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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Numerical Investigation of a Central Fuel Property Hypothesis Under Boosted Spark-Ignition Conditions

In the present work, a central fuel property hypothesis (CFPH), which states that fuel properties are sufficient to provide an indication of a fuel’s performance irrespective of its chemical composition, was numerically investigated. In particular, the objective of the study was to determine whether Research Octane Number (RON) and Motor Octane Number (MON), as fuel properties, are sufficient to describe a fuel’s knock-limited performance under boosted spark-ignition (SI) conditions within the framework of CFPH. To this end, four TPRF-bioblendstock surrogates having different compositions but matched RON (=98) and MON (=90), were first generated using a non-linear regression model based on artificial neural network (ANN). Additionally, three unconventional bioblendstocks were included in the analysis: di-isobutylene (DIB), isobutanol, and Anisole. Skeletal reaction mechanisms were generated for the TPRF-DIB, TPRF-isobutanol, and TPRF-anisole blends from a detailed kinetic mechanism. Thereafter, numerical simulations were performed for the fuel surrogates using the skeletal mechanisms and a virtual cooperative fuel research (CFR) engine model, under a representative boosted operating condition. In the computational fluid dynamics (CFD) model, the G-equation approach was employed to track the turbulent flame front and the well-stirred reactor model combined with the multi-zone binning strategy was used to capture auto-ignition in the end-gas. In addition, laminar flame speed (LFS) was tabulated for each blend as a function of pressure, temperature, and equivalence ratio a priori, and the lookup tables were used to prescribe laminar flame speed as an input to the G-equation model. Parametric spark timing sweeps were performed for each fuel blend to determine the corresponding knock-limited spark advance (KLSA) and 50% burn point (CA50) at the respective KLSA timing. It was observed that despite same RON, MON, and engine operating conditions, the TPRF-anisole blend exhibited markedly different knock-limited performance from the other three blends. This deviation from the octane index (OI) expectation was shown to be caused by differences in laminar flame speed. However, it was found that relatively large fuel-specific differences in LFS (>20%) would have to be present to cause any appreciable deviation from the OI framework. Otherwise, RON and MON would still be robust enough to predict a fuel’s knock-limited performance.

42 ENGINEERING↗
DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1971

Global distribution of total ozone derived from Nimbus 3 satellite during April July, 1969 and its implication to upper tropospheric circulation

Utilizing a stepwise multiple regression scheme, monthly mean global maps of total ozone are produced from the spectral intensity measurements made by the infrared interferometer spectrometer (IRIS) onboard the Nimbus 3 satellite for the period April 18 to July 22, 1969. These maps show that over the equatorial regions total ozone increases steadily from April to July. Further, latitudinal variation of total ozone derived from these maps shows that the ozone increases at all latitudes over the Southern Hemisphere uniformly from April to July while over the Northern Hemisphere the monthly decrease at all latitudes is nonuniform. In general, these maps bear a close resemblance to the upper tropospheric circulation. It is observed that the total ozone is best correlated with the 200 mb geopotential heights. This relationship enables the utilization of total ozone as a quasi-stream function to determine geostrophic winds at 200 mb level. Further, even at low latitudes where the total ozone does not bear simple relationship to the geopotential heights the course of the easterly jet stream with the help of the ozone measurements can be identified.

Prabhakara, C.↗
NASA NTRS1997

Evaluation of heart rate changes: electrocardiographic versus photoplethysmographic methods

The heart rate (HR) variation to forced deep breathing (HRDB) and to the Valsalva maneuver (Valsalva ratio; VR) are the two most widely used tests of cardiovagal function in human subjects. The HR is derived from a continuously running electrocardiographic (ECG) recording. Recently, HR derived from the arterial waveform became available on the Finapres device (FinapHR), but its ability to detect rapid changes in HR remains uncertain. We therefore evaluated HRDB and VR derived from FinapHR using ECG-derived HR (ECGHR) recordings as the standard. We also compared the averaged HR on Finapres (Finapav) with beat-to-beat Finapres (FinapBB) values. Studies were undertaken in 12 subjects with large HR variations: age, 34.5 +/- 9.3 (SD) years; six males and six females. FinapBB values were superimposable upon ECGHR for both HRDB and VR. In contrast, Finapav failed to follow ECGHR for HRDB and followed HRECG with a lag for the VR. To evaluate statistically how closely FinapHR approximated ECGHR, we undertook regression analysis, using mean values for each subject. To compare the two methods, we evaluated the significance of the difference between test and standard values. For HRDB, FinapBB reproducibly recorded HR (R2 = 0.998), and was significantly (p = 0.001) better than Finapav (R2 = 0.616; p < 0.001). For VR, HRBB generated a VR that was not significantly different from the correct values, while HRav generated a value that was slightly but consistently lower than the correct values (p < 0.001). We conclude that FinapHR reliably records HR variations in the beat-to-beat mode for cardiovascular HR tests.

Non-NASA Center↗
NASA NTRS1995

The tonotopic map in the embryonic chicken cochlea

The purpose of the present study was to determine the tonotopic map in the chicken cochlea at 19 days of incubation (E19) by obtaining characteristic frequencies (CFs) for primary afferents, labeling the characterized neurons, and documenting their projections to the papilla. The lowest and highest CFs recorded were 188 and 1623 Hz respectively. The embryonic tonotopic map coincided with maps reported for post-hatch chicks. There were no evidence that neurons selective to low frequencies project inappropriately to more basal locations of the embryonic papilla. Linear regression was used to estimate the frequency gradient (b = 0.037 +/- 0.012 In Hz/% [b +/- SEb]) and intercept (In C, where C = 111 Hz) of the semilog plot of frequency versus cochlear position (in % distance from apex). From these estimates the octave distribution was calculated to be 18.7%/octave or 0.58 mm/octave. These quantities were not significantly different from those found in post hatch chickens. We conclude that the tonotopic map of the avian cochlea for CFs between 100 and 1700 Hz is stable and relatively mature from age E19 to post-hatch day 21 (P21). The most striking sign of immaturity in the E19 embryo is the limited range of high CFs. We offer the hypothesis that, between the ages of E19 and P21, improvements in middle ear admittance alone or in combination with functional maturation of the cochlear base may be the principal factors responsible for the appearance of adult-like high CF limits and not an apically shifting tonotopic map.

NASA Discipline Neuroscience↗
DOE OSTI2015

A model of the methane cycle, permafrost, and hydrology of the Siberian continental margin

A two-dimensional model of a sediment column, with Darcy fluid flow, biological and thermal methane production, and permafrost and methane hydrate formation, is subjected to glacial–interglacial cycles in sea level, alternately exposing the continental shelf to the cold atmosphere during glacial times and immersing it in the ocean in interglacial times. The glacial cycles are followed by a "long-tail" 100 kyr warming due to fossil fuel combustion. The salinity of the sediment column in the interior of the shelf can be decreased by hydrological forcing to depths well below sea level when the sediment is exposed to the atmosphere. There is no analogous advective seawater-injecting mechanism upon resubmergence, only slower diffusive mechanisms. This hydrological ratchet is consistent with the existence of freshwater beneath the sea floor on continental shelves around the world, left over from the last glacial period. The salt content of the sediment column affects the relative proportions of the solid and fluid H 2 O-containing phases, but in the permafrost zone the salinity in the pore fluid brine is a function of temperature only, controlled by equilibrium with ice. Ice can tolerate a higher salinity in the pore fluid than methane hydrate can at low pressure and temperature, excluding methane hydrate from thermodynamic stability in the permafrost zone. The implication is that any methane hydrate existing today will be insulated from anthropogenic climate change by hundreds of meters of sediment, resulting in a response time of thousands of years. The strongest impact of the glacial–interglacial cycles on the atmospheric methane flux is due to bubbles dissolving in the ocean when sea level is high. When sea level is low and the sediment surface is exposed to the atmosphere, the atmospheric flux is sensitive to whether permafrost inhibits bubble migration in the model. If it does, the atmospheric flux is highest during the glaciating, sea level regression (soil-freezing) part of the cycle rather than during deglacial transgression (warming and thawing). The atmospheric flux response to a warming climate is small, relative to the rest of the methane sources to the atmosphere in the global budget, because of the ongoing flooding of the continental shelf. The increased methane flux due to ocean warming could be completely counteracted by a sea level rise of tens of meters on millennial timescales due to the loss of ice sheets, decreasing the efficiency of bubble transit through the water column. The model results give no indication of a mechanism by which methane emissions from the Siberian continental shelf could have a significant impact on the near-term evolution of Earth's climate, but on millennial timescales the release of carbon from hydrate and permafrost could contribute significantly to the fossil fuel carbon burden in the atmosphere–ocean–terrestrial carbon cycle.

Archer, D.↗
NASA NTRS2022

Comparison of U.S. and Russian Slow Crack Growth Data and Models

Both the US and Russian space programs use similar predictive models for design of fused silica windows on the International space station. The Russian model can be derived from the power expression for slow crack growth (SCG) or “static fatigue.” The US uses both power and exponential models. Despite the similarity of models and data fitting approach (linear regression and right censoring), different SCG parameters have been derived by US and Russian parties for the same material (Russian fused silica) tested in a similar manner. The difference appears to be related to the use of short-term strength data along with the longer-term static fatigue data, with the power law parameter n being very sensitive to the conversion of strength data into equivalent static data. This hybrid approach is feasible if strength data is measured with a constant stress rate and is appropriately converted to a static equivalent. More research into the approach is needed. However, because of the nonlinear behavior of fused silica in log(v) – log(K(I) ) space and the sensitivity of parameter estimation to fit range, the exponential model is a better choice regardless of test method. Functions are given to convert parameters from the Russian model to those in the US model. However, accurate conversion is hampered by the lack of inert strength data. When the same test technique is used, US and Russian materials exhibit very similar parameters.

Silica, strength, impact, crack growth, windows, I↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2004-2011 Passenger Cars and LTVs” (LBNL Phase 1)

In its 2012 report NHTSA simulated the effect four fleetwide mass reduction scenarios would have on the change in annual fatalities. NHTSA estimated that the most aggressive of these scenarios (reducing mass 5.2% in heavier light trucks and 2.6% in all other vehicles types except lighter cars) would result in a small reduction in societal fatalities. LBNL replicated the methodology NHTSA used to simulate six mass reduction scenarios, including the mass reductions recommended in the 2015 NRC committee report, and estimated in 2021 and 2025 by EPA in the TAR, using the updated data through 2012. The analysis indicates that the estimated x change in fatalities under each scenario based on the updated analysis is comparable to that in the 2012 analysis, but less beneficial or more detrimental than that in the 2016 analysis. For example, an across the board 100-lb reduction in mass would result in an estimated 157 additional annual fatalities based on the 2012 analysis, but would result in only an estimated 91 additional annual fatalities based on the 2016 analysis, and an additional 87 fatalities based on the current analysis. The mass reductions recommended by the 2015 NRC committee report would result in a 224 increase in annual fatalities in the 2012 analysis, a 344 decrease in annual fatalities in the 2016 analysis, and a 141 increase in fatalities in the current analysis. The mass reductions EPA estimated for 2025 in the TAR would result in a 203 decrease in fatalities based on the 2016 analysis, but an increase of 39 fatalities based on the current analysis. These results support NHTSA’s conclusion from its 2012 study that, when footprint is held fixed, “no judicious combination of mass reductions in the various classes of vehicles results in a statistically significant fatality increase and many potential combinations are safety-neutral as point estimates.”Like the previous NHTSA studies, this updated report concludes that the estimated effect of mass reduction while maintaining footprint on societal U.S. fatality risk is small, and not statistically significant at the 95% or 90% confidence level for all vehicle types based on the jack-knife method NHTSA used. This report also finds that the estimated effects of other control variables, such as vehicle type, specific safety technologies, and crash conditions such as whether the crash occurred at night, in a rural county, or on a high-speed road, on risk are much larger, in some cases two orders of magnitude larger, than the estimated effect of mass or footprint reduction on risk. Finally, this report shows that after accounting for the many vehicle, driver, and crash variables NHTSA used in its regression analyses, there remains a wide variation in risk by vehicle make and model, and this variation is unrelated to vehicle mass. Although the purpose of the NHTSA and LBNL reports is to estimate the effect of vehicle mass reduction on societal risk, this is not exactly what the regression models are estimating. Rather, they are estimating the recent historical relationship between mass and risk, after accounting for most measurable differences between vehicles, drivers, and crash times and locations. In essence, the regression models are comparing the risk of a 2600-lb Dodge Neon with that of a 2500-lb Honda Civic, after attempting to account for all other differences between the two vehicles. The models are not estimating the effect of literally removing 100 pounds from the Neon, leaving everything else unchanged. In addition, the analyses are based on the relationship of vehicle mass and footprint on risk for recent vehicle designs (model year 2004 to 2011). These relationships may or may not continue into the future as manufacturers utilize new vehicle designs and incorporate new technologies, such as more extensive use of strong lightweight materials and specific safety technologies. Therefore, throughout this report we use the phrase “the estimated effect of mass (or footprint) reduction on risk” as shorthand for “the estimated change in risk as a function of its relationship to mass (or footprint) for vehicle models of recent design.”

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Cadmium and Alzheimer’s disease mortality in U.S. adults: Updated evidence with a urinary biomarker and extended follow-up time

Cadmium has been linked to impaired cognitive function in adults and may cause behavioral, physiological and molecular abnormalities characteristic of Alzheimer's disease (AD) in animals. Evidence linking cadmium and AD in humans is limited, but supportive. In the most recent epidemiologic study, blood cadmium in U.S. adults was positively associated with elevated AD mortality 7–13 years later. The association between urinary cadmium – an arguably more appropriate biomarker for studying chronic diseases – and AD mortality has not yet been explored. Further study of cadmium and AD mortality in an independent population, with longer follow-up, and stratified by sex is also needed. We sought to answer these questions using the U.S. National Health and Nutrition Examination Survey (NHANES) (1999–2006 cycles) and NHANES III (interviews in 1988–1994) datasets, separately linked to AD mortality as of 2011. We used survey-weighted Cox regression models predicting age at AD death and adjusted for race/ethnicity, sex, smoking status, education and urinary creatinine. An interquartile range (IQR; IQR=0.51 ng/mL) increase in urinary cadmium was associated with 58% higher rate of AD mortality (hazard ratio (HR)=1.58, 95% CI: 1.20, 2.09. p-value=0.0009, mean follow-up: 7.5 years) in NHANES 1999–2006 participants. In contrast, in NHANES III participants, an IQR (IQR=0.78 ng/mL) increase in urinary cadmium was not associated with AD mortality (HR=0.85, 95% CI: 0.63, 1.17, p-value=0.31, mean follow-up: 13 years). Also in the NHANES III sample however, when the maximum follow-up time was restricted to 12.7 years (i.e. the same as NHANES 1999–2006 participants) and urinary creatinine adjustments were not made, urinary cadmium was associated with elevated AD mortality (HR=1.11, 95% CI: 1.02, 1.20, p-value=0.0086). Our study partially supported an association between cadmium and AD mortality, but the sensitivity of results to follow-up time and creatinine adjustments necessitate cautious interpretation of the association. Further studies, particularly those on toxicological mechanisms, are required to fully understand the nature of the “cadmium-AD mortality” association. - Highlights: • Urine Cd positively associated with Alzheimer’s disease mortality in NHANES 99-06. • Urine Cd NOT associated with AD in NHANES III, except under some conditions. • Results partly supportive of “Cd-AD mortality” link. • Inconsistency and insufficient toxicological data suggest cautious interpretation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2017

Blood cadmium concentrations and environmental exposure sources in newcomer South and East Asian women in the Greater Toronto Area, Canada

Background: Immigrant women are often identified as being particularly vulnerable to environmental exposures and health effects. The availability of biomonitoring data on newcomers is limited, thus, presenting a challenge to public health practitioners in the identification of priorities for intervention. Objectives: In fulfillment of data needs, the purpose of this study was to characterize blood concentrations of cadmium (Cd) among newcomer women of reproductive age (19–45 years of age) living in the Greater Toronto Area (GTA), Canada and to assess potential sources of environmental exposures. Methods: A community-based model, engaging peer researchers from the communities of interest, was used for recruitment and follow-up purposes. Blood samples were taken from a total of 211 newcomer women from South and East Asia, representing primary, regional origins of immigrants to the GTA, and environmental exposure sources were assessed via telephone survey. Metal concentrations were measured in blood samples (diluted with 0.5% (v/v) ammonium hydroxide and 0.1% (v/v) octylphenol ethoxylate) using a quadrupole ICP-MS. Survey questions addressed a wide range of environmental exposure sources, including dietary and smoking patterns and use of nutritional supplements, herbal products and cosmetics. Results: A geometric mean (GM) blood Cd concentration of 0.39 µg/L (SD:±2.07 µg/L) was determined for study participants (min/max: <0.045 µg /L (LOD)/2.36 µg/L). Several variables including low educational attainment (Relative Ratio (RR) (adjusted)=1.50; 95% CI 1.17–1.91), milk consumption (RR (adjusted)=0.86; 95% CI 0.76–0.97), and use of zinc supplements (RR (adjusted)=0.76; 95% CI 0.64–0.95) were observed to be significantly associated with blood Cd concentrations in the adjusted regression model. The variable domains socioeconomic status (R{sup 2}{sub adj}=0.11) and country of origin (R{sup 2}{sub adj}=0.236) were the strongest predictors of blood Cd. Conclusion: Blood Cd concentrations fell below those generally considered to be of human health concern. However, negative health effects cannot be entirely excluded, especially for those that fall in the upper percentile range of the distribution, given the mounting evidence for negative health outcomes at low environmental exposure concentrations. - Highlights: • Blood Cd and exposure sources were assessed for 211 newcomer women in Toronto. • Blood Cd was slightly elevated compared to that in Canadian-born populations. • SES and country of origin were the strongest predictors of blood Cd. • Intake of essential micronutrients is a likely important modulator of blood Cd. • Identified need for more research on newcomers as a function of country of origin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Predicting Sooting Tendencies from Chemical Structure with Experimental and Theoretical Insight

Particulate matter (PM) emissions from internal combustion engines negatively impact public health and global climate. These problems are exacerbated by newer gasoline direct injection engines, which are more fuel-efficient, but also produce more soot than traditional spark ignited engines. Reducing soot formation is therefore of paramount importance in the development of new fuels. A fuel's sooting tendency is a quantitative parameter that describes the sooting behavior of a pure compound or fuel mixture. The yield sooting index (YSI), developed by McEnally and Pfefferle, accurately measures sooting tendencies using small sample quantities. Using an experimental sooting tendency database, we have developed a predictive model for sooting behavior from a quantitative structure-activity relationship (QSAR). It was developed so that input molecules are first decomposed into individual carbon-type fragments for which the sooting tendency contribution can be assigned based on a Bayesian linear regression against the experimental database. The model's predictive accuracy is comparable to its training performance using leave-one-out cross-validation. We have used this model to provide quantitative insights into the effects of chemical structure on soot formation, but excitingly, we have also been able to readily identify the presence of more complicated kinetic sooting mechanisms for structures which are extreme outliers. Oxygenated aromatics can be produced readily from biomass as renewable sources and oxygenated aromatics with very similar structures tend to have a much lower sooting tendency, for example methoxybenzene (anisole, 107), 2-methylphenol (m-cresol, 103), 2-ethylphenol (120), 3-ethylphenol (138) and 1-phenylethanol (142). Thus, the presence of just one oxygen atom in an aromatic compound can drastically alter the reaction pathways leading to soot precursors. We have applied density functional theory (DFT) calculations and flow reactor experiments to examine how oxygenation alters reaction pathways in a combustion environment. This study has allowed us to gain understanding on how the location of an oxygenated functional group influences soot formation. Our work provides a blueprint for the design of oxygenated fuels from biomass, which minimize the production of soot in low oxygen environments.

09 BIOMASS FUELS↗
DOE OSTI2018

Urinary bisphenol A concentrations are associated with reproductive parameters in young men

Highlights: • Urinary BPA concentrations were analyzed to estimate environmental exposure. • Serum reproductive hormone levels and semen parameters were evaluated. • Negative associations between BPA and sperm counts were found. • Increasing BPA concentrations were associated with altered LH levels. • BPA exposure may adversely impact reproductive function in young men. Bisphenol A (BPA) is a pervasive environmental toxicant with known reproductive effects on sperm parameters and hormone levels. Several observational studies have investigated the associations between BPA exposure and male reproductive function, but findings are inconsistent. The objective of this study was to assess the associations between urinary BPA concentrations and semen quality and reproductive hormone levels in a cross-sectional study with 215 healthy young university students (18–23 years old), investigated between 2010 and 2011 in Southern Spain (Murcia Region). All subjects provided urine, blood serum and semen samples on a single day. Urinary BPA concentrations were measured by dispersive liquid–liquid microextraction and ultra-high performance liquid chromatography with tandem mass spectrometry detection. Semen quality was evaluated by measuring volume, concentration, motility, morphology and total sperm count (TSC). Serum samples were analyzed for reproductive hormones, including follicle-stimulating hormone (FSH), luteinizing hormone (LH), testosterone, inhibin B and estradiol. Relationships between urinary BPA concentrations and semen quality parameters and reproductive hormone levels were examined using linear regression, adjusting for potential confounders and covariates. Ninety-five percent of the men had detectable urinary BPA concentrations with unadjusted median (5th–95th) of 2.8 (0.16–11.5) ng/mL. After adjustment for important covariates, there was a significant positive association between urinary BPA concentrations and serum LH levels (β = 0.07, 95%CI: 0.02;0.12, p-value < 0.01). Urinary BPA concentration was also significantly and inversely associated with sperm concentration (β = − 0.04, 95%CI: − 0.07;− 0.02, p-value < 0.01) and TSC (β = − 0.05, 95%CI: − 0.08;− 0.02, p-value < 0.01). No significant associations were found between BPA and other semen parameters or reproductive hormone levels. Our results support the hypothesis that BPA exposure may be associated with a reduction in Leydig cell capacity (increased LH levels) and decreased sperm counts in young men.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Degradation and Modeling of Large-Format Commercial Lithium-Ion Cells as a Function of Chemistry, Design, and Aging Conditions

Demand for large-format (>10 Ah) lithium-ion batteries has increased substantially in recent years, due to the growth of both electric vehicle and stationary energy storage markets. The economics of these applications is sensitive to the lifetime of the batteries, and end-of-life can either be due to energy or power limitations. Despite this, there is little information from cell manufacturers on the sensitivity of cell degradation to environmental conditions or battery use. This work reports accelerated aging test data from four commercial large-format lithium-ion batteries from three manufacturers, with varying design (thickness, casings, ...), chemistry (lithium-iron-phosphate (LFP) or lithium-nickel-manganese-cobalt-oxide positive electrodes (NMC), with graphite (Gr) negative electrodes), and capacity (50 to 250 Amp hours). The tested LFP|Gr cell is found to be relatively insensitive to cycling conditions like temperature or voltage window, while NMC|Gr cells have varying sensitivity. Degradation trends are further investigated by training predictive models: simple polynomial trend lines, a semi-empirical reduced-order model, and an empirical reduced-order model identified using machine-learning based on symbolic regression. Calendar and cycle life are simulated over a variety of conditions to directly compare the various batteries. Cell size and thickness are found to substantially impact sensitivity to temperature during cycle aging, while electrode chemistry impacts depth-of-discharge sensitivity. Real-world battery lifetime is evaluated by simulating residential energy storage and commercial frequency containment reserve systems in several U.S. climate regions. Predicted lifetime across cell types varies from 7 years to 20+ years, though all cells are predicted to have at least 10 year life in certain conditions.

battery lifetime↗
NASA NTRS2021

The M-dwarf Ultraviolet Spectroscopic Sample. I. Determining Stellar Parameters for Field Stars

Accurate stellar properties are essential for precise stellar astrophysics and exoplanetary science. In the M-dwarf regime, much effort has gone into defining empirical relations that can use readily accessible observables to assess physical stellar properties. Often, these relations for the quantity of interest are cast as a nonlinear function of available data; in Bayesian modeling, however, the reverse is needed. In this article, we introduce a new Bayesian framework to self-consistently and simultaneously apply multiple empirical calibrations to fully characterize the mass, luminosity, radius, and effective temperature of a field age M-dwarf. This framework includes a new M-dwarf mass–radius relation with a scatter of 3.1% at fixed mass. We further introduce the M-dwarf Ultraviolet Spectroscopic Sample (MUSS), and apply our methodology to provide consistent stellar parameters for these nearby low-mass stars, selected as having available spectroscopic data in the ultraviolet. These targets are of interest largely as either exoplanet hosts or benchmarks in multiwavelength stellar activity. We use the field MUSS stars to define a low-mass main sequence in the solar neighborhood through Gaussian Process (GP) regression. These results enable us to empirically measure a feature in the GP derivative at M ⊙ that indicates where the MUSS transitions from fully to partly convective interiors.

J. Sebastian Pineda↗
DOE OSTI2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The star cluster mass–galactocentric radius relation: Implications for cluster formation

Whether or not the initial star cluster mass function is established through a universal, galactocentric-distance-independent stochastic process, on the scales of individual galaxies, remains an unsolved problem. This debate has recently gained new impetus through the publication of a study that concluded that the maximum cluster mass in a given population is not solely determined by size-of-sample effects. Here, we revisit the evidence in favor and against stochastic cluster formation by examining the young (≲ a few ×10{sup 8} year old) star cluster mass–galactocentric radius relation in M33, M51, M83, and the Large Magellanic Cloud. To eliminate size-of-sample effects, we first adopt radial bin sizes containing constant numbers of clusters, which we use to quantify the radial distribution of the first- to fifth-ranked most massive clusters using ordinary least-squares fitting. We supplement this analysis with an application of quantile regression, a binless approach to rank-based regression taking an absolute-value-distance penalty. Both methods yield, within the 1σ to 3σ uncertainties, near-zero slopes in the diagnostic plane, largely irrespective of the maximum age or minimum mass imposed on our sample selection, or of the radial bin size adopted. We conclude that, at least in our four well-studied sample galaxies, star cluster formation does not necessarily require an environment-dependent cluster formation scenario, which thus supports the notion of stochastic star cluster formation as the dominant star cluster-formation process within a given galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗