| Aerospace engineering stands at the forefront of innovation, where every advancement holds the promise of safer, more efficient, and an environmentally friendly aircraft. As the demand for a technologically advanced aircraft grows, engineers are turning to cutting-edge technologies such as simulation, artificial intelligence (AI) and digital twins to redefine the future of flying (or defining the flightpath to future). Let us delve into the latest trends in aerospace engineering, and how the convergence of simulation and AI technology is helping bring innovation in design and better handling the product in operation.
Sushil Mane,
Senior Director, Technology & Customer Support
Simulation: A Virtual Playground for Aerospace Innovation
Simulation has long been a cornerstone of aerospace engineering, providing engineers with a virtual playground to test and refine aircraft designs before they take to the skies. Today, advancements in simulation technology have enabled engineers to fasten the design process by simulating at the speed of design.
Engineers use simulation driven design tools early in the concept stage to solve conflicting design challenges, meet performance and manufacturing constraints whilst being light weight. There is a rapid advancement of AI in simulation; AI integrated generative design workflows enable engineers to rapidly sift through many design concepts and suggest the best optimum to carry forward for validation.
Meshless FE solvers are gaining huge acceptance in aerospace as they validate multiple design concepts with blazingly fast simulations. These tools enable engineers to develop highly accurate digital models of aircraft systems and its structural components, allowing for detailed analysis and optimization at every stage of the design process.
Systems modeling and simulation helps break silos in the design of complex systems. Model based systems engineering workflows helps in the integration of multi-physics modeling, which enables engineers to simulate the complex interactions between different physical phenomena such as fluid dynamics, structural mechanics, and thermal effects. This holistic approach to simulation allows engineers to gain deeper insights into aircraft performance, identify potential design flaws, and optimize aircraft systems for maximum efficiency and safety.
Environmental concerns also are driving new ideas in aerospace design. To address these requirements simulation platforms with integrated material database, provide valuable data on the carbon footprint and global warming potential of materials. Engineers can search and compare various materials in the database and provide trade off statistics on its strength characteristics and carbon footprint data. The most effective use of these data is in optimizing the design; engineers can now set targets for strength, durability or vibration responses along with the CO2 targets.
AI: The Co-Pilot of Tomorrow’s Aircraft
Artificial intelligence (AI) is revolutionizing aerospace engineering by augmenting human capabilities and enabling new levels of autonomy and intelligence in aircraft systems. AI algorithms can analyze vast amounts of data from sensors, flight instruments, and historical flight data to optimize aircraft performance, enhance safety, and improve the overall flying experience.
AI platforms that are low code/no code are empowering engineers with rapid evaluation of datasets and dashboard KPIs and the influencing parameters. AI tools with powerful ML algorithms can build predictive models of synthetic data (simulation) or test data that provide scalar predictions of systems performance.
AI assisted simulation is also in turn democratizing the use of AI in design. Engineers can now use historic simulation results of non-parametric designs and use geometric deep learning techniques to build predictive models. These trained ML models can now be used to predict the behavior of the CAD or FE variants of new designs. These AI assisted simulation workflows are rapidly shrinking design cycle times.
One of the most exciting applications of AI in aerospace engineering is in autonomous flight systems. AI-powered autopilot systems can analyze real-time sensor data and make split-second decisions to navigate complex flight conditions, avoid obstacles, and respond to changing environmental factors. This level of autonomy not only enhances safety but also opens new possibilities for unmanned aerial vehicles (UAVs) and urban air mobility (UAM) systems.
Digital Twin: A Mirror Image of Aircraft Performance
Digital twin technology is transforming the way aircraft are designed, manufactured, and operated by creating virtual replicas of physical aircraft systems and components. A digital twin is an accurate digital or virtual representation of a product or service that mimics the complex behavior of the system. These digital twins can simulate real-world operating conditions, monitor performance in real-time, and predict maintenance needs, enabling engineers to optimize aircraft performance, reduce downtime, and enhance safety.
Integrated simulation and AI tools help engineers build physics based digital twins during the design process; this helps validate system performance and optimize the design. Operational digital twins use sensor data from physical assets to build predictive models that can help monitor and control physical systems in operation. One of the key components of these digital twins is the Reduced Order Model (ROM) based AI or romAI. Engineers can now import simulation or test data and use neural net based ML models that can then be deployed in systems engineering workflows for real time prediction of virtual or physical systems.
For instance, an interesting application of a digital twin in the design process is in the development of a helicopter radome. A digital twin of the radar antenna’s structural and electromagnetic systems can help correct the radar transmission loss due to in-flight vibration.
By integrating data from sensors, avionics systems, and onboard computers, digital twins provide engineers with a comprehensive view of an aircraft’s health and performance.
The Convergence of Simulation, AI, and Digital Twin: A New Era of Aerospace Engineering
The convergence of simulation, AI, and digital twin technology is ushering in a new era of aerospace engineering, where innovation knows no bounds. By combining the power of simulation to model and analyze complex systems, AI to optimize performance and enhance autonomy, and digital twin to monitor and predict aircraft behavior, engineers are pushing the boundaries of what is possible in aviation.
From next-generation aircraft design to autonomous flight systems and predictive maintenance, these transformative technologies are revolutionizing every aspect of aerospace engineering, paving the way for safer, more efficient, and environmentally sustainable flight. As we look to the future, the sky is truly the limit for aerospace innovation powered by simulation, AI, and digital twin technology.
