Accurately characterizing fuel inventory is critical for risk‑informed design and operation of future nuclear fusion devices. The Idaho National Laboratory has developed a mass‑balance model for fusion fuel‑cycle analysis using the Tritium Migration Analysis Program (TMAP‑8); however, this model is inherently static, relying on fixed parameter values that do not capture the temporal effects of equipment degradation, maintenance schedules, or stochastic system failures. To address this limitation, we have developed a two‑way close‑coupling methodology between TMAP‑8 and the Event Modeling Risk Assessment using Linked Diagrams (EMRALD) dynamic probabilistic modeling tool. This framework enables the fusion fuel‑cycle mass‑balance model to interact with a time‑dependent representation of plant performance, allowing operational states and degradation mechanisms to directly influence fuel inventory predictions.

In the coupled approach, EMRALD simulates system‑level reliability behaviors—including component aging, repair cycles, maintenance outages, and random failures—while TMAP‑8 computes corresponding changes in fuel‑cycle flows and inventory levels. The two models exchange data iteratively, producing a fully dynamic, risk‑informed representation of fuel inventory under evolving plant conditions. Several case studies using representative failure and degradation parameters will be analyzed to demonstrate how integrating dynamic PRA with fuel‑cycle modeling can reveal inventory vulnerabilities that static analyses may overlook.

This work establishes a methodological foundation for applying dynamic PRA principles to fusion fuel‑cycle design, enabling more accurate evaluation of operational margins, storage requirements, and system resilience. The resulting framework provides a pathway toward next‑generation, risk‑informed fuel‑cycle analysis tools for advanced fusion energy systems.