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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Utilization of Hydroturbines in Wastewater Treatment Plants

Wastewater treatment plants (WWTPs) are a significant energy consumer, yet there are several opportunities for implementing on-site power generation systems. Within the treatment process, the high flow rate of effluent is produced and discharged to a nearby water body by gravity. Thus, hydroturbines can be utilized to generate power in such an application due to a difference in elevation and high flow rate. This paper presents a case study of introducing a hydroturbine in a WWTP in Wisconsin and evaluating the power output in addition to determining the energy savings. The WWTP considered in this study has an effluent flow rate of 190 MGD (million gallons per day) and elevation difference of 3 m (10 ft) between the final stage of treatment and the discharge point. Based on the parameters above; hubless rim-drive Kaplan type hydroturbine (RDT) is the optimal choice to be used in such an application. The RDT is designed and optimized by using in-house code. A computational fluid dynamics (CFD) software is applied to evaluate the performance of the proposed model, and the system is simulated through homer software to validate the results generated by the CFD. The expected savings is estimated to be 1564 MWh/yr.

Energy & Fuels↗
DOE OSTI2023

A Backend-agnostic, Quantum-classical Framework for Simulations of Chemistry in C ++

As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Furthermore, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Remark on Algorithm 1012: Computing Projections with Large Datasets

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD. This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE, due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Blockchain Based Remote Data Integrity Checking Tool v1.0

Computer System Security: The software is used to perform remote data integrity checking without relying on a single third party. In order to remove the trusted third party our tool uses distributed ledger (Blockchain) technology. The ledger is used to store evidences (in the form of hash values). These values are used as an input in the integrity checking process.

Wonjiga, Amir↗
DOE OSTI2020

GauXC v0.1.0

GauXC is a modern, modular C++ library for the numerical exchange-correlation (XC) integration required for the Gaussian basis set discretization of Kohn-Sham density functional theory (DFT). The modular nature of GauXC allows for an extensible software design which enables performance portability on an array of CPU and GPU architectures.

Williams-Young, David↗
DOE OSTI2018

trueSIG

Sensitivity testing software package with improved performance for small samples.

Sjue, Sky↗
DOE OSTI2024

minervachem

Minervachem is a tool for cheminformatics and machine learning in chemistry. It includes both algorithms from existing literature and algorithms which we have developed. Its features center around two main themes: molecular representations and algorithms, both for molecular machine learning. It provides a scikit-learn transformer interface for molecular featurization and an estimator interface for machine learning algorithms. It also provides visualization tools for explaining machine learning predictions. We aim to continue developing this software to improve its performance and usability and expand its capabilities within the realm of molecular machine learning, cheminformatics, and visualization.

Lubbers, Nicholas↗
DOE OSTI2023

CLIP Representation Enhanced Predicate Estimation

This software provides functions to perform visual predicate estimation by leveraging the pre-trained vision-language model CLIP. In addition to training the prediction estimator with a labeled dataset, it also performs performance evaluation and visualization of results.

Jayaraman Thiagarajan, Jayaraman↗
DOE OSTI2024

Maritime Battery Electrification Simulator (MariBES) v1

MariBES is a Python-based software designed for calculating emissions and energy consumption in maritime transportation. This software is capable of performing calculations for multiple vessels, facilitating emission analysis at regional, national, and international scales. It also allows for the examination of energy consumption under various resource such as heavy fuel oil, diesel, and battery-electric, enabling the assessment of different decarbonization strategies in the maritime sector. MariBES utilizes public data on ship activities combined with detailed vessel specifications, significantly enhancing the accuracy of its simulations. This approach marks a considerable advancement over previous models that were constrained by limited spatial and temporal resolution. It features a temporal resolution based on 5-minute intervals and a spatial resolution using precise coordinates.

Moon, HeeSeung↗
DOE OSTI2024

RetroTide v1.0

RetroTide is a retrobiosynthesis software tool, designed to guide the design of biological pathways to make valuable small molecules. It takes as input a desired small molecule chemical structure, and outputs a design for a polyketide synthase enzyme (PKS) that has the closest possible chemical structure, as accessible by PKS chemistry. It is developed as a Python software library, and internally performs a search of the PKS design space, while simulating the chemicals produced by each design.

Backman, Tyler↗
DOE OSTI2022

UAV remote sensing imagery - Miscanthus trials 2020 - Energy Farm - UIUC

Aerial imagery utilized as input in the manuscript "Deep convolutional neural networks exploit high spatial and temporal resolution aerial imagery to predict key traits in miscanthus" . Data was collected over M. Sacchariflorus and Sinensis breeding trials at the Energy Farm, UIUC in 2020. Flights were performed using a DJI M600 mounted with a Micasense Rededge multispectral sensor at 20 m altitude around solar noon. Imagery is available as tif file by field trial and date (10). The post-processing of raw images into orthophoto was performed in Agisoft Metashape software. Each crop surface model and multispectral orthophoto was stacked into an unique raster stack by date and uploaded here. Each raster stack includes 6 layers in the following order: Layer 1 = crop surface model, Layer 2 = Blue, Layer 3 = Green, Layer 4 = Red, Layer 5 = Rededge, and Layer 6 = NIR multispectral bands. Msa raster stacks were resampled to 1.67 cm spatial resolution and Msi raster stacks were resampled to 1.41 cm spatial resolution to ease their integration into further analysis. 'MMDDYYYY' is the date of data collection, 'MSA' is M. Sacchariflorus trial, 'MSI' is Miscanthus Sinensis trial, 'CSM' is crop surface model layer, and 'MULTSP' are the five multispectral bands.

bioenergy↗
DOE OSTI2024

Characterization of Soil and Rock Magnetic Properties along Multiple Hillslope Transects at Teller Road Site, Seward Peninsula, Alaska, 2018 and 2023

The magnetometer data was collected in multiple directions across the watershed hillslope at the NGEE Arctic Teller Road site at mile marker 27 (TL_MM27) on the Seward Peninsula, Alaska over multiple years in March 2018 and April 2023. The magnetic data were collected using a Geometrics Inc. G-858 gradiometer and G-857 base station in 2018 and the G-864 gradiometer and G857 base station in 2023. The data was collected (in all instances) by towing the gradiometer behind a snow machine around the watershed with the two sensors in a vertical profile with constant spacing during the continuous survey in that specific year. Magnetic total field measurements were collected by gradiometer and base station, and the data processing was performed in Geometrics MagMap2000 software. The processing steps were limited to removal of data spikes (despiking), reading dropouts, and correction/removal of bad GPS points. All offsets between sensors and GPS are stated within the data files and metadata, alongwith the processed and raw data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).In this data submission there are two sets of raw magnetic data (.bin and .stn for 2018 and base for 2023; raw rover mag for 2023 is in .csv) inside two .zip files that identify the year the mag data was collected. The data are proprietary format to Geometrics and can be opened and processed with MagMap2000 which can be downloaded for free at Geometrics website. There are also two processed data files *.csv for each year and two metadata files *.csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Decrease time-to-solution through improved linear-system setup and solve

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acoustically-incompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step We describe in this report our efforts to decrease the setup and solution time for the mass-continuity Poisson system with respect to the benchmark timing results reported in FY18 Q1. In particular, we investigate improving and evaluating two types of algebraic multigrid (AMG) preconditioners: Classical Ruge-Stfiben AMG (C-AMG) and smoothed-aggregation AMG (SA-AMG), which are implemented in the Hypre and Trilinos/MueLu software stacks, respectively. Preconditioner performance was optimized through existing capabilities and settings.

17 WIND ENERGY↗
DOE OSTI2018

Nuclear Packaging Internship Final Report

The goal of this internship is for the student to become proficient in modeling the stress and deformation of mechanical structures in the ABAQUS FEA software. The research specifically performed by the student during the internship concerns measuring the crush strength of honeycomb stainless steel through FEA modeling. The student is working in the nuclear packaging department at the Savannah River National Laboratory (SRNL). The nuclear packaging department is developing a new package, dubbed 9982, which is a new Type B shipping package being proposed by the DOE Environmental Management program for the safe shipment of small quantities of nuclear materials (e.g. fissile contents). It is being proposed to implement honeycomb stainless steel as an energy absorber in the design for the new Type B nuclear shipping package. However, to properly implement this material, more data must be collected concerning the crush strength of different honeycomb stainless steel structures. The research makes measuring the crush strength of different honeycomb stainless steels through analytical means a viable option. This saves time and money as opposed to performing real drop tests and can verify the material’s performance as an impact energy absorber.

42 ENGINEERING↗
DOE OSTI2019

TRIUMF BLM

Jefferson Lab developed and provided 6 BLM VME Modules to Triumf for Electron Linac. The work includes providing digital copies of the user manual, firmware, EPICS software and PCB files, performing acceptance testing and modifications to meet the requirement.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Compact Solid-State High-Voltage Switch

Researchers at Sandia have developed a semiconductor-based high-voltage switch, with experimental results showing potential for enhanced radiation hardness, for use in multiple power conversion applications. Gallium nitride (GaN) metal-oxide semiconductor field effect transistors (MOSFETs) were modeled using commercial and Sandia CHARON simulation software to understand their performance and for future prediction of device operation in radiation environments.

42 ENGINEERING↗
DOE OSTI2022

Fast BLT Code

This note discusses numerical algorithmic software design considerations and performance estimates for a fast BLT coupling code [1] written in c++. The aim of this code is to conduct faster parameter studies over line orientations. The original matlab code was written by Mike Rivera. Art Barnes ported this code to julia. I rewrote portions of the code for speed improvement, mainly to eliminate some redundant computation when calculating many line orientations. But this code is still far from optimal.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Regression for Assessing Contaminant Plume Trends

The Department of Energy’s (DOE) Hanford site has significant dissolved-phase groundwater contaminant plumes, for which remediation activities are ongoing. Assessing concentration trends is critical for making decisions regarding remedy implementation or performance. The SOCRATES single-page web application provides a quality-assured framework for data access and consistent data analytics using standard methods. To provide trend information for remedial decisions, statistical analyses based on ordinary least squares (OLS) are already supported by SOCRATES. The presence of censored data, however, can skew the result of an OLS regression. Other methods, such as the Akritas-Theil-Sen (ATS) or Tobit regressions can be more powerful. These two regression methods are used by Hanford site personnel to analyze groundwater contaminant concentration data, which often include nondetects (censored data). The ATS method uses comparisons of observation-pair ranks and the Tobit method uses a maximum likelihood estimation method. The Tobit method can also accommodate covariates, such as river stage or groundwater level fluctuations. Both ATS and Tobit regressions can be performed with the statistics software R, but to incorporate these methods into SOCRATES, their R code was ported into JavaScript. For quality assurance verification, the new code was tested with hexavalent chromium concentration data from the Hanford 100-K West Area and compared to regression analyses that were previously conducted on this data using R.

100-K Area↗