Postgraduate DiplomaAutomotive & Motorsport Applications open

Postgraduate Diploma in Aerodynamic Optimisation with Adjoint/Surrogates

Online 800-hour Postgraduate Diploma in Aerodynamic Optimisation with Adjoint/Surrogates. 8 modules with FIA Formula 1 Technical Regulations, FIA Formula 1 Sporting Regulations and SU2. Dedicated tutor and open enrolment.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Formulate aerodynamic optimisation problems.

02

Apply the adjoint method to sensitivity calculation.

03

Build metamodels to reduce simulation cost.

04

Automate optimisation workflows.

05

Optimise single-seater aerodynamic components.

06

Apply multi-objective and robust optimisation.

07

Validate optimised designs with high-fidelity methods and testing.

Profile

Who it's for

  • Aerodynamics engineer who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • CFD engineer who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • Optimisation engineer who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • Engineering data scientist who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • Numerical methods engineer who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • Aerodynamic development engineer who wants to specialise in Aerodynamic Optimisation with Adjoint/Surrogates.
  • Graduates in engineering or science who wish to enter motorsport.
Standards & tools

What you'll work with

  • FIA Formula 1 Technical Regulations
  • FIA Formula 1 Sporting Regulations
  • ASME V&V 20
  • AIAA G-077
  • SAE J1594
  • ISO/IEC 17025
Syllabus

8 modules · 800 h

  1. 1.1Formulation of the optimisation problem
  2. 1.2Objective functions: downforce, drag, balance
  3. 1.3Geometric and regulatory constraints
  4. 1.4Gradient-based optimisation
  5. 1.5Gradient-free and evolutionary algorithms
  6. 1.6Geometry parameterisation
  7. 1.7Parameterisation using free-form deformation
  8. 1.8Mesh morphing
  9. 1.9Comparison of methods
  10. 1.10Case study: optimisation of a wing aerofoil section
  1. 2.1Continuous and discrete adjoint equations
  2. 2.2Surface sensitivities
  3. 2.3Adjoint for RANS
  4. 2.4Frozen versus full turbulence
  5. 2.5Shape and mesh sensitivity
  6. 2.6Stability of the adjoint solver
  7. 2.7Interpreting sensitivity maps
  8. 2.8Computational cost and HPC resources
  9. 2.9Limitations in separated flows
  10. 2.10Laboratory: adjoint with SU2 on an aerofoil
  1. 3.1Design of experiments
  2. 3.2Kriging and Gaussian processes
  3. 3.3Radial basis functions
  4. 3.4Neural networks as surrogates
  5. 3.5Bayesian optimisation with acquisition functions
  6. 3.6Surrogate error and validation
  7. 3.7Adaptive sampling enrichment
  8. 3.8Dimensionality reduction
  9. 3.9Multi-fidelity CFD and wind tunnel models
  10. 3.10Practical: Kriging of aerodynamic downforce
  1. 4.1Mesh automation
  2. 4.2Simulation scripting
  3. 4.3Case management and HPC resources
  4. 4.4Tool integration
  5. 4.5Automatic post-processing of results
  6. 4.6Process convergence
  7. 4.7Data management
  8. 4.8Reproducibility of results
  9. 4.9Regulatory CFD constraints
  10. 4.10Workshop: automated chain with modeFRONTIER
  1. 5.1Front wing optimisation
  2. 5.2Floor and diffuser
  3. 5.3Rear wing and beam wing
  4. 5.4Sidepod shape optimisation
  5. 5.5Wheel components
  6. 5.6Component interaction
  7. 5.7Ride height sensitivity
  8. 5.8Design robustness
  9. 5.9Manufacturing constraints
  10. 5.10Case study: optimisation of the floor edge
  1. 6.1Pareto front
  2. 6.2NSGA-II type genetic algorithms
  3. 6.3Objective weighting
  4. 6.4Robust optimisation under uncertainty
  5. 6.5Uncertainty in flow conditions
  6. 6.6Stable aerodynamic balance
  7. 6.7Final design selection
  8. 6.8Decision-making
  9. 6.9Communicating results
  10. 6.10Practical: downforce–drag Pareto front
  1. 7.1High-fidelity CFD validation
  2. 7.2Wind tunnel testing
  3. 7.3Track correlation
  4. 7.4Prediction errors
  5. 7.5Lessons from real projects
  6. 7.6Design risk management
  7. 7.7Documentation and results archiving
  8. 7.8Transfer to manufacturing
  9. 7.9Process improvement
  10. 7.10Case study: validation of an optimised design
  1. 8.1Component definition
  2. 8.2Geometry parameterisation
  3. 8.3Sensitivity study with the adjoint
  4. 8.4Surrogate construction
  5. 8.5Multi-objective optimisation of the configuration
  6. 8.6Robustness analysis
  7. 8.7High-fidelity CFD validation
  8. 8.8Conclusions and future work
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is SU2, STAR-CCM+, OpenFOAM, modeFRONTIER, Dakota, in free, academic or evaluation versions. Tests are studied on real procedures from automated CFD optimisation campaigns, wind tunnel validation of optimised designs and robustness studies under varying conditions. Assessment is continuous per module plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:faculty with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Component definition

Formulate aerodynamic optimisation problems.

Geometry parameterisation

Apply the adjoint method to sensitivity calculation.

Sensitivity study with the adjoint

Build metamodels to reduce simulation cost.

Surrogate construction

Automate optimisation workflows.

Multi-objective optimisation of the configuration

Optimise single-seater aerodynamic components.

Career opportunities

Where this takes you

Aerodynamics engineerCFD engineerOptimisation engineerEngineering data scientistNumerical methods engineerAerodynamic development engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient level of Spanish to follow technical regulatory reading.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme consists of 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.

The programme works with SU2, STAR-CCM+, OpenFOAM, modeFRONTIER, Dakota. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

A SEIUM own diploma upon passing the continuous assessment and the final integrating project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Consult the recognition policy when you enrol.

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