Because the vehicles are likely to encounter conditions that cannot be foreseen, revolutionary
approaches to verification and validation of the models, simulations and systems must also be
developed. Additionally, the ability to modify and evaluate the consequences of modification to
mission parameters in near-real time will be required. Moreover, the consequences of failure
during a long-duration space mission, where the vehicle is far from home, will almost certainly
be catastrophic.
Future generations of vehicles will rely on increasingly complex, heterogeneous and
multifunctional material forms with increasingly complex failure modes. Thus, the extensive
legacy of historical flight information incorporated in the various standards and handbooks that
were based on decades of aircraft and spacecraft design experience, will likely be insufficient to
either certify future extreme vehicles or to guarantee mission success. Additionally, the
extensive physical testing that provided the confidence needed to assure the success of previous
missions has become increasingly expensive to perform. Thus, a complete and fundamental
understanding of physical processes related to degradation at the material, structural and system
level and throughout the vehicle’s life-cycle is needed to move beyond the past decades of
empirical and heuristic design rules that result in inefficiencies and unquantifiable reliability.
Complex missions, particularly those where external support is difficult or impossible, will
necessitate complete real-time management of complex materials, structures and systems that
will ultimately lead to “self-aware” vehicles. The numerous resulting engineering challenges
will necessitate a shift from current empirical-based standard engineering practice to an
additional emphasis on cradle-to-grave sustainment and reliability that will include (1) new
multidisciplinary physics-based methods to ensure robust certification, and (2) new
multidisciplinary methodolgies to ensure life-cycle sustainability. If various best-physics (i.e.,
the most accurate, physically realistic and robust) models can be integrated with one another and
with on-board sensor suites, they will form a basis for certification of vehicles by simulation and
for real-time, continuous, health management of those vehicles during their missions. They will
form the foundation of a Digital Twin
15-18
.
A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built
vehicle or system that uses the best available physical models, sensor updates, fleet history, etc.,
to mirror the life of its corresponding flying twin. The Digital Twin is ultra-realistic and may
consider one or more important and interdependent vehicle systems, including airframe,
propulsion and energy storage, life support, avionics, thermal protection, etc. The extreme
requirements of the Digital Twin motivate the integration of design of materials and
revolutionary approaches for material processing. Manufacturing anomalies that may affect the
vehicle are also explicitly considered, evaluated and monitored. In addition to the backbone of
high-fidelity physical models of the as-built structure, the Digital Twin integrates sensor data
from the vehicle’s on-board integrated vehicle health management (IVHM) system, maintenance
history and all available historical and fleet data obtained using data mining and text mining.
By combining all of this information, the Digital Twin continuously forecasts the health of the
vehicle or system, the remaining useful life and the probability of mission success. The Digital
Twin can also predict system response to safety critical events and uncover previously unknown