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Postgraduate Diploma in Characterisation & Wear Analytics During the Race

Online 800-hour Postgraduate Diploma in Characterisation & Wear Analytics During the Race. 8 modules with ISO 8855, SAE J2047 and Python (scikit-learn). Assigned 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

Characterise race tyres through standardised tests.

02

Measure and quantify tread wear.

03

Apply data analytics to tyre degradation.

04

Estimate tyre condition during the race.

05

Plan compound evaluation tests.

06

Build correlated predictive models.

07

Translate results into strategy and set-up decisions.

Profile

Who it's for

  • Tyre engineer who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Performance data analyst who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Sports data scientist who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Strategy engineer who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Test engineer who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Modelling engineer who wants to specialise in Characterisation & Wear Analytics During the Race.
  • Engineering or science graduates seeking to enter motorsport.
Standards & tools

What you'll work with

  • ISO 8855
  • SAE J2047
  • SAE J670
  • ISO 28580
  • UN ECE R117
  • ETRTO Standards Manual
  • FIA Formula 1 Sporting Regulations
Syllabus

8 modules · 800 h

  1. 1.1Objectives of characterisation
  2. 1.2Indoor vs outdoor testing
  3. 1.3Input variables: load, slip angle, pressure
  4. 1.4Lateral and longitudinal force curves
  5. 1.5Characterisation at different temperatures
  6. 1.6Variation between units
  7. 1.7Pre-run break-in protocols
  8. 1.8Supplier data
  9. 1.9Tyre databases
  10. 1.10Case study: analysis of a slip angle sweep
  1. 2.1Abrasive and fatigue wear mechanisms
  2. 2.2Measuring tread depth
  3. 2.3Laser profile scanning
  4. 2.4Tyre mass loss
  5. 2.5Wear by tread zone
  6. 2.6Influence of slip energy
  7. 2.7Wear on different asphalts
  8. 2.8Graining and its quantification
  9. 2.9Correlation with driving style
  10. 2.10Practical: profile measurement with a scanner
  1. 3.1Relevant telemetry variables
  2. 3.2Estimation of tyre forces
  3. 3.3Energy indicators per wheel
  4. 3.4Feature engineering
  5. 3.5Degradation regression models
  6. 3.6Applied machine learning
  7. 3.7Validation of predictive models
  8. 3.8Trend visualisation
  9. 3.9Outlier management
  10. 3.10Laboratory: predictive degradation model in Python
  1. 4.1Force observers
  2. 4.2Extended Kalman filters
  3. 4.3Internal temperature estimation
  4. 4.4Available grip estimation
  5. 4.5Detection of performance loss
  6. 4.6Cliff prediction
  7. 4.7Alerts to the race engineer
  8. 4.8Integration with strategy
  9. 4.9Sensor limitations
  10. 4.10Case study: real-time grip estimator
  1. 5.1Tyre test design
  2. 5.2Compound evaluation runs
  3. 5.3Control of external variables
  4. 5.4Standardised evaluation procedures
  5. 5.5Post-session measurement protocols
  6. 5.6Returning tyres to the supplier
  7. 5.7Allocation and usage rules
  8. 5.8Cost and logistics
  9. 5.9Safety in testing
  10. 5.10Workshop: compound test plan
  1. 6.1Correlation between rig and track
  2. 6.2Model fitting with race data
  3. 6.3Prediction uncertainty
  4. 6.4Hybrid physics and data models
  5. 6.5Transfer between circuits
  6. 6.6Continuous model updating
  7. 6.7Benchmarking against rivals
  8. 6.8Communicating results
  9. 6.9Knowledge management
  10. 6.10Case study: transferring a model between circuits
  1. 7.1Race compound selection
  2. 7.2Pressure recommendations
  3. 7.3Set-up adjustments for wear
  4. 7.4Data-based pit stop strategy
  5. 7.5Tyre allocation during the weekend
  6. 7.6Tyre reports
  7. 7.7Real-time decisions
  8. 7.8Coordination with the strategist
  9. 7.9Assessing accuracy of decisions
  10. 7.10Practical: weekend tyre report
  1. 8.1Definition of objectives
  2. 8.2Data collection
  3. 8.3Tyre characterisation
  4. 8.4Wear analysis
  5. 8.5Development of the predictive model
  6. 8.6In-race estimator
  7. 8.7Model validation
  8. 8.8Strategic recommendations for the team
  9. 8.9Dissertation and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, making it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), MATLAB, MoTeC i2 Pro, MF-Tyre/MF-Swift, Power BI, in free, academic or evaluation versions. Tests are studied using real wear measurement procedures with laser scanners, flat-belt characterisation tests and telemetry analysis of long runs. Assessment is continuous across each module, plus the final integrative project.

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

Definition of objectives

Characterise race tyres through standardised tests.

Data collection

Measure and quantify tread wear.

Tyre characterisation

Apply data analytics to tyre degradation.

Wear analysis

Estimate tyre condition during the race.

Development of the predictive model

Plan compound evaluation tests.

Career opportunities

Where this takes you

Tyre engineerPerformance data analystSports data scientistStrategy engineerTest engineerModelling 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 Spanish to follow technical regulation texts.

Ready to start?

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

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FAQ

Frequently asked questions

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

The programme uses Python (scikit-learn), MATLAB, MoTeC i2 Pro, MF-Tyre/MF-Swift and Power BI. 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 integrative project. This is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please check the recognition policy when you enrol.

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