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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Partitioning CO2 fluxes with isotopologue measurements and modeling to understand mechanisms of forest carbon sequestration

1. Objectives This project combines automated in situ observations of the isotopologues of CO2 with root observations, novel experimental manipulations of belowground processes, and isotope-enabled ecosystem modeling to investigate mechanisms of below- vs. aboveground carbon sequestration at the Harvard Forest Environmental Measurements Site (EMS). The proposed objectives, which have now been largely accomplished, include: A. Partitioning of net ecosystem CO2 exchange (NEE) into photosynthesis and respiration using long-term continuous observations of the isotopic composition of NEE, and analysis of their dynamics ; B. Investigation of the influence of vegetation phenology on the timing and magnitude of carbon allocated belowground using measurements of root growth and indices of belowground autotrophic vs. heterotrophic respiration (via trenched plots and isotope measurements); C. Testing whether plant allocation of carbon belowground stimulates the microbial decomposition of soil organic matter, using in situ rhizosphere simulation experiments wherein realistic quantities of artificial isotopically-labeled exudates are released into the soil; and D. Synthesis and interpretation of the above data using the Ecosystem Demography Model 2 (ED2). 2. Highlights Accomplishments: • Our isotopic eddy flux record has completed its 5th full year and has been used to independently estimate ecosystem-scale respiration and photosynthesis. • Soil surface chamber isotopic flux measurements were carried out during three growing seasons, in conjunction with a trenching manipulation. Key findings to date (listed by objective): A. Partitioning of Net Ecosystem Exchange: 1. Ecosystem respiration is lower during the day than at night—the first robust evidence of the inhibition of leaf respiration by light (the “Kok effect”) at the ecosystem scale. 2. Because it neglects the Kok effect, the standard NEE partitioning approach overestimates ecosystem photosynthesis (by ~25%) and daytime respiration (by ~100%) in the first half of the growing season at our site, and portrays ecosystem photosynthetic light-use efficiency as declining when in fact it is stable until autumnal senescence. B. Vegetation Phenology and belowground allocation: Findings: 1. Autotrophic respiration (Ra) showed a seasonal pattern, peaking in mid-summer when trees were most active. 2. The effective age of the substrate for belowground respiration is less than 2 weeks. 3. Above and belowground phenology are more synchronous in deciduous hardwood stands than evergreen hemlock stands. 4. The decline in root respiration rates in the fall is related to temperature rather than acclimation of root respiration or substrate limitations. Methodological Issues: 5. The isotopic signatures of autotrophic and heterotrophic respiration are too similar for isotopic partitioning of belowground respiration into these two components at our site—in keeping with the recent findings of Bowling et al. (2015) in a subalpine conifer forest. 6. Artifacts of the trenching method, such as changes in soil moisture and increased carbon substrate from the newly severed roots, are significant and need to be quantified when determining daily to annual estimates of autotrophic and heterotrophic respiration. C. Effects of simulated exudates on priming of microbial decomposition: The stoichiometry of root exudates influences both the amount and the mechanism by which priming occurs. At low C:N, SOC loss is caused by an increase in microbial efficiency. At high C:N, SOC loss is caused by an increase in microbial biomass. D. Modeling with the Ecosystem Demography Model (ED2): 1. Incorporation of 13C tracking to create an isotopically-enabled Ecosystem Demography v2 model (ED2) 2. State-of-the-art parameter optimization methodology developed for improving ED2 model predictions and parameters. 3. Significantly improved model predictions of growth- and maintenance-related carbon fluxes and 13C fluxes

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Constitutive Model of Radiation Aging Effects in Filled Silicone Elastomers under Strain

Filled silicone elastomers, an essential component in many technological applications, are often subjected to controlled or unintended radiation for a variety of reasons. Radiation exposure can lead to permanent mechanical and structural changes in the material, which is manifested as altered mechanical response, and in some cases, a permanent set. For unfilled elastomers, network theories developed and refined over decades can explain these effects in terms of chain-scission and cross-link formation and a hypothesis involving independent networks formed at different strain levels of the material. In this work, we expose a filled silicone rubber to gamma radiation while being under finite elongational strain and show that the observed mechanical and structural changes can be quantitatively modeled within the same theoretical framework developed for unfilled elastomers as long as nuances associated with the Mullins effect are accounted for in a consistent manner. In this work, we employ Ogden’s incompressible hyperelastic model within the framework of Tobolsky’s two-network scheme to describe the observed permanent set and mechanical modulus changes as a function of radiation dosage. In the process, we conclude that gamma radiation induces both direct cross-linking at chain crossings (H-links) and main-chain-scission followed by cross-linking (Y-links). We provide an estimate of the ratio of chain-scission to cross-linking rates, which is in reasonable agreement with previous experimental estimate from Charlesby–Pinner analysis. We use density functional theory (DFT)-based quantum mechanical calculations to explore the stability of –Si and –SiO radicals that form upon a radiation-induced chain-scission event, which sheds light on the relative rates of Y-linking and H-linking processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

UV-Fluorescence Imaging of Silicon PV Modules After Outdoor Aging and Accelerated Stress Testing

Fast, non-destructive, outdoor-compatible methods for photovoltaic module characterization are essential for monitoring module quality without disrupting energy production. UV-fluorescence (UVF) imaging of the encapsulant layer can be used for crack detection in the field. We show that UVF patterns vary widely between modules and types of applied stress. We propose that combining accelerated stress testing (e.g. thermal cycling and damp heat) with field investigations can help build understanding of different types of UVF, which are formed and subsequently quenched at varying rates. Ultimately, such understanding could enable estimating when cracks were formed.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

Determination of single crystal thermal expansion in Uranium-6wt%Niobium shape memory alloy using in-situ diffraction and modeling of textured polycrystalline samples

In-situ neutron diffraction and ex-situ dilatometry experiments were performed on a chemically banded, quenched uranium-6wt% niobium shape memory alloy to study the impact of deformation-(detwinning-) induced texture on the evolution of thermally induced strains (and associated stresses). Thermal heating and cooling cycles between 5K and 473K were performed in-situ with neutron diffraction, and the lattice strain evolution is reported for the observed monoclinic α" phase. Comparisons between the measured diffraction strains and macroscopic dilatometry experiments reveal relationships between micro- and macro-level thermal expansion. Softening of the texture during heating suggests that twin boundary motion can accommodate the large internal thermal strains which are approximately 2% greater than that typically observed during the 175K heating interval used to age more randomly oriented polycrystalline material. Assuming weak constraint of neighboring grains in randomly textured polycrystals, the single crystal thermal expansion tensor is extracted from measurements of lattice strains over the range 5K to 473K. Predictions of polycrystalline thermal expansion, based upon this single crystal thermal expansion tensor, are shown to compare favorably with bulk thermal expansion observations of the as quenched microstructure. However, such a lower-bound estimate is insufficient to explain all aspects of the behavior of the textured material, where the matrix is not isotropic. In conclusion, it is hypothesized that relaxation processes which occur within the quenched microstructure during heating are responsible for the distinct thermal expansion behavior observed during the initial heating cycle compared to cooling and subsequent cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distinct Gas-Particle Partitioning and Viscosity Characteristics of Secondary Organic Aerosols Derived from α-Pinene versus Ocimene

Secondary organic aerosols (SOA) have complex, multicomponent composition that controls particle viscosity and gas-particle partitioning, key factors to their atmospheric evolution. This study investigates the chemical composition, volatility and viscosity of SOA formed by ozonolysis of cyclic α-pinene (PSOA) and acyclic ocimene (OSOA) monoterpenes. Using Temperature-Programmed Desorption combined with Direct Analysis in Real-Time ionization and High-Resolution Mass Spectrometry, we determined the molecular composition and saturation mass concentration of individual SOA constituents. These data enabled gas-particle partitioning and viscosity estimates under varied atmospheric conditions. PSOA, composed of higher molecular weight and less oxidized species, shows higher condensability and viscosity under high total organic mass (tOM) loadings. Here, in contrast, OSOA, consisting of more oxidized, lower molecular weight species, exhibits greater sensitivity to tOM, with viscosity increasing significantly upon dilution. Poke-flow experiments support this trend, indicating that OSOA undergoes more dynamic compositional and phase changes during atmospheric aging. These observations reveal distinct dynamic trends in the atmospheric transformations and reactivity of SOA from cyclic and acyclic monoterpenes, with the latter showing greater compositional changes during aging that alter viscosity and diffusion. This highlights the importance of incorporating such dynamic transformations into atmospheric models to improve predictions of SOA atmospheric loadings, lifetimes, and impacts.

cyclic and acyclic monoterpenes↗
DOE OSTI2018

Combining spectroscopic and isotopic techniques gives a dynamic view of phosphorus cycling in soil

Current understanding of phosphorus (P) cycling in soils can be enhanced by integrating previously discrete findings concerning P speciation, exchange kinetics, and the underlying biological and geochemical processes. Here, we combine sequential extraction with P K-edge X-ray absorption spectroscopy and isotopic methods ( 33 P and 18 O in phosphate) to characterize P cycling on a climatic gradient in Hawaii. We link P pools to P species and estimate the turnover times for commonly considered P pools. Dissolved P turned over in seconds, resin-extractable P in minutes, NaOH-extractable inorganic P in weeks to months, and HCl-extractable P in years to millennia. Furthermore, we show that in arid-zone soils, some primary mineral P remains even after 150 ky of soil development, whereas in humid-zone soils of the same age, all P in all pools has been biologically cycled. The integrative information we provide makes possible a more dynamic, process-oriented conceptual model of P cycling in soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗
DOE OSTI2017

Scale-invariant Cosmology and CMB Temperatures as a Function of Redshifts

Cosmological models assuming the scale invariance of the macroscopic empty space show an accelerated expansion, without calling for some unknown particles. Several comparisons between models and observations (tests on distances, m−z diagram, Ω{sub Λ} versus Ω{sub m} plot, age versus H {sub 0}, H(z) versus z, transition braking-acceleration) have indicated an impressive agreement. We pursue the tests with the CMB temperatures T{sub CMB} as a function of redshifts z. CO molecules in DLA systems provide the most accurate excitation temperatures T{sub exc} up to z≈2.7. Such data need corrections for local effects like particle collisions, optical depths, UV radiation, etc., We estimate these corrections as a function of the (CO/H{sub 2}) ratios from far-UV observations of CO molecules in the Galaxy. The results show that it is not sufficient to apply theoretical collisional corrections to get the proper values of T{sub CMB} versus z. Thus, the agreement often found with the standard model may be questioned. The T{sub CMB}(z) relation needs further careful attention and the same for the scale-invariant cosmology in view of its positive tests.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Connecting the Light Curves of Type IIP Supernovae to the Properties of Their Progenitors

Abstract Observations of core-collapse supernovae (CCSNe) reveal a wealth of information about the dynamics of the supernova ejecta and its composition but very little direct information about the progenitor. Constraining properties of the progenitor and the explosion requires coupling the observations with a theoretical model of the explosion. Here we begin with the CCSN simulations of Couch et al., which use a nonparametric treatment of the neutrino transport while also accounting for turbulence and convection. In this work we use the SuperNova Explosion Code to evolve the CCSN hydrodynamics to later times and compute bolometric light curves. Focusing on Type IIP SNe (SNe IIP), we then (1) directly compare the theoretical STIR explosions to observations and (2) assess how properties of the progenitor’s core can be estimated from optical photometry in the plateau phase alone. First, the distribution of plateau luminosities ( L 50 ) and ejecta velocities achieved by our simulations is similar to the observed distributions. Second, we fit our models to the light curves and velocity evolution of some well-observed SNe. Third, we recover well-known correlations, as well as the difficulty of connecting any one SN property to zero-age main-sequence mass. Finally, we show that there is a usable, linear correlation between iron core mass and L 50 such that optical photometry alone of SNe IIP can give us insights into the cores of massive stars. Illustrating this by application to a few SNe, we find iron core masses of 1.3–1.5 M ⊙ with typical errors of 0.05 M ⊙ . Data are publicly available online on Zenodo: doi: 10.5281/zenodo.6631964 .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

Measuring ocean waves from space; Proceedings of the Symposium, Johns Hopkins University, Laurel, MD, Apr. 15-17, 1986

Papers are presented on ocean-wave prediction; the quasi-universal form of the spectra of wind-generated gravity waves at different stages of their development; the limitations of the spectral measurements and observations of the group structure of surface waves; the effect of swell on the growth of wind wave; operational wave forecasting; ocean-wave models, and seakeeping using directional wave spectra. Consideration is given to microwave measurements of the ocean-wave directional spectra; SIR research; estimating wave energy spectra from SAR imagery, with the radar ocean-wave spectrometer, and SIR-B; the wave-measurement capabilities of the surface contour radar and the airborne oceanographic lidar; and SIR-B ocean-wave enhancement with fast-Fourier transform techniques. Topics discussed include wave-current interaction; the design and applicability of Spectrasat; the need for a global wave monitoring system; the age and source of ocean swell observed in Hurricane Josephine; and the use of satellite technology for insulin treatment.

Beal, Robert C.↗
NASA NTRS1994

The impactor flux in the Pluto-Charon system

Current impact rates of comets on Pluto and Charon are estimated. It is shown that the dominant sources of impactors are comets from the Kuiper belt and the inner Oort cloud, each of whose perihelion distribution extends across Pluto's orbit. In contrast, long-period comets from the outer Oort cloud are a negligible source of impactors. The total predicted number of craters is not sufficient to saturate the surface areas of either Pluto of Charon over the age of the Solar System. However, heavy cratering may have occurred early in the Solar System's history during clearing of planetesimals from the outer planets' zone.

Weissman, Paul R.↗
NASA NTRS1998

Disproportionate loss of thin filaments in human soleus muscle after 17-day bed rest

Previously we reported that, after 17-day bed rest unloading of 8 humans, soleus slow fibers atrophied and exhibited increased velocity of shortening without fast myosin expression. The present ultrastructural study examined fibers from the same muscle biopsies to determine whether decreased myofilament packing density accounted for the observed speeding. Quantitation was by computer-assisted morphometry of electron micrographs. Filament densities were normalized for sarcomere length, because density depends directly on length. Thick filament density was unchanged by bed rest. Thin filaments/microm2 decreased 16-23%. Glycogen filled the I band sites vacated by filaments. The percentage decrease in thin filaments (Y) correlated significantly (P < 0.05) with the percentage increase in velocity (X), (Y = 0.1X + 20%, R2 = 0.62). An interpretation is that fewer filaments increases thick to thin filament spacing and causes earlier cross-bridge detachment and faster cycling. Increased velocity helps maintain power (force x velocity) as atrophy lowers force. Atrophic muscles may be prone to sarcomere reloading damage because force/microm2 was near normal, and force per thin filament increased an estimated 30%.

Non-NASA Center↗
NASA NTRS2014

Improved Discrimination of Volcanic Complexes, Tectonic Features, and Regolith Properties in Mare Serenitatis from Earth-Based Radar Mapping

Radar images at 70 cm wavelength show 4-5 dB variations in backscatter strength within regions of relatively uniform spectral reflectance properties in central and northern Mare Serenitatis, delineating features suggesting lava flow margins, channels, and superposition relationships. These backscatter differences are much less pronounced at 12.6 cm wavelength, consistent with a large component of the 70 cm echo arising from the rough or blocky transition zone between the mare regolith and the intact bedrock. Such deep probing is possible because the ilmenite content, which modulates microwave losses, of central Mare Serenitatis is generally low (2-3% by weight). Modeling of the radar returns from a buried interface shows that an average regolith thickness of 10m could lead to the observed shifts in 70 cm echo power with a change in TiO2 content from 2% to 3%. This thickness is consistent with estimates of regolith depth (10-15m) based on the smallest diameter for which fresh craters have obvious blocky ejecta. The 70 cm backscatter differences provide a view of mare flow-unit boundaries, channels, and lobes unseen by other remote sensing methods. A localized pyroclastic deposit associated with Rima Calippus is identified based on its low radar echo strength. Radar mapping also improves delineation of units for crater age dating and highlights a 250 km long, east-west trending feature in northern Mare Serenitatis that we suggest is a large graben flooded by late-stage mare flows.

Radar echoes↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗
DOE OSTI2018

Geochemical Model of E h and pH Transitions in Pore Fluids during Saltstone and SDU Concrete Aging: Saltstone Disposal Facility PA Revision

The chemical and physical properties of the cementitious materials (dry cement, blast furnace slag, and fly ash) used to construct the Saltstone Disposal Units (SDUs) and saltstone waste form play a key role in the design and long-term performance of the Saltstone Disposal Facility. Chemical degradation of the reducing saltstone and concrete over time will impact radionuclide release and transport due to the changing chemical composition of groundwater (GW) percolating through the porous saltstone waste form. An equilibrium reaction path model was developed using The Geochemist’s Workbench® (GWB) software to estimate the timescales (expressed as number of pore volumes) for chemical degradation of the reducing cementitious materials as indicated by changes in redox potential (E h ) and pH.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Understanding the Effects of Vehicle Platoons on Crash Type and Severity

Crash type is an informative indicator to infer driving behaviors and conditions that cause a crash. For example, rear-end and side-swipe crashes are typically caused by improper vehicle interaction such as sudden lane-changing or speed control while hit-object crashes are likely the result of a single driver’s mistake. This study investigated the impact of vehicles travelling as a group (platoon) and its configuration (i.e., types of vehicles consisting of the platoon) on crash type and severity since the vehicles could affect each other when travelling in close proximity. This study applied Generalized Structure Equation Modeling (GSEM) to capture the complex relationships among the various crash factors such as traffic condition, driver characteristics, environmental conditions, and vehicle interaction to the crash attributes including type and severity. This study collected over 3 million individual vehicle data from 39 traffic count sites in California to estimate the vehicle interactions and driving behaviors. The microscopic traffic data are matched to 1417 crash reports. Results showed that vehicles traveling in platoons are associated with more rear-end and side-swipe crashes. Speed difference in the platoon had a positive effect on hit-object crashes if the platoon comprises vehicles of homogeneous type – either trucks or non-trucks. In addition, human factors such as age and gender were identified as significant influential factors in all type of crashes, however truck involvement particularly played an important role amongst side-swipe crashes. Crash severity was negatively affected by total flow, and rear-end crashes were more likely to be severe compared with hit-object crashes. Based on findings, this study suggests practical operational strategies to reduce traffic instability associated with platooned vehicle patterns. Understanding the high-risk factors for different crash types and severities would provide valuable insights for decision-makers and transportation engineers to develop targeted intervention strategies in consideration of road users and traffic conditions such as fleet mix and speed.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

60 APPLIED LIFE SCIENCES↗

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