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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Additive Manufacturing of Lattice Structures for Catalyst Applications

Abstract The design and fabrication of Inconel 718 open-pore lattice structures via Laser Powder Bed Fusion (LPBF) has been investigated in this research, focusing on applications such as catalyst supports in jet fuel production. The study explores the impact of laser power and scanning speed on the geometrical resolution of these structures aiming to achieve high porosity (porosity > 60%) and specific pore sizes ranging from 500–1000 μm, intending to serve as catalyst supports, replacing conventionally manufactured foams to reduce costs. Results demonstrate the significant influence of processing parameters on the geometrical aspects of printed lattice structures, with laser power having a more pronounced effect on geometrical accuracy than scanning speed. Additionally, the mechanical properties of the printed lattice structures showed a correlation with the lattice strut sizes, as lattices with less porosity and thicker struts resulted in higher maximum shear stress.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness

Abstract We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate $$\xi $$ ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.

Physics↗
DOE OSTI2024

Operational experience and R&D results using the Google Cloud for High-Energy Physics in the ATLAS experiment

The ATLAS experiment at CERN relies on a Worldwide Distributed Computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud providers: lightweight configuration and operation, elasticity and availability of diverse infrastructures. Here this paper examines the seamless integration of cloud computing services as a conventional Grid site within the ATLAS workflow management and data management systems, while also offering new setups for interactive, parallel analysis. It underscores pivotal results that enhance the on-site computing model and outlines several R&D projects that have benefited from large-scale, elastic resource provisioning models. Furthermore, this study discusses the impact of cloud-enabled R&D projects in three domains: accelerators and AI/ML, ARM CPUs and columnar data analysis techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Security assessment and impact analysis of cyberattacks in integrated T&D power systems

In this paper, we examine the impact of cyberattacks in an integrated transmission and distribution (T&D) power grid model with distributed energy resource (DER) integration. We adopt the OCTAVE Allegro methodology to identify critical system assets, enumerate potential threats, analyze and prioritize risks for threat scenarios. Based on the analysis, attack strategies and exploitation scenarios are identified which could lead to system compromise. Specifically, we investigate the impact of data integrity attacks in inverted-based solar PV controllers, control signal blocking attacks in protective switches and breakers, and coordinated monitoring and switching time-delay attacks. Index Terms—Cyberattacks, security assessment, impact analysis, case studies, integrated power systems.

14 SOLAR ENERGY↗
DOE OSTI2021

Plant Cell Identity in the Era of Single-Cell Transcriptomics

High-throughput single-cell transcriptomic approaches have revolutionized our view of gene expression at the level of individual cells, providing new insights into their heterogeneity, identities, and functions. Recently, technical challenges to the application of single-cell transcriptomics to plants have been overcome, and many plant organs and tissues have now been subjected to analyses at single-cell resolution. In this review, we describe these studies and their impact on our understanding of the diversity, differentiation, and activities of plant cells. We particularly highlight their impact on plant cell identity, including unprecedented views of cell transitions and definitions of rare and novel cell types. Lastly, we also point out current challenges and future opportunities for the application and analyses of single-cell transcriptomics in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Impact of Catalyst Ink Dispersing Solvent on PEM Fuel Cell Performance and Durability

The dispersing solvent used for fuel cell catalyst ink preparation plays a vital role in establishing the resulting morphology of the electrode layers, which in turn will impact the performance of proton exchange membrane (PEM) fuel cells. In this study, we report the impact of various ionomer dispersion solvents on PEM fuel cell performance and durability; two aqueous (1-propanol/water and 2-propanol/water) and several non-aqueous dispersing solvents (ethylene glycol and 1,2-butanediol) are compared. The cathode catalyst layer (CCL) fabricated using inks prepared with 1-propanol/water (3:1, w/w) exhibited the best initial performance followed by the CCL prepared using ethylene glycol. The CCLs made from non-aqueous ethylene glycol and 1,2-butanediol exhibited the best durability upon accelerated stress testing. Scanning transmission electron microscopy combined with energy dispersive X-ray spectroscopy indicated that, after the stress test, the distribution of both the Nafion ionomer and Pt nanoparticles within the CCLs prepared with non-aqueous ionomer dispersions underwent less change than those prepared with aqueous dispersions, which is responsible for the improved durability

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Phase Stability of Garnet Solid-Electrolyte Interfacing with Various Cathodes in All Solid-State Batteries

Garnet-structured Li 6.75 La 3 Zr 1.75 Ta 0.25 O 12 (LLZTO) is one of the most promising electrolyte materials for solid-state Li batteries (SS-LiB). The design and fabrication of a good cathode/electrolyte interface is an important criterion for the SS-LiB. In this work, we performed a systematic study on the impact of cathode crystal structure and chemical compositions on their chemical stabilities against the LLZTO at elevated temperatures, which are required for their adhesion during cell fabrication processes. X-ray Diffraction (XRD) and Rietveld refinement analyses revealed the chemical stabilities of various cathode materials in contact with the LLZTO. While layered LiCoO 2 cathode showed good stability in contact with LLZTO to 900 °C, LiNiO 2 or Ni-rich LiNi x Mn y Co 1–x–y O 2 (NMC) cathodes suffered from the formation of La 4 NiLiO 8 due to La-diffusion from LLZTO. Furthermore, Mn-rich LiMn 2 O 4 spinel and layered LiNi 1/3 Mn 1/3 Co 1/3 O 2 cathodes suffered from the formation of La 2 Zr 2 O 7 due to Li-diffusion and production of Li 2 MnO 3 . As a result, LiNi 0.6 Mn 0.2 Co 0.2 O 2 , having an ideal balance of Ni/Mn/Co composition, or Li 2 MnO 3 containing cathodes such as Li 1.2 Ni 0.15 Mn 0.55 Co 0.1 O 2 were found to have excellent phase stability as the cathodes for LLZTO-based SS-LiBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Capacity Fade of Graphite/NMC811: Influence of Particle Morphology, Electrolyte, and Charge Voltage

LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) is an important Li-ion battery cathode material; however, there is a tradeoff between delivered capacity and capacity retention. As the charge potential increases the capacity rises but at the expense of capacity retention. The decrease in capacity retention has been ascribed to several factors including particle cracking, surface reconstruction, transition metal dissolution, and electrolyte reactivity. The present study compares 4.1 and 4.3 V charging limits in commercially relevant graphite/NMC811 pouch cells for single crystal (SC) and polycrystalline (PC) NMC811 with ethylene carbonate (EC)-containing or EC-free electrolytes. The electrochemistry is rationalized through analysis of electrochemical impedance spectroscopy, positive electrode X-ray photoelectron spectroscopy, soft X-ray absorption spectroscopy, X-ray diffraction, and negative electrode mapping by X-ray fluorescence. Graphite/SC-NMC811 cells show high-capacity retention at 4.1 V but exhibit degradation at 4.3 V charging potentials. The EC-free electrolyte cells led to higher capacity fade, especially when charged to 4.3 V. Cathode dissolution and deposition on the negative electrode from PC-NMC811 cells was higher than for samples from SC-NMC811 cells. This study reveals the impact of material type, charge voltage, and electrolyte composition on the reactions at the positive electrode, their influence on the negative electrode, and evolution with cycle number.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AMOC stability and diverging response to Arctic sea ice decline in two climate models

This study compares the impacts of Arctic sea ice decline on the Atlantic meridional overturning circulation (AMOC) in two configurations of the Community Earth System Model with different horizontal resolution. In a suite of model experiments, we impose radiative imbalance at the ice surface, replicating a loss of sea ice cover comparable to that observed during 1979–2014, and we find dramatic differences in the AMOC response between the two models. In the lower-resolution configuration, the AMOC weakens by about one-third over the first 100 years, approaching a new quasi-equilibrium. By contrast, in the higher-resolution configuration, the AMOC weakens by ~10% during the first 20–30 years followed by a full recovery driven by invigorated deep water formation in the Labrador Sea and adjacent regions. We investigate these differences using a diagnostic AMOC stability indicator, which reflects the AMOC freshwater transport in and out of the basin and hence the strength of the basin-scale salt-advection feedback. This indicator suggests that the AMOC in the lower-resolution model is less stable and more sensitive to surface perturbations, as confirmed by hosing experiments mimicking Arctic freshening due to sea ice decline. Differences between the models’ mean states, including the Atlantic Ocean mean surface freshwater fluxes, control the differences in AMOC stability. Our results demonstrate that the AMOC stability indicator is indeed useful for evaluating AMOC sensitivity to perturbations. We emphasize that, despite the differences in the long-term adjustment, both models simulate a multidecadal AMOC weakening caused by Arctic sea ice decline, relevant to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Early-stage Testing of Thermal Power Dispatch Simulator for Subjective Mental Workload and Evolution Time

The excess thermal energy produced by nuclear power plants (NPPs) during low electricity demands can be utilized in industrial processes, such as hydrogen production, through a thermal power dispatch (TPD) system. Initial testing of the first iteration of a single-train TPD design with a manual control mode at the Idaho National Lab (INL) revealed a high operator workload and degraded control capability. The current study evaluated the impact of an enhanced dual-train TPD design on operators’ subjective mental workload and evolution task-time while completing two operating scenarios in manual and automatic control modes. The results showed no statistically significant difference between participants’ mental workload using both control modes. Evolution time in automatic control mode took a shorter time than in manual control, with participants completing all evolutions in less than the 10-min set as the design specification limit. The shorter evolution time is discussed within the context of plant safety and operational efficiency.

Gideon, Olugbenga↗
DOE OSTI2024

Mitigative Strategies for Recovering From Large Language Model Trust Violations

In this study, we investigated strategies to address trust issues arising from errors in large language models (LLMs). The study examined the impact of confidence scores, system capability explanations, and user feedback on trust restoration post-error. 68 participants viewed the responses of an LLM to 20 general trivia questions, with an error introduced on the third trial. Each participant was presented with one mitigation strategy. Participants rated their overall trust in the model and the reliability of the answer. Results showed an immediate drop in trust after the error; however, there were no differences across the three strategies in trust recovery. All conditions had a logarithmic trend in trust recovery following error. Differences in overall trust were predicted by perceived reliability of the answer, suggesting that participants were evaluating results critically and using that to inform their trust in the model. Qualitative data supported this finding; participants expressed lasting distrust despite the LLM’s later accuracy. Results showcase the need to prioritize accuracy in LLM deployment, because early errors may irrevocably damage user trust calibration and later adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Reduced legacy precipitation decreases microbial community growth efficiency and alters soil organic carbon in a California grassland

Changes in global patterns can leave a lasting legacy in semiarid grasslands by reshaping microbial growth dynamics and carbon cycling during the first wet-up in the autumn—a period known for intense microbial activity and significant carbon emissions. To study the lasting impacts of decreased winter rain, we implemented two precipitation regimes (100% vs. 50% mean annual precipitation) in California Mediterranean-climate grassland field plots. After the dry season, soils were rewetted in the laboratory with H 2 18 O and sampled at 0 h, 3 h, 24 h, 48 h, 72 h, and 168 h post rewet. We quantified CO 2 efflux, measured microbial growth and mortality via quantitative 18 O stable isotope probing and 16S rRNA gene amplicon sequencing, and characterized the soil organic carbon chemical composition, metagenomes, and metatranscriptomes.

16S gene amplicon sequencing↗
DOE OSTI2020

Microstructure study of U–35 wt.% Zr alloy after quick annealing at 650 °C

Uranium-35wt.%zirconium (U-35wt.%Zr) alloy was annealed for 1 hour and 24 hours at 650 ºC and characterized to understand the early stage microstructure evolution. Dendritic microstructure with fine (~300 nm in length) α-U precipitates clustered between dendrite branches were observed in the 1-hour annealed sample. After 24-hour annealing 650 ºC, the α-U precipitates coarsened, and the dendritic microstructure disappeared due to microstructure homogenization. Furthermore, microchemical homogenization observed with Energy-Dispersive X-ray Spectroscopy analysis suggests that α-U precipitates are approaching thermodynamic equilibrium in the 24-hour annealed sample. The findings from this study have potential impacts on the manufacturing and computer modeling of metallic nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗