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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integration of computer vision system to track the alignment SRF cavities into the test cryostat for PIP-II at Fermilab

PIP-II cryomodules use a computer vision system (H-BCAMs system) to monitor the alignment of SRF cavities and focusing lenses during assembly, testing, and operation. This contribution details the integration of the H-BCAMs into the Spoke Test Cryostat (STC) at Fermilab, which is utilized for cold testing SRF cavities prior to their integration into the string assembly. Thermal and structural finite element analyses were employed to estimate the cavities’ deformations, to be validated during cold testing in the STC using H-BCAMs. Notably, this marks the first instance of H-BCAMs integration into a cryostat and operation within a cryogenic environment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

DPO↗
DOE OSTI2024

Field Emission Properties of Cu-Filled Vertically Aligned Carbon Nanotubes Grown Directly on Thin Cu Foils

Copper-filled vertically aligned carbon nanotubes (Cu@VACNTs) were grown directly on Cu foil substrates of 0.1 mm thicknesses at different temperatures via plasma-enhanced chemical vapor deposition (PECVD). By circumventing the need for additional catalyst layers or intensive substrate treatments, our in-situ technique offers a simplified and potentially scalable route for fabricating Cu@VACNTs with enhanced electrical and thermal properties on thin Cu foils. Comprehensive analysis using field emission scanning microscopy (FESEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS) mappings, and X-ray diffraction (XRD) revealed uniform Cu filling within the VACNTs across a range of synthesis temperatures (650 °C, 700 °C, and 760 °C). Field emission (FE) measurements of the sample synthesized at 700 °C (S700) showed low turn-on and threshold fields of 2.33 V/μm and 3.29 V/μm, respectively. The findings demonstrate the viability of thin Cu substrates in creating dense and highly conductive Cu-filled VACNT arrays for advanced electronic and nanoelectronics applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Vertically Aligned Carbon Nanotubes Grown on Copper Foil as Electrodes for Electrochemical Double Layer Capacitors

This study reports a binder-free, catalyst-free method for fabricating vertically aligned carbon nanotubes (VACNTs) directly on copper (Cu) foil using plasma-enhanced chemical vapor deposition (PECVD) for electrochemical double-layer capacitor (EDLC) applications. This approach eliminates the need for catalyst layers, polymeric binders, or substrate pre-treatments, simplifying electrode design and enhancing electrical integration. The resulting VACNTs form a dense, uniform, and porous array with strong adhesion to the Cu substrate, minimizing contact resistance and improving conductivity. Electrochemical analysis shows gravimetric specific capacitance (C grav ) and areal specific capacitance (C areal ) of 8 F g −1 and 3.5 mF cm −2 at a scan rate of 5 mV/s, with low equivalent series resistance (3.70 Ω) and charge transfer resistance (0.48 Ω), enabling efficient electron transport and rapid ion diffusion. The electrode demonstrates excellent rate capability and retains 92% of its initial specific capacitance after 3000 charge–discharge cycles, indicating strong cycling stability. These results demonstrate the potential of directly grown VACNT-based electrodes for high-performance EDLCs, particularly in applications requiring rapid charge–discharge cycles and sustained energy delivery.

25 ENERGY STORAGE↗
DOE OSTI2025

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and estimation across disciplines, and (b) the varying technical requirements for establishing trustworthy uncertainties in diverse problem contexts. In this position paper, we aim to clarify the depth of these challenges by identifying these inconsistencies and articulating how different contexts impose distinct epistemic demands. We examine the current landscape of estimation targets (e.g., prediction, inference, simulation-based inference), uncertainty constructs (e.g., frequentist, Bayesian, fiducial), and the approaches used to map between them. Drawing on the literature, we highlight and explain examples of problematic mappings. To help address these issues, we advocate for standards that promote alignment between the \textit{intent} and \textit{implementation} of uncertainty quantification (UQ) approaches. We discuss several axes of trustworthiness that are necessary (if not sufficient) for reliable UQ in ML models, and show how these axes can inform the design and evaluation of uncertainty-aware ML systems. Our practical recommendations focus on scientific ML, offering illustrative cases and use scenarios, particularly in the context of simulation-based inference (SBI).

Trivedi, Shubhendu [MIT] (ORCID:0000000312374301)↗
NASA NTRS1965

Simple optical system used to align spectrograph

Optically fast, portable spectrograph incorporates auxiliary optics in a boresight technique to use the zero order of the grating for visual alignment. This device obtains moderately resolved spectra of a multitude of light sources.

Exton, R. J.↗
NASA NTRS1965

Self-aligning fixture used in lathe chuck jaw refacing

Self-aligning tool positions and rigidly holds lathe chuck jaws for refacing and truing of the clamping surface. The jaws clamp the fixture in the manner of clamping a workpiece. The fixture can be modified to accommodate four-jawed checks.

Linn, C. C.↗
NASA NTRS1965

Oil-damped mercury pool makes precise optical alignment tool

Mercury pool with a cover layer of high viscosity oil provides a reference reflector for precise alignment of optical instruments. The cover layer effectively damps any ripples in the mercury from support structure vibrations.

Thekaekara, M. P.↗
NASA NTRS1966

Turbine blade root design concept promises superior alignment

Blade-to-hub mounting concept assures excellent alignment integrity and results in elimination of some welding problems associated with designs. With this design, if rework is required, blade removal and replacement may be readily accomplished without damage to blade positioning media on the wheel hub.

King, O. D.↗
NASA NTRS1966

Quick attach and release fluid coupling assembly is self-aligning, self-sealing

Fluid coupling assembly that is self-aligning, self-sealing and contains a bellow ball and socket coupling for quick attach and release is highly reliable and can handle cryogenic fluids where icing is encountered. The fluid coupling assembly is used in many fluid systems but is particularly applicable to cryogenic systems.

Herold, C. P.↗