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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 OSTI2021

Software Quality Assurance for EBR-II Fuels Irradiation and Physics Database (FIPD)

The Fuels Irradiation and Physics Database (FIPD) is an ongoing DOE project on archival of the EBR-II metal-alloy fuel irradiation experiments. As part of its use in support of license applications, the Quality Assurance Program Plan (QAPP) was drafted and endorsed by NRC in an effort to demonstrate its compliance with regulatory expectations. Software Quality Assurance (SQA) for the physics portion of FIPD is intended to qualify the calculated quantities such as fuel and cladding temperatures, neutron fluence and axially varying burnup estimates for irradiated fuel elements. This report covers the initial evaluation of SQA status of three neutron physics and thermo-fluid codes (REBUS, RCT and SE2RCT) that form the basis of calculated quantities for as-irradiated characteristics of the tested metallic fuel elements. The report also introduces an SQA plan to address the identified deficiencies. The REBUS, RCT, and SE2RCT codes are all part of the Argonne Reactor Code (ARC) code system. There is considerable knowledge and experience on REBUS and RCT but relatively less on SE2RCT. During FY2021, efforts focused on an assessment of how the data in the EBR-II Physics and Analysis DataBase (PADB) is generated with SE2RCT and used in FIPD. Additional tasks included considerations of uncertainties for power estimates in REBUS and RCT calculations and their impact on the combined RCT methodology. The RCT software usage in FIPD was assessed this year and the input/output details studied. A “requirements” document was created that identifies the key features of the RCT software being used in FIPD that need to have SQA documentation. A brief discussion on the history of RCT and its input is included in this report along with the basic SQA roadmap laid out in the requirements document. The SE2RCT software usage in FIPD is still being studied noting that there is no current manual. As part of the work done this year, two bugs were identified in the SE2RCT software which have a minor impact on the accuracy of the results it produces. No requirements document has been created, but one identified feature of SE2RCT being used that needs verification was its fuel pin temperature calculation. The work completed this year confirms that the approximations which will be included in the software verification report for SE2RCT are accurate. In addition to software quality assurance work for RCT and SE2RCT, an automated verification framework is proposed to simplify the software quality assurance process. The purpose of this framework is to streamline code verification and documentation while minimizing repetitive tasks for code developers and reviewers. The reduction of repeated input (between reference solution, software, and documentation input) throughout the SQA process reduces potential for human errors during the preparation of the supporting software quality records. The automation of the verification and documentation process proposed for this project leverages the existing verification structure already in place for the SAS4A/SASSYS-1 code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Cool GTL for the Conversion of Biogas to Jet Fuel

Cool GTL is a new process under development for conversion of biogas from digestors or CO2 +H2 to high quality drop in jet fuel. Cool GTL is designed to be a streamlined simplified low cost process which can be cost effective for small scale biogas applications. Cool GTL has the advantage that it uses a high activity bi-reforming catalyst, in a streamlined electric reformer design, to make the synthesis gas for a high conversion slurry fischer Tropsch process followed by a trailing wax cracking reactor. In this way high quality jet fuel can be directly produced. Recent experimental test data will be presented showing product quality. The life cycle analysis LCA will also be presented as well.

09 BIOMASS FUELS↗
DOE OSTI2021

Orbital Angular Momentum from Self‐Assembled Concentric Nanoparticle Rings

Abstract Ring‐shaped nanostructures can focus, filter, and manipulate electromagnetic waves, but are challenging to incorporate into devices using standard nanofabrication techniques. Directed self‐assembly (DSA) of block copolymers (BCPs) on lithographically patterned templates has successfully been used to fabricate concentric rings and spirals as etching masks. However, this method is limited by BCP phase behavior and material selection. Here, a straightforward approach to generate ring‐shaped nanoparticle assemblies in thin films of supramolecular nanocomposites is demonstrated. DSA is used to guide the formation of concentric rings with radii spanning 150–1150 nm and ring widths spanning 30–60 nm. When plasmonic nanoparticles are used, ring nanodevice arrays can be fabricated in one step, and the completed devices produce high‐quality orbital angular momentum (OAM). Nanocomposite DSA simplifies and streamlines nanofabrication by producing metal structures without etching or deposition steps; it also introduces interparticle coupling as a new design axis. Detailed analysis of the nanoparticle ring assemblies confirms that the supramolecular system self‐regulates the spatial distribution of its components, and thus exhibits a degree of flexibility absent in DSA of BCPs alone, where structures are determined by polymer‐pattern incommensurability. The present studies also provide guidelines for developing self‐regulating DSA as an alternative to incommensurability‐driven methods.

Vargo, Emma↗
DOE OSTI2024

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗
DOE OSTI2025

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Experimental evaluation of neural probe’s insertion induced injury based on digital image correlation method

Purpose: The application of neural probes in clinic has been challenged by probes’ short lifetime when implanted into brain tissue. The primary goal is to develop an evaluation system for testing brain tissue injury induced by neural probe’s insertion using microscope based digital image correlation method. Methods: A brain tissue phantom made of silicone rubber with speckle pattern on its surface was fabricated. To obtain the optimal speckle pattern, mean intensity gradient parameter was used for quality assessment. The designed testing system consists of three modules: (a) load module for simulating neural electrode implantation process; (b) data acquisition module to capture micrographs of speckle pattern and to obtain reactive forces during the insertion of the probe; (c) postprocessing module for extracting tissue deformation information from the captured speckle patterns. On the basis of the evaluation system, the effects of probe wedge angle, insertion speed, and probe streamline on insertion induced tissue injury were investigated. Results: The optimal quality speckle pattern can be attained by the following fabrication parameters: spin coating rate—1000 r/min, silicone rubber component A: silicone rubber component B: softener: graphite = 5 ml: 5 ml: 2 ml: 0.6 g. The probe wedge angle has a significant effect on tissue injury. Compared to wedge angle 40° and 20°, maximum principal strain of 60° wedge angle was increased by 40.3% and 87.5%, respectively; compared with a relatively higher speed (500 μm/s), the maximum principle strain within the tissue induced by slow insertion speed (100 μm/s) was increased by 14.3%; insertion force required by probe with convex streamline was smaller than the force of traditional probe. Based on the experimental results, a novel neural probe that has a rounded tip covered by a biodegradable silk protein coating with convex streamline was proposed, which has both lower insertion and micromotion induced tissue injury. Conclusions: The established evaluation system has provided a simulation environment for testing brain tissue injury produced by various insertion conditions. At the same time, it eliminates the adverse effect of biological factors on tissue deformation during the experiment, improving the repeatability of measurement results. As a result, the evaluation system will provide support on novel neural probe design that can reduce the acute tissue injury during the implantation of the probe.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

The Case for Optimized Edge-Centric Tractography at Scale

The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗
DOE OSTI2025

Porous carbon from lignocellulosic biomass with emphasis on corn plant waste residue for energy storage

The rising global demand for sustainable energy storage materials has driven the search for environmentally friendly and cost-effective electrode options. Hydrothermal conversion of lignocellulosic biomass has gained attention due to its low energy requirements and operation at relatively low temperatures, presenting a green alternative to traditional thermochemical methods. The resulting solid product, hydrochar, has been used as an adsorbent and soil amendment; however, chemical/thermal treatment significantly enhances its physical properties. These structural modifications transform hydrochar into an effective porous carbon electrode, offering abundant sites for electrolyte ion transport, critical for high-performance devices like supercapacitors and batteries. This review first discusses various waste biomass and sustainable feedstocks available globally. It compares two primary thermochemical conversion techniques, pyrolysis and hydrothermal carbonization/liquefaction, and examines their respective solid products, biochar and hydrochar, analyzing differences in their physical and chemical characteristics. The focus is placed on hydrochar, summarizing activation methods to produce porous carbon suitable for energy storage applications. Additionally, this review will include a dedicated section on the application of porous carbon derived from corn plant waste residue, considering that corn is one of the most abundant crops grown worldwide, which makes it an important and promising source for sustainable porous carbon production. The role of machine learning models in optimizing hydrothermal processes to produce high-quality hydrochar is also discussed, emphasizing how data-driven approaches can streamline process development. Finally, the review identifies the current challenges and prospects for lignocellulosic biomass-derived porous carbon as a sustainable electrode material in next-generation energy storage technologies.

25 ENERGY STORAGE↗
DOE OSTI2024

In-Plane Liftout and Push-to-Pull for In Situ Mechanical Testing of Irradiated Inconel X-750

A streamlined sample preparation method for nanomechanical testing is needed to improve the quality of specimens, reduce the cost, and increase the versatility of specimen fabrication. This work outlines an in-plane liftout focused ion beam (FIB) fabrication procedure to prepare electron-transparent specimens for in situ transmission electron microscopy (TEM) nanomechanical testing. Ion etching and electron backscatter diffraction (EBSD) techniques were used to lift out a [110] oriented grain from a neutron-irradiated bulk X-750 alloy. Careful control of voltages and currents ensured precision. Top surface thinning sweeps prevented resurfacing and redeposition while dog-bone geometries were shaped with a 1:4 gauge width-to-milling pattern diameter ratio. Nanotensile testing in the TEM with a picoindenter allowed for the estimation of an ultimate tensile strength of 2.41 GPa, and inspection revealed a high density of bubbles in the X-750 matrix. The proposed fabrication procedure is significant for preparing samples from radioactive materials, studying complex structures that are orientation-dependent, and analyzing desired planar areas.

36 MATERIALS SCIENCE↗
DOE OSTI2015

MO-F-CAMPUS-T-02: An Electronic Whiteboard Platform to Manage Treatment Planning Process

Purpose: In an effort to improve patient safety and streamline the radiotherapy treatment planning (TP) process, a software based whiteboard had been developed and put in use in our facility Methods: The electronic whiteboard developed using SQL database (DB) and PHP/JavaScript based web interface, is published via department intranet and login credentials. The DB stores data for each TP process such as patient information, plan type, simulation/start dates, physician, dosimetrist, QA and the current status in planning process. Users interact with the DB per plan and perform status updates in real time as the planning process progresses. All user interactions with the DB are recorded with timestamps so as to calculate statistical information for TP process management such as contouring times, planning and review times, dosimetry, physics and therapist QA times. External beam and brachytherapy plans are categorized according to complexity (ex: IMRT, 3D, HDR, LDR etc) and treatment types and applicators. Each plan category is assigned specific timelines for each planning process. When a plan approaches or passes the predetermined timeline, users are alerted via color coded graphical cues. When certain process items are not completed in time, pre-determined actions are triggered such as a delay in treatment start date. Results: Our institution has been using the electronic whiteboard for two years. Implementation of pre-determined actions based on the statistical information collected by the whiteboard improved our TP process. For example, the average time for normal tissue contouring decreased from 0.73±1.37 to 0.24±0.33 days. The average time for target volume contouring decreased from 3.2±2.84 to 2.37±2.54 days. This increase in efficiency allows more time for quality assurance processes, improving patient safety. Conclusion: The electronic whiteboard has been an invaluable tool for streamlining our TP processes. It facilitates timely and accurate communication between all parties involved in the TP process increasing patient safety.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2017

Harmonization of experimental approach and data collection to streamline analysis of biomass composition from algae in an inter-laboratory setting

In order to establish and design long-term algae cultivation experiments, inter-laboratory projects need to harmonize the requirements of techno-economic and life-cycle analysis models, with standardized data inputs. In order to provide a consistent foundation and allow for integration and analysis of the results in computational technical and resource analysis models, we implemented closely coordinated, harmonized and objective analytical protocols along with a common language for measuring growth and productivity for the major algal components. We describe here the process by which we developed a harmonization framework for analysis across five geographically diverse testbed sites. Our goal was to align analytical procedures to ensure consistent reporting on biomass and lipid content, quality and yields to eliminate measurement variability as a source of uncertainty in production data. Developing standards for analysis that streamline reporting on composition and expected fuel yields from biomass is one of the major outcomes of this work and this provides a starting place for further advanced characterization of algae to support the techno-economical process analyses and account for the mass balance accounting of algal biomass. In conclusion, initial analysis of data obtained from field studies shows trends in compositional shifts of lipid and protein content of the biomass that are in support of the physiological experiments demonstrated in the first geographically distributed unified outdoor cultivation trials.

09 BIOMASS FUELS↗
DOE OSTI2025

WREF Halo Streamline scanning lidar / Derived data / RHI

This dataset contains streamwise velocity retrieved from the Range Height Indicator (RHI) scans done by the UC Davis scanning Lidar. The quality control of the radial wind speed is performed following the algorithm of Beck & Kuhn (2017). Instantaneous realizations characterized by wind misalignment greater than 60 degrees (based on the closest VAD scan in time) are discarded.

17 WIND ENERGY↗
DOE OSTI2015

Groundwater monitoring report streamlining techniques - 15305

Compliance with national and state environmental regulations (e.g. RCRA and CERCLA [SuperFund] requires Savannah River Site (SRS) to extensively collect and report groundwater monitoring data, with potential fines for missed reporting deadlines. Large reports, which may contain dozens to hundreds of monitoring stations, are especially challenging to present data in a way that is quick and easy to interpret. Initially analyzing hundreds to thousands of records of data to be summarized in the report can be daunting, and producing maps can be extremely time consuming. Several utilities have been developed at SRS to facilitate production of the regulatory reports which include data tables, charts, maps, and cross-sections. Components of each report are generated in accordance with complex sets of regulatory requirements specific to each site monitored. SRS developed a relational database to incorporate the detailed reporting rules with the groundwater data, and created a set of automation tools to interface with the information and generate the report components. Maps produced with ArcGIS use a technique that can be repeatedly used to display data very quickly and in a consistent form. Cross-sections developed with RockWorks provide a more technical and geological analysis and representation of well and boring data, while allowing for automated data placement. These process improvements have streamlined the repetitive job of creating data tables, charts, maps, and cross-sections. It has also enhanced report quality and consistency by centralizing the information, and has reduced manpower and production time through automated efficiencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗