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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Understanding Biomass Burning Aerosol via Integrated Analyses of Aerosol Mass Spectrometry Data from DOE Campaigns and ACRF Long Term Measurements

Biomass burning (BB) is one of the largest sources of aerosol particles in the atmosphere and a significant source of trace gases important to atmospheric chemistry. Organic aerosols from biomass burning sources (BBOA) are an important but poorly characterized component of the earth’s climate system. BBOA composition and life cycle processes are associated with enormous complexities and contribute significantly to model uncertainties. Hence it is important to improve the ability to simulate the concentration and properties (chemical, optical, and hygroscopic) of BBOA and the atmospheric aging processes that affect these properties. Here, we propose to analyze ambient data collected from the DOE Biomass Burning Observation Project (BBOP) field campaign and from a long term measurement site of the Atmospheric Radiation Measurement (ARM) program – the Southern Great Plains (SGP), where transported BB plumes and elevated BBOA episodes were frequently observed. This project is aimed at 1) gaining detailed, quantitative understanding of the chemical and physical properties of BBOA and their chemical changes during atmospheric aging via advanced analysis of aerosol measurement data acquired at the summit of Mt. Bachelor (MBO) during the BBOP campaign and through integration with concurrent measurements of trace gases, particle properties, and meteorological conditions performed at MBO and from the G-1 aircraft; 2) gaining better understanding of BBOA properties and its roles in radiative forcing via analysis of routine data from the ARM long-term measurement sites; and 3) integrating the results from 1) and 2) into a global database of aerosol mass spectrometry and collaborate with modelers on evaluating and improving numerical model performance as part of the ASR program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Exploring drought-responsive crucial genes in Sorghum

Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
DOE OSTI2022

Uncertainty analysis for VERA problem 2 using the cell-code Condor v2.8.05

Condor is a cell-level neutronic calculation code that applies multi-group collision probabilities with heterogeneous response coupling method within generic geometry configurations. Under the Condor's code continuous development, the incorporation of up-to-date methodologies and state-of-the-art practices in reactor analysis represents a driving force. In this work, the capabilities of Condor v2.8.05 to develop an uncertainty analysis for realistic PWR-kind fuel assemblies are studied. The Total Monte Carlo approach is applied to quantify the impact of fabrication tolerances in the code's results for reactivity and power distributions, by means of randomly sampled input values using the VERA problem 2 as basis. The VERA problem 2 proposes a series of Westinghouse 2D 17 x 17-type fuel lattices, to be calculated reflected at beginning-of-life without Xe. The configurations correspond to a modern PWR. Selected neutronic parameters from Condor runs are thus analyzed in terms of the observed spread as well as the obtained distributions for the randomly perturbed cases, showing the capability of the code to handle the required input data, as well as its ability to provide valuable insights regarding uncertainty quantification.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A role for Rab30 in retrograde trafficking and maintenance of endosome-TGN organization

Highlights: • Rab30 localised primarily to TGN and recycling endosomes. • Rab30 plays a major role in the spatial organization of the TGN and recycling endosomes. • Deficiency of Rab30 results in a perturbation in endosome-TGN retrograde transport. • Interactive partners of Rab30 include Arf1 and Arf4. • Findings highlight the interconnected network between the TGN and endosomal system. Rab30 is a poorly characterized small GTPase. Here we show that Rab30 is localised primarily to the TGN and recycling endosomes in a range of cell types, including primary neurons; minor levels of Rab30 were also detected throughout the Golgi stack and early endosomes. Silencing of Rab30 resulted in the dispersal of both early and recycling endosomes and TGN compartments in HeLa cells. By analyzing cargo trafficking in Rab30-silenced and Rab30-overexpressing HeLa cells, we demonstrate that Rab30 plays a role in retrograde trafficking of TGN38 from endosomes to the Golgi, but has no apparent role in the endocytic recycling of the transferrin receptor to the plasma membrane. Five interactive partners with Rab30 were identified by pull-down and MS analysis using GFP-tagged Rab30 mutant, Rab30(Q68L). Two of the interactive partners identified were Arf1 and Arf4, known regulators of endosome to TGN retrograde transport. Knockdown of Arf1 and Arf4 results in GFP-Rab30 decorated tubules arising from the recycling endosomes, suggesting association of Rab30 with tubular carriers. Overall our data demonstrates a role for Rab30 in regulating retrograde transport to the TGN and maintenance of endosomal-TGN organization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Quantification and evaluation of strain reduction from small-bubble gas injection in Spallation Neutron Source target vessels

Small-bubble gas injection has been routinely utilized in the operation of Spallation Neutron Source (SNS) mercury targets since 2017 to mitigate cavitation-induced erosion damage to target vessels. Strain measurements of target vessels collected in-situ during initial operation with gas injection were used to study the gas injection effect on the structural response of targets to proton pulses. A significant strain reduction owing to gas injection was found by comparing the strain measurement data during operation with and without gas injection. The research presented here focuses on quantifying strain reductions in SNS targets and evaluating the effect of small-bubble gas injection by comparing different bubbler types and target designs. The strain measurement results show the gas injection significantly reduced strain in SNS target vessels; strain values decreased by 30% to 80% for targets operating with gas injection. Stress and strain responses of SNS targets were simulated to numerically evaluate the gas injection effect. Based on the predicted stresses with and without gas injection, the fatigue lifetimes of SNS jet-flow design target were estimated using fe-safe fatigue analysis software. The simulations show these reductions should improve the fatigue life of target vessels and allow SNS targets to meet their fatigue design goal.

Fatigue life estimation↗
DOE OSTI2023

Geo-temporal patterns to design cost-effective interventions for zoonotic diseases -the case of brucellosis in the country of Georgia

Control of zoonosis can benefit from geo-referenced procedures. Focusing on brucellosis, here the ability of two methods to distinguish disease dissemination patterns and promote cost-effective interventions was compared. Geographical data on bovine, ovine and human brucellosis reported in the country of Georgia between 2014 and 2019 were investigated with (i) the Hot Spot (HS) analysis and (ii) a bio-geographical (BG) alternative. More than one fourth of all sites reported cases affecting two or more species. While ruminant cases displayed different patterns over time, most human cases described similar geo-temporal features, which were associated with the route used by migrant shepherds. Other human cases showed heterogeneous patterns. The BG approach identified small areas with a case density twice as high as the HS method. The BG method also identified, in 2018, a 2.6–2.99 higher case density in zoonotic (human and non-human) sites than in non-zoonotic sites (which only reported cases affecting a single species) –a finding that, if corroborated, could support cost-effective policy-making. Three dissemination hypotheses were supported by the data: (i) human cases induced by sheep-related contacts; (ii) human cases probably mediated by contaminated milk or meat; and (iii) cattle and sheep that infected one another. This proof-of-concept provided a preliminary validation for a method that may support cost-effective interventions oriented to control zoonoses. To expand these findings, additional studies on zoonosis-related decision-making are recommended.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility

Abstract The Soybean Free Air Concentration Enrichment (SoyFACE) facility is the longest running open-air carbon dioxide and ozone enrichment facility in the world. For over two decades, soybean, maize, and other crops have been exposed to the elevated carbon dioxide and ozone concentrations anticipated for late this century. The facility, located in East Central Illinois, USA, exposes crops to different atmospheric concentrations in replicated octagonal ~280 m 2 Free Air Concentration Enrichment (FACE) treatment plots. Each FACE plot is paired with an untreated control (ambient) plot. The experiment provides important ground truth data for predicting future crop productivity. Fumigation data from SoyFACE were collected every four seconds throughout each growing season for over two decades. Here, we organize, quality control, and collate 20 years of data to facilitate trend analysis and crop modeling efforts. This paper provides the rationale for and a description of the SoyFACE experiments, along with a summary of the fumigation data and collation process, weather and ambient data collection procedures, and explanations of air pollution metrics and calculations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Experimental Evaluation of Deformation and Fracture Mechanisms in Highly Irradiated Austenitic Steels

The present report documents recent experimental results of analysis using scanning electron microscopy/electron backscatter diffraction (SEM-EBSD) of plastic deformation mechanisms and strain localization phenomena in austenitic steels irradiated by neutrons. Experiments were performed with specimens irradiated to 125 dpa and, additionally, with specimens that experienced radiation-induced swelling up to 3%. Section 1 briefly analyzes the deformation localization in irradiated steels and its consequences on the material performance. The section describes the advantages and importance of the SEM-EBSD approach combined with in situ mechanical testing capability. Section 2 briefly introduces the experimental tools and methods (i.e., SEM/EBSD in situ tensile frame, electric discharge machine to manufacture irradiated specimens) and describes the investigated materials (i.e., element composition, irradiation conditions, and general microstructure). Section 3 describes the key experimental results and provides a brief analysis and comparison with the datasets obtained earlier within the same task (i.e., low-dose specimens). The discussion focuses on EBSD microstructure maps with strain localization features, misorientation evolution as a function of strain, and observed deformation mechanisms. Section 4 evaluates data collected in recent years on highly irradiated steel and estimates the possible misorientation evolution under irradiation. The section introduces and discusses the concept of in-service-induced damage as an irradiation-assisted stress-corrosion cracking precursor. Section 5 summarizes the work performed. As expected, the present work results are beneficial for exploring and understanding degradation mechanisms in highly irradiated in-core materials found in light water reactors after long-term in-service life.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Analysis of Design Alternatives of Actively Cooled RF Window for MPEX

The Materials Plasma Exposure eXperiment (MPEX) has been designed as a linear plasma divertor simulator in order to address plasma material interaction (PMI) science for next-generation fusion devices. It will have the capability to test neutron irradiated samples with plasma fluxes of greater than 10 24 m -2 s -1 . It is expected to operate steady state for up to 10 6 s to consider PMI affects through reactor end of life. The conceptual design of MPEX was completed in 2019, with preliminary design having begun in 2020. The plasma source for MPEX is a helicon antenna, where the energized helical antenna sits outside of the vacuum in order to minimize impurities in the plasma. It is expected to receive up to 200 kW of continuous power, and so the antenna and the window must be actively cooled. The water-cooled copper antenna has been operated at full power on the Proto-MPEX device (which is a test facility to demonstrate the plasma source and heating systems). The water-cooled window, however, is a novel component that must meet numerous competing requirements. It requires a low dielectric loss to allow the Radio Frequency (RF) power to create the plasma within the vacuum boundary. It must be structurally robust to handle the significant heat flux from the plasma and any heat from dielectric coupling. It must be compatible with the coolant (preferably water). It requires a vacuum seal that minimizes impurities into the plasma and does not compromise the structural integrity of the window. Two window designs have been tested. Results from these tests, where temperatures are measured and heat fluxes inferred from infrared camera data, have been correlated with thermal-structural simulations. When these simulations are extrapolated to the full power steady-state heat fluxes that are expected in MPEX, the designs do not appear to have the necessary structural robustness. This study explores design alternatives for the MPEX helicon antenna window, presents analysis results for several of the alternatives, and shows a viable solution that satisfies the requirements for MPEX operation.

Tipton Jr, Joseph↗
DOE OSTI2024

Downstream Separation of Formic Acid with Anion-Exchange Resin from Electrocatalytic Carbon Dioxide (CO 2 ) Conversion: Adsorption, Kinetics, and Equilibrium Modeling

The electrocatalytic carbon dioxide reduction reaction (CO 2 RR) that produces liquid formic acid (FA) is one potential route to minimize the CO 2 concentration in the atmosphere by utilizing CO 2 as a feedstock. However, one of the main disadvantages of this route is the high energy demand required for purification through conventional distillation. This method of purification has been shown to result in a large carbon footprint in life-cycle analysis (LCA). Therefore, finding an alternative purification technique that can integrate with an electrochemical CO 2 RR process with the potential to lower the carbon footprint would be of great interest. Under the experimental conditions of the CO 2 RR, FA remains in its basic form as the formate anion in the catholyte. Anion exchange resins with their basic properties can separate the formate anion from the catholyte mixture as an alternative to energy intensive distillation. In this work, three anion exchange resins with increasing basicity order: Amberlite IRA-96 < Amberlite IRA-910 < Ambersep 900 are tested to separate FA in the presence of aqueous solution of potassium bicarbonate catholyte with varying levels of resin and FA. Kinetics and equilibrium studies data for the FA adsorption are interpreted using several kinetics and isotherm models. The kinetics data fit well with a pseudo-first-order model at high initial FA concentrations and a pseudo-second-order model at low initial FA concentrations. The experimental data can be best explained with the Freundlich isotherm model. The fitted results show that strongly basic Ambersep 900 is more effective at separating FA, with a maximum adsorption capacity of 336.7 mg/g compared to Amberlite IRA-96 (275.2 mg/g) and Amberlite IRA-910 (209.2 mg/g) in neutral water. However, weakly basic Amberlite IRA-96 (110.8 mg/g) can separate FA more efficiently from potassium bicarbonate catholyte than Amberlite IRA-910 (99.9 mg/g) and Ambersep 900 (40.0 mg/g). As a result, competitive adsorption occurs in the presence of potassium bicarbonate catholyte, but the formic acid adsorption capacity is still sufficient for adequate separation.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Reliability-Informed Economic and Energy Evaluation for Design for Remanufacturing: A Case Study on a Hydraulic Manifold

Design for Remanufacturing (DfRem) is an attractive approach for sustainable product development. Evaluation of DfRem strategies, from both economic and environmental perspectives, at an early design stage can allow the designers to make informed decisions when choosing the best design option. Studying the long-term implications of a particular design scenario requires quantifying the benefits of remanufacturing for multiple life cycles while considering the reliability of the product. In addition to comparing designs on a one-to-one basis, we find that including reliability provides a different insight into comparing design strategies. We present a reliability-informed cost and energy analysis framework that accounts for product reliability for multiple remanufacturing cycles within a certain warranty policy. The variation of reuse rate over successive remanufacturing cycles is formulated using a branched power-law model which provides probabilistic scenarios of reusing or replacing with new units. To demonstrate the utility of this framework, we use the case study of a hydraulic manifold, which is a component of a transmission used in some agricultural equipment, and use real-world field reliability data to quantify the transmission’s reliability. Three design improvement changes are proposed for the manifold and we quantify the costs and energy consumption associated with each of the design changes for multiple remanufacturing cycles.

design for remanufacturing↗
DOE OSTI2022

Space flight associated changes in astronauts’ plasma-derived small extracellular vesicle microRNA: Biomarker identification

This pilot study suggests relatively short (median 12 days long) low-Earth orbit (LEO) spaceflight induces changes in circulating plasma small extracellular vesicle (sEV) microRNA expression. Normalization of small RNA sequencing (sRNAseq) data and quantitative polymerase chain reaction (qPCR) validation confirmed miR-4732-3p is significantly upregulated up to 3 days post-landing, and enrichment analysis suggests this miRNA is expressed in various central nervous system tissues and hematopoietic cells and may be linked to different organ disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Life-cycle energy use and greenhouse gas emissions of palm fatty acid distillate derived renewable diesel

This study aims to quantify life-cycle fossil energy use and greenhouse gas (GHG) emissions for palm fatty acid distillate (PFAD) derived renewable diesel (RD) taking into consideration different feedstock classifications that are applicable to PFAD (residue, byproduct, or coproduct), and incorporating updated data for key processes. Under the three classifications, the PFAD to RD pathway was modeled using the Greenhouse gases, Regulated Emissions, and Energy Use in Technologies (GREET®) model. PFAD-derived RD could reduce fossil energy consumption by 77%–88%, relative to petroleum diesel. GHG emissions are very sensitive to PFAD classification and coproduct handling methods. Considering the production of palm oil and PFAD and economic value, we maintain that PFAD should be treated as a byproduct in palm oil refineries. With this treatment, PFAD-derived RD could achieve 84% GHG emissions reductions, compared to the emissions of petroleum diesel. We also employed a substitution method to address the substitution of PFAD by other materials in the marketplace. Compared to coproduct allocation results, we found substituting PFAD by tallow, soy oil, barley, and canola oil results in lower GHG emissions. Due to high induced land-use change emissions associated with palm farming, if PFAD is treated as a coproduct with refined palm oil, PFAD-derived RD may not deliver GHG reductions. A sensitivity analysis identified key parameters such as palm fruit yield, oil extraction efficiency in oil mills, and energy use intensity for RD production affects LCA results significantly; future efforts to improve these parameters could result in further GHG reductions.

09 BIOMASS FUELS↗
DOE OSTI2024

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use at http://pnnl-compbio.github.io/decomprolute.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
DOE OSTI2020

Physics-Informed Machine Learning for Epidemiological Models

One challenge of using compartmental SEIR models for public health planning is the difficulty in manually tuning parameters to capture behavior reflected in the real-world data. This team conducted initial, exploratory analysis of a novel technique to use physics-informed machine learning tools to rapidly develop data-driven models for physical systems. This machine learning approach may be used to perform data assimilation of compartment models which account for unknown interactions between geospatial domains (i.e. diffusion processes coupling across neighborhoods/counties/states/etc.). Results presented here are early, proof-of-concept ideas that demonstrate initial success in using a physically informed neural network (PINN) model to assimilate data in a compartmental epidemiology model. The results demonstrate initial success and warrant further research and development.

60 APPLIED LIFE SCIENCES↗