In this contribution, the simulation-based digital twin of a high-speed turbomachine, i.e. a fan with a nominal speed of 16500 rpm, that has been developed for the aerospace industry with multiple uses in various platforms, and the corresponding findings are presented. The relevant simulation-based digital twin is created by first running various multi-physics engineering simulations, i.e. computational solid mechanics, computational fluid dynamics, and low-frequency electromagnetic analyses with Ansys software. Subsequent dynamic reduced order models (ROM) are formed and integrated together to create the digital twin that are bidirectionally connected with the asset. The digital twin is also validated via experimental data that are obtained with various setups for several parameters read at discrete locations via sensors, based on fidelity comparisons both between experiments and simulations, and between simulations and ROMs. Several operational scenarios including those with stress tests are run and the asset is checked against fatigue and thermal requirements for the rotor-stator assembly and the motor circuit respectively. This is done with an inhouse code that incrementally reads time, temperature, and rotational speed data and in which the relevant fatigue and thermal criteria are defined. This code is also used to simulate and analyze what-if scenarios. It is seen that vibration fatigue dominates all possible sources of failure in most cases. As a novel aspect, the fatigue induced by starts and stops of the fan is expressed as equivalent operating hours (EOH) depending on time and temperature parameters of the corresponding scenarios, as opposed to be taken as a constant value for all scenarios. Using this novelty, it is also conceptually demonstrated that simulation-based digital twins can play a significant role in operation and maintenance when combined with conventional data-driven predictive maintenance techniques. Approaches to simulation-based digital twins in context with multi-physics and ROM fidelity are also discussed based on several digital twin maturity models.