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Sustainability and Availability Big Wins for Digital Twins

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Digital twin tech gives commanders confidence in vehicular availability and sustainability by predicting downtime before it happens.

The U.S. military operates the world’s largest aircraft fleet by flying over 13,000 aircraft, all of which must be sustained to ensure mission readiness. As the fleet ages, high rates of operational availability become more challenging to achieve, and recently, rates have declined. This is exacerbated by the relatively small number of advanced aircraft in the fleet whose mission readiness has sometimes been as low as 50%.

This significantly impacts warfighters in two critical ways. First, with more aircraft in maintenance and more funding spent on sustainment, less capability remains available to warfighters. Further, fewer resources are directed to modernization efforts, hindering warfighters’ ability to stay ahead of future threats.

“The challenge of maximizing asset uptime and optimizing maintenance investment is not unique to the military,” notes Robert Weiss, Ansys consultant and EVP & GM, Lockheed Martin Advanced Development Programs (Skunk Works) (Ret.). “The commercial sector — particularly the industrial equipment and energy industries — also grapple with best practices for sustaining high value, complex operational assets that may be fielded for decades.

A common solution pursued by these companies is physics-based digital twins, explained Weiss. In fact, 50 percent of large industrial companies say they expect to use digital twins by next year. For example, a leading global industrial equipment company uses digital twins to perform predictive maintenance of machines, including wind turbines. This helps engineers decide whether to increase turbine power or reduce it to prevent the motors from overheating.

The commercial aviation sector has reported that a digital twin could improve maintenance, repair, and overhaul cycle times by 30 percent. Such an improvement would certainly boost operational availability and mission readiness statistics.

Under substantial pressure to improve readiness metrics and reduce sustainment costs — potentially amounting to approximately 70 percent of a program’s overall life cycle cost — 75 percent of a branch of U.S. military executives reportedly favor digital twins.

Digital Twins and Sustainability

“In simple terms, digital twins are virtual replicas of deployed physical assets,” stated Weiss. “The two are digitally connected, enabling performance and health data from the physical asset to be connected to its virtual counterpart, which, in turn, generates predictive, decision-ready insights. A digital twin can even be used to establish “virtual sensors” in cases where an asset’s physical sensors are unavailable. All this is used to reduce uncertainties – and therefore more effectively sustain the fleet.”

Armed with these insights, sustainment leaders can address many critical questions: Does the asset need to be brought in for maintenance as scheduled or can it be safely delayed? Could an adjustment be made in the field to keep the asset mission ready?

Perhaps leaders want a deployed asset to perform an out-of-specification operation. Its digital twin rapidly determines the physical asset’s ability to perform the task, defines a safe operational envelope, and suggests required modifications.

And if an asset is required to remain fielded for an overly extended period, its digital twin predicts when, how, and why the asset will need to be maintained, avoiding potentially unnecessary time-based scheduling.

“The impact of digital twins goes beyond the asset and extends to logistics,” Weiss explains.” This has significant implications for mission readiness if an asset deploys to remote or hazardous locations. By understanding the condition of any given asset at any given time, sustainment leaders can anticipate maintenance requirements, ensuring the right components and personnel are in the right place at the right time, making real the concept of condition-based maintenance plus (CBM+).”

CBM – sometimes called Predictive Maintenance (PdM) - is a maintenance methodology that utilizes sensors to assess the health of the system. The health information, in addition to other inputs, helps to drive the maintenance activities. In a CBM environment, operating platforms, embedded sensors, inspections, and other triggering events determine when restorative maintenance tasks are required based on evidence of need.

Combined, these end benefits drive affordable and resource-optimized sustainment operations and data-informed decisions to significantly increase operational availability. In a single-use case assessment of two aircraft engine components, a U.S. military service branch reported potential savings of approximately $42 million annually by using a digital twin.

Not just any digital twin

The predictive ability and subsequent benefits of a digital twin remain fundamentally dependent on the accuracy of its modeling and simulation foundation.

“Only through physics-based simulations can the highest-fidelity representation of an asset’s real-world behavior be obtained,” notes Weiss.

Historically, detailed physics-based simulations struggled to provide the required insights within the actionable timeframe required by an operational asset. The situation has greatly improved today. Physics-based reduced-order models and high-performance computing enable organizations’ digital twins to simultaneously deliver the required fidelity and speed, observed Weiss.

“The deployment of digital twins at scale has yet to be realized, though the benefits for sustainment leaders, maintenance operations, and mission readiness are clear,” said Weiss. “A digital twin is only as good as the underlying simulation capabilities it relies on. Recent developments indicate that the era of the physics-based digital twin — simultaneously delivering tremendous fidelity and speed — is upon us."

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John Blyler is a Design News senior editor, covering the electronics and advanced manufacturing spaces. With a BS in Engineering Physics and an MS in Electrical Engineering, he has years of hardware-software-network systems experience as an editor and engineer within the advanced manufacturing, IoT and semiconductor industries. John has co-authored books related to system engineering and electronics for IEEE, Wiley, and Elsevier

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