Digital twin course in turbines
About our
The Digital Twins in Turbines course explores the creation and application of accurate virtual turbine models, using real-time data to simulate, predict, and optimize engine performance. It focuses on the integration of sensors, data analysis, artificial intelligence (AI), and machine learning (ML) to improve efficiency, reduce maintenance costs, and extend turbine lifespan. It includes the use of CFD simulation and FEM tools to analyze component behavior under diverse operating conditions, facilitating predictive monitoring and design optimization.
This course provides practical knowledge of digital twin platforms, big data processing, and the implementation of predictive maintenance strategies. Participants will learn to develop and use digital twins to diagnose faults, predict component wear, and optimize turbine performance in real time, improving energy efficiency and operational safety. This training prepares professionals such as maintenance engineers, data analysts, and simulation specialists to apply the latest technologies in the aviation industry.
Digital twin course in turbines
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
399 $
Competencies and results
What you will learn
1. Mastery of Turbine Design and Simulation with Digital Twins
- Design and optimize turbines using advanced simulation software and digital twins.
- Understand the fundamental principles of aerodynamics and fluid mechanics as applied to turbines.
- Model and simulate turbine behavior under various operating and environmental conditions.
- Analyze flap-lag-torsion, whirl flutter, and fatigue couplings.
- Integrate data from sensors and monitoring systems to create accurate and up-to-date digital twins.
- Evaluate turbine performance, including efficiency, power, and emissions.
- Dimension laminates in composites, joints, and bonded joints using FE.
- Apply finite element analysis (FEA) techniques for structural simulation and Optimization.
Understand the concepts of digital twins and their application in the turbine lifecycle.
Implement damage tolerance and NDT (UT/RT/thermography).
Develop predictive maintenance strategies based on digital twins and data analysis.
Use visualization and data analysis tools to interpret simulation results.
2. Turbine Performance Optimization: Digital Twins and Rotors
- Advanced turbine modeling using digital twins, including computational fluid dynamics (CFD) and structural analysis (FEA).
- Evaluation and optimization of turbine performance through sensor data analysis and the application of machine learning algorithms.
- Design and analysis of rotors, considering aerodynamics, structural dynamics, and strength of materials.
- Study of the effects of vibration on turbines, including the identification and mitigation of resonances.
- Application of optimization techniques to improve energy efficiency and reduce fuel consumption.
- Implementation of predictive maintenance and asset management strategies to extend turbine lifespan.
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. Predictive Modeling and Rotor Analysis in Turbines with Digital Twins
4. Predictive Modeling and Analysis of Turbine Rotors with Digital Twins
- Apply advanced predictive modeling techniques to simulate the dynamic behavior of turbine rotors.
- Understand and analyze complex phenomena such as flap-lag-torsion coupling, which influence the stability and performance of rotor blades.
- Evaluate the risk of whirl flutter, a self-induced vibration that can be catastrophic, and design mitigation strategies.
- Estimate the service life of components through fatigue analysis and crack propagation modeling.
- Design and dimension rotor structures using composite materials, including laminate calculations and joint optimization.
- Use finite element analysis (FEA) tools to simulate the structural behavior of the components, including connections and bonded joints.
Apply damage tolerance concepts to assess the damage tolerance of structures and ensure safety.
Implement non-destructive testing (NDT) techniques such as ultrasonic testing (UT), radiography (RT), and thermography for the inspection and evaluation of component integrity.
5. In-depth Analysis of Turbine Rotors: Digital Twins and Performance Optimization
5. In-Depth Analysis of Turbine Rotors: Digital Twins and Performance Optimization
Evaluation of rotor structural dynamics: analysis of phenomena such as flap-lag torsion, whirl flutter instabilities, and fatigue damage accumulation.
Design and analysis of rotor structural elements using finite element (FE) methods, including the dimensioning of composite laminates, the study of mechanical joints, and the simulation of bonded joints.
Application of damage tolerance methodologies for structural integrity assessment and the implementation of non-destructive testing (NDT) techniques, such as ultrasonic testing (UT), radiography (RT), and thermography.
6. Digital Analysis of Turbine Rotors: Design and Optimization
6. Digital Analysis of Turbine Rotors: Design and Optimization
You will understand the fundamentals of rotor analysis, including the evaluation of aerodynamics and structural mechanics.
You will master the use of numerical simulation tools (CFD and FEA) for rotor design and optimization.
You will learn to identify and mitigate the effects of flutter and vibration in rotors.
You will analyze flap-lag-torsion couplings, whirl flutter, and fatigue.
You will specialize in blade design, including material selection and shape optimization.
You will apply optimization techniques to improve turbine performance and efficiency.
You will dimension laminates in composites and joints. and bonded joints with FE.
You will become familiar with industry standards and regulations for turbine design and manufacturing.
You will implement damage tolerance and NDT (UT/RT/thermography).
You will evaluate the service life of turbine components and predictive maintenance strategies.
Who this program is for:
Digital twin course in turbines
- Graduates in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals from OEM rotorcraft/eVTOL, MRO, consulting, technology centers.
- Flight Testing, certification, avionics, control, and dynamics seeking specialization.
- Regulators/authorities and UAM/eVTOL professionals requiring compliance skills.
Recommended qualifications: foundation in aerodynamics, control, and structures; ES/EN B2+/C1. We offer bridging tracks if you need them.
- 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.
1.1 Introduction to Digital Twins in Turbine Design
1.2 Turbine Design Principles: Fluid Flow and Aerodynamics
1.3 3D Modeling of Turbine Components
1.4 CFD Flow Simulation: Techniques and Tools
1.5 Creating Digital Twins: Interconnecting Data and Models
1.6 Validation and Verification of Digital Models
1.7 Applications of Digital Twins in Turbine Design
1.8 Sensitivity Analysis and Optimization of the Initial Design
1.9 Case Studies: Practical Examples of Turbine Design with Digital Twins
1.10 Integrating the Digital Twin into the Turbine Lifecycle
2.2 Introduction to Turbine and Rotor Optimization
2.2 Turbine Rotor Design and Simulation: Key Principles
2.3 Digital Twins: Essential Tools for Turbine Analysis
2.4 Turbine Performance Optimization Using Digital Twins
2.5 Data Analysis and Predictive Modeling for Rotors
2.6 Rotor Design and Analysis: Efficiency Optimization
2.7 Advanced Rotor Optimization: Simulation and Digital Twins
2.8 Rotor Failure Analysis and Continuous Improvement
2.9 Integrating Digital Twins into the Turbine Lifecycle
2.20 Case Studies: Turbine and Rotor Optimization
3.3 Principles of Rotor Optimization: Introduction to Digital Twins
3.2 CFD and FEA Modeling for Rotor Analysis
3.3 Optimization Techniques: Parametric Design and Genetic Algorithms
3.4 Sensitivity Analysis and Design of Experiments (DOE) in Rotors
3.5 Materials and Advanced Manufacturing for Optimized Rotors
3.6 Integration of Digital Twins for Performance Monitoring
3.7 Failure Analysis and Design for Durability in Rotors
3.8 Case Studies: Rotor Optimization in Different Applications
3.9 Model Validation and Verification Strategies
3.30 Future Trends in Rotor Optimization with Digital Twins
4.4 Introduction to Predictive Modeling of Turbine Rotors with Digital Twins
4.2 Fundamentals of Digital Simulation for Rotor Analysis
4.3 Creation and Validation of Digital Twins of Turbine Rotors
4.4 Data Analysis and Experimental Design with Predictive Models
4.5 Application of Machine Learning Algorithms for Prediction
4.6 Rotor Design Optimization Based on Predictions
4.7 Performance and Durability Evaluation with Predictive Models
4.8 Integration of Digital Twins into the Rotor Lifecycle
4.9 Case Studies: Predictive Modeling in Different Types of Turbines
4.40 Trends and Future of Predictive Modeling in the Shipbuilding Industry
5.5 Introduction to Turbine Design and Digital Twins
5.5 Principles of CFD Simulation for Turbines
5.3 3D Modeling of Turbine Components
5.4 Creating Digital Twins for Turbines
5.5 Setting Up Simulation Environments
5.6 Initial Flow and Performance Analysis
5.7 Validating Results and Calibrating the Model
5.8 Case Study: Designing a Specific Turbine
5.9 Design Considerations for Different Applications
5.50 Design and Simulation Tools and Software
5.5 Introduction to Turbine Performance Optimization
5.5 Turbine Performance Data Analysis
5.3 Optimizing Blade and Rotor Geometry
5.4 Using Digital Twins for Optimization
5.5 Algorithmic Optimization Methods
5.6 Optimizing Flow Within the Turbine
5.7 Sensitivity Analysis and Design of Experiments
5.8 Implementation of Changes and Impact Assessment
5.9 Optimization Examples in Existing Turbines
5.50 Simulation and Verification of Optimized Results
3.5 Introduction to Advanced Rotor Analysis
3.5 Detailed Rotor Modeling in Digital Twins
3.3 Stress and Strain Analysis in Rotors
3.4 Failure Simulation and Service Life Analysis
3.5 Design for Fatigue Resistance
3.6 Optimization of Rotor Materials and Geometry
3.7 Vibration and Rotational Dynamics Analysis
3.8 Case Study: Common Rotor Failures
3.9 Structural and Dynamic Analysis Tools
3.50 Validation of Models and Results
4.5 Introduction to Predictive Modeling with Digital Twins
4.5 Data Collection and Analysis for Predictive Modeling
4.3 Development of Predictive Performance Models
4.4 Integration of Sensors and Data in the Digital Twin
4.5 Failure Prediction and Predictive Maintenance
4.6 Data-Driven Maintenance Optimization
4.7 Using Machine Learning in Predictive Modeling
4.8 Case Study: Predictive Modeling on a Real Turbine
4.9 Developing a Remote Monitoring System
4.50 Integrating Predictive Models into Operation
5.5 Introduction to In-Depth Rotor Analysis
5.5 Advanced Rotor Modeling with Digital Twins
5.3 Complex Stress and Strain Analysis
5.4 Design Considerations for Harsh Environments
5.5 Failure Analysis and Root Cause Diagnosis
5.6 Design Optimization for Extreme Durability
5.7 Using Advanced Simulation Tools
5.8 Case Study: Analyzing a Damaged Rotor
5.9 Developing Failure Mitigation Strategies
5.50 Design for Reliability
6.5 Introduction to Design and Digital Rotor Optimization
6.5 Parametric Design of Turbine Rotors
6.3 Optimization Based on Genetic Algorithms
6.4 Design Sensitivity Analysis
6.5 Design for Manufacturing and Assembly
6.6 Use of Digital Twins in the Design Process
6.7 Integration of Design with Simulation
6.8 Case Study: Design and Optimization of a Rotor
6.9 CAD and CAE Tools for Rotor Design
6.50 Design Validation and Virtual Prototyping
7.5 Introduction to Digital Rotor Modeling
7.5 3D Modeling Techniques for Rotors
7.3 Flow Simulation and Structural Analysis
7.4 Use of Digital Twins for Modeling
7.5 Integration of Sensor Data into the Model
7.6 Vibration Analysis and Dynamic Response
7.7 Design Optimization for Performance
7.8 Case Study: Digital Modeling of a Rotor Specific
7.9 Modeling and Simulation Tools
7.50 Model Validation and Results
8.5 Introduction to Digital Twin Modeling for Rotors
8.5 Creating Digital Twins for Rotors
8.3 Real-Time Data Integration
8.4 Performance Simulation and Failure Analysis
8.5 Design and Performance Optimization
8.6 Predictive Analytics and Proactive Maintenance
8.7 Using Artificial Intelligence in Digital Twins
8.8 Case Study: Application of Digital Twins in Rotors
8.9 Benefits of Implementing Digital Twins
8.50 The Future of Digital Twins in the Turbine Industry
6.6 Fundamentals of Turbine Rotor Design
6.2 Principles of Rotor Optimization
6.3 Digital Rotor Modeling: Methodologies and Tools
6.4 Rotor Flow Simulation: CFD Analysis
6.5 Structural Analysis of Rotors: Strength and Durability
6.6 Parametric Rotor Design for Optimization
6.7 Digital Twins in Rotor Design and Analysis
6.8 Validation and Verification of Rotor Models
6.9 Multiobjective Optimization Strategies for Rotors
6.60 Case Studies: Applied Rotor Design and Optimization
- 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”).