In a Q&A session, Matthieu Paquet, principal application engineer at Ansys explains why digital engineering will underpin the future of autonomy in aerospace and defence.

Ansys is a global leader in simulation engineering, with a mission to power innovation that drives human advancement. For over 50 years, the company has been working with companies in every sector, including aerospace and defence (A&D) to help them push boundaries and make the impossible, possible.

Matthieu Paquet works as a principal application engineer at Ansys and has been with the company for over eight years. As such, his time at Ansys has been invaluable for communicating the importance of leveraging cutting-edge technologies to support autonomy efforts in the A&D industry.

Q) Tell us about the overall state of autonomy in A&D at the moment, and the forecast for what’s to come.

Autonomy has far-reaching applications across the entire A&D sector. Usually, the first example that people think of is pilot assistance in commercial aircraft, but autonomy can be hugely beneficial across the industry. For instance, it can be useful in emergency services, delivery and logistics, and emerging areas like space operations.

I’d say that the goal now is to support the continuous development of autonomous systems for specific missions and tasks. This could look like, for instance, an aircraft independently making critical decisions, or a pilot assisting with remotely operating or managing aircraft. In fact, we’re already seeing incredible advancements in autonomy, such as the introduction of eVTOLs, that are being driven by technological innovations in AI and machine learning. But in order to deliver the next generation of intelligent systems, every single application needs to rely on a robust and scalable architecture that can perform in dynamic and unpredictable environments. Additionally, navigating the evolving regulatory environment is vital for the successful deployment of autonomous systems.

Q) What are some of the challenges that organisations are facing when it comes to developing and testing autonomy applications?

Understanding the challenges starts with breaking down the architecture of an autonomous system. It needs to do five things: collect information and data, perceive the situation around it, analyse accordingly, make decisions, and execute actions. Ensuring these aspects can connect to each other and work in tandem requires a strong comms system and data link.

Naturally, the process of developing an autonomous system is highly complicated and time-consuming, and ensuring its safety and redundancy is the highest priority; navigating the regulatory environment adds another layer of complexity here. Engineers have to verify that the system operates efficiently and effectively in what are often uncertain environments, and the level of testing needed to ensure this can be costly. As such, advancements in AI, machine learning, and simulation environments are crucial to the development process. Scalability concerns, such as moving from prototypes to full-scale operations, and ensuring interoperability with existing systems and infrastructure, further complicate matters. Not to mention, there’s the challenge of bolstering the public’s trust in autonomous systems and addressing ethical considerations.

Evidently, there are lot of factors to be considered, so organisations need advanced tools and technologies to help them comprehensively test and validate autonomy applications, without increasing costs or time to market.

Q) How can digital engineering help to support and advance autonomy efforts in A&D?

Digital engineering empowers organisations through three key vectors: ensuring performance and accuracy, enabling safety and reliability, and minimising time and cost. Essentially, leveraging digital engineering can often be the difference between success and failure.

To break it down, digital engineering encompasses a wide range of technologies, including simulation, AI/ML, and cloud computing. It creates a digital thread that everyone works from when designing and testing an autonomy application, offering a holistic approach. For instance, one team could be deploying simulations to test sensors, while another leverages AI/ML to train software. By working on the same digital thread, teams ensure that every aspect of an autonomy application works in tandem. They can also test the application multiple times to validate its safety and effectiveness, with any errors or issues detected and rectified within minutes.

The positive impact of digital engineering extends far beyond those designing autonomy applications. At an organisational level, productivity increases, and time-to-market reduces since there's less need for physical materials or testing in the product development process. Costs are drastically reduced and optimised, allowing organisations to innovate successfully even on a budget. Moreover, digital engineering produces empirical evidence that an autonomy application is safe and ready for deployment in the real world. Organisations can demonstrate continuous verification and validation across the development process, helping them adhere to stringent safety standards. Clear evidence of a system’s safety also helps bolster public trust in commercial aviation applications.

The future of digital engineering includes specific tools and platforms, ensuring seamless integration and interoperability between different system components. Collaborations and partnerships play a significant role, facilitating better communication and innovation.

Overall, implementing digital engineering is non-negotiable for organisations aiming to advance autonomy in A&D. By ensuring performance, safety, and cost-effectiveness, digital engineering is essential for driving innovation and maintaining competitive advantage in this rapidly evolving field.

Q) What do you think the future of autonomy in A&D will look like?

I think that the progress in advancing autonomy applications will only continue, and the sector could, one day, achieve full autonomy. This is by no means a quick or simple process, but I do believe that the already rapid rate of innovation will accelerate, which will have a huge impact; innovations like improved AI algorithms, enhanced machine vision, and robust cybersecurity measures will be pivotal here. Collaborations and partnerships between industry, academia, and regulatory bodies will also be crucial.

These combined efforts will help build public trust and ensure the successful integration of autonomous technologies into mainstream applications. And, as technologies like AI continue to develop, and adoption continues to increase, more organisations will be able to push the boundaries of innovation and support autonomy efforts across various sectors within A&D.

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