Outline

Overview

The unit will provide an avenue to apply the mathematics concurrently taught in year one, enhance computing skills and data analysis as well as furthering and cementing knowledge provided in AMME1705. The unit is aimed at providing an overview on conducting experiments, data acquisition and data analysis, thus expanding and providing further foundation on the faculty vision of large digital data analysis.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	(AERO1560 or ENGG1800) and (ENGG1810 or INFO1110) and (AMME1705 or ENGF1112) and AMME1802 and AERO1400
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Nicholas Lawson, nicholas.lawson@sydney.edu.au
Lecturer(s)	Nicholas Lawson, nicholas.lawson@sydney.edu.au
Tutor(s)	Nishanth Menakath, nishanth.menakath@sydney.edu.au
	Darren Rajeepan, darren.rajeepan@sydney.edu.au
	Caroline Hamilton Smith, caroline.hamiltonsmith@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Group Lab Report 2 Group report of Laboratory 2 data processing, analysis and presentation	16%	Mid-semester break Due date: 07 Apr 2024 at 23:59 Closing date: 31 May 2024	10 - 20 pages
Outcomes assessed:
Presentation	Tutorial Assessment 1 Group task set and data & method presented at end of each tutorial class	2.5%	Week 04 Due date: 15 Mar 2024 at 16:59 Closing date: 15 Mar 2024	up to 10 mins
Outcomes assessed:
Assignment	Individual Lab Report 1 Individual report of Lab 1 data processing, analysis and presentation	16%	Week 05 Due date: 24 Mar 2024 at 23:59 Closing date: 31 May 2024	10 - 20 pages
Outcomes assessed:
Presentation	Tutorial Assessment 2 Group task set and data & method presented at end of each tutorial class	2.5%	Week 06 Due date: 28 Mar 2024 at 14:59 Closing date: 28 Mar 2024	up to 10 mins
Outcomes assessed:
Presentation	Tutorial Assessment 3 Group task set and data & method presented at end of each tutorial class	2.5%	Week 08 Due date: 19 Apr 2024 at 16:59 Closing date: 19 Apr 2024	up to 10 mins
Outcomes assessed:
Assignment	Group Lab Report 3 Group report of Laboratory 3 data processing, analysis and presentation	16%	Week 09 Due date: 28 Apr 2024 at 23:59 Closing date: 31 May 2024	10 - 20 pages
Outcomes assessed:
Presentation	Tutorial Assessment 4 Group task set and data & method presented at end of each tutorial class	2.5%	Week 10 Due date: 02 May 2024 at 14:59 Closing date: 02 May 2024	up to 10 mins
Outcomes assessed:
Assignment	Group Lab Report 4 Group report of Laboratory 4 data processing, analysis and presentation	16%	Week 11 Due date: 12 May 2024 at 23:59 Closing date: 31 May 2024	10 - 20 pages
Outcomes assessed:
Presentation	Lab Group Presentation Group presentation on a selected group laboratory with copy of presentation	10%	Week 13 Due date: 24 May 2024 at 17:59 Closing date: 31 May 2024	up to 20 mins
Outcomes assessed:
Assignment	Group Lab Report 5 Group report of Laboratory 5 data processing, analysis and presentation	16%	Week 13 Due date: 26 May 2024 at 23:59 Closing date: 31 May 2024	10 - 20 pages
Outcomes assessed:
= group assignment ?

Assessment summary

Assignments: Assignment reports will cover the 5 laboratories completed by the groups of students which will cover a cylinder wake, a heated cylinder, a boundary layer transition, a wing bending measurement and a free flight glider. Different groups will be assigned for each report and each group member will receive a weighted mark based on a Sparkplus survey from the rest of the group members for group reports. The first lab will require an individual lab report. All group reports require every group member to complete a SparkPlus survey to receive their mark.
Tutorial Assessments: In 4 of the 5 tutorial sessions, students will be set a basic data processing and data analysis task in their current group and will be required to present data and a solution to the problem at the end of the tutorial session, with a maximum presentation and demonstration of their methods of 10 mins, to all the students. An assessment grading of the final solution will be made based on this demonstration, presentation of the method and the answers given to questions from the lecturer, tutor and their peers. All students must attend these sessions to receive a mark. Non-attendence by any given group member will result in a nil mark for that group member.
Lab Group Presentation: A group presentation of up to 20 mins long must be made by the current set groups of students on a laboratory completed throughout the semester. This laboratory will be set by the lecturer up to two weeks ahead of the presentation date. The assessment mark for each group will be made based on the presentation quality and content from the group and the answers given to questions from the lecturer, tutor and their peers.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (sydney.edu.au) (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	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 – 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 – 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 – 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 – 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard. In such cases the student will receive the mark awarded by the faculty up to a maximum of 49.

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:

The penalty for lateness is 5% per day. The penalty would apply from the next calendar day after the deadline. The penalty is a percentage of the available mark and is applied to the mark gained after the submitted work is marked (e.g., an assignment worth 100 marks is 1 day late. The content is given a mark of 75. With the 5% penalty, the final mark is 70).

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.

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	Independent study – class preps and report writing	Independent study (63 hr)
Week 01	Intro, data, definitions & error analysis	Lecture (2 hr)
	Laboratory 1 – Background and briefing	Lecture (2 hr)
	Laboratory 1 – Signal / data processing	Lecture (2 hr)
Week 02	Laboratory 2 – Background and briefing	Lecture (3 hr)
Week 02	Laboratory 2 – Signal / data processing	Lecture (3 hr)
Week 03	Laboratory 3 – Background and briefing	Lecture (2 hr)
	Laboratory 3 – Signal / data processing	Lecture (1 hr)
	Laboratory 1 – Data collection	Practical (7 hr)
Week 04	Laboratory 4 – Background and briefing	Lecture (2 hr)
Week 04	Tutorial 1 – Signal / data processing / group work	Tutorial (3 hr)
Week 05	Laboratory 2 – Data collection	Practical (7 hr)
Week 06	Laboratory 4 – Signal / data processing	Lecture (2 hr)
Week 06	Tutorial 2 – Signal / data processing / group work	Tutorial (3 hr)
Week 07	Laboratory 3 – Data collection	Practical (7 hr)
Week 08	Laboratory 5 – Background and briefing	Lecture (2 hr)
	Laboratory 5 – Signal / data processing	Lecture (1 hr)
	Tutorial 3 – Signal / data processing / group work	Tutorial (3 hr)
Week 09	Laboratory 5 – Signal / data processing	Lecture (2 hr)
Week 09	Laboratory 4 – Data collection	Practical (7 hr)
Week 10	Tutorial 4 – Signal / data processing / group work	Tutorial (3 hr)
Week 10	Laboratory 5 – Data collection	Practical (7 hr)
Week 11	Tutorial 5 – Signal / data processing / group work	Tutorial (3 hr)
Week 13	Laboratory Group Presentation	Presentation (3 hr)

Attendance and class requirements

Lectures : A total of 24 hours of lectures over the 13 week semester.
Tutorial: Tutorials with examples and group work on open ended problems, to encourage innovation in data processing and analysis methods. Five sessions with a duration of 3 hours / session. Due to the group nature of the tutorials, all students are required to attend.
Independent Study: In order to complete assignments and to understand the concepts and applications presented, students will be required to engage in self-study, including in programming languages such as MATLAB.

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

All compulsory readings for this unit can be accessed through Canvas.

Recommended texts and resources:

Measurement and instrumentation : theory and application, Morris, A.S., Langari, R., 2021, 3rd Edition, London, England : Academic Press (available online from USyd library)

Springer Handbook of Experimental Fluid Mechanics (2007)
Editors: Cameron Tropea, Alexander L. Yarin, John F. Foss

The Scientist and Engineer's Guide to Digital Signal Processing, by Steven W. Smith (available online - http://www.dspguide.com/pdfbook.htm)

Additional Online text resources will be supplied for private study as required throughout the course. It is also highly recommended the students complete background reading and practice of MATLAB as there is a strong programming component in the course. Online learning resources for MATLAB are available and should be studied by the students prior and during the course.

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.

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

LO1. investigate available literature to build up background information on set laboratory problems
LO2. understand concepts of aeronautical measurements including pressure, thermal, optical, structural and airborne measurands
LO3. apply scientific principles to a particular situation to obtain solutions to experimental problems
LO4. complete a set of group laboratory reports and an individual report on aspects of aeronautical measurements including pressure, thermal, optical, structural and airborne measurands
LO5. work as a team to efficiently manage a set of laboratory experiments, present solutions and produce results that meet set deadlines
LO6. present laboratory data and a group laboratory report to a group of colleagues
LO7. understand lab and health and safety requirements

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

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 4. ENGINEERING APPLICATION EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria.

Responding to student feedback

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

Additional worked examples and tutorial exercises have been developed for the unit and will be presented and discussed in the lectures and tutorials.

Additional information

Additional costs

N/A

Site visit guidelines

N/A

Work, health and safety

Students will be briefed on the health and safety requirements in the laboratories, ahead of attending the laboratory sesssions.

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

AERO2710: Analysis of Aerospace Engineering Data

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

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

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

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