboratory sessions is compulsory and will be 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 following textbook can be accessed through the Library eReserve, available on Canvas.

  • Stephen J. Chapman, FORTRAN FOR SCIENTISTS & ENGINEERS (4). 9780073385891

The following text book is also a required:

  • Introduction to High Performance Computing for Scientists and Engineers by Georg Hager: ISBN 978-1439811924

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.

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

  • LO1. A major project will be undertaken in groups. Each member will have responsibilities for delivering these complex and technically demanding projects. Group members will have to work closely and understand all aspects of the project to deliver a successful software solution.
  • LO2. Students will have to engage with engineering standards for computational mechanics and ensure their testing of their software meets these standards. This includes appropriate bench-marking of solutions, professionally presenting these and indicating the range of applicability for their solution
  • LO3. Students will design numerical simulation software in small groups. The students will select the underlying numerical method, choice of computation architecture, coding language and design the code structure.
  • LO4. Students will be required to select the most appropriate numerical tools to solve engineering problems and how to represent these problems in a simulation. This requires and understanding of solution behaviour, what aspects of a problem are critical and what aspects can be simplified.
  • LO5. Students will apply advanced numerical methods to a range of complex engineering problems. Students will be required to write their own software.
  • LO6. Students will become proficient in advanced numerical methods, their suitability and application to numerical modelling of engineering problems.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Changes to online teaching.

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.