Note: This unit version is currently under review and is subject to change!
AMME5060: Advanced Computational Engineering (2019 - Semester 2)
Unit: | AMME5060: Advanced Computational Engineering (6 CP) |
Mode: | Normal-Day |
On Offer: | Yes |
Level: | Postgraduate |
Faculty/School: | School of Aerospace, Mechanical & Mechatronic Engineering |
Unit Coordinator/s: |
Dr Williamson, Nicholas
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Session options: | Semester 2 |
Versions for this Unit: |
Campus: | Camperdown/Darlington |
Pre-Requisites: | None. |
Brief Handbook Description: | 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, compact finite difference schemes, numerical dispersion and diffusion and efficient linear solvers. Students will develop to skills and confidence to write their own computational software. Applications in fluid and solid mechanics will be covered. |
Assumed Knowledge: | Linear algebra, calculus and partial differential equations and be familiar with Taylor series, the finite difference method, the finite element method (linear, quadratic elements), numerical stability, accuracy, direct and iterative linear solvers and be able to write Matlab Scripts to solve problems using these methods. Recommend AMME3060 or similar course. |
Timetable: | AMME5060 Timetable |
T&L Activities: | Independent Study: Approximately 5 hours per week of independent study outside of scheduled hours are required to complete the course assessments. |
Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.
Attribute Development Method | Attribute Developed |
Students will become proficient in advanced numerical methods, their suitability and application to numerical modelling of engineering problems. | (1) Maths/ Science Methods and Tools (Level 5) |
Students will apply advanced numerical methods to a range of complex engineering problems through assignments and a large project. Students will be required to write their own software. | (2) Engineering/ IT Specialisation (Level 5) |
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. | (3) Problem Solving and Inventiveness (Level 4) |
Students will design numerical simulation software in small groups. The students will selection the underlying numerical method, choice of computation architecture and coding language. | (4) Design (Level 4) |
The 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. | (7) Project and Team Skills (Level 4) |
Students will have to engage with engineering standards for computational mechanics and ensure their testing of their software meets these standards. This includes appropriate benchmarking of solutions, professionally presenting these to clients and indicating the range of applicability for their solution. | (8) Professional Effectiveness and Ethical Conduct (Level 3) |
For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
(7) Project and Team Skills (Level 4)Assessment Methods: |
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Assessment Description: |
Quiz: Two quizzes will be set, each worth 15% of the total mark. These will be held in during the lecture slots. Assignments: Two individual short assignments each worth 10% of the total mark. Text-based similarity detecting software (Turnitin) will be used to detect plagiarism. Lab Assignments: Weekly lab assignments will be set worth a total of 10%. Weekly assignments are due by the following laboratory and will be marked in the laboratory session only. Some solutions will be provided 7 days after the due date. No submissions will be accepted 7 days after the due date. Major Project: One major design project worth 40% of the total mark will be set. This project will have a group work component however all students must submit individual reports focusing on their individual contributions. There may be statistically defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes. Assignments submitted after the due date will receive a 5% penalty per day. Assignments more than 10 days late receive 0. |
Prescribed Text/s: |
Note: Students are expected to have a personal copy of all books listed.
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Recommended Reference/s: |
Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
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Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
Week | Description |
Week 1 | Lecture: Introduction |
Lecture: Compiled Languages, Fortran, C | |
Week 2 | Lecture: High Performance Computing |
Lecture: Parallel Programing, MPI | |
Week 3 | Lecture: Parallel Linear Solvers |
Lecture: Efficient Coding | |
Week 4 | Lecture: Krylov Space Solvers |
Lecture: Krylov Space solvers | |
Week 5 | Lecture: Multigrid Solvers |
Lecture: Linear Solvers | |
Week 6 | Lecture: Spectral Methods |
Lecture: Spectral Methods | |
Assessment Due: Assignment 1 | |
Week 7 | Lecture: Quiz 1 |
Lecture: Numerical Dispersion | |
Assessment Due: Quiz 1 | |
Week 8 | Lecture: Numerical Dissipation and Dispersion |
Lecture: Higher Order Schemes | |
Week 9 | Lecture: Applications, CFD |
Week 10 | Lecture: Applications, hyperbolic equations |
Assessment Due: Assignment 2 | |
Week 11 | Lecture: Quiz 2 |
Lecture: Applications | |
Assessment Due: Quiz 2 | |
Week 12 | Lecture: Guest Lecture |
Lecture: Review | |
Week 13 | Project Presentations |
Assessment Due: Major Project |
Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
Attribute | Practiced | Assessed |
(7) Project and Team Skills (Level 4) | Yes | 6% |
(8) Professional Effectiveness and Ethical Conduct (Level 3) | Yes | 4% |
(4) Design (Level 4) | Yes | 8% |
(3) Problem Solving and Inventiveness (Level 4) | Yes | 4% |
(2) Engineering/ IT Specialisation (Level 5) | Yes | 23% |
(1) Maths/ Science Methods and Tools (Level 5) | Yes | 55% |
These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.