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Applications

Operator Training Simulators

High-fidelity plant simulators that let operators build skill and rehearse rare events safely before the plant exists.

Why simulate the operator's world

Operators must handle situations that are too rare, too dangerous, or too expensive to practice on real hardware, especially situations that must be handled correctly the first time. A training simulator reproduces the control room and plant response faithfully enough that skills learned in it transfer to the real system, including the muscle memory of responding to alarms and transients.

What the simulator must reproduce

Kronos motion — fidelity

Fidelity versus speed

A perfect physics model may run slower than real time, which defeats interactive training. Simulators use reduced-order or surrogate models tuned to match the full physics in the regimes that matter, trading unnecessary detail for the responsiveness that makes training realistic. The art is knowing which details matter for the operator's decisions.

Scenario design

The value of a simulator is in its scenarios. Good training decks include normal operation, common faults, rare compound faults, and deliberately ambiguous situations that build judgment. Scenarios are drawn from the safety analysis so that the events operators rehearse are the ones the design most needs them to handle well.

python
def run_scenario(sim, script, operator):
    log = []
    for t, event in script:
        sim.inject(event, t)
        action = operator.respond(sim.state(t))
        log.append((t, event, action, sim.evaluate(action)))
    return log

Kronos framing

Because the Hyperion breeder is not yet built (construction begins in the second quarter of 2027), simulator development runs in parallel with design, using the digital twin as its physics core. Training a workforce before first operation is one reason the twin is built to be faithful, not merely illustrative.