Fatigue is the most common cause of failure in structures subject to cyclic loading. Fatigue failure is a two-stage phenomenon consisting of a crack initiation stage followed by a propagation stage. Cracks can initiate due to fatigue but also as a result of manufacturing processes and material features such as inclusions or voids. Once a crack is present, if it is subject to a sufficiently high cyclic stress, it will propagate until failure. However, in some instances a crack can propagate into a low-stressed region causing the crack to stall and never propagate to failure. In this case the crack may be acceptable in-service as it might not compromise the durability of the component (damage tolerant approach). Fatigue crack growth refers to the propagation (or non-propagation) of cracks in structures subject to cyclic loading. Fatigue crack growth and damage tolerant analyses use fracture mechanics principles and therefore, the knowledge of pre-existing cracks is necessary. These pre-existing cracks can be detected in components using non-destructive techniques or assumed. From the second half of the 20th century, significant research effort was put in understanding and describing how cracks propagate under cyclic loading, including how to characterise the threshold, propagation and fast fracture regions, both from an experimental and numerical point of view, as well as how to account for mean stress effect and crack retardation. Unfortunately, this research effort is scattered in a multitude of scientific publications. The purpose of this paper is to provide in a single document a comprehensive collection of the most relevant fatigue crack growth laws and models allowing a more effective review and comparison of the available tools and facilitating decision making.

As the global move toward a carbon-neutral future continues, new sustainability laws are placing greater focus on fatigue and reliability in emerging technologies. From electric vehicles and hydrogenpowered aircraft to offshore wind turbines and nuclear fusion reactors, the challenge is clear: how do we design systems that remain safe, durable, and maintainable over many years? At the same time, new solution technologies are changing how we address these challenges. Tools such as Digital Twins, Digital Passports, Machine Learning, AI, Smart Testing, and Systems Engineering are helping engineers improve qualification, validation, and lifecycle management.

This keynote provides a cross-sector view of fatigue and reliability in the green economy. It begins with a review of current challenges in electrification, hydrogen systems, fusion energy, new materials, and circular manufacturing. It then presents a modern framework for fatigue and reliability engineering, combining traditional methods with digital tools across three key areas:

1. Design Space

The design space is evolving rapidly. Physics-informed AI is enhancing tools like Failure Mode and Effects Analysis (FMEA). Emerging FMEA should include the Voice of Business, Voice of Customer, and Voice of Regulator, along with qualification test requirements for each failure mode. These elements align technical risks with broader stakeholder needs and ensure reliability targets are embedded from the start.

Smart testing combines physical and virtual tests to build confidence faster:

• Simulation helps define realistic loading profiles for accelerated fatigue tests

• Physical tests provide data to improve simulation accuracy

• Uncertainty Quantification and probabilistic analysis help validate fatigue predictions

• Combining both methods gives more confidence than small-sample physical tests alone

Advanced reliability modelling, such as System-of-Systems simulation, helps engineers understand how multiple failure modes affect large systems—such as battery packs with many connected

components. This supports better maintenance planning and helps meet goals for reliability,

availability, maintainability, and sustainability.

2. Operation Space

After deployment, systems must be monitored and maintained. This space focuses on:

• Capturing real-world stress using operational data lakes, including the use of Physicsinformed

Machine Learning to extract meaningful insights

• Measuring in-service strength using FRACAS (Failure Reporting, Analysis, and Corrective

Action System) and maintenance records, with Machine Learning techniques to explore

failure metrics more effectively

By linking design models with operational and maintenance data, engineers can build Digital Twins and Digital Passports. These virtual tools evolve with the product and create a feedback loop between design intent and field performance.

3. Design Feedback

The feedback loop from operation to design is becoming more dynamic. Technologies like Software-Defined Vehicles (SDVs) and Human–Machine Interfaces (HMIs) introduce new reliability challenges across hardware, software, and human-in-the-loop systems. We explore how vehicle simulators are used to capture these complex interactions and support a zero-prototype ambition for future development.

AI-powered reliability analysis can use historical FRACAS data to improve future FMEA processes.

This helps engineers predict problems and improve designs before production. The combination of data, simulation, and AI is changing how we learn from the field and apply those lessons to next-generation technologies.

This keynote aims to inspire new thinking about fatigue and reliability. It encourages academics and engineers to adopt digital tools and systems thinking to design sustainable, long-lasting technologies for tomorrow’s world.