About the Job
Achieving the goals of climate neutrality by reducing the impact of aviation is a task that requires a carefully drafted roadmap for the development of disruptive technologies. The Aeroelastic and Structural Design Lab (ASD Lab) group at University Carlos III of Madrid (UC3M) is committed to the environmental cause, and its vision is: to develop physic-based aircraft design methodologies including all the concurrent development aspects (manufacturing, assembly, certification, operation, environmental footprint) from the early stage with the aim of reducing novel configurations, material and technologies time to market. Emphasis is placed on the tighter discipline coupling (aero-structural, aeroelasticity, airframe-propulsion) imposed by efficient aircraft architectures.
ASD Lab is offering one position at the doctoral or Post-doc levels on the Airframe design tools for multi-fidelity structural sizing and system integration of new generation quiet and green electric aircraft.
The position is partly funded by Horizon Europe project INDIGO (https://cordis.europa.eu/project/id/101096055) and partly by research projects with relevant aircraft integrators and aeronautical industries.
Responsibilities
This researcher position aims at developing multi-fidelity design procedure for performance optimisation of new generation green and quite aircraft. The activity will be mainly focus on developing the structural sizing tools defining the airframe internal structures and mass distribution. Different level of fidelity as well as several optimisation architecture strategies will be assessed and compared in term of accuracy and computational cost. Minimum weight structural optimisation methods will be developed fostering the implementation of advanced composite materials and multifunctional structures. Preliminary weight estimations will be computed by ROMs obtained from both modified advanced beam-based FEM and GFEM-based airframe sizing procedures. AI and machine learning techniques will be applied to generate surrogate models.
Mid/high fidelity GFEM will enhance the procedure accuracy by including design details such as assembly techniques and component joints. Size and free size optimisations will be applied to optimise material distribution and composite ply shape for minimum weight aeroelastic tailoring. Staking sequences internal optimisation will be coupled with size procedure to enable the automatic generation of composite laminates to maximise weight saving while fulfilling ply book rules and manufacturing constraints. Ply drops and thickness variation strategies will be assessed to seek a suitable trade-off between weight saving and manufacturability.
The activity of the candidate will include:
- Developing shell-accurate beam element FE model for multi-material hybrid tapered cross-sections
- Defining modelling strategy to include system integration into GFEM
- Developing stress analysis and mass estimation DoE and surrogate models
- Performing minimum weight optimisation
- Developing advanced composite material optimisation
- Developing a strategy to effectively include certification and manufacturing constraints in airframe optimisation routines
- Developing surrogate models based on AI and machine learning
- Using in-house FE codes (Augusto) and commercial software (Nastran, Abaqus, OptiStruct)
- Write reports and deliverables
- Disseminate the work at international technical conferences and greater audience events
- Support R&T activities within ASD Lab
Qualifications
- For the PhD studentship: hold a MSc (or MSc student with 60 ECTS passed at contract signature) in aerospace engineering or a relevant discipline.
- For the Research Fellowship: hold a PhD and demonstrated experience in airframe design and aerostructure analysis.
- Students with a background in aircraft design, aerostructures and FE analysis are particularly encouraged to apply
- Have an outstanding academic record, critical and creative thinking.
- Be proficient in English (oral and written).
- Deal independently and proactively with scientific and engineering challenges; be self-motivated and capable of working under pressure to meet deadlines.
- FE, programming (Pyton) and coding skills will be an added value
- AI and machine learning methods experience is favourable
Funding
- PhD position (20.000€ – 26.000€ p.a., negotiable), for 3/4 years
- PostDoc position (26.000€ – 42.000€ p.a., based on assessment of merit) for 2 years.
Benefits
The successful applicants will:
- Work as part of the European and company-funded research projects
- Become part of a young, dynamic, highly qualified, collaborative team
- Experience flexible working environment and schedule
- Opportunity to participate in international conferences to present research activities.
- Opportunity to carry out research internships abroad.
- Be involved in several research activities within ASD Lab
- Have health coverage under the National Health System.
- Salary raises and production prices based on performance
Applicants are expected to formally apply by the 7th of September. For further information, please contact Dr Andrea Cini: https://www.linkedin.com/in/andrea-cini-b87bb2206/