Scramjet Design Optimisation Module

Engineering, IT, Mathematics and Statistics

PLEASE NOTE

  • Due to the sensitivity and security of this project, students must have Australian Citizenship to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

PROJECT BACKGROUND

Modern vehicles increasingly require complex, multidisciplinary optimisation approaches in order to achieve conceptual designs capable of exploring optimal mission performance parameters as opposed to optimal component performance. This project seeks to integrate into an existing DST MDO tool a head of class air breathing (scramjet) design optimisation module that can operate in conjunction with other vehicle design modules to investigate system level performance impacts of design choices.

This will encompass, at a fidelity to be determined, inlet, combustor, nozzle design features to determine engine performance as a function of incoming air and atmospheric conditions. Heating and insulation as well as materials choices and mass should be accounted for and exposed to the global genetic algorithm. The module can ideally be configured to represent different classes of airbreathing propulsion (i.e. ramjet, dual mode scramjet, etc).

RESEARCH TO BE CONDUCTED?

  • Rapid scramjet inlet design
  • Scramjet flowpath performance model
  • Airbreathing/vehicle design integration
  • Verification and validation activities against available scramjet data

SKILLS AND QUALIFICATIONS REQUIRED

We are looking for a PhD student with skills in the following areas:

  • Scramjet/air breathing engine design and analysis
  • Supersonic/hypersonic Aerodynamics and flow effects
  • Shape optimisation techniques
  • Matlab programming (with C++ and/or Python programming skills desirable)

EXPECTED OUTCOMES

The outcome of the research will be:

  • A verified module capable of rapidly producing propulsion data for a range of parametrically variable engine and vehicle designs, over a range of flight conditions, for implementation in a combined vehicle-trajectory optimisation toolkit.
  • Time permitting, the extension of this module into rapid aerodynamic database generation for complex parametrically defined vehicles.
  • Augmented DST capability in a critical tool servicing a number of high priority programs for Defence

ADDITIONAL DETAILS

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

LOCATION:
Adelaide, SA or Brisbane, QLD
DURATION:
5 months
CLOSING DATE:
19/08/2020
ELIGIBILITY:
Domestic students only
REF NO:
APR – 1468

INTERNSHIP CONTACT

CONNECT WITH APR.INTERN

Suggested Internships

DEFENCE SCIENCE TECHNOLGY GROUP (APR – 1535)

Location:
Adelaide, SA
Material-Object Detection in Hyperspectral Images using Machine-Learning

NSW DEPARTMENT OF PLANNING, INDUSTRY & ENVIRONMENT (APR – 1531)

Location:
Sydney, NSW or can be completed remotely
Assessing the Efficacy of Hazard Reductions in Bushfire Control through Eyewitness Accounts

LOCKHEED MARTIN STELARLAB (APR – 1529)

Location:
Melbourne, VIC or can be completed remotely
Gaining Insights from Complex Suggestions