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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 541 records · Page 30

ccp137/SUGAR

This package (SUrface-wave Grader with ARtificial intelligence or SUGAR) automatically assigns a quality score to surface-wave seismograms (SAC format) using a trained artificial neural network model (included). Specifically, the python script 01_apply_ann.py calculates probability scores for a list of SAC files. You may consider seismograms with probability scores larger than 0.5 as acceptable data. Note no scores will be given to seismograms that do not pass an initial check (e.g., insufficient number of data points). See https://doi.org/10.1002/essoar.10507941.3 for more details.

Chai, Chengping [Oak Ridge National Lab. (ORNL), O↗

Energy Saving Quantification on Ductless Heat Pump (DHP) in Existing Homes

In residential retrofit applications, ductless mini-split heat pumps (DHP) are often reported to have high-energy savings potential, depending on the system they are supplementing or replacing. However, recently, there have been a number of utility studies and analyses indicating these energy savings are not being achieved when the existing system is left in place for backup heating or air conditioning. The Pacific Northwest National Laboratory conducted a three-phase project to help determine which control strategies would have the most energy savings impact in various climate zones around the United States. The first phase developed the standalone simulation model for the PNNL Lab Homes and investigated the energy-saving potential from different control strategies and HVAC system configurations. The second phase focused on conducting experiments in the PNNL Lab Homes, which tested the most promising solutions that were modeled in the first phase. The third phase used the field data to calibrate the simulation model and then extrapolated the results to different climate locations and different building sizes. This report focuses on the third phase of the study, including five major parts: model calibration, parametric model setup, results, a sensitivity analysis of air leakage rate, and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Late-Stage Research Development and Demonstration Sub-activities Updates – FY24 Q3

Oak Ridge National Laboratory (ORNL), in collaboration with the Pacific Northwest National Laboratory (PNNL), the National Renewable Energy Laboratory (NREL), the Lawrence Berkeley National Laboratory (LBNL), and the Hummingbird Firm (a specialized consulting firm focused on promoting diversity, equity, and inclusion considerations), has initiated a national initiative known as the Heat Pump (HP) and Heat Pump Water Heater (HPWH) Field Validation Partnership. This effort involves active participation from numerous critical entities involved in research and market transformation within the field. The ORNL team is responsible for leading Late-Stage Research Development and Demonstration (LSRDD) among the four different topics. The overall outcomes of this project will be: (1) A structured Field Validation Partnership between DOE, the national labs, research, implementation, and market transformation organizations. This will result in unique way to coordinate field validation plans and collect relevant data from around the country into the HP and HPWH Field Validation Database. (2) The Field Validation Partnership will result in a continuous stream of information between DOE and the major industry players in the space of HPs and HPWHs. If desired, DOE could use this information to inform roadmaps related to HP and HPWH market adoption and research going forward. (3) The structure of this Partnership provides a mechanism for sharing lessons learned directly between Late-Stage RD&D, Building Integration Barriers, Regional Market and Policy and Workforce Development efforts. The result will be training content that is well-reviewed by the Partnership which will lead to a workforce that meets the industry’s quality and workforce supply demands. (4) The structure of this Partnership also provides an opportunity for regions to share lessons learned on policy and market transformation with each other through the Market and Policy core Committee. This report includes an update in Late-Stage Research Development and Demonstration.

99 GENERAL AND MISCELLANEOUS↗

Waveform Simulation Framework: User Manual with Tutorials

This manuscript describes the Waveform Simulation Framework (WSF), a Python-based framework that provides a unified, programmable interface for generating synthetic seismograms for applications such as seismic array design, method development, and special event analysis. WSF standardizes how users define sources, receivers, and velocity models while abstracting simulator-specific configuration details, enabling workflows that are largely independent of the underlying numerical engine. The document provides installation guidance and tutorial-driven examples for three WSF simulator wrappers—WSF PyFK, WSF SW4, and WSF SPECFEM2D—illustrating end-to-end workflows from forward waveform simulation to common post-processing tasks (e.g., visualization and backprojection) using consistent data products (e.g., ObsPy Stream objects and SAC files).

97 MATHEMATICS AND COMPUTING↗

Need help planning for the future of electric vehicles?

This resource provides a sampling of key U.S. Department of Energy (DOE) and national lab resources available to states and local jurisdictions to help them plan for the future of electric vehicles. In particular, these data, information, and modeling resources can help jurisdictions with community planning and developing charging infrastructure.

department of energy, DOE, electric vehicles, Volk↗

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Jeske, Torri↗

Streaming readout for next generation electron scattering experiments

Current and future experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper, we report results of the on-beam validation of the Jefferson Lab SRO framework. In this work, we exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

47 OTHER INSTRUMENTATION↗

ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight [1] and E2Clab [2] are available as open-source tools.

Rosendo, Daniel↗

Proposed Analytical Methods for Determining Filter Media Properties

High Efficiency Particulate Air (HEPA) filters, commonly used in nuclear filtration applications, are an integral part of the waste management processes in nuclear plants. HEPA filters are 99.97% efficient filtration devices characterized by their high resistance to air flow, or pressure drop. From theoretical models, the initial pressure drop across a clean filter is proven to be a function of the filter media fiber diameters and porosity of the media. These filter properties are relatively difficult to obtain with traditional manual methods; therefore, there is a need to develop analytical methods to find the fiber diameter and porosity to determine the pressure drop. Typically, these values can be found by substituting a calculated representative value based upon measured values, such as equivalent fiber diameter based upon a clean pressure drop. However, these values may also be directly recorded and measured by incorporating Scanning Electron Microscope (SEM) image analysis and other measurement methods to analyze the filter media and determine its physical properties without the need for media testing. DiameterJ, an open source Java plug-in, used with ImageJ, can process images of filter media taken by an SEM to find statistical data such as mean fiber diameter and porosity. To produce the raw data, SEM images of two filter media type samples are taken and segmented in DiameterJ using the traditional and statistical region merging segmentation algorithms. Manual segmentation is necessary after the initial segmentation by the algorithms as the images tend to be too complex for the algorithms to output with the necessary accuracy. However, complications exist in the manual segmentation process as these methods can be time intensive and prone to the individual bias of the user. This in turn can skew the final mean fiber diameter result, and lead to either an over or under prediction of the pressure drop. It was also discovered that the porosity data produced by DiameterJ is inaccurate, as the SEM analyzes the three-dimensional filter media by projecting its geometry onto a plane and analyzing it as a two-dimensional binary image. Thus, the porosity is artificially inflated through the segmentation process, rendering this result incorrect. Alternatively, density determination, gravimetric analysis, and thickness testing of the filter media is collectively used to determine the filter fiber porosity. Together, the SEM image analysis and analytical lab methods produce results through direct measurements which allow for the prediction of the initial pressure drop from clean filter media without the need for prior media testing to collect pressure data. By improving upon this proposed analytical method in the future, there is potential to streamline the process of finding these filter properties into a more direct methodology for determining the pressure drop across HEPA filters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach

Channel turbulence is a formidable obstacle for freespace optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be > 98% accurate with multiple ML training parameters. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.

Zobaer Islam, Md [School of Electrical and Compute↗

Status Update of Permanent Magnet Radiation Resiliency Studies at CEBAF

The proposed energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) incorporates Fixed-Field Alternating-gradient (FFA) arcs utilizing permanent magnet technology. Given the radiation environment within the CEBAF tunnel enclosure, validating the long-term magnetic stability of these materials is a critical step for the project's technical feasibility. This contribution presents an overview of the ongoing permanent magnet radiation resiliency program at Jefferson Lab. We briefly review the experimental methodology used to monitor demagnetization in situ and summarize the operational experience from the initial data-taking campaign. Furthermore, we discuss the upgrades implemented for the second exposure campaign, currently underway, which aims to refine dose correlation and reduce systematic uncertainties. We report on the general status of the program and the roadmap for certifying permanent magnet optics for the proposed upgrade energies.

Bodenstein, R. [Thomas Jefferson National Accelera↗

Final Scientific/Technical Report: Real Time Particle Size and Settling Velocity for In-Situ Monitoring of Deep-Sea Polymetallic Nodule Mining

In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.

47 OTHER INSTRUMENTATION↗

Internship Final Report on the unsupervised learning sensor fusion (ULSF) approach

This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.

97 MATHEMATICS AND COMPUTING↗

TTDAQ: A Continuous Flow, Timing and Trigger DAQ System

Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Solar Energy: Bans or Moratoriums (2022)

This dataset identifies counties and municipalities that had a solar energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where solar energy bans or moratoriums exist across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗

Solar PV Water Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from bodies of water based on county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗

Air Source Heat Pumps in Very Cold Climates

Air source heat pumps (ASHPs) in heating mode use a refrigeration cycle to remove heat from the outside air and transfer it into a building. Therefore, ASHPs have the potential to save energy compared to conventional heat sources that create the heat as opposed to transferring it from outside. Many cold climate ASHPs available on the market today can operate at outside temperatures of -25 degrees C (-13 degrees F) or even lower. As a result of technological advances and other factors, there is growing interest in ASHPs in cold climates, including very cold regions such as Alaska. However, guidance on the selection, installation, and operation of ASHPs in these very cold climates is limited, as significant data gaps exist regarding the performance of ASHPs in very cold environments. To address these data gaps and guide future innovations, our research team has studied the field performance of several ASHP installations in Alaska and done lab evaluations of several ASHP models using a cold chamber. While a cold chamber cannot fully reflect field conditions, it allowed for changing one variable at a time and gaining additional understanding of the behavior of ASHPs that would be difficult to gain from field studies only. This study focused on two main variables: the temperature in the chamber (representing the outdoor temperature) and the level of thermal loading of the ASHP. The results from the field as well as the lab show that ASHPs can operate with relatively high efficiency even in very cold climates if used in appropriate situations and in an appropriate way. It was found that not only the outside temperature, but also the level of thermal loading is a significant factor affecting the ASHP efficiency and needs to be carefully considered when sizing and operating ASHPs in very cold climates.

air source heat pump↗

Validation of Hadron Mass Correction Schemes in Deep Inelastic Scattering at Low Energy Transfer

One method of describing the quark-gluon of the structure proton relies on factorizing its cross-section into a process-dependent short distance element and an independent long distance element that quantifies the quark and gluon momentum distributions within the proton. This research focuses on validating the kinematic range at which factorization theorems still accurately describe the cross-section, in particular for events that simulate scattering on large momentum quarks, that are sensitive to the details of the color mechanism. In this regime, effects which otherwise scale away at large energies, become non-negligible at low energies. The goal of this research is to assess the viability of an extended version of ?collinear factorization? recently proposed by J. Guerrero and A. Accardi, which includes kinematic corrections at quark level, known as ?hadron mass corrections?, to account for the masses of the quarks and the target. To accomplish this, deep inelastic scattering events are simulated using a realistic model of deep inelastic scattering, in which one may analytically calculate both the structure functions and the quark distribution functions. These simulated experiments are then used to test the viability of the proposed factorization theorem by fitting the quark distributions and comparing these to the analytically calculable ones. It is found that the viability of hadron mass corrections in low energy scattering experiments, such as those at Jefferson Lab, extends beyond the kinematic range suggested by analytic calculations alone, provided that residual higher twist corrections are simultaneously fitted to the data.

Krause, Andrew↗