Search NASA⌕ Search

SEARCH · Search NASA

Results for “NPP”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

258 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI for Interpreting Nuclear Power Plant Documents for Power Uprates

To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Using a Large Language Model for Accurate Technical Language Generation in the Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Machine learning (ML) methods for predictive maintenance (PdM) are emerging as effective proactive strategies for diagnosing equipment degradation and enabling effective decision-making. However, explainability and trustworthiness of artificial intelligence are two salient challenges that need to be addressed for wider deployment of these technologies in nuclear power plants (NPPs). Large language models (LLMs) offer a unique approach to tackle these challenges by explaining PdM, work orders, diagnosis results, and ML algorithms to users, who may not be familiar with ML and PdM in general. Moreover, by dynamically retrieving relevant information from technical documents and evaluating factuality of LLM generation, the accuracy and relevance of LLM generations can be improved. This work demonstrates using LLMs to explain the causes and consequences of circulating water system failures based on multiyear NPP work orders. This work tests the capability of multimodal LLM approaches in explaining the differences in the circulating water system from both the Salem and Hope Creek NPPs using both text and image resources. This work also demonstrates the use of multimodal LLMs in describing the diagnosis tab of a predictive maintenance software named VIsualization for PrEdictive maintenance Recommendation (VIPER) to users.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Stem respiration and growth in a central Amazon rainforest

Tropical forests cycle a large amount of CO 2 between the land and atmosphere, with a substantial portion of the return flux due tree respiratory processes. However, in situ estimates of woody tissue respiratory fluxes and carbon use efficiencies (CUEW) and their dependencies on physiological processes including stem wood production (Pw) and transpiration in tropical forests remain scarce. In this work, we synthesize monthly Pw and daytime stem CO 2 efflux (ES) measurements over one year from 80 trees with variable biomass accumulation rates in the central Amazon. On average, carbon flux to woody tissues, expressed in the same stem area normalized units as ES, averaged 0.90 ± 1.2 µmol m -2 s -1 for Pw, and 0.55 ± 0.33 µmol m -2 s -1 for daytime ES. A positive linear correlation was found between stem growth rates and stem CO 2 efflux, with respiratory carbon loss equivalent to 15 ± 3% of stem carbon accrual. CUEW of stems was non-linearly correlated with growth and was as high as 77-87% for a fast-growing tree. Diurnal measurements of stem CO 2 efflux for three individuals showed a daytime reduction of ES by 15-50% during periods of high sap flow and transpiration. The results demonstrate that high daytime ES fluxes are associated with high CUE W during fast tree growth, reaching higher values than previously observed in the Amazon Basin (e.g. maximum CUEW up to 77-87%, versus 30-56%). The observations are consistent with the emerging view that diurnal dynamics of stem water status influences growth processes and associated respiratory metabolism.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Annual Tropical‐Rainforest Productivity Through Two Decades: Complex Responses to Climatic Factors, [CO 2 ] and Storm Damage

Abstract Yearly changes in tropical carbon‐cycling have been a major biotic determinant of the interannual variation in the rise of atmospheric carbon dioxide ([CO 2 ]). The environmental responses underlying these changes remain poorly understood. A 21‐year field study (1997–2018) in a Costa Rican rainforest has produced the first multi‐decade record of landscape‐scale annual aboveground productivity for a tropical ecosystem. While none of the four production components (wood production; leaf‐, twig‐, and reproductive litterfall) increased over two decades, all significantly varied among years. Multi‐factor environmental models explained a large fraction of the inter‐year changes in two production components. Annual changes in three climatic factors and [CO 2 ] jointly accounted for 84% of the variation in annual wood production. While the strongest response of wood production was to decline in years of slightly warmer nights, it also significantly declined in years with more dry‐season hours hotter than 28°C. The yearly changes in reproductive litterfall were largely (52%) determined by the combined effects of increasing [CO 2 ] and declining annual radiation. Twig litterfall declined over the 21 years. Annual leaf litterfall, the largest component of field‐assessed aboveground production, showed no significant environmental responses. An extreme storm in the study's final year disrupted the environmental controls on production that prevailed through the prior 20 years. While important uncertainties are introduced by some of the field methods for assessing aboveground forest production, the findings of this first long‐term field study of annual landscape‐scale productivity raise deep concerns for the future of tropical forests as global warming proceeds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Earth System Model Simulations of Ameriflux sites using dynamic roots and dynamic leaf to root allocation

This data package contains point-scale Earth System Model simulations run with DOE's E3SM Model. The package includes four sets of simulations, one set of simulations run in the default configuration, a second set of simulations run with the dynamic root module enabled, the third set with dynamic leaf to fine root allocation, and the fourth with the combination of dynamic roots and dynamic allocation. These simulations were run in point mode a Ameriflux sites, US-MMS, US-Moz, US-Syv, and US-NR1. These datasets can be used to assess the impact that dynamic roots have on Land Surface fluxes such as carbon and water. The simulations were run with atmospheric forcing from the Global Soil Wetness Project. We provide specifically here Gross Primary Production, Transpiration, relative root fraction per soil layer as well as Air Temperature, Precipitation and all the parameterizations used for the simulations. Our purpose for generating and analyzing these simulations was to assess the role that root dynamics and foraging for water has in the recovery timescale of ecosystems following climate perturbation. All data provided are in the "netcdf" format using standard CF-1 (Climate and Forecast) Convention. The data are all machine readable using various software platforms including Matlab, NCO, Panoply and R.

54 ENVIRONMENTAL SCIENCES↗