Dr Stephen Hall

Senior Lecturer First Year Eng

Faculty of Engineering, Architecture and Information Technology
s.hall@uq.edu.au
+61 7 336 51287

Overview

CAREER OBJECTIVE To push design and research boundaries in demanding engineering applications, by applying the best of my knowledge and skills

Research Interests

  • Computational Fluid Dynamics
    Advanced automotive Urban landscape and city modelling Turbulent flows
  • Engineers Without Borders, Australia
    Humanitarian Thesis Projects www.ewb.org.au/whatwedo/education-research/research-program/university-research-program/available-research-projects

Qualifications

  • Bachelor of Engineering, University of South Australia
  • Master of Engineering Sciences, University of New South Wales
  • Doctor of Philosophy, University of New South Wales

Available Projects

  • Humanitarian Thesis Projects

    www.ewb.org.au/whatwedo/education-research/research-program/university-research-program/available-research-projects

    EWB's University Research Program provides an opportunity for students and academics at Australiasian universities to

    support development organisation working in Australia or overseas through real world research projects. If you want your

    final year research project to help engineer a better world then this program is for you.

  • Aerodynamics plays a major role in high performance vehicles and is recognised as the differentiating factor in many forms of high level motorsports, such as Formula 1, CART racing and NASCAR. Performance and race winning designs tend to correlate with aerodynamic performance and success in ongoing, rapid aerodynamic development. Designers make use of large, high speed wind tunnels, track testing and Computational Fluid Dynamics (CFD) and it is not uncommon for a racing team’s Aero Budget to be far larger than for any other department. This however does not guarantee success.

    The many different aerodynamics designs and shapes present on modern Formula 1 cars, indicates that aerodynamics is very complex and that there is currently no clear path to a “best all-round design”. This can be attributed to the inherent difficulties in understanding the complex flow, such as no general analytical solution of the controlling equations, the unknown nature of turbulence and the difficulties in making physical aerodynamic measurements. The path forward to improve and optimise aerodynamic performance is seldom clear and many different methods and ideas prevail amongst designers and aerodynamicists.

    CFD offers perhaps the most promising path forward and is the focus of this proposed study. Improving the accuracy and reliability of current CFD models will be the first step. A CFD aerodynamicist, in the racing industry is always under pressure to provide CFD results and rarely has time to perform a rigorous Verification and Validation (V&V) study of CFD models. Through V&V this study will develop verified and more reliable CFD solutions that can then be used for prediction, with improved confidence. The second step will be to apply formal optimisation methods, such as the Adjoint and Gradient Methods to guide and speed-up aerodynamic design and development. With these improved CFD models and approaches it is intended that this study will generate deeper understandings for a complex flow and new and original design directions in High Performance Automotive Aerodynamics.

    References

    Ferziger J. H., Peric, M., Computational Methods for Fluid Dynamics. (2002), Springer

    Hirsch C., Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, 2nd Ed. (2007), Elsevier

    AIAA Computational Fluid Dynamics Committee, Guide for the Verification and Validation of Computational Fluid Dynamics Simulations. (2002), AIAA G-077-1998, AIAA Standards

  • The field of Computational Fluid Dynamics (CFD) developed rapidly during the final part of the last century and is now a well-established and sophisticated method of analysis. There are several major codes available and these are heavily applied in industry, design, prediction and research. Despite this, the core algorithms of these codes have changed little from early research methods and they often remain “stiff”. That is; their regions of application are tightly bounded and they can be unforgiving when applied incorrectly. Frequently, the codes may fail to generate a solution, or worse they may produce a wrong solution that is just plausible enough to be accepted.

    For this reason, user knowledge and skill remains a primary factor in the success or otherwise of a CFD model and study. The long sort after “grail” of a CFD code that it is fully automatic and user independent, remains a far-off research goal. With this in mind, it is surprising that CFD user training, or CFD focused learning and teaching has remained a poorly developed aspect of applied CFD. A typical user takes a theory course during undergraduate studies and may observe the output of a CFD program, but the real deep learning in applied CFD is generally on-the-job and piecewise between projects. This is rarely a comprehensive or efficient method of learning.

    One commercial program has a fully programmable, inbuilt wizard to provide a more comprehensive interaction between the user and the program. It is proposed that this will be used to firstly measure user learning, for both novice and experienced users. Findings from this will be applied to develop effective teaching methods that may be applied through the wizard, or relevant POD casts, or though more formal mentoring. The overall measure of success that will drive this study is to increase user learning rates, decrease user errors and improve CFD simulation reliability.

    References

    Felder R., Brent R., Teaching and Learning STEM. (2016), Jossey-Bass a Wiley Brand

    Ferziger J. H., Peric, M., Computational Methods for Fluid Dynamics. (2002), Springer

    StarCCM+, CCM User Guide, Version 12.02. (2016), CD-adapco, London UK

View all Available Projects

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Humanitarian Thesis Projects

    www.ewb.org.au/whatwedo/education-research/research-program/university-research-program/available-research-projects

    EWB's University Research Program provides an opportunity for students and academics at Australiasian universities to

    support development organisation working in Australia or overseas through real world research projects. If you want your

    final year research project to help engineer a better world then this program is for you.

  • Aerodynamics plays a major role in high performance vehicles and is recognised as the differentiating factor in many forms of high level motorsports, such as Formula 1, CART racing and NASCAR. Performance and race winning designs tend to correlate with aerodynamic performance and success in ongoing, rapid aerodynamic development. Designers make use of large, high speed wind tunnels, track testing and Computational Fluid Dynamics (CFD) and it is not uncommon for a racing team’s Aero Budget to be far larger than for any other department. This however does not guarantee success.

    The many different aerodynamics designs and shapes present on modern Formula 1 cars, indicates that aerodynamics is very complex and that there is currently no clear path to a “best all-round design”. This can be attributed to the inherent difficulties in understanding the complex flow, such as no general analytical solution of the controlling equations, the unknown nature of turbulence and the difficulties in making physical aerodynamic measurements. The path forward to improve and optimise aerodynamic performance is seldom clear and many different methods and ideas prevail amongst designers and aerodynamicists.

    CFD offers perhaps the most promising path forward and is the focus of this proposed study. Improving the accuracy and reliability of current CFD models will be the first step. A CFD aerodynamicist, in the racing industry is always under pressure to provide CFD results and rarely has time to perform a rigorous Verification and Validation (V&V) study of CFD models. Through V&V this study will develop verified and more reliable CFD solutions that can then be used for prediction, with improved confidence. The second step will be to apply formal optimisation methods, such as the Adjoint and Gradient Methods to guide and speed-up aerodynamic design and development. With these improved CFD models and approaches it is intended that this study will generate deeper understandings for a complex flow and new and original design directions in High Performance Automotive Aerodynamics.

    References

    Ferziger J. H., Peric, M., Computational Methods for Fluid Dynamics. (2002), Springer

    Hirsch C., Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, 2nd Ed. (2007), Elsevier

    AIAA Computational Fluid Dynamics Committee, Guide for the Verification and Validation of Computational Fluid Dynamics Simulations. (2002), AIAA G-077-1998, AIAA Standards

  • The field of Computational Fluid Dynamics (CFD) developed rapidly during the final part of the last century and is now a well-established and sophisticated method of analysis. There are several major codes available and these are heavily applied in industry, design, prediction and research. Despite this, the core algorithms of these codes have changed little from early research methods and they often remain “stiff”. That is; their regions of application are tightly bounded and they can be unforgiving when applied incorrectly. Frequently, the codes may fail to generate a solution, or worse they may produce a wrong solution that is just plausible enough to be accepted.

    For this reason, user knowledge and skill remains a primary factor in the success or otherwise of a CFD model and study. The long sort after “grail” of a CFD code that it is fully automatic and user independent, remains a far-off research goal. With this in mind, it is surprising that CFD user training, or CFD focused learning and teaching has remained a poorly developed aspect of applied CFD. A typical user takes a theory course during undergraduate studies and may observe the output of a CFD program, but the real deep learning in applied CFD is generally on-the-job and piecewise between projects. This is rarely a comprehensive or efficient method of learning.

    One commercial program has a fully programmable, inbuilt wizard to provide a more comprehensive interaction between the user and the program. It is proposed that this will be used to firstly measure user learning, for both novice and experienced users. Findings from this will be applied to develop effective teaching methods that may be applied through the wizard, or relevant POD casts, or though more formal mentoring. The overall measure of success that will drive this study is to increase user learning rates, decrease user errors and improve CFD simulation reliability.

    References

    Felder R., Brent R., Teaching and Learning STEM. (2016), Jossey-Bass a Wiley Brand

    Ferziger J. H., Peric, M., Computational Methods for Fluid Dynamics. (2002), Springer

    StarCCM+, CCM User Guide, Version 12.02. (2016), CD-adapco, London UK

  • Computational Fluid Dynamics (CFD) is a branch of Fluid Mechanics, which strictly includes two main disciplines; Fluid Statics and Fluid Dynamics. Statics considers fluids at rest, while Dynamics is about liquids and gases in motion. Fluid Dynamics itself divides into two important fields of study; namely Hydrodynamics and Aerodynamics, concerned with water and air, respectively. Fluid Dynamics and CFD has many widely varying applications, including calculating forces and moments on aircraft, determining the mass flow rate of petroleum through pipelines, predicting evolving weather patterns, understanding nebulae in interstellar space and modelling explosions.

    There are many modern vehicles dependant on this science and numerical technique. Of particular interest to my potential supervisor and I are road and racing automobiles. There is considerable scope for technological progress in automobile aerodynamics, for improved vehicle control and improved energy efficiency, through lower aerodynamic drag. This is especially important for racing cars, along with the additional need to increase aerodynamic downforce to generate increasingly fast cornering speeds. In racing cars it is also important to know the precise aerodynamic load on each part of the vehicle, so that they can be design for minimum mass but strong enough to resist the aerodynamic loads.

    The essential automotive challenge for aerodynamics and CFD is to continually improve existing solutions, generate new solutions where possible and maintain or exceed the current rate of development in automotive aerodynamics.

    Perhaps the first thing to consider in this research proposal is that CFD has some disadvantages when applied to automotive aerodynamics. For example, the accuracy of CFD simulations is difficult to estimate in some situations and can lead to doubt and uncertainty in the results. In addition, the complexity of many CFD models can place accountability of the process into question. The training and skill level of the individual CFD practitioner becomes a very significant factor in the success, or otherwise of a simulation. Another major challenge for CFD is the application of several incomplete models that are frequently used to solve complex situations, such as turbulent flow.

    The output of a CFD simulation must be carefully considered to evaluate its value and accuracy through the formal methods of Verification and Validation. A primary goal of this research is to describe a path, in which the afore mentioned problems may be solved or minimised, so the predictions CFD gives us, becomes closer to reality.

    It is proposed that this research will proceed by firstly investigating and gathering information on the problem and considering deeply what may be the strengths and weaknesses of CFD in this area of technology. After this is well understood, the work of breaking down and addressing these weaknesses will begin and methods of eliminating and testing them will be rigorously investigated. Finally the best methods and processes will be applied and the resulting solutions generated and evaluated. An improved outcome in applied CFD and automotive aerodynamics; that is improved processes, improved solutions and identified paths to improved automotive technology will be the major measures of success in this research.

    References:

    1. Ferziger J. H., Peric, M., Computational Methods for Fluid Dynamics, (2002), Springer

    2. Hirsch C., Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, 2nd Ed. (2007), Elsevier