Kieran Mackle

Doctor of Philosophy

2020 - 2024

Thesis Title

Co-design of Hypersonic Vehicle Shape and Trajectory

Research Summary

Conducted research on hypersonic vehicle design optimisation. Developed novel methods for approximating partial derivatives of aerodynamic characteristics for high-dimensional optimisation. Awarded an industry scholarship from BAE Systems and completed a paid research placement with the Defence Science and Technology Group. Collaborated with interdisciplinary teams, creating integrated vehicle design and optimisation approaches. Developed simulations and automated optimisation frameworks for high-performance computing clusters.

Bachelor of Mechanical and Aerospace Engineering (Hons I)

2016 - 2019

Honors and Distinctions

Awarded the Dean's Commendation for Academic Excellence every semester. Graduated with a GPA of 6.8.

Efficient and Flexible Methodology for the Aerodynamic Shape Optimization of Hypersonic Vehicle Concepts in a High-Dimensional Design Space

AIAA SciTech Forum and Exposition | 2024

• Addressed challenges of aerodynamic shape optimization for hypersonic vehicles with many design variables due to high CFD computational costs.
• Proposed an efficient, scalable, and CFD solver-agnostic method for gradient-based optimization using approximate Jacobians from lower-order aerodynamic models.
• Enabled calculation of pressure sensitivities to design parameters without additional CFD simulations.
• Validated accuracy by comparing estimated pressure sensitivities to finite difference CFD solutions.
• Optimized a generic hypersonic waverider for maximum Lift-to-Drag ratio (L/D) while respecting an internal volume constraint.
• Achieved a 33% increase in L/D with the optimized configuration obtained in just over 4 hours on an 8 CPU workstation, parameterized by 16 design variables.

Developing a Co-Design Framework for Hypersonic Vehicle Aerodynamics and Trajectory

AIAA SciTech Forum and Exposition | 2024

• Addressed the need for a novel design method for hypersonic vehicles to perform across their entire mission trajectory.
• Critiqued traditional design methods for their inability to capture complex and non-linear subsystem interactions in hypersonic flight.
• Identified challenges from competing design requirements, such as packaging constraints, aerodynamic needs, and flight path objectives.
• Proposed a computationally tractable approach for simultaneous co-design of vehicle geometry and flight trajectory to optimize mission objectives.
• Demonstrated that the proposed method reduces the number of computational fluid dynamic simulations while scaling favorably with the number of design parameters.
• Optimized a hypersonic glider (waverider) defined by 16 variables for maximum range while adhering to an internal volume constraint.
• Achieved an 11.6% improvement in optimal vehicle configuration over the nominal design through the co-designed framework.

Understanding the Impacts of Aerodynamic Uncertainty on Optimal Trajectories for Hypersonic Vehicles

AIAA Aviation Forum | 2021

• Highlighted challenges in hypersonic vehicle design due to strong non-linear interactions and inherent uncertainties.
• Explored the impact of different aerodynamic modeling techniques and analysis tools on vehicle performance.
• Addressed the trade-off between computational efficiency and the introduction of modeling uncertainty in aerodynamic tools.
• Utilized a delta wing configuration of the North American X-15 for a minimum time-to-climb maneuver at Mach 6.
• Developed two aerodynamic databases: a 'design' database using an inviscid flow solver (Cart3D) and a 'truth' database that includes viscous drag effects.
• Conducted flight simulations to assess the impact of model mismatch on open-loop performance using both databases.
• Demonstrated a significant impact on performance from relatively small changes in vehicle aerodynamics.
• Emphasized the necessity for co-design of vehicle aerodynamics and control systems to achieve near-optimal flight performance.