The Evolution of 'Design Build Fly' Led Teaching in Aerospace Engineering

This paper describes the evolution of a ‘Design - Build-Fly’ (DBF) approach to the delivery and assessment of a Stage Three Aircraft Design module. It focuses on the primary learning outcomes around the design and manufacturing functions associated with the development of a remotely controlled aircraft. The work covers a six year period from 2011 to present mapping the transformation of the module from report based assessment to a more hands on approach resulting in a fully functioning remotely controlled aircraft. Results show that both the staff and student experience improved across key performance metrics including student feedback, learning and competency development. Challenges still remain in methods of placing students within teams and maintaining technical rigour in reporting as students develop vocational skills and more reflective writing styles.

Designing low-emission aero-engines using adjoint methods

The aim of this work is to investigate an efficient CAD based adjoint process chain for calculating sensitivities of the objective function to the CAD parameter in time scales acceptable for industrial design processes.

Linking Parametric CAD with Adjoint Surface Sensitivities

The goal of this work is to present an efficient CAD-based adjoint process chain for calculating parametric sensitivities (derivatives of the objective function with respect to the CAD parameters) in timescales acceptable for industrial design processes. The idea is based on linking parametric design velocities (geometric sensitivities computed from the CAD model) with adjoint surface sensitivities. A CAD-based design velocity computation method has been implemented based on distances between discrete representations of perturbed geometries. This approach differs from other methods due to the fact that it works with existing commercial CAD packages (unlike most analytical approaches) and it can cope with the changes in CAD model topology and face labeling. Use of the proposed method allows computation of parametric sensitivities using adjoint data at a computational cost which scales with the number of objective functions being considered, while it is essentially independent of the number of design variables. The gradient computation is demonstrated on test cases for a Nozzle Guide Vane (NGV) model and a Turbine Rotor Blade model. The results are validated against finite difference values and good agreement is shown. This gradient information can be passed to an optimization algorithm, which will use it to update the CAD model parameters.