Spacecraft Digital Twins?

Digital Twin technology has been revolutionizing many industries and processes globally over the last few years. I first learned of the concept only a couple of years ago. Wondering about its applicability to the space enterprise, I quickly realized that while digital twins may face some very unique challenges that the space application poses on any future implementation of the technology, they can bring tremendous value and completely transform the entire industry.

But first, what is a digital twin? A digital twin is a faithful digital representation of a physical object or system. By “faithful digital representation” I do not just mean a mathematical model and a simulation environment for the object or system that are detached from the current state of the actual physical instance of the object. A digital twin replicates the physical object or system using near-real-time streaming sensor data. In today’s Cloud Era, a digital twin typically lives and is analyzed in the cloud, and is combined with other data related to the object or system such as its environment. The twin is then made available to people in a variety of roles, so they can remotely understand its status, its history, its needs, and interact with it to perform any number of functions. One of the key functions of digital twins is the prediction and detection of anomalous system behaviors, where an artificial intelligence or a machine learning algorithm compares the streaming data against system design performance expectations, model simulations, and, very importantly, how other actual instances of the object or system, via their aggregated digital twin behaviors, to establish normal operation, are behaving.

Applications of digital twins range from manufacturing, to Airbus’ use of the technology in the design and development phases of its aircraft, to General Electric’s real-time jet engine early degradation detection, to the construction, maintenance and analysis of smart buildings, to Thornton Tomasetti’s use of the technology for the monitoring of medical devices and patients in healthcare. Digital twins are now also coming to a United States Department of Defense agency near you! The Navy’s Office of Naval Research (ONR) is investing nearly $10M in the technology to enhance the “resiliency, efficiency, adaptability, and autonomy of naval power systems and platforms” and to “accelerate transition of health monitoring and predictive maintenance technology for costly electrical components aboard Navy ships”. The Assistant Secretary of the Air Force for Acquisition, Technology and Logistics, Dr. Will Roper, has been advocating for the use of digital twin technology across the US Air Force for the design of jets (Boeing’s T-7 Red Hawk Trainer aircraft is already leveraging digital twin technology), missiles and satellites.

Note, especially for satellites, that Dr. Roper specifically used the term design and not operation. While digital twins are applicable to satellite design and manufacturing processes pre-launch —manufacturing is a natural place for the technology— its use in satellite operations, as is done by, say, GE for jet engines, will face challenges that are unique to the operational space environment.

(I note here that the broader technology which connects data flows and produces a holistic view of an asset's data across its entire lifecycle is known as a digital thread. Digital twins are an integral part of the digital thread, but the latter encompasses a broader set of digital concepts and technologies beyond digital twins. I will return to the concept of digital threads in a future post. Both Digital threads and digital twins are integral to the Model Based Enterprise and Model Based Systems Engineering.)

The unique challenges facing digital twin technology for an operational space system are not difficult to guess. In the pre-Cloud Era, one would have started with the latency problem. But with technologies like Amazon’s AWS and Microsoft’s Azure Space, having to wait for ground contact for data transmission may become unnecessary, and where the analytics themselves, digital twins included, may be moved closer to the operational edge. There will always be a latency challenge, but it will not be as bad as having to wait until the satellite’s next pass over a ground station. Two bigger challenges, though, loom, especially for the today’s small satellite revolution. First, there is bandwidth. For a twin to be a faithful replica of an operational spacecraft, we would need to transmit far more data about the satellite’s state than is typically contained in telemetry data today. Second, and of even more relevance to small satellites, to get to that data volume level needed to realize a faithful digital twin, we would need to add far many more sensors with many more sensor types onboard the spacecraft. However, more sensors means more weight. And, in space, weight matters, especially for small satellites.

With these challenges, it is tempting to just dismiss the concept of digital twins for operational space systems. As in the examples I mention above for terrestrial and aerial applications, there is tremendous value that digital twin technology can bring to any enterprise. With its predictive analytics capabilities, for example, digital twins can result in the prolongation of the operational life of a spacecraft, or aid in the prevention or, if prevention is impossible, in the preparation for and mitigating the impact of an impending catastrophic event that may result in the spacecraft’s total loss. Would the added weight and cost justify the value gained from employing a digital twin technology? This question presents us with a rich field for research and development that will also have to touch on questions related to latency, bandwidth, and what other sensors, and how many, do we need to add to the current suite of onboard sensors in order to generate enough data for a digital twin to deliver on its promises.

Exploring that trade space seems to me to be unavoidable since the use of digital twins for operational space systems is, somewhat, underway. In a March, 2020 article, for example, Air Force Magazine mentions that the US Air Force’s Space and Missile Systems Center (SMC) has long been pervasively employing the Model Based Systems Engineering framework on which digital twin technology is typically based. In fact, as mentioned in the article, Booz Allan Hamilton created a digital replica of an an-orbit Lockheed Martin’s Block IIR GPS satellite to detect system cybersecurity vulnerabilities.

One can even argue that, as Prof. Moriba Jah did recently, any one of the myriad commercial, government and academic Space Situational Awareness (SSA) and Space Traffic Management (STM) services, such as University of Texas’ ASTRIAGraph, is a de facto SSA/STM nascent digital twin of the space object population. For SSA/STM, however, the replica is limited by the relative lack of diversity in sensor types, geometric perspective and distance from the “objects”. The SSA/STM application, however, benefits from the fact that it is heavily dominated by the physics. Therefore, it is both simulation- and data-heavy, but with low data diversity. Typical terrestrial or aerial digital twin applications, on the other hand, are usually data-heavy, and have high data diversity, and these two properties dominate the need for a heavy reliance on simulations.

So digital twins are not really all that new. We are simply just embarking on unleashing what I think will be a revolutionary transformation of how we design, manufacture, analyze and operate space systems across the entire enterprise. And that is a very exciting prospect!