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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Experimental and Phenomenological Investigations of the MiniBooNE Anomaly

This thesis covers a range of experimental and theoretical efforts to elucidate the origin of the $4.8\sigma$ MiniBooNE low energy excess (LEE). We begin with the follow-up MicroBooNE experiment, which took data along the BNB from 2016 to 2021. This thesis specifically presents MicroBooNE's search for $\nu_e$ charged-current quasi-elastic (CCQE) interactions consistent with two-body scattering. The two-body CCQE analysis uses a novel reconstruction process, including a number of deep-learning-based algorithms, to isolate a sample of $\nu_e$ CCQE interaction candidates with $75\%$ purity. The analysis rules out an entirely $\nu_e$-based explanation of the MiniBooNE excess at the $2.4\sigma$ confidence level. We next perform a combined fit of MicroBooNE and MiniBooNE data to the popular $3+1$ model; even after the MicroBooNE results, allowed regions in $\Delta m^2$-$\sin^2 2_{\theta_{\mu e}}$ parameter space exist at the $3\sigma$ confidence level. This thesis also demonstrates that the MicroBooNE data are consistent with a $\overline{\nu}_e$-based explanation of the MiniBooNE LEE at the $<2\sigma$ confidence level. Next, we investigate a phenomenological explanation of the MiniBooNE excess combining the $3+1$ model with a dipole-coupled heavy neutral lepton (HNL). It is shown that a 500 MeV HNL can accommodate the energy and angular distributions of the LEE at the $2\sigma$ confidence level while avoiding stringent constraints derived from MINER$\nu$A elastic scattering data. Finally, we discuss the Coherent CAPTAIN-Mills experiment--a 10-ton light-based liquid argon detector at Los Alamos National Laboratory. The background rejection achieved from a novel Cherenkov-based reconstruction algorithm will enable world-leading sensitivity to a number of beyond-the-Standard Model physics scenarios, including dipole-coupled HNLs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Toward Better Understanding of Microphysical Processes and Resulting Precipitation Physics: A Merger Of Observations and Cloud Models (Final Report)

A large database of global disdrometer observations and a diverse set of simulations from the Regional Atmospheric Modeling System (RAMS) were compared using principal component analysis (PCA) in order to better understand warm and ice-based precipitation processes. The analysis demonstrated that six distinct precipitation groups (PGs) with common characteristics were revealed in both the observations and model simulations. These PGs were defined by similar co-variability of rain parameters. However, the model showed large concentrations of drops with a given size compared to the observations. Detailed investigations determined the parameterization of drop breakup was forcing the drops to an equilibrium size with a stronger constraint than was indicated by the observations. A series of sensitivity studies permutating the rain shape parameter in the RAMS 2-moment bulk scheme demonstrated how the assumed shape of the rain distribution influences the microphysics as well as precipitation characteristics. Results were compared to the same simulations performed with a bin microphysics scheme where the rain shape parameter is freely evolving. In the bin scheme, a wide range of shape parameters were found, with horizontal and vertical variability, in addition to being a function of storm lifetime. The results of this study highlight the limitations of a fixed assumed rain distribution in 2-moment microphysics schemes. Model simulations were used to probe the microphysical origins of the six PGs. Rain budgets from a supercell case showed that several of the groups hypothesized to be associated with strong ice microphysical processes had significant contributions from melting hail, but the complexity of ice and warm-rain processes in the supercell precluded definitive determination of microphysical origins for many of the PGs. A case study from the Mid-latitude Continental Convection and Clouds Experiment (MC3E) compared the model PGs to disdrometer observations, and demonstrated the spatial and temporal variability of the PGs which is not possible from disdrometer point measurements alone. The analyses performed during this project demonstrated how statistical analysis can provide a robust method for comparing model simulations and observations, which ultimately resulted identification of limitations in some current microphysics parameterizations as well as insights into precipitation variability which ultimately benefits both observations and modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Comparative Study of Layer Heating and Continuous Heating Methods on Prediction Accuracy of Residual Stresses in Selective Laser Melted Tube Samples

Thermal distortion and residual stresses are two important factors that affect the quality and reliability of steel parts manufactured by laser powder bed fusion (LPBF) processes. A cost-effective model for evaluation of those heat effects is needed to refine the manufacturing process and provides insights into the product design and heat treatment. In this study, the layer heating method and sophisticated track-layer scanning method were applied to simulate the thermo-mechanical response of IN625 tube parts built by LPBF. Based on the similarity of temperature field in each layer deposit, a swept mesh was constructed to perform the thermal analysis for top layer, with the rest of layers referring to the temperature by node number offsetting. A novel explicit finite element analysis code accelerated by graphics processing unit was used for the massive-element numerical analysis. The computational accuracy and efficiency of the layer heating and track-layer scanning methods were compared in detail. It is shown that layer heating method can efficiently capture the pattern of stress distribution with reasonable accuracy in stress magnitude. The grouped track-layer scanning method can predict the residual stress and strain more accurately at a higher cost (5 ~ 10×). The elastic strain distribution was compared with the measurement by X-ray diffraction, confirming the accuracy of residual stress prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Biomethanation to Upgrade Biogas to Pipeline Grade Methane

NREL is working closely with DOE, Electrochaea GmbH, and Southern California Gas Company (SoCalGas) to reduce costs of a biomethanation process capable of megawatt-scale deployment that upgrades organic biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a single-celled methanogenic archaea that converts low-carbon low-cost hydrogen (H2) and waste carbon dioxide (CO2) to produce renewable methane (CH4). The process upgrades the biogenic CO2 - while allowing the CH4 to pass through - from biogas sources like dairies, wastewater treatment plants, and landfills. The CH4 produced is a drop-in direct replacement fuel and producers can participate in the growing number of carbon markets; like California's Low Carbon Fuel Standard and the Federal Renewable Fuel Standard. NREL and Argonne National Laboratory have completed a life cycle analysis using the GREET model to show that the biomethanation process produces RNG that is carbon negative even when H2 production via low-temperature water electrolysis is driven by the existing carbon intensity of California's electricity grid. And of course, even further carbon negative (-233 kg CO2e/kWh) when the electricity is produced from low-carbon sources like wind and solar. Leveraging lessons learned from operating SoCalGas' 700L 18-bar bioreactor system, NREL is designing and building a flexible RD&D platform that will enable field trials at biogas and other CO2 sources. A custom 16' long trailer will house a 20L 18-bar bioreactor, 3 - 25 kW proton exchange membrane electrolyzer, and dosing, thermal, and controls systems to support operations with only power, biogas, and water feedstocks required by the field locations. The end-of-project goal is to demonstrate pipeline quality RNG production (> 95% CH4, < 4% H2, <1% CO2, < 0.2% O2 and < 4 parts per million H2 sulfide) using real biogas feedstocks - thereby recycling both greenhouse gases for injection into the natural gas network or to be used onsite.

biogas upgrading↗
DOE OSTI2026

Evaluation of AI-Enabled Digital Documented Safety Analysis: A Case Study

Safety basis documentation development and review under U.S. Department of Energy (DOE) authorization have emerged as critical constraint throttling deployment of advanced nuclear reactors, with traditional processes demanding extraordinary resource investment that delays the delivery of these technologies. Traditional Documented Safety Analysis (DSA) processes rely on static documents with limited traceability [U.S. DOE]. The regulatory review and engagement processes are similarly constrained, often requiring significant effort and extensive manual verification. The scale of this challenge is exemplified by the U.S. Nuclear Regulatory Commission (NRC) review of the NuScale application, which required over 250,000 staff hours and the evaluation of approximately two million pages of documentation [Bergman 2021]. The volume and complexity of information within nuclear licensing applications or authorization reviews demands innovative approaches to document generation and data management.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Opportunities for Solar Industrial Process Heat in the United States

This presentation summarizes the first national analysis of opportunities for solar technologies to provide industrial process heat (IPH) in the United States. The industrial sector is a major end-user of energy and IPH constitutes a majority of industrial fuel energy. New disaggregations of IPH demands are made at the temporal, geographic, and operational levels. Solar heat generation by seven solar thermal technologies and PV-connected electrotechnologies are modeled using county solar resources and land availability and matched to relevant IPH demands. Parabolic trough collectors (PTC), when combined with thermal energy storage (TES), not only have the largest opportunity in terms of distribution over geography and time, but also in terms of applicable IPH demands. PTC with TES represents the displacement of nearly 2,500 trillion Btus of combustion fuels, which corresponds to 137 million metric tons of CO2, or about 15% of all industrial combustion CO2 emissions. TES, along with site-level analysis, are identified as areas of further analysis.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Techno‐economic feasibility analysis of engineered energycane‐based biorefinery co‐producing biodiesel and ethanol

Abstract High feedstock cost and low oil yields per unit of land from temperate oilseed crops limit the growth of commercial‐scale biodiesel production. Recently, highly productive crops, such as sugarcane and energycane, have been engineered to accumulate triacylglycerides (TAGs) that allow the production of far more industrial vegetable oil than previously possible. A proof‐of‐concept suggests that biodiesel production from engineered energycane will be possible. However, before making efforts for scale‐up, it is critical to understand the commercial feasibility and economic competitiveness of this process. This study performs techno‐economic analysis of a unique biorefinery processing energycane to co‐produce biodiesel and ethanol. Comprehensive process simulation models were developed for two scenarios: (i) biodiesel from TAGs and ethanol from fermentation of sugars in juice and (ii) biodiesel from TAGs and ethanol from fermentation of sugars in juice and hydrolysis of carbohydrates in bagasse. Based on the target levels, the analysis was performed for energycane containing 0%, 5%, and 7.7% TAGs (d.b.). The biodiesel from engineered energycane was found economically viable and competitive to soybean biodiesel. Although the capital investment is higher compared to the soybean biodiesel plant, the biodiesel production costs ($0.66–$0.9/L) were lower than soybean biodiesel ($0.91/L). Biorefinery‐scenario‐1 processing energycane containing 7.7% TAG produces biodiesel with profitability (IRR 7.84) slightly lower than soybean biodiesel (IRR 8.3), but yields five times of biodiesel per unit land and is self‐sustainable for energy requirements. The surplus electricity can displace fossil electricity and provide environmental benefits. Monte Carlo simulation indicated that biorefinery is profitable with a 29%–65% probability (NPV > 0) which is largely controlled by feedstock composition and biodiesel market price. It is important to note that energycane can be grown on the marginal rainfed lands in S.E. USA, where soybean would not be viable. Biodiesel from engineered energycane would therefore be complementary to soydiesel in the United States.

09 BIOMASS FUELS↗
DOE OSTI2020

Site 200 #1400 Block Area New Trailers Project, Soil Sampling and Analysis Plan/Quality Assurance Plan

Lawrence Livermore National Laboratory's (LLNL) Project Management Office (PMO) is proposing to install a series of modular trailers and install associated underground utilities in the 1400 Block at the Livermore Site. This Sampling and Analysis Plan (SAP) outlines the processes and procedures to collect environmental and geotechnical soil samples from the project area and to send the samples to the appropriate laboratory or laboratories for analysis or testing. The resulting environmental laboratory data will determine whether excavated soils can be reused on the project, or which type of landfill or landfills the soils may be sent for disposal. This SAP has been prepared and is organized to be consistent with LLNL's Soils Screening and Management Plan (SSMP) (LLNL, 2019), as well as the U.S. Environmental Protection Agency's (EPA) Data Quality Objectives programs (EPA, 2006).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Intermediate Level Trigger Supernova Pointing Trigger for DUNE Using In-Storage AI

We propose a near-data approach to reduce the buffered supernova data on the DUNE DAQ front-end computers using ML techniques. Based on simulations, we are able to reduce the data by 5 orders of magnitude to 0(100)MB which can then be transferred quickly over ethernet to a single server that executes a full reconstruction and pointing analysis to determine the SN direction. The entire process of reducing the data and executing reconstruction and pointing analysis pipeline takes less time than that required to transfer all the data back to Fermilab before performing more processing to determine the direction of the supernova, which was the baseline plan for DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

High Entropy Analysis Code

This code describes the process to estimate material properties using elemental properties and machine learning.

Kunz, MatthewR↗
DOE OSTI2023

Nuclear Data: From Measurement, Evaluation, Processing to Practical Applications [Slides]

Any analysis, design of a nuclear system will be as good as the knowledge of the nuclear data used whether differential and integral. ORNL is engaged in generating high level nuclear data for reactor, shielding, and safety applications. Nuclear data remains a major source of data uncertainties. Uncertainties on nuclear data rely upon measurements and evaluations. Tight connections with experimental facilities around the world have helped ORNL to address issues on ND and uncertainty. It is extremely important for ORNL to retain expertise on nuclear data in support of the research and expertise activities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Impact of a track trigger at a multi-TeV muon collider

In the panorama of future particle accelerators, the Muon Collider seems to be the best choice for several reasons that will be explored in section 1.2. The main challenge is represented by the instability of the muons. At the desired instantaneous luminosities, an intense flow of secondary and tertiary particles from the interactions of the beam decay products with the machine elements reaches the detector, compromising its performance. Several studies are currently underway to mitigate the background and to have an efficient trigger. My work represents a first step towards understanding the basic characteristics of a trigger selection, which will subsequently guide the design of the trigger system. With this aim I produced and analyzed Higgs and di-Higgs signals with MadGraph and CERN-ROOT. In first section there is an introduction to Muon Collider, a brief history, a discussion of its benefits, a description of the detector and of the effects of the Beam Induced Background. Several operating center-of-mass energies are possible for this machine, in this report a center-of-mass of 3 TeV is assumed. Then I describe the event generation process. Finally I describe the entire analysis, in particular I analyzed the efficiency and the number of events for each process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

High-Multiplicity Muon Airshower Analysis at NOvA Far Detector

We process and analyze muon airshower data from the NOvA far detector using various image processing algorithms, such as Fast Fourier Transformation, and Hough line transformation. From the processed event images, we calculate multiple parameters for our study. We are looking for physics features, including East-West Asymmetry, anisotropies in right ascension, and seasonal variation. Additionally, we have developed an algorithm to count the multiplicity of muons in the airshower events using the single muon data.

Lima, Aklima Khanam [Syracuse U.]↗
DOE OSTI2020

Advanced Test Reactor Experiment Safety Analysis

This presentation contains general information about the Advanced Test Reactor experiment safety analysis program. Accidents and dose consequence are discussed in general terms. Pictures of ATR facilities and experiments are included. In cases where experiments are shown, identification numbers have been removed. Department of Energy limits on dose consequence are provided. These limits are found in many non-CUI documents and a derived from publicly available sources. A summary of the presentation content is as follows: -Define Experiment Safety Analysis -Discuss what we are protecting and the applicable limits -Show how safety analysis fits into the experiment process -Explore the scope of experiment activities and associated accidents -Present how we categorize accidents and establish accident frequencies -Identify how controls are implemented and process improvements

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Observation of Potential Contaminants in Processed Biomass Using Fourier Transform Infrared Spectroscopy

With rapidly increased interests in biomass, diverse chemical and biological processes have been applied for biomass utilization. Fourier transform infrared (FTIR) analysis has been used for characterizing different types of biomass and their products, including natural and processed biomass. During biomass treatments, some solvents and/or catalysts can be retained and contaminate biomass. In addition, contaminants can be generated by the decomposition of biomass components. Herein, we report FTIR analyses of a series of contaminants, such as various solvents, chemicals, enzymes, and possibly formed degradation by-products in the biomass conversion process along with poplar biomass. This information helps to prevent misunderstanding the FTIR analysis results of the processed biomass.

09 BIOMASS FUELS↗