Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Acceleration Data”

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.

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

At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Petabyte Class Storage at Jefferson Lab (CEBAF)

By 1997, the Thomas Jefferson National Accelerator Facility will collect over one Terabyte of raw information per day of Accelerator operation from three concurrently operating Experimental Halls. When post-processing is included, roughly 250 TB of raw and formatted experimental data will be generated each year. By the year 2000, a total of one Petabyte will be stored on-line. Critical to the experimental program at Jefferson Lab (JLab) is the networking and computational capability to collect, store, retrieve, and reconstruct data on this scale. The design criteria include support of a raw data stream of 10-12 MB/second from Experimental Hall B, which will operate the CEBAF (Continuous Electron Beam Accelerator Facility) Large Acceptance Spectrometer (CLAS). Keeping up with this data stream implies design strategies that provide storage guarantees during accelerator operation, minimize the number of times data is buffered allow seamless access to specific data sets for the researcher, synchronize data retrievals with the scheduling of postprocessing calculations on the data reconstruction CPU farms, as well as support the site capability to perform data reconstruction and reduction at the same overall rate at which new data is being collected. The current implementation employs state-of-the-art StorageTek Redwood tape drives and robotics library integrated with the Open Storage Manager (OSM) Hierarchical Storage Management software (Computer Associates, International), the use of Fibre Channel RAID disks dual-ported between Sun Microsystems SMP servers, and a network-based interface to a 10,000 SPECint92 data processing CPU farm. Issues of efficiency, scalability, and manageability will become critical to meet the year 2000 requirements for a Petabyte of near-line storage interfaced to over 30,000 SPECint92 of data processing power.

Chambers, Rita↗
DOE OSTI2021

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

25 ENERGY STORAGE↗
DOE OSTI2025

60 years of science in ICF: from conception to scientific breakeven on the National Ignition Facility

The recent achievements of a burning plasma, fusion ignition, and scientific energy gain with deuterium-tritium (DT) fuel at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) represents a major milestone in the development of inertial confinement fusion (ICF) and all of fusion research. In these experiments, fuel pressures well in excess of hundreds of GBars were achieved in the compressed fuel, and robust alpha heating of the fuel, far in excess of the energy provided by the implosion, were demonstrated for the first time. These achievements occurred 60 years after the inception of ICF and the first laser demonstration, and were made possible by more than five decades of research at laser facilities around the world. Advances in laser technology both in wavelength and precision, motivated by improved understanding of laser-plasma interaction physics and the demands of targets; improvements in target fabrication inspired by the need to control and minimize hydrodynamic instabilities in the implosion; and multi-dimensional simulations and diagnostics have been critical to this achievement. This paper will summarize the scientific and technical advances, the surprises, and the challenges that had to be overcome to achieve these goals.

fusion↗
DOE OSTI2026

Interpolation of computed gamma-ray detector response functions

Gamma-ray spectra measured by traditional detectors contain features that result from a combination of the effects of detector materials/geometry, the incident gamma-ray energy, and the angle of entry. The features, such as the full-energy photopeak, Compton continuum, annihilation peak, and escape peaks, are governed by simple relationships depending on incident energy and have been known for a long time. Monte Carlo computer simulations of gamma rays interacting with a detector will show these features, and with a resolution function applied, the results should look similar to real measurements. The traditional approach to creating a detector response function requires many separate simulations of monoenergetic gamma rays striking the detector. This paper presents a new approach to developing computed detector response functions. The new approach involves a much smaller number of monoenergetic gamma-ray simulations and uses interpolation to quickly generate the responses of gamma rays that were not simulated. During the interpolation process, the underlying physics equations are used to accurately compute the response of a given energy gamma ray from the small set of simulations. Such work enables accelerated generation of synthetic radiation detector data.

Detector response↗
DOE OSTI2022

Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

Mechanics↗
DOE OSTI2020

A Holistic Algorithmic Approach to Improving Accuracy, Robustness, and Computational Efficiency for Atmospheric Dynamics

Atmospheric weather and climate models must perform simulations very quickly to be useful. Therefore, modelers have traditionally focused on reducing computations as much as possible. However, in our new era of increasingly compute-capable hardware, data movement is now the prohibiting expense. This study examines the computational benefits of a new algorithmic approach to modeling atmospheric dynamics on scales relevant to weather and climate simulation. Rather than minimizing computations, this new approach considers the larger problem more holistically, including spatial accuracy, temporal accuracy, robustness (i.e., oscillations), on-node efficiency, and internode data transfers together at once. Numerical experiments demonstrate how computations can be strategically increased to simultaneously address each of these constraints while reducing data movement to adapt to modern accelerated hardware. The new algorithm can achieve at times up to 80% peak floating point throughput in single precision on the Nvidia Tesla V100 GPU, where the traditional approach is shown to only achieve single-digit floating point efficiency. Further, the new algorithm is twice as fast as a standard Runge--Kutta time integrator, and high-order accuracy with Weighted Essentially Non-Oscillatory (WENO) limiting came at less than 30% additional runtime cost on a GPU, thus increasing the accuracy per degree of freedom.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2024

Integration Development and Testing of Rear Transition Monitor for Beam Current Monitoring System

Addressing baseline effects in accelerator environments is crucial for accurate data acquisition and analysis, since baseline effects can obscure signal clarity and impact the reliability of beam current monitoring systems. There are many potential contributors to baseline noise, such as variations in beam dynamics, electromagnetic interference from nearby equipment, or RF interference. Previous applications of noise reduction systems don t sufficiently filter sources of asynchronous noise, so a new algorithm was implemented. A simulation dataset was created to replicate beam conditions and a Red Pitaya FPGA was used to collect data through the streaming application. A Python script was developed to implement noise reduction algorithms and efforts were made to integrate real-time data streaming with the Redis platform and Acnet Front End infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

Thrust measurements of a hollow-cathode discharge

Thrust measurements of a hollow cathode mercury discharge were made with a synthetic mica target on a torsion pendulum. Thrust measurements were made for various target angles, tip temperatures, flow rates, keeper discharge powers, and accelerator electrode voltages. The experimental thrust data are compared with theoretical values for the case where no discharge power was employed.

Snyder, A.↗
NASA NTRS1972

A quantitative investigation of the solar modulation of cosmic-ray protons and helium nuclei

The differential energy spectra of cosmic ray protons and He nuclei were measured at energies up to 315 MeV/nucleon using balloon-borne and satellite-borne instruments. These spectra are presented for solar quiet times for the years 1966 through 1970. The data analysis is verified by extensive accelerator calibrations of the detector systems and by calculations and measurements of the production of secondary protons in the atmosphere. The spectra of protons and He nuclei in this energy range are dominated by the solar modulation of the local interstellar spectra. Numerical solutions to the transport equation are presented for a wide range of parameters.

Garrard, T. L.↗
NASA NTRS1973

Study of an experimental technique for application to structural dynamic problems

An experimental program was conducted to determine the feasibility of using subscale plastic models to determine the response of full-scale aerospace structural components to impulsive, pyrotechnic loadings. A monocoque cylinder was impulsively loaded around the circumference of one end, causing a compressive stress wave to propagate in the axial direction. The resulting structural responses of two configurations of the cylinder (with and without a cutout) were recorded by photoelasticity, strain gages, and accelerometers. A maximum dynamic stress concentration was photoelastically determined and the accelerations calculated from strain-gage data were in good agreement with those recorded by accelerometers. It is concluded that reliable, quantitative structural response data can be obtained by the experimental techniques described in this report.

Snell, R. F.↗
NASA NTRS1973

Slosh testing of a spherical mercury propellant tank with positive-expulsion diaphragm

A preliminary investigation was conducted to evaluate the lateral slosh characteristics of a 23-cm diameter mercury propellant tank with a positive-expulsion diaphragm and 17.5% ullage. Data are presented for tank sinusoidal acceleration levels between 0.05 and 0.5 g at frequencies ranging from 5 to 50 Hz. Results indicate that slosh characteristics are highly nonlinear at acceleration levels approaching those in flight, and depend heavily on the shape and stiffness of the diaphragm. Nyquist plots of driving force over acceleration are shown to be a useful tool for determining natural frequency, damping, and modal mass characteristics. A computerized nonlinear least squares method for extracting the modal parameters from the Nyquist plots is described and results of applying the method are presented.

Ross, R. G., Jr.↗