Digital transformation is key for the Aerospace & Defense (A&D) industry to deliver next-generation systems with increased agility. The availability of high-performance compute power and big data analytics together with model-based engineering are creating the foundation of this digital future, making it possible to virtually test, optimize and validate systems faster and at a lower cost than ever before.

New technologies and best practices exist in other industries which can be leveraged in the defense industry, accelerating new platform development and ensuring operational resiliency. Disruptive technology companies, for example, are already leveraging digital engineering strategies to drive autonomous vehicle development. As the sheer number of road-test/flight-test hours needed to ensure autonomous vehicle safety are impossible to achieve with physical testing, vehicle makers are turning to simulation. With simulation, a digital environment can be used to virtually drive or fly test vehicles over millions of operating hours to validate their safety.

However, the step toward a digital engineering strategy is disruptive in nature and poses unique challenges for the defense industry.

Product cycles in the Department of Defense (DoD) span decades and need to accommodate new technology — developed long after the initial technology deployment. Legacy data and systems that span decades need to be factored in.

A perfect example of a legacy weapons system is the B-52, developed before computer-aided design, and therefore lacking a complete set of digital models to reference. Now, the B-52 is getting its own digital architecture. By leveraging a digital design strategy — incorporating physics insights from modeling and simulation — new technologies such as engines providing increased efficiency and longer deployment times are achievable. Unique digital assets can be created for each engine, modeling not just these assets “as designed”, but “as manufactured”, “as operated” and “as maintained” in the field. The challenge, of course, is developing a digital model foundation for the plane long after its original design, to support migration of legacy subsystems into their digital counterparts, like hydraulics, pneumatics and electrical subsystems upgrades, dependent on leveraging legacy wiring and infrastructure. The digital design strategy for the defense industry needs to enable a seamless flow of model-based data which can connect the past, present and future of these consistently evolving platforms.

The Digital Engineering Strategy, published by the DoD in June 2018, highlights how an integrated digital model-based approach is key to making data-driven decisions throughout the development phase and into operations and sustainment, improving decision making for acquisitions and operations as well as rolling out new advanced features and addressing new threats.

The key to driving decisions throughout the design process is the ability to predict the outcome of every decision with confidence. This requires accurate modeling for the impact of every potential decision.

There is a huge trust element to this digital future. We have the technical framework, but how do we install confidence in the modeling? This is where the engineering data from simulation and high-fidelity physics-based models can be used to support mission-critical decisions. Digital models generated from simulation can be single sources of truth to be referenced across design, deployment and sustainment cycles. These digital models can span the product lifecycle starting from model-based design and architecture specifications, through the optimization of the physical design, and into the deployment and sustainment phase with Digital Twins.

 

Designing for the Product Lifecycle

Products in the DoD space are high-investment and planned for use over decades, in highly evolving threat environments with complex requirements. The challenge becomes how to implement digitalized systems onto legacy programs, in order to achieve more warfighter capability. Such a program needs to be designed not just for a successful roll-out, but for continuous development over the product lifecycle. A top-Down design of model-based architecture can help in planning upgrades and establishing compliance of safety-critical software early in the product lifecycle.

For such an environment, it is essential that the digital design model database is maintained in an open format so that introduction of new tools during the product cycle and access to existing model data is seamless. At the same time, open and reusable embedded software solutions and certified code generation ensure compliance to strict aerospace standards like DO-178C, ARINC 661 and the Open Group’s FACE Standard, as well as ensuring functional safety of the systems and resilience to the threat of cyberattack.

Embedded software solutions have been used for more than 15 years for the development of automated systems. A few examples include fly-by-wire on the Airbus A380 and the Dassault Aviation Falcon 7X. Thanks to certified code generation, these systems can meet the objectives of the DO-178C, ISO 26262 and IEC 50128 safety standards (A&D, Automotive, Rail) in record time. More than 70% of flight time can thus be efficiently automated using these techniques.

Modeling/Simulation and Embedded Software Across the Lifecycle:

In the digital thread, modeling and simulation is pervasive across the product lifecycle, enabling digital models to evolve for each asset. Simulation can be used upstream in the design cycle to make early trade-offs to optimize size, weight, and power (SWaP). As the design evolves, simulation can validate the final design decisions and build confidence in achieving positive manufacturing outcomes. As a result, the final product will produce an equivalent physics-accurate model that includes the embedded software controls that can serve as a golden reference long after the asset is deployed in the field, as well as the basis for establishing a digital twin strategy. This development approach creates a closed feedback loop in a virtual environment, which offers the best possible design and verification platform for product lifecycle support, training and the manufacturing process.

 

Digital Twins: From Development to Operation and Sustainment

With the aging of legacy defense systems, efficient maintenance schedules are needed to root-cause and guide repairs, ensuring readiness, resilience and endurance of national security assets. Digital twins are key in enabling a predictive maintenance regimen, where digital twins running in parallel with real-world operations can provide early warning for systems not functioning properly.

Digital twins can track the performance of a unit deployed in the field with digital accuracy, providing a reference model not only of how the asset is designed, but also how it is manufactured and maintained. As a result, the digital twin evolves over time and can drive an efficient and cost-effective predictive maintenance schedule enabled by the combination of simulation, big data processing techniques and artificial intelligence capabilities. Digital twins can factor in operational conditions by comparing the expected performance under typical maintenance and use conditions to the actual performance observed in the field — providing fleetwide guidance for aircraft maintenance, as well as help planning for future modernization and upgrades.

As the defense industry faces the challenges of legacy system migration, reduced readiness levels of weapons systems threaten to impact our national security if digital engineering is not embraced with urgency. Model-based digital design methodologies provide a tremendous opportunity to improve fleet readiness, reduce cost and enable roll-out of new technologies. However, they also require a digital design strategy that provides high-fidelity models and traceable design histories to enable sustainable platforms with maximum operational readiness.

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Written by Margaret Schmitt, Senior Director, Digital Solutions Enablement and Strategy, ANSYS, Inc.