Digital twin course in motorsport
About our
Digital Twin in Motorsport Course
Explore the application of digital twins to optimize performance and reliability in motorsport. This course focuses on creating and simulating accurate digital models of vehicles, using telemetry data, sensor analysis, and finite element modeling (FEM) to predict and improve on-track behavior. The course covers the analysis of components such as engines, chassis, and tires, as well as the simulation of race strategies and environmental conditions. It emphasizes the use of data analysis, 3D visualization, and machine learning tools for informed decision-making.
This course will provide participants with the ability to create and use digital twins to optimize the performance of racing vehicles, reduce development costs, and accelerate the innovation process. It will prepare professionals for roles such as simulation engineers, data analysts, and race strategists, empowering them to make data-driven decisions in a high-pressure racing environment. Key topics such as data management, aerodynamic optimization, and race strategy analysis will be covered, linked to FIA regulations and industry standards.
Digital twin course in motorsport
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
575 $
Competencies and results
What you will learn
1. **Performance Optimization with Digital Twins: Motorsport Course**
- You will understand the fundamentals of digital twins and their application in the field of Motorsport.
- You will master the use of advanced simulations to optimize vehicle performance, including aerodynamic analysis and thermal management.
- You will learn to model and analyze the structure of racing vehicles, identifying critical points and optimizing load distribution.
- You will delve into the simulation of suspension and steering systems, seeking the perfect balance between grip and handling.
- You will analyze flap-lag-torsion couplings, whirl flutter, and fatigue.
- You will dimension laminates in composites, joints, and bonded joints with FE.
Implement damage tolerance and NDT (UT/RT/thermography).
You will use telemetry data and simulations to fine-tune vehicle setup and optimize track performance.
You will develop data-driven decision-making skills, using digital twins as a key tool.
You will understand the lifecycle of a digital twin and its integration into the vehicle design and development process.
2. **Advanced Design and Analysis: Digital Twins in Motorsport**
- Optimize aerodynamic performance with advanced CFD simulations.
- Develop finite element (FEA) models to simulate the vehicle structure.
- Integrate telemetry and sensor data for real-time monitoring.
- Build accurate digital twins of the chassis, engine, and components.
- Study vehicle dynamics and track behavior.
- Evaluate the impact of different configurations and settings.
- Apply machine learning techniques for prediction and optimization.
- Analyze flap-lag-torsion, whirl flutter, and fatigue couplings.
- Dimension laminates in composites, joints, and bonded joints using FE.
- Implement damage tolerance and NDT (UT/RT/thermography).
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. **Comprehensive Exploration of the Digital Twin: Motorsport**
- Master the analysis of aircraft systems by studying critical couplings: flap-lag-torsion, essential for the design and control of flight surfaces, and understand phenomena such as whirl flutter, critical for stability, and the evaluation of structural fatigue, key for durability and safety.
- Deeply explore the design and simulation of composite structures, including the dimensioning of laminates using finite element (FE) methods, optimizing their strength and weight. Analyze the integrity of joints and bonded joints, applying FE to predict their behavior under load and ensure structural integrity.
- Apply advanced strategies in the evaluation of structural integrity, including the implementation of damage tolerance techniques, allowing for the prediction of failure behavior and the planning of preventive maintenance.
Use non-destructive testing (NDT) methods, such as ultrasonic testing (UT), radiography (RT), and thermography, to detect and evaluate defects without damaging the structure.
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5. **Mastering the Digital Twin: Simulation and Analysis in Motorsport**
- Develop advanced simulations using digital twins to understand and predict the behavior of critical components in motorsport.
- Delve into the analysis of crucial aeroelastic phenomena, including flap-lag-torsion couplings, whirl flutter, and fatigue assessment in materials subjected to extreme cyclic loading.
- Master the dimensioning of laminated structures made of composites, including the design and analysis of bonded joints using Finite Element (FE) methods.
- Apply damage tolerance methodologies to evaluate the service life of components and understand the impact of defects.
- Become familiar with non-destructive testing (NDT) techniques, such as UT (Ultrasound), RT (Radiography), and thermography for the inspection and detection of flaws in materials and structures.
6. **Digital Twins: Prediction and Optimization of Performance in Motorsport**
- Fundamentals of Digital Twins in Motorsport: Understand the architecture, workflow, and benefits of digital twins for simulating, predicting, and optimizing the performance of motorsport vehicles. This includes integrating design, simulation, testing, and operational data.
- Modeling and Simulation of Critical Components: Learn to create digital models of key vehicle components, such as chassis, suspension, aerodynamics, and engine. 3D modeling, finite element analysis (FEA), and multi-body dynamics simulation (MBS) techniques will be covered to analyze behavior under different racing conditions.
- Data Analysis and Model Validation: Master the analysis of data from sensors and telemetry to validate and calibrate digital models. This involves using statistical analysis tools, machine learning, and data visualization techniques to identify patterns and trends.
Performance Prediction and Optimization: Using digital twins to predict vehicle performance under specific race conditions, including simulating different setups, strategies, and scenarios. Optimization methods, such as genetic algorithms and simulation-based optimization, will be explored to improve design and career strategy.
Specific Applications:
Structural Systems Analysis: Analyze flap-lag-torsion, whirl flutter, and fatigue couplings.
Materials Design and Analysis: Dimension laminates in composites, joints, and bonded joints using FE.
Structural Integrity Assessment: Implement damage tolerance and NDT (UT/RT/thermography).
Digital Twin Lifecycle Management: Understand the importance of digital twin lifecycle management, including updates of models, data management, and documentation. Best practices for ensuring the accuracy, reliability, and long-term usefulness of digital twins will be addressed.
Case Studies and Real-World Applications: Analyze case studies of motorsport teams using digital twins to gain a competitive advantage. Practical applications, challenges, and lessons learned in the real world of racing will be discussed.
Who this program is for:
Digital twin course in motorsport
- Graduates in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals working in the Motorsport sector, including racing teams, sports car manufacturers, technology providers, and specialized consultants.
- Experts in areas such as race car design, aerodynamics, performance engineering, race strategy, and software development.
- Data analysts, simulation and modeling engineers, and other professionals seeking to enhance their skills in using digital twins to optimize on-track performance.
Recommended qualifications: Basic knowledge of vehicle dynamics, simulation, and programming; proficiency in CAD and CFD software; EN/ES B2+ / C1 level. Consider our introductory courses if you need support.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — Introduction to the Digital Twin in Motorsport
1.1 History and Evolution of the Digital Twin in Motorsport
1.2 Fundamental Concepts of the Digital Twin: Definition and Components
1.3 Initial Applications of the Digital Twin in the Motorsport World
1.4 Key Advantages and Benefits of the Digital Twin in Motorsport
1.5 Integrating the Digital Twin into the Lifecycle of a Racing Car
1.6 Essential Tools and Software for Creating Digital Twins
1.7 Data Collection and Management for Simulation
1.8 Connecting the Digital Twin to the Real World: Sensors and Actuators
1.9 Case Studies: Success Stories of the Digital Twin in Motorsport
1.10 Future Challenges and Opportunities of the Digital Twin in the Industry
2.2 Chassis and Aerodynamic Design: Advanced Software and Methodologies
2.2 Suspension and Braking System Simulation: Complex Models
2.3 Composite Materials Analysis: Structural and Weight Optimization
2.4 Engine Modeling and Simulation: Performance and Efficiency
2.5 Control System Integration: Strategy Simulation
2.6 Telemetry Data Analysis: Correlation and Validation
2.7 Design for Additive Manufacturing: Prototyping and Production
2.8 Engine Cooling Optimization: Thermal Management
2.9 Computational Fluid Flow (CFD) Analysis: Aerodynamics
2.20 Model Validation and Verification: Testing and Trials
3.3 Introduction to Digital Twins in Motorsport
3.2 Performance Optimization Methodology
3.3 Data Collection and Analysis in Motorsport
3.4 Modeling and Simulation of Key Components
3.5 Digital Twin Validation and Calibration
3.6 Performance Optimization Strategies
3.7 Practical Applications in Motorsport
3.8 Continuous Improvement and Feedback
3.9 Case Studies and Real-World Examples
3.30 Tools and Software for Optimization
4. Performance Optimization with Digital Twins: Motorsport Course
4.4 Introduction to Performance Optimization in Motorsport
4.2 Fundamentals of Digital Twins
4.3 Data Collection and Analysis in Motorsport
4.4 Performance System Modeling
4.5 Simulation and Model Validation
4.6 Performance Optimization Strategies
4.7 Practical Applications: Lap Time Improvement
4.8 Optimization of Key Components
4.9 Case Study: Analysis and Optimization of a Race Car
4.40 Implementation and Monitoring of Optimization
2. Advanced Design and Analysis: Digital Twins in Motorsport
2.4 Principles of Advanced Design with Digital Twins
2.2 Design of Specific Components: Engine, Suspension, Aerodynamics
2.3 Integration of Digital Twins into the Design Process
2.4 Structural and Fatigue Analysis
2.5 CFD Analysis (Computational Fluid Dynamics)
2.6 Heat Transfer Analysis
2.7 Design Optimization for Performance
2.8 Design for Manufacturing (DFM) and Design for Assembly (DFA)
2.9 Race Scenario Simulation and Analysis
2.40 Virtual Prototype Development
3. Simulation and Analysis of Critical Components: Digital Twin Motorsport Course
3.4 Identification of Critical Components in Motorsport
3.2 Engine Modeling and Simulation
3.3 Transmission and Gearbox Simulation
3.4 Braking System Modeling and Simulation
3.5 Tire Performance Analysis
3.6 Suspension System Simulation
3.7 Component Aerodynamics Analysis
3.8 Component Structural Integrity Validation
3.9 Failure Simulation and Risk Analysis
3.40 Component Design Optimization Critical Thinking
4. Comprehensive Exploration of the Digital Twin: Motorsport
4.4 Introduction to Comprehensive Exploration of the Digital Twin in Motorsport
4.2 Digital Twin Architecture and Components
4.3 Data Integration in the Digital Twin
4.4 Vehicle Behavior Modeling
4.5 Simulation of Racing Environments
4.6 Data Analysis and Visualization
4.7 Performance Optimization Under Different Conditions
4.8 Development of Race Strategies with Digital Twins
4.9 Evaluation of the Digital Twin Lifecycle
4.40 Comprehensive Exploration Case Studies
5. Digital Twin Mastery: Simulation and Analysis in Motorsport
5.4 Principles of Advanced Simulation in Motorsport
5.2 Modeling of Complex Systems
5.3 Advanced Data Analysis Techniques
5.4 Sensitivity Analysis and Optimization
5.5 Simulation of Race Strategies
5.6 Risk Analysis and Reliability
5.7 Real-Time Performance Optimization
5.8 Hardware-in-the-Loop (HIL) Integration
5.9 Design of Experiments (DOE)
5.40 Model Implementation and Validation
6. Digital Twins: Performance Prediction and Optimization in Motorsport
6.4 Introduction to Performance Prediction
6.2 Predictive Modeling with Digital Twins
6.3 Trend and Pattern Analysis
6.4 Machine Learning Techniques in Motorsport
6.5 Predictive Performance Optimization
6.6 Component Wear Prediction
6.7 Failure Prediction and Predictive Maintenance
6.8 Pit Stop Strategy Simulation
6.9 Tire Strategy Optimization
6.40 Case Study: Prediction and Optimization in a Race
7. Strategy Development and Evaluation: Digital Twins in Motorsport
7.4 Introduction to Strategy Development Race
7.2 Modeling Race Variables
7.3 Simulating Race Scenarios
7.4 Analyzing Different Strategies
7.5 Optimizing Strategies in Real Time
7.6 Evaluating Strategy Performance
7.7 Designing Early Warning Systems
7.8 Integrating with Telemetry Systems
7.9 Simulating Incidents and Weather Conditions
7.40 Case Study: Developing and Evaluating Race Strategies
8. Predictive Analysis and Optimization: Digital Twins in Motorsport
8.4 Fundamentals of Predictive Analysis in Motorsport
8.2 Predictive Modeling Techniques
8.3 Analyzing Historical Data
8.4 Predicting Vehicle Performance
8.5 Predicting Wear and Failures
8.6 Optimizing Maintenance
8.7 Optimizing Race Strategies
8.8 Integrating with Data Management Systems
8.9 Validating Predictive Models
8.40 Practical Applications and Case Studies
5.5 Introduction to Digital Twins and Their Application in Motorsport
5.5 Benefits of Performance Optimization
5.3 Fundamentals of Simulation and Modeling
5.4 Architecture and Components of a Digital Twin
5.5 Digital Twin Workflow: Data to Results
5.6 Integration with Data Acquisition (DAQ) Systems
5.7 Case Studies: Real-World Examples in Motorsport
5.8 Tools and Software: Introduction to the Environment
5.5 Principles of Parametric Design and 3D Modeling
5.5 Selection of CAD and Simulation Software
5.3 Creating Component Models: Engine, Chassis, Aerodynamics
5.4 Implementing Boundary Conditions and Loads
5.5 Modeling Systems: Powertrain, Suspension, Brakes
5.6 Validation and Calibration of Models
5.7 Multidisciplinary Modeling and System Coupling
5. 8 Sensitivity Analysis and Design Optimization
3.5 Simulation of Critical Components: Engine and Transmission
3.5 Fatigue and Durability Analysis of Components
3.3 Fluid Flow Simulation: Cooling and Aerodynamics
3.4 Thermal System Modeling
3.5 Impact Simulation and Structural Analysis
3.6 Material Selection and Weight Optimization
3.7 Integration with FEA and CFD Simulation Software
3.8 Case Studies: Component Failures and Improvements
4.5 Digital Twin Data Analysis: Acquisition and Management
4.5 Visualization and Analysis of Simulation Results
4.3 Identification of Bottlenecks and Weak Points
4.4 Sensitivity Analysis and Design of Experiments
4.5 Digital Twin Validation with Real Data
4.6 Scenario Analysis: Race Conditions and Strategies
4.7 Visualization Techniques Data: Dashboards and Reports
4.8 Real-Time Data Integration and Predictive Control
5.5 Advanced Systems Simulation: Vehicle Dynamics
5.5 Track Performance Analysis: Lap Times
5.3 Race Strategy Optimization: Pit Stops
5.4 Tire Analysis: Wear and Performance
5.5 Track and Weather Condition Simulation
5.6 Driver and Control Model Integration
5.7 Optimization Techniques: Genetic Algorithms
5.8 Case Studies: Race Data Analysis
6.5 Predictive Performance Modeling
6.5 Historical Data Analysis for Prediction
6.3 Machine Learning Techniques in Motorsport
6.4 Performance Optimization: Engine, Aerodynamics
6.5 Component Wear Prediction
6.6 Race Strategy Optimization
6.7 Risk Analysis and Mitigation
6.8 Data-Driven Decision-Making Strategies
7.5 Race Strategy Design and Evaluation
7.5 Simulation of Different Scenarios and Options
7.3 Optimization of Pit Stop Strategies
7.4 Risk and Benefit Analysis of Each Strategy
7.5 Development of Strategy Analysis Software
7.6 Integration with Race Information Systems
7.7 Real-Time Communication and Decision-Making
7.8 Case Studies: Implementation in Real Races
8.5 Predictive Analysis: Failures and Maintenance
8.5 Long-Term Performance Optimization
8.3 Use of Historical and Real-Time Data
8.4 Machine Learning Techniques in Motorsport
8.5 Failure Prediction and Maintenance Planning
8.6 Vehicle Lifecycle Optimization
8.7 Data-Driven Decision-Making Strategies
8. 8 Integration with Quality Control
6.6 Predictive Vehicle Performance Modeling
6.2 Aerodynamic and Setup Optimization
6.3 Propulsion System and Energy Management Simulation
6.4 Race Strategy Analysis and Pit Stop Simulation
6.5 Tire Strategy Optimization
6.6 Real-Time Data Analysis and Decision Making
6.7 Driver Behavior and Vehicle Dynamics Simulation
6.8 Component Reliability Prediction
6.9 Car Setup Strategy Optimization
6.60 Case Study: Performance Optimization in Specific Races
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).