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The University of Southampton
Engineering
Phone:
(023) 8059 7662
Email:
D.J.J.Toal@soton.ac.uk

Dr David Toal MEng, PhD, AMRAeS, MAIAA

Lecturer

Dr David Toal's photo

Dr David Toal is Lecturer within Engineering and Physical Sciences at the University of Southampton.

Current Position & Responsibilities

David is currently a Lecturer within the Computational Engineering & Design Group at the University of Southampton and a member of the Rolls-Royce University Technology Centre for Computational Engineering, module coordinator for SESA 6051 – Systems Reliability and a tutor on SESG 6019 - Design Search and Optimisation 2 and FEEG2001 – System Design & Computing.

General Research Interests

David is interested in the development of optimisation and automation technologies to improve design.

Career History

David graduated from Queen’s University Belfast in 2006 with a first class honours degree in Aeronautical Engineering and was then sponsored by Airbus to complete a PhD in design optimisation at the University of Southampton. Upon completion of his PhD in 2009, David was appointed as a research fellow within the Rolls-Royce University Technology Centre for Computational Engineering. In 2012 David was appointed as a senior research fellow and then as a New Frontiers Fellow. He is now a Lecturer within the Faculty of Engineering & the Environment.

2015 - present University of Southampton Faculty of Engineering & the Environment Lecturer
2012 – 2015 University of Southampton Faculty of Engineering & the Environment New Frontiers Fellow
2006 – IMechE Whittle Reactionaries Prize runner-up
2006 – Bombardier Prize for best final year student

Collaborations

David has worked in collaboration with a number of aerospace companies including Rolls-Royce and Airbus on the development of techniques for design optimisation and automation.

Research interests

 
Research Interests

David is interested in the development of optimisation algorithms and design automation technologies to enable rapid design. This includes, but is not exclusive to:

  • Surrogate model development
  • Methods for rapidly predicting whole computational domains
  • Optimisation algorithms using first & second order derivatives
  • CAD automation
  • Geometry manipulation, interrogation & identification
  • Evolutionary optimisation algorithms
  • Modelling and prediction of design uncertainties
Applications of Research

David has applied his design optimisation and automation algorithms to:

  • Wing and aerofoil design
  • Gas turbine combustor design
  • Whole gas turbine engine design

 

An example turbofan combustor
An example turbofan combustor
A parametric engine model
A parametric engine model
Predicted engine temperatures
Predicted engine temperatures

Research group

Computational Engineering and Design

Research project(s)

Embedding intelligence & knowledge in CAD

Traditionally computer aided design (CAD) models represent only geometric information, however, by embedding intelligence and knowledge within the CAD environment the process of design can be streamlined and accelerated.

Mixing cheap and expensive simulations in an optimisation

Traditionally automated design optimisation methods use the results from computational simulations to drive an improvement in a design. However, the expense of such simulations can considerably restrict the scope of an optimisation. An alternative is to mix expensive and cheap simulations within a single design optimisation to improve performance.

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Articles

Conferences

Thesis

  • Toal, D. J. J. (2009). Proper orthogonal decomposition & kriging strategies for design
Title Module Code Programme Role
Systems Reliability SESA 6051 MSc Unmanned Vehicle Systems Design Coordinator
Design Search and Optimisation 2 - Case Studies SESG 6019 MEng/MSc Tutor
Introduction to Advanced Mechanical Engineering Science SESM 6025 MSc Tutor
Dr David Toal
Computational Engineering and Design Group Engineering Centre of Excellence Building 176 University of Southampton Boldrewood Campus Burgess Road Southampton SO16 7QF

Room Number: 176/5011

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