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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Estimating Spatial Distribution Impacts of Rooftops Solar PV on Dynamic Hosting Capacity Evaluation for a Real Distribution Feeder

This paper models the sensitivity of dynamic hosting capacity analysis to the spatial distribution of distributed PV scenarios, random, close and far from substation deployments, at various penetration levels on a real distribution feeder, in a quasi-static time series (QSTS) power flow simulation using high resolution time-aware metrics. These spatial distribution scenarios were chosen to capture a wide range of potential operation impacts of these deployments using a set of thermaland voltage-based metrics such as instantaneous violations and moving averages for both thermal and voltage constraints. This study uses actual load and PV data up-scaled from 1-hour time step to 1-minute resolution to fully characterize the interaction between the daily changes in load and PV output, and their impacts on distribution system operations.

distribution system↗
DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗
DOE OSTI2021

Critical phenomena of the layered ferrimagnet Mn 3 Si 2 Te 6 following proton irradiation

The critical phenomena and magnetic entropy of the quasi-2D ferrimagnetic crystal, Mn 3 Si 2 Te 6 (MST), is analyzed along the easy axis (H || ab) as a function of proton irradiance. The critical exponents β and γ do not fall into any particular universality class upon proton irradiation. However, for pristine and irradiated samples, the critical exponents lie closer to mean field-like interactions; therefore, long-range interactions are presumed to be sustained in MST. The effective spatial dimensionality reveals that MST remains at d =3 under proton irradiation, whereas spin dimensionality transitions from an initial n =1 to n =2 and n =3 for 1 × 10 15 and 5 × 10 15 H + /cm 2 , indicating XY and Heisenberg interactions, respectively. The spin correlation function reveals an increase in magnetic correlations at 5 × 10 15 H + /cm 2 . Maximum change in magnetic entropy at 3 T is the largest for 5 × 10 15 H + /cm 2 at 2.45 J/kg K, in comparison to 1.60 J/kg K for pristine MST. These results intriguingly align with previous findings on MST where magnetization increased by ~50% at 5 × 10 15 H + /cm 2 , in comparison to its pristine counterpart [Martinez et al., Appl. Phys. Lett. 116, 172404 (2020)]. Magnetic entropy derived from heat capacity analysis shows no large deviations across the proton irradiated samples suggesting that the antiferromagnetic (AFM) coupling between the Mn sites is stable even after proton irradiation. This implies that magnetization is enhanced through a strengthening of the super-exchange interaction between Mn atoms mediated through Te rather than a weakening of the AFM component.

2D materials↗
DOE OSTI2024

Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data

The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.

Distribution network↗
DOE OSTI2022

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2024

State Requirements for Electric Distribution System Planning

Utilities have conducted distribution planning since they first began building and operating electricity systems. But filing these plans for regulatory and stakeholder review is a relatively recent phenomenon. This report summarizes legislative and regulatory requirements for regulated electric utilities to file some type of distribution system plan in 20 U.S. jurisdictions. Some plans focus on expedited cost recovery for certain types of distribution system improvements; other plans focus on investments for grid modernization or distributed energy resources. Increasingly, states are adopting requirements for Integrated Distribution Plans. Such plans provide holistic grid investment strategies that address state and local policies and increasing complexity at the grid edge. The report covers the following topics for distribution system plans, highlighting advanced practices: -State goals and objectives -Procedural requirements -Forecasting loads and distributed energy resources -Hosting capacity analysis -Baseline information requirements -Grid modernization strategy -Grid needs assessment -Non-wires solutions -Reliability and resilience analyses -Stakeholder engagement -Equity -Pilots -Coordination with other planning processes The report includes links to legislation; regulatory requirements, proceedings, and orders; and filed utility plans. The U.S. Department of Energy’s Office of Electricity provided funding support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

The rapid expansion of artificial intelligence (AI) and machine learning is driving unprecedented electricity demand from data centers. It is predicted that by 2030, 90% of AI workloads will be inference-based, requiring interconnection of multiple low-latency edge data centers (<20 MW) sited closer to end users - often on already constrained distribution feeders. Although individually small, these loads can aggregate to large loads per feeder, straining infrastructure, creating multi-year interconnection delays, and driving up customer costs. This paper proposes a data center-focused grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, building load flexibility, and waste heat reuse to expand effective feeder and substation headroom. Such approaches can reduce interconnection delays, lower costs for ratepayers, and accelerate AI-ready infrastructure deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Distributed Generation Market Demand (dGen) model

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

Array↗
NASA NTRS1972

Externally pressurized gas-lubricated journal bearings with herringbone grooves - Load capacity and stability analysis.

A small eccentricity analysis was performed to predict load capacity and stability. Numerical results were obtained for a range of feeding parameter, pressure ratio, groove length and orifice recess volume for compressibility numbers from 0 to 50. These results were obtained from a digital computer program. Results showed that the addition of herringbone grooving to an externally pressurized bearing increases stability, but reduces load capacity at low compressibility numbers. A fully-grooved bearing is more stable than a partially-grooved bearing. Orifice recesses reduce stability, especially at high compressibility numbers.

Fleming, D. P.↗
NASA NTRS2012

Debris Examination Using Ballistic and Radar Integrated Software

The Debris Examination Using Ballistic and Radar Integrated Software (DEBRIS) program was developed to provide rapid and accurate analysis of debris observed by the NASA Debris Radar (NDR). This software provides a greatly improved analysis capacity over earlier manual processes, allowing for up to four times as much data to be analyzed by one-quarter of the personnel required by earlier methods. There are two applications that comprise the DEBRIS system: the Automated Radar Debris Examination Tool (ARDENT) and the primary DEBRIS tool.

Griffith, Anthony↗
NASA NTRS2024

Xplo-SA9T: Optimization of Sorbent Technology With Existing and New Amine Variants for Carbon Dioxide Removal and Humidity Control

Scrubbing of the spacesuit air stream to control humidity and regulate carbon dioxide (CO 2 ) relies on amine-loaded resins, colloquially known as sorbents. The efficacy of Rapid Cycle Amine technology relies on the propensity of nitrogens toward adsorption of CO 2 while simultaneously offering regenerative properties under vacuum or positive pressure. The commonly utilized sorbent, SA9T, was recreated and evaluated further to increase stability and serve as a benchmark comparison for newly synthesized amine sorbent technologies. Ongoing storage studies with eight different environments will allow the development of applicable storage protocols and provide insight into possible ammonia generation pathways. Attempts to alleviate the known issues with SA9T included the alteration of the primary amine (tetraethylenepentamine), the choice of resin, and the particle size distribution. In-house CO 2 breakthrough experiments were conducted using a specialized apparatus that can simulate spacesuit conditions, offering high-quality triplicate capacity analysis affording direct comparison to known adsorbents in the literature. Thermo-Desorption Gas Chromatography Mass Spectrometry studies on the synthesized materials revealed any reagent or solvent offgassing while Nuclear Magnetic Resonance spectroscopy tracked the stability of the bound amine. Scanning Electron Microscope images of sieved materials provided a more accurate size interpretation facilitating synthetic design and pressure drop elimination. All efficient sorbents were synthesized to a commercial batch scale subsequent to evaluation including Xplo-SA9T.

John R Tidwell↗
NASA NTRS2016

Analysis of the Capacity Potential of Current Day and Novel Configurations for New York's John F. Kennedy Airport

In 2015, a series of systems analysis studies were conducted on John F. Kennedy Airport in New York (NY) in a collaborative effort between NASA and the Port Authority of New York and New Jersey (PANYNJ). This work was performed to build a deeper understanding of NY airspace and operations to determine the improvements possible through operational changes with tools currently available, and where new technology is required for additional improvement. The analysis was conducted using tool-based mathematical analyses, video inspection and evaluation using recorded arrival/departure/surface traffic captured by the Aerobahn tool (used by Kennedy Airport for surface metering), and aural data archives available publically through the web to inform the video segments. A discussion of impacts of trajectory and operational choices on capacity is presented, including runway configuration and usage (parallel, converging, crossing, shared, independent, staggered), arrival and departure route characteristics (fix sharing, merges, splits), and how compression of traffic is staged. The authorization in March of 2015 for New York to use reduced spacing under the Federal Aviation Administration (FAA) Wake Turbulence Recategorization (RECAT) also offers significant capacity benefit for New York airports when fully transitioned to the new spacing requirements, and the impact of these changes for New York is discussed. Arrival and departure capacity results are presented for each of the current day Kennedy Airport configurations. While the tools allow many variations of user-selected conditions, the analysis for these studies used arrival-priority, no-winds, additional safety buffer of 5% to the required minimum spacing, and a mix of traffic typical for Kennedy. Two additional "novel" configurations were evaluated. These configurations are of interest to Port Authority and to their airline customers, and are believed to offer near-term capacity benefit with minimal operational and equipage changes. One of these is the addition of an Optimized Profile Descent (OPD) route to runways 22L and 22R, and the other is the simultaneous use of 4 runways, which is not currently done at Kennedy. The background and configuration for each of these is described, and the capacity results are presented along with a discussion of drawbacks and enablers for each.

Glaab, Patricia↗
NASA NTRS2011

Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds

Tidal marshes are highly productive ecosystems that support migratory birds as roosting and over-wintering habitats on the Pacific Flyway. Microphytobenthos, or more commonly 'biofilms' contribute significantly to the primary productivity of wetland ecosystems, and provide a substantial food source for macroinvertebrates and avian communities. In this study, biofilms were characterized based on taxonomic classification, density differences, and spectral signatures. These techniques were then applied to remotely sensed images to map biofilm densities and distributions in the South Bay Salt Ponds and predict the carrying capacity of these newly restored ponds for migratory birds. The GER-1500 spectroradiometer was used to obtain in situ spectral signatures for each density-class of biofilm. The spectral variation and taxonomic classification between high, medium, and low density biofilm cover types was mapped using in-situ spectral measurements and classification of EO-1 Hyperion and Landsat TM 5 images. Biofilm samples were also collected in the field to perform laboratory analyses including chlorophyll-a, taxonomic classification, and energy content. Comparison of the spectral signatures between the three density groups shows distinct variations useful for classification. Also, analysis of chlorophyll-a concentrations show statistically significant differences between each density group, using the Tukey-Kramer test at an alpha level of 0.05. The potential carrying capacity in South Bay Salt Ponds is estimated to be 250,000 birds.

Hsu, Wei-Chen↗
DOE OSTI2024

Computational Analysis of Hydraulic Capacity of Ohio DOT Catch Basin No. 6 in On-Grade and Sag Locations

Stormwater runoff from streets and highways is typically captured by drainage structures strategically placed when roadways use curb or curb and gutter systems. These structures include catch basins with grates, inlets, or combination grates/inlets that collect and discharge storm water runoff to buried pipe conveyance systems. The performance of these drainage structures is measured in terms of hydraulic efficiency, which is defined as the percentage of flow captured by the basin as compared to the total flow draining to the structure. Understanding the performance of these drainage structures allows for a safe and economical design which prevents flooding along Ohio roadways.

42 ENGINEERING↗
NASA NTRS1974

Yield analysis of large capacity magnetic bubble circuits with redundancy design

The fabrication yield of an on-chip modifiable redundant circuit design for a 100M bit serial shift register is evaluated. The yield model is a redundancy design in which there is a primary loop and a set of secondary loops which can be enabled/disabled without introducing blanks to the data stream. This function has a finite yield, the loop-modification yield factor, which must be greater than 0.9 to make the system more economical than the simple nonredundant design. It is further established that small loop capacities greatly degrade the yield because of the effect of the modification yield factor, while large loop capacities degrade the yield because of defects in the operating area. As the modification yield increases the optimum loop capacity decreases. An optimum value for the number of redundant loops exists for each loop capacity. Other factors that affect the yield are the garnet film and the processed circuit.

Bailey, R. F.↗