A Cyclical Structural Analysis of U.S. Nuclear Power Outages (2015-2023) Using the Adhishthana Principles

Nuclear power remains a critical component of the United States energy infrastructure, yet outages, arising from weather disturbances, equipment failures, grid instability, and other systemic events, continue to present operational and forecasting challenges. This study applies the Adhishthana Principles, a structural analytical framework composed of eighteen cyclical phases, to U.S. nuclear outage data from 2015 to 2023 in order to identify recurring patterns that are not easily captured through event-specific or statistical methods. Using aggregated monthly outage counts derived from the EAGLE-I based Event-Correlated Outage Dataset, we examined year-by-year outage trajectories and mapped them onto the Adhishthana cycle.

Across all nine years of data, outages exhibited a consistent structural formation: a Cākra between Phases 4 and 8, followed by a decisive transition in Phase 9. In most years, this transition resulted in a breakdown of the Cākra and initiated the Move of Pralayā, with outage counts falling sharply in accordance with Adhishthana expectations. Detailed analyses of the 2016 and 2019 datasets illustrate this behavior, with post-breakdown declines of 41.87% and 48.15%, respectively. While the present study focuses on the Cākra-Pralayā sequence, the results indicate that additional insights may be obtained through finer-grained analyses of plant-level data and other phases within the Adhishthana framework.

The findings demonstrate that nuclear outages in the United States conform to a recurring cyclical rhythm, suggesting the presence of an underlying structural pattern independent of specific outage causes. These results highlight the potential value of the Adhishthana Principles as a complementary tool for interpreting outage behavior and improving the anticipatory capabilities of nuclear grid operators and policymakers. Future work incorporating micro-level data and phase-specific modeling may further enhance the predictive utility of this approach.

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