Contact Dr Andrea Spinelli

Areas of expertise

  • Aircraft Design
  • Computing, Simulation & Modelling
  • Gas Turbines & Propulsion
  • Systems Engineering

Background

Andrea Spinelli is a Research Fellow at Cranfield University in the topic of Engineering Design for complex systems, specifically focusing on probabilistic design space exploration methodologies and machine-learning based methods for multi-disciplinary optimisation.

He graduated from Politecnico di Milano (Italy) in 2020 with a B.Sc. in Aerospace Engineerign and a M.Sc. in Aeronautical Engineering, focusing on aircraft propulsion and aerodynamics. His Master Thesis titled "A study of unconventional wings and tails" presents a multi-disciplinary parametric analysis of non-elliptical lift distributions, and their application on a business jet. The proposed design had a Vee tail, which required stability and control analysis. The final outcome was a 10% reduction in fuel burn over the reference aircraft.

His PhD was sponsored by the EU-funded FutPrint50 project, whose goal is to understand the key technologies and system interactions to accelerate the introduction of a hybrid-electric 50 seater regional turboprop airplane. He has devised a Design-Space Exploration methodology by combining Set-Based Design and Multidisciplinary Optimisation, allowing to reduce the computational cost by 80%. This methodology has been applied on the investigation of optimal energy management strategies, which is being used to drive the design of a 50 seater hybrid-electric turboprop by FutPrint50 partners.

Currently he is working to extend the developed framework by including further uncertainty analysis for the Airbus-sponsored ONEHeart project.

Research opportunities

Probabilistic and Causal-based Machine Learning

Artificial Intelligence

Multi-Disciplinary Optimisation

Systems Engineering Design

Aircraft Design

Current activities

Research is currently focused on:

Application of Set-Based design with Multidisciplinary Optimization for supporting designer decisions in complex integrated systems.

Development of a Bayesian network approach for requirements analysis and decision-making support in setting up engineering optimisation problems.

Clients

  • Airbus SE

Publications

Articles In Journals

Conference Papers