SEARCH · Engineering Papers

Results for “Data transfer”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Transverse Enhancement, Longitudinal Quenching and Coulomb Sum Rule in e-$^{12}$C and e-$^{16}$O Scattering

We present a short summary of a phenomenological analysis of all available electron scattering data on 12 C 12 C (about 6600 differential cross section measurements) and on 16 O 16 O (about 250 measurements) within the framework of the quasielastic superscaling model (including Pauli blocking). All quasielastic and inelastic cross section measurements are included down to the lowest momentum transfer ? q (including photo-production data). We find that there is enhancement of the transverse quasielastic response function and quenching of the longitudinal response function at low ? q (in addition to Pauli blocking). We extract parameterizations of a ? ? ? ? ? ? ? ? ? ? ? ? ? ? multiplicative low ? q "Longitudinal Quenching Factor" and an ? ? ? ? ? ? ? ? additive "Transverse Enhancement" contribution. Additionally, we find that the excitation of nuclear states contribute significantly (up to 30\%) to the Coulomb Sum Rule. We extract the most accurate determination of the sum rule to date and find it to be in disagreement with random phase approximation based calculations but in reasonable agreement with recent theoretical calculations such as "First Principle Green's Function Monte Carlo".

Bodek, Arie↗

DOE OSTI2025

Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning

Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to ab initio calculations. However, atomic energy predictions, often assumed to lack physical meaning, remain underexplored. In this study, we demonstrate that inaccuracies in atomic energy predictions reduce the robustness and transferability of Neural Network Potentials (NNPs) and atomic energy error can be masked in total energy predictions due to error cancellation. Here, we validate this finding using challenging configurations involving deformation and failure under tensile loading. By pretraining atomic energy predictions using empirical potentials and applying transfer learning with density functional theory (DFT) data, we achieve notable improvements in the accuracy of total energy, forces, and stress predictions. Furthermore, this approach enhances the robustness and transferability of NNPs, emphasizing the importance of atomic energy predictions in developing high-quality and reliable MLIPs.

Active Learning↗

DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Gen3 CSP Materials: Critical Review of Limited Existing and New Survey Data

Abstract Novel high temperature (≥ 700°C) Heat Transfer Medias (HTMs, e.g., molten salts) and corrosion-resistant Containment Materials (CMs, e.g., metal alloys or ceramics) are necessary for concentrated solar power (CSP) given the emphasis on higher temperatures and high cycle efficiency in the 3rd generation CSP (Gen3 CSP) technologies. In early 2019, we sent out an online survey to the Gen3 CSP community to fully assess the communal needs for thermophysical properties measurements of which HTMs and CMs, and what temperature range and other testing environments would be ideal for those materials. Based on the recorded responses, seven unique HTMs and twenty-six unique CMs were identified. Since then the list has been constantly updated, following our interactions and inputs from the Gen3 CSP community, with some new materials substituting their older counterparts. Currently, there are total of ten unique HTMs and twenty-nine unique CMs that are under consideration by the Gen3 CSP community. By analyzing the available body of research to date and combining it with our survey data from within the Gen3 CSP community, this paper presents trends of what people in the CSP world are thinking regarding materials worth investigating and suggests which thermophysical property measurements are critical to advance high-temperature CSP systems.

Gunawan, Andrey↗

DOE OSTI2024

In tube condensation of low global warming potential refrigerants in an axial micro-fin aluminum tube

Environmental regulations have driven the development of refrigerants with low global warming potential (GWP). To design heat exchangers using these new refrigerants, data are needed concerning the heat transfer coefficient and pressure drop in two-phase flow. Another change is the increasing use of aluminum tubes rather than copper tubes to reduce heat exchanger cost. Hence, this study presents an experimental investigation of flow condensation using an expanded axial micro-fin aluminum tube with a fin-tip diameter of 5.96 mm. Here, the experiments included single compounds R-32, R-1234yf, and R-1234ze(E), zeotropic mixtures with low glide (R-454B), and zeotropic mixtures with high-glide (R-454C and R-455A). Experiments were conducted at condensation temperatures ranging from 40 °C to 50 °C, reduced pressures ranging from 0.21 to 0.55, and mass fluxes ranging from 150 to 350 kg/(m 2 s). Data obtained for these refrigerants constitute one of the first reports for high-glide refrigerants using axial micro-fin aluminum tubes. An evaluation of heat transfer degradation of zeotropic mixtures due to mass transfer resistance at the liquid/vapor interface is presented. This information can be used to design heat exchangers for next generation air conditioning and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows

In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities by scheduling data analytics close to the data source. Hybrid in-situ processing splits data analytics into two stages: the data processing that runs in-situ aims to extract regions of interest, which are then transferred to staging services for further in-transit analytics. To facilitate this type of hybrid in-situ processing, the data staging service needs to support complex intermediate data representations generated/consumed by the in-situ tasks. Unstructured (or irregular) mesh is one such derived data representation that is typically used and bridges simulation data and analytics. However, how staging services efficiently support unstructured mesh transfer and processing remains to be explored. This paper investigates design options for transferring and processing unstructured mesh data using staging services. Using polygonal mesh data as an example, we show that hybrid in-situ workflows with staging-based unstructured mesh processing can effectively support hybrid in-situ workflows, and can significantly decrease data movement overheads.

data-driven↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

58 GEOSCIENCES↗

DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Quasielastic electromagnetic scattering cross sections and world data comparisons in the GENIE Monte Carlo event generator

The usage of Monte Carlo neutrino event generators (MC$\textit{ν}$EGs) is a norm within the high-energy $\textit{ν}$ scattering community. The relevance of quasielastic (QE) energy regimes to $\textit{ν}$ oscillation experiments implies that accurate calculations of $\textit{ν}$A cross sections in this regime will be a key contributor to reducing the systematic uncertainties affecting the extraction of oscillation parameters. In spite of this, many MC$\textit{ν}$EGs utilize highly phenomenological, parametrized models of QE scattering cross sections. Moreover, a culture of validation of MC$\textit{ν}$EGs against prolific electron ($\textit{e}$) scattering data has been historically lacking. In this work, we implement new eA cross sections obtained from nuclear ab initio approaches in GENIE, the primary MC$\textit{ν}$EG utilized by the FNAL community. In particular, we utilize results from quantum MC methods which solve the many-body nuclear problem in the short-time approximation (STA), allowing consistent retention of two-nucleon dynamics which are crucial to explain available nuclear electromagnetic (electroweak) data over a wide range of energy and momentum transfers. This new implementation in GENIE is fully tested against the world QE electromagnetic data, finding agreement with available data below ~2 GeV of beam energy with the aid of a scaling function formalism. The STA is currently limited to study $\textit{A}$ ≤ 12 nuclei, however, its semi-inclusive multibody identity components are exportable to other many-body computational techniques such as auxiliary field diffusion MC which can reach $\textit{A}$ ≤ 40 systems while continuing to realize the factorization contained within the STA’s multinucleon dynamics. Together, these developments promise to make future experiments such as DUNE more accurate in their assessment of MC$\textit{ν}$EG systematics, $\textit{ν}$ properties, and potentially empower the discovery of physics beyond the Standard Model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2025

Parts per Billion Concentrations of Volatile Organic Gases collected with a PTRMS at the EPCAPE Mount Soledad site from 05-20-2023 to 06-15-2023

This dataset contains the parts per billion concentrations of various volatile organic gases. Data was collected using a Proton Transfer Reaction Mass Spectrometer (PTRMS). Concentrations for all compounds were calculated using a reaction constant (k-value) of 2.0E-9 cm^3/s. On June 14, a calibration was preformed for the gases methanol, acetone, isoprene, butanone, benzene and toluene. Calibrations curves were used to correct the concentrations of these gases. Negative concentration values were replaced with zeros. Other gases were not calibrated for. Data has undergone one minute averaging. Time data is in coordinated universal time (UTC). Time has been recorded in seconds since 1/1/1904.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

MLClouds [SWR-24-24]

The National Solar Radiation Database (NSRDB) is NREL’s flagship solar data resource. With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the NSRDB is a crucial public data asset. A fundamental input to accurate surface irradiance in the NSRDB is high quality cloud property data. Cloud properties are used in radiative transfer calculations and are sourced from satellite imagery. Improving the accuracy of cloud property inputs is a tractable method for improving the accuracy of the irradiance data in the NSRDB. For example, in July of 2018, an average location in the Continental United States is missing cloud property data for nearly one quarter of all daylight cloudy timesteps. This project aims to improve the cloud data inputs to the NSRDB by using machine learning techniques to exploit the NSRDB’s massive data resources. More accurate cloud property input data will yield more accurate surface irradiance data in the NSRDB, providing direct benefit to researchers at NREL and to public data users everywhere.

Buster, Grant↗

DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, SeyedMohammad↗

DOE OSTI2023

Data compression system using base values and methods thereof

Seyedzadehdelcheh, Seyed Mohammad↗

DOE OSTI2024

Measurement of the Neutron Magnetic Form Factor at Large Momentum Transfer Using the Super-Bigbite Apparatus in Jefferson Lab Hall-A

The nucleon elastic electromagnetic form factors help us study the electromagnetic structure of the nucleon, benchmark theoretical models, and improve our understanding of non-perturbative quantum chromodynamics and confinement. The Nobel Prize-winning electron-nucleon scattering experiments by Robert Hofstadter and collaborators in the 1950s at Stanford High Energy Physics Lab were the first nucleon form factor measurements performed using leptonic probes. The Super Bigbite Spectrometer (SBS) program at Hall-A of Jefferson Lab represents the latest efforts to measure nucleon form factors. This ambitious program aims to significantly extend the current data set in terms of square momentum transfer (Q2) with high precision. The advent of novel detector technologies, like Gas Electron Multipliers (GEM), which provide excellent position resolution (< 100 ?m) while withstanding high background particle rates (several hundred MHz/cm2) over a large active area, has paved the way for open-geometry, moderate solid angle spectrometers, which are central to all form factor experiments in the SBS program. The first experimental run group in SBS ran successfully between September 2021 and February 2022, collecting data for the measurement of the magnetic form factor of the neutron Gn M at five squared momentum-transfer values: 3.0, 4.5, 7.5, 9.8, and 13.5 (GeV /c)2. This extends the existing high-precision data for Gn M by about a factor of four. The ratio technique was used, which involved the simultaneous measurement of exclusive quasielastic scattering of D(e,e?n)p and D(e,e?p)n from a deuterium target. Pre-preliminary results for D(e,e?n)p and D(e,e?p)n quasi-elastic ratio, and the neutron magnetic form factor Gn M , for Q2 points 3.0, 9.8, and 13.5 (GeV /c)2 are presented.

Mudiyanselage, Anuruddha Rathnayake↗

DOE OSTI2022

Efficient Progressive Transfer Learning for Full-Waveform Inversion With Extrapolated Low-Frequency Reflection Seismic Data

Not provided.

Engineering↗

DOE OSTI2025

pyAXEAP1-GUI package

SF-25-076 pyAXEAP1-GUI package is an X-ray emission spectroscopy (XES) data analysis package in the Python language. It can transfer the collected images from a 2D detector into spectra and display XES and RXES data as 3Dimages, 2D images and 1D profiles.

Berno, Alexander↗