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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗
DOE OSTI2022

Semi-empirical simulation of in-motion radiation detection systems

The Replicative Assessment of Spectroscopic Equipment (RASE) is an open-source software that uses experimental data as the basis to simulate the response of commercial radiation detectors to sources in various situations, particularly in the context of nuclear security and safeguards applications. Dynamic RASE introduces the capability to simulate scenarios where sources and detector are in relative motion. Position-dependent experimentally acquired gamma spectra are ingested by Dynamic RASE to build maps that describe the detector response over all space. These response maps are used to replicate the time-dependent energy spectra collected as sources move on a path near the detector. Here, a Gaussian process is used to build each map, incorporating a novel kernel adapted to the special case of radiation detection. The approach has been validated against experimental data acquired using a NaI-based detector for 137 Cs and 54 Mn sources. The capability to create accurate simulations using either long-dwell static measurements or dynamic pass-by measurements as source data has been demonstrated. Quantitative relative performance, benefits, and shortcomings are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Structure of the HIV-1 Full-Length Capsid Protein in a Conformationally Trapped Unassembled State Induced by Small-Molecule Binding

The capsid (CA) protein plays crucial roles in HIV infection and replication, essential to viral maturation. The absence of high-resolution structural data on unassembled CA hinders the development of antivirals effective in inhibiting assembly. Unlike enzymes that have targetable, functional substrate-binding sites, the CA does not have a known site that affects catalytic or other innate activity, which can be more readily targeted in drug development efforts. We report the crystal structure of the HIV-1 CA, revealing the domain organization in the context of the wild-type full-length (FL) unassembled CA. The FL CA adopts an antiparallel dimer configuration, exhibiting a domain organization sterically incompatible with capsid assembly. A small compound, generated in situ during crystallization, is bound tightly at a hinge site ('H site'), indicating that binding at this interdomain region stabilizes the ADP conformation. Electron microscopy studies on nascent crystals reveal both dimeric and hexameric lattices coexisting within a single condition, in agreement with the interconvertibility of oligomeric forms and supporting the feasibility of promoting assembly-incompetent dimeric states. Solution characterization in the presence of the H-site ligand shows predominantly unassembled dimeric CA, even under conditions that promote assembly. Our structure elucidation of the HIV-1 FL CA and characterization of a potential allosteric binding site provides three-dimensional views of an assembly-defective conformation, a state targeted in, and thus directly relevant to, inhibitor development. Based on our findings, we propose an unprecedented means of preventing CA assembly, by 'conformationally trapping' CA in assembly-incompetent conformational states induced by H-site binding.

Du, Shoucheng↗
DOE OSTI2025

Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER

Global Storm-Resolving Models (GSRMs) are becoming increasingly vital for advancing climate modeling and improving the prediction of extreme weather events. Houston, a coastal region frequently affected by deep convective storms, offers an ideal setting to evaluate the ability of GSRMs to simulate deep convection. This study assesses the performance of the Doubly Periodic Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere Model (DP-SCREAM) using observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model. The DP-SCREAM is demonstrated to be applicable to coastal regions, partially due to the forcing data sets already capturing the influence of breezes. DP-SCREAM also replicates biases persistent in the global version of SCREAM: the underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and the popcorn convection, characterized by small and disorganized convective cells generating the strongest precipitation. To investigate these issues, two sensitivity experiments were conducted: increasing the mixing length and scaling up the buoyancy flux within the Simplified Higher Order Closure scheme. Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence. Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells. In conclusion, an additional resolution sensitivity test at 0.5 km grid spacing demonstrated that a refined horizontal resolution alone is insufficient to resolve these biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A cancer-associated polymorphism in ESCRT-III disrupts the abscission checkpoint and promotes genome instability

Cytokinetic abscission facilitates the irreversible separation of daughter cells. This process requires the endosomal-sorting complexes required for transport (ESCRT) machinery and is tightly regulated by charged multivesicular body protein 4C (CHMP4C), an ESCRT-III subunit that engages the abscission checkpoint (NoCut) in response to mitotic problems such as persisting chromatin bridges within the midbody. Importantly, a human polymorphism in CHMP4C (rs35094336, CHMP4C T232 ) increases cancer susceptibility. Here, we explain the structural and functional basis for this cancer association: The CHMP4C T232 allele unwinds the C-terminal helix of CHMP4C, impairs binding to the early-acting ESCRT factor ALIX, and disrupts the abscission checkpoint. Cells expressing CHMP4C T232 exhibit increased levels of DNA damage and are sensitized to several conditions that increase chromosome missegregation, including DNA replication stress, inhibition of the mitotic checkpoint, and loss of p53. Our data demonstrate the biological importance of the abscission checkpoint and suggest that dysregulation of abscission by CHMP4C T232 may synergize with oncogene-induced mitotic stress to promote genomic instability and tumorigenesis.

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

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2016

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replace the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.

25 ENERGY STORAGE↗
DOE OSTI2018

Residential Building Energy Code Field Study

This document presents a methodology for assessing baseline energy efficiency in new single-family residential buildings and quantifying related savings potential. The approach was developed by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy (DOE) Building Energy Codes Program with the objective of assisting states as they assess energy efficiency in residential buildings and implementation of their building energy codes, as well as to target areas for improvement through energy codes and broader energy-efficiency programs. It is also intended to facilitate a consistent and replicable approach to research studies of this type and establish a transparent data set to represent baseline construction practices across U.S. states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Alterations in the nuclear proteome of HIV-1 infected T-cells

Virus infection of a cell involves the appropriation of host factors and the innate defensive response of the cell. The identification of proteins critical for virus replication may lead to the development of novel, cell-based inhibitors. In this study we mapped the changes in T-cell nuclei during human immunodeficiency virus type 1 (HIV-1) at 20 hpi. Using a stringent data threshold, a total of 13 and 38 unique proteins were identified in infected and uninfected cells, respectively, across all biological replicates. An additional 15 proteins were found to be differentially regulated between infected and control nuclei. STRING analysis identified four clusters of protein–protein interactions in the data set related to nuclear architecture, RNA regulation, cell division, and cell homeostasis. Immunoblot analysis confirmed the differential expression of several proteins in both C8166-45 and Jurkat E6-1 T-cells. These data provide a map of the response in host cell nuclei upon HIV-1 infection. - Highlights: • We identify changes in the expression of nuclear proteins during HIV-1 infection. • 163 nuclear proteins were found differentially regulated during HIV-1 infection. • Bioinformatic analysis identified several nuclear pathways altered by HIV infection. • Candidate factors were validated in two independent cell lines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Omics-Lethal Human Viruses, Interferon-Stimulated Response Experiment IFNaHUH001

The purpose of this experiment was to evaluate the human host cellular response to treatment with and without interferon alpha/beta (IFNα/β) treatment. Sample time course data was obtained from human hepatoma carcinoma cells (HUH-7) expressing transcriptional activation protein VP30, stably transfected with the Ebola VP30 gene for enabling replication of VP30-deficient Ebola virus for transcriptome expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset download have a direct relationship to a primary sample submission corresponding to a specific interferon response to viral infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Validation of the DESI DR2 measurements of baryon acoustic oscillations from galaxies and quasars

The Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from Data Release 1 (DR1), providing improved statistical precision in baryon acoustic oscillation (BAO) constraints across multiple tracers, including bright galaxies, luminous red galaxies, emission line galaxies, and quasars. In this paper, we validate the BAO analysis of DR2. We present the results of robustness tests on the blinded DR2 data and, after unblinding, consistency checks on the unblinded DR2 data. All results are compared with those obtained from a suite of mock catalogs that replicate the selection and clustering properties of the DR2 sample. We confirm the consistency of DR2 BAO measurements with DR1 while achieving a reduction in statistical uncertainties due to the increased survey volume and completeness. The combined BAO precision, including both statistical and systematic errors, improves from ∼0.52% in DR1 to 0.30% in DR2—a factor of 1.7 gain. We assess the impact of analysis choices, including different data vectors (correlation function vs power spectrum), modeling approaches and systematics treatments, and an assumption of the Gaussian likelihood, finding that our BAO constraints are stable across these variations and assumptions with a few minor refinements to the baseline setup of the DR1 BAO analysis. We summarize a series of pre-unblinding tests that confirmed the readiness of our analysis pipeline, the final systematic errors, and the DR2 BAO analysis baseline. The successful completion of these tests led to the unblinding of the DR2 BAO measurements, ultimately leading to the DESI DR2 cosmological analysis, with their implications for the expansion history of the Universe and the nature of dark energy presented in the DESI key paper (companion paper).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Nanopolysaccharide Builder: A User-Friendly Tool for Atomistic Models of Polysaccharide-Based Nanostructures

Here, we introduce Nanopolysaccharide Builder (NPB), a user-friendly software tool designed to construct polysaccharide nanostructures─mainly those based on cellulose, chitin, and chitosan─using experimental data or user-defined parameters. NPB enables the generation of cellulose and chitin allomorphs with customizable biochemical topologies and also facilitates the construction of large bundles that replicate nanostructures found in biological support systems, including plant cell walls and arthropod cuticles. The software outputs atomic Cartesian coordinates in Protein Data Bank (PDB) format and also provides atom connectivity files in PSF and PARM formats, ensuring seamless integration with major molecular dynamics (MD) engines such as NAMD, CHARMM, GROMACS, AMBER, OpenMM, and LAMMPS. Built on an interactive visualization framework, NPB features a graphical user interface (GUI) and supports both macOS and Linux operating systems. By enabling detailed atomic-scale studies of polysaccharide evolution in extracellular matrices and cell walls of algae, bacteria, fungi, and plants, NPB is poised to advance AI-guided research in sustainable chemical development and biomass utilization.

Wan, Zhangmin [Univ. of British Columbia, Vancouve↗
DOE OSTI2021

Blodgett Forest CA Warming Experiment phospholipid fatty acid and branched glycerol dialkyl glycerol tetraethers data 2018 from: "Whole-soil warming decreases abundance and modifies the community structure of microorganisms in the subsoil but not in surface soil".

This data package contains phospholipid fatty acid and branched glycerol dialkyl glycerol tetraethers data used for the paper: “Whole-soil warming decreases abundance and modifies the community structure of microorganisms in the subsoil but not in surface soil”. SOIL, https://doi.org/10.5194/soil-2021-14.On April 2018 (after 4.5 years of warming), we collected soil cores down to 90 cm depth from the whole soil profile warming experiment at the University of California Blodgett Forest Research Station, in the Sierra Nevada foothills near Georgetown, CA at 1370 m above sea level. Cores were collected from 6 experimental plots (three replicated blocks); samples were collected in 10 cm increments from 0 to 90 cm depth.This data package includes a file in .csv format and a file in .xlsx format, which can be accessed and processed using MS Excel or R. Phospholipid fatty acid and branched glycerol dialkyl glycerol tetraethers concentrations are provided as processed data files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Genetic characterization of an adapted pandemic 2009 H1N1 influenza virus that reveals improved replication rates in human lung epithelial cells

The 2009 influenza pandemic originated from a swine-origin H1N1 virus, which, although less pathogenic than anticipated, may acquire additional virulence-associated mutations in the future. To estimate the potential risk, we sequentially passaged the isolate A/Hamburg/04/2009 in A549 human lung epithelial cells. After passage 6, we observed a 100-fold increased replication rate. High-throughput sequencing of viral gene segments identified five dominant mutations, whose contribution to the enhanced growth was analyzed by reverse genetics. The increased replication rate was pinpointed to two mutations within the hemagglutinin (HA) gene segment (HA{sub 1} D130E, HA{sub 2} I91L), near the receptor binding site and the stem domain. The adapted virus also replicated more efficiently in mice in vivo. Enhanced replication rate correlated with increased fusion pH of the HA protein and a decrease in receptor affinity. Our data might be relevant for surveillance of pre-pandemic strains and development of high titer cell culture strains for vaccine production. - Highlights: • We observed a spontaneous mutation of a 2009-pandemic H1N1 influenza virus in vitro. • The adaptation led to a 100-fold rise in replication rate in human A549 cells. • Adaptation was caused by two mutations in the HA gene segment. • Adaptation correlates with increased fusion pH and decreased receptor affinity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Reproducibility and Replication of Experimental Particle Physics Results

Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of non-replicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiments require large investments of time and effort to design, construct, and operate. Large collaborations produce and check the results, and many papers are signed by more than three thousand authors. This paper gives an introduction to what experimental particle physics is and to some of the tools that are used to analyze the data. It describes the procedures used to ensure that results can be computationally reproduced, both by collaborators and by non-collaborators. It describes the status of publicly available data sets and analysis tools that aid in reproduction and recasting of experimental results. It also describes methods particle physicists use to maximize the reliability of the results, which increases the probability that they can be replicated by other collaborations or even the same collaborations with more data and new personnel. Examples of results that were later found to be false are given, both with failed replication attempts and one with alarmingly successful replications. While some of the characteristics of particle physics experiments are unique, many of the procedures and techniques can be and are used in other fields.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Correlation between Toluene Exposure and Toxicity Effects of Toluene on Drosophila Melanogaster Fecundity and Offspring Survivability

Toluene is a volatile organic compound that is a common indoor contaminant. According to the U.S. Department of Health and Human Services, chronic toluene exposure negatively affects organ systems, such as nervous and respiratory, and acts as a fetotoxic agent in the development of humans. Toluene exposure causes damage to human myelin sheaths, thus we propose that damage to the fly nervous system will be similar to that seen in humans exposed to toluene. Our ultimate goal is to determine the effects of toluene exposure on developmental and neuronal processes in D. melanogaster . In this first phase, we hypothesize that toluene‐exposed adults, at the interpolated Lethal Exposure (LE 50 ), will demonstrate decreased fecundity and that offspring from toluene‐exposed females will have decreased survivability from the egg to adult stage. We developed a continuous flow system that delivers known concentrations of gaseous toluene to populations of D. melanogaster . Ten to twenty adult female flies per trial were exposed for 24 hours to known concentrations of toluene. Surviving females were collected, counted and mated to non‐exposed adult males. The toluene‐exposed females were placed on a colored agar plate to lay eggs for 24 hours. Eggs were counted to determine fecundity (number of eggs produced by a single female fly). Survivability of eggs to later life stages, was measured by counting number of larvae, pupae, and adults divided by number eggs, larvae, and pupae respectively. The interpolated LE 50 of gaseous toluene was between 1000 and 1500 ppm after 24‐hour exposure. Fecundity of toluene‐exposed females decreased by approximately 50% when compared to airexposed females. The survivability of offspring of air‐exposed flies from egg to larval stages was 84% higher than published studies. We observed that larval eclosure from eggs laid by toluene‐exposed females was 35–50% higher on days 1 and 2 when compared to larval eclosure from eggs of air‐exposed exposed females. On days 3–6 the eclosure rate was relatively similar. These data showed toluene's effects on fecundity and survivability were measurable, however, additional replicates are in progress to fully assess the life table characteristics of offspring of toluene‐exposed female flies. Further studies will focus on toluene's effect on nervous and reproductive organs of fruit flies, along with a study on the mutagenic effects of toluene on fly DNA. Support or Funding Information U.S. DOE, Hispanic‐Serving Institutions, STEM Articulation Grant P031C110128 This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

MacGregor, Michael Charles↗