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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.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FARM supervisory capabilities for thermal energy storage

The FARM (Feasible Actuator Range Modifier) module is a component of the RAVEN-based FORCE framework for the analysis of Integrated Energy Systems (IES). FARM aids HERON in the solution of the power dispatch problem by evaluating feasible set-point signals to be issued to the control systems of the different IES unit components. Set-points need to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To enforce all these limits, a two-stage approach is adopted. First, the power dispatcher algorithm in HERON module estimates set-points that meet the constraints on the production variables, e.g., power levels and power ramp rate limits. These constraints are called explicit constraints. Then, if necessary, FARM adjusts these set-points to ensure the respect of the limits on the process variables of interest, given the knowledge of the system dynamics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). In this report, an overview of the major capabilities of the latest release of FARM is provided, along with a summary of the tool demonstration campaign conducted at the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility. These results assess the performance of the control system architecture embedding FARM both as a Validator of the HERON power dispatcher and as a real time Supervisory control scheme. Additionally, the report outlines the areas that FARM might benefit from, along with proposed solutions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Accelerating Nuclear-Integrated Data Centers in the USA: SWOT Analysis, Power-Thermal Management Strategies, and Industrial-Scale Demonstration and Potential Deployment

Driven by the growth in digital services, cloud computing, AI, and manufacturing, data centers face rising energy demands that challenge traditional power sources and cooling efficiency. This study explores using nuclear power to meet these demands, focusing on accelerated reactor technology deployment and highlighting needs such as N+1/N+2 power supplies and integrated power-thermal management. A SWOT analysis addresses grid connectivity, reactors, and site selection, particularly DOE sites. Reactor technology demonstration and deployment could be accelerated by leveraging test facilities such as MARVEL, MAGNET, TED, FAS, DOME, LOTUS, ATR, Energy System Proving Grounds, and upcoming Energy Launch Pads, along with modeling and simulation tools such as RELAP5, MOOSE, VERA, RAVEN, and FORCE. The potential power and thermal management options, including various cooling technologies, waste-heat utilization, and an industrial-scale demonstration plan, aim to accelerate the integration of nuclear power and data centers in the USA, while emphasizing community and stakeholder engagement and synergistic efforts.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Uncertainty quantification in MELCOR Safety analysis of ARIES reactor designs

MELCOR-TMAP is a combined thermal-hydraulics and tritium tracking code developed to simulate severe accident scenarios in fission and fusion power plants. Here, we demonstrate the results of MELCOR-TMAP analyses on historical ARIES program reference designs. By coupling MELCOR-TMAP with the open source RAVEN probabilistic risk analysis framework’s Bayesian UQ capabilities, we also demonstrate key uncertainties in material properties with the highest impact on tritium inventory and plant risk.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Evaluating sulfurization as a blue carbon sink in a southern California salt marsh

Blue carbon ecosystems such as seagrass meadows, mangrove forests, and salt marshes are important carbon sinks that can store carbon for millennia. Recently, organic matter sulfurization and pyritization have been proposed as mechanisms of net carbon storage in blue carbon ecosystems. At our study site, organic sulfur that is resistant to acid hydrolysis (protokerogen) is an order of magnitude less abundant than pyrite sulfur, suggesting a dominance of pyritization over sulfurization. The C/N ratios and carbon isotope compositions suggest that nearly half of total organic carbon and ≥ 80% of protokerogen is composed of marsh plant material. Sediment protokerogen appears to be sulfurized based on its low δ 34 S values (− 10‰), abundance of disulfides, and higher S/C ratio (~ 1.0%) relative to potential biogenic sulfur sources. However, the interpretation of protokerogen δ 34 S values is complicated by the wide range in sulfur isotope compositions of marsh plants. Evidence for sulfurization occurs within the shallowest sediments across different vegetation zones, yielding consistent products, while pyritization appears to be more sensitive to alterations in sediment redox conditions. Based on organic sulfur and pyrite content, sulfurization may be a more spatially consistent process than pyritization, with implications for carbon storage. The relative abundance of pyrite and protokerogen organic sulfur indicates that pyritization is favored at our study site, but this is likely to vary across the spectrum of blue carbon ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Representativity error scaling of models of the high temperature test facility

Error scaling is a critical step toward the validation of modeling capabilities for high-temperature gas-cooled reactors (HTGRs). Extensive effort is being made to bring HTGRs into the validation basis of numerous thermal-hydraulics codes. Here, this paper demonstrates how systems-level codes can be leveraged to perform error scaling analyses between an experimental facility and a plant-to-be facility. Specifically, we focus on two conduction cooldown experiments from the High Temperature Test Facility (HTTF) and the General Atomics 350 MW th modular high-temperature gas-cooled reactor (MHTGR-350). The error scaling methodology employed in this study is representativity, which in the context of this work was used to quantify how well experiments captured the physics of the plant facility by comparing sensitivity vectors between the two facilities. In addition, two different RELAP5–3D models of the HTTF were included in the comparison to determine if modeling methodologies notably impact the representativity results. The key figures of merit are the maximum block temperature and the coolant outlet temperature. The time-dependent maximum block temperature had a low representativity of below 0.1 for both models across both transients. The coolant outlet temperature had a higher representativity of around 0.6 for both models during the pressurized conduction cooldown, but it was below 0.2 for the depressurized conduction cooldown. Overall, the HTTF experiments were not representative of the transients in the MHTGR-350. However, these results can play a significant role in informing future potential experiments for HTGR systems that iterate on what the HTTF accomplished.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Optimizing pressurized-water reactor equilibrium cycle using a novel loading pattern encoding and rule-based genetic crossover operators

This work presents an extended multi-batch approach applied in shuffling scheme optimization for equilibrium cycle for pressurized water reactors using Genetic Algorithms (GAs). A new ruled based GA crossover operator called Inherited Location and Batch (ILB) was introduced to enhance offsprings reproduction efficiency specialized for equilibrium cycle optimization problem. This approach was implemented within the Plant ReLoad Optimization (PRLO) framework and validated using a generic reactor model based on the AP1000 design, with core parameters calculated via the CASMO/SIMULATE software package. The ILB approach is then applied for both single and multi-objective problems in maximizing cycle length and core average exposure while minimizing the average enrichment of the 57 fresh fuel assemblies (FAs) per cycle. The optimal solutions are selected based on their dominance to the objectives from all feasible solutions. This research identified three optimal solutions satisfied safety constraints: The first solution minimizes feed enrichment costs with a cycle length of 338.8 days and core exposure of 25.39 MWd/MT; the second solution extends cycle length to 361.2 days, with the highest core exposure of 26.84 MWd/MT, using 3.75 wt% average fuel enrichment; the third solution balances both objectives with a cycle length of 349.6 days, core exposure of 25.82 MWd/MT with a slight enrichment increase compared to the first solution. Collectively, these findings underscore the efficiency and effectiveness of the proposed approach in achieving practical multi-objective optimal equilibrium cycle designs using GAs optimizer.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Microbial Community Changes across Time and Space in a Constructed Wetland

Constructed wetlands are artificial ecosystems designed to replicate natural wetland processes. Microbial communities play a pivotal role in cycling essential elements, particularly sulfur, which is crucial for trace metal fixation and remobilization in these ecosystems. By their response to their environment, microbial communities act as biological indicators of the wetland performance. To address knowledge gaps pertinent to the changes in trace metal bioavailability in relation to microbial activities in the H-02 constructed wetland, we performed this study to investigate temporal and spatial variations in microbial communities by using molecular biology tools. Quantitative polymerase chain reaction and next generation sequencing techniques were employed to analyze archaeal and bacterial groups associated with sulfur and methane cycling. Alpha diversity indices were used to assess species richness, evenness, and dominance. Results indicated high gene abundance of Desulfuromonas (5.37 × 10 6 g.cell –1 ), methane oxidizing bacteria (6.92 × 10 6 g.cell –1 ), and methanogenic microorganisms (3.02 × 10 5 g.cell –1 ) during cool months. Warm months were marked by sulfate reducing bacteria dominance (3.31 × 10 6 g.cell –1 ), potentially due to competitive interactions and environmental conditions, higher temperatures, and lower redox potential. Spatial variability among microbial groups was insignificant, but trends in gene abundance indicated complex factors influencing these groups. Next generation sequencing data demonstrated Firmicutes as the most abundant phylum with over 50% regardless of the season or sampling location. Cool months exhibited higher alpha diversity than warm months. Overall, this study showed that seasonal changes significantly impacted the microbial communities in the H-02 constructed wetland that are associated with the sulfur cycle and eventually trace metal biogeochemistry, revealing two distinct mechanisms of the sulfur cycle between the two main seasons, whereas spatial variability effects were not conclusive.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning

Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Surface Texture of Macroplastic Pollution in Streams Alters the Physical Structure and Diversity of Biofilm Communities

ABSTRACT Biofilms can develop on nearly any surface, and in aquatic ecosystems they are essential components of biogeochemical cycles and food webs. Plastic waste in waterways is a new type of surface for biofilm colonisation. To analyse the influence of plastic pollution on the development and diversity of microbial freshwater biofilms that colonised them, we incubated 388 cm 2 veneers of high‐density polyethylene (HDPE) with two veneer textures, smooth and rough, and tulip tree wood ( Liriodendron tulipifera ), in three rural headwater streams at the Savannah River Site (Aiken, SC, USA). We collected biofilms from veneers after 14, 28 and 56 days of incubation and analysed 16S rRNA genes and biofilm properties. We found that plastic negatively affected species richness of biofilms compared with wood, but that evenness was greatest on rough textured HDPE. Beta diversity was primarily influenced by stream site. Beta diversity differed more between wood and plastic veneers than with plastic surface texture and became more different over time. Wood had nine times more biomass than rough HDPE and 40 times more biomass than smooth HDPE. Given the projected increase of macroplastic pollution in aquatic ecosystems, our findings emphasise the need to further understand its effects on biofilm characteristics.

Lopez Avila, Fabiola [Odum School of Ecology Unive↗
DOE OSTI2024

A Full-scale Demonstration of Pressurized Water Reactor Core Design Optimization using Multi-Cycle Optimization Methodology

The U.S. nuclear sector encounters a difficulty in upholding essential safety standards while also securing economic viability for continued operation. Safety stands as a pivotal factor across all facets of operations within light-water reactor nuclear power plants. Achieving economic feasibility alongside safety can be facilitated through the utilization of a risk-informed framework, exemplified by the ongoing development within the Risk-Informed Systems Analysis Pathway under the auspices of the U.S. Department of Energy's LWRS Program. This initiative advocates for a diverse array of research and development endeavors aimed at optimizing both safety and economic efficacy within nuclear power plants, particularly pertinent as many plants contemplate second license renewals. The Risk-Informed Systems Analysis Pathway has two main goals: deploy methodologies and technologies that better represent safety margins and cost and safety factors and develop advanced applications that enable cost-effective plant operation. This report assesses the potential for resolving multi-cycle plant reload challenges through real-world scenarios utilizing the Plant ReLoad Optimization (PRLO) framework. This framework offers reactor core design developers analytic tools of reactor safety and fuel performance with the assistance of artificial intelligence (AI) to enhance core design solutions. Multi-objective genetic algorithm alongside acceleration techniques is explored as an enabling technology for improving fuel efficiency while upholding safety thresholds. The demonstration of multi-cycle core design optimization is performed. This report investigates the practical application of the PRLO platform in addressing real-world core design challenges, supporting AI efforts, and contrasting outcomes with those derived from heuristic or conventional algorithms.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗