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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Breast Cancer Molecular Subtype as a Predictor of Radiation Therapy Fractionation Sensitivity

The predictive benefit of breast cancer molecular subtypes for systemic therapy approaches has been well established; yet, there is a paucity of data regarding their use as a predictor of radiation therapy fractionation sensitivity. The purpose of this study was to determine whether rates of local recurrence (LR) for patients treated with hypofractionated (HF) radiation therapy, in comparison to conventional fractionation, differ across breast cancer molecular subtypes in a large, prospectively collected cohort treated with modern systemic therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Geometric compatibility measure m' for twin transmission: A predictor or descriptor?

In this work, the geometric compatibility factor m' is critically analyzed to assess whether it can be used to interpret/predict twin transmission (TT) across grain boundaries (GBs). This geometric measure is widely used to relate the likelihood of TT to the misalignment of both the shear and plane-normal directions within a twin set (i.e., incoming and outgoing twin). Here, using a large set of electron back scattering diffraction (EBSD) data, a detailed statistical analysis of twin-GB interactions is performed for {${1\bar{01}}2$} tensile twins in hexagonal close-packed (HCP) metals Mg, Zr, and Ti at different strain levels. In addition, a full-field crystal plasticity model is employed to quantify the role of local stresses and the applicability of m' as a criterion for the TT process. This combined study addresses the following three main questions: (i) What is the fidelity of m' in describing experimentally observed TTs? (ii) Can m' be used as a metric to predict/anticipate TT? (iii) Does m' naturally capture local stress effects? As a descriptor, m' cannot rationalize ~25% of TT events observed in Mg or more than 50% of TT events in Zr and Ti. As a predictor, the m'-measure does not predict TT events in over ~50% of twin-GB interactions analyzed. Further, the applicability of m' to describe and predict TT events decreases with an increase in elastic anisotropy, plastic anisotropy, and macroscopic strain levels. Finally, the twinning simulations reveal that m' does not capture the key effects of local stresses on variant selection upon twin transmission. The local stress induced by the twinning shear transformation plays a dominant role in driving the TT process compared to the geometric alignment of the constituting twins, i.e., m'.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Organic Photovoltaic Efficiency Predictor: Data-Driven Models for Non-Fullerene Acceptor Organic Solar Cells

In the design of organic solar cells, there has been a need for materials with high power conversion efficiencies. Scharber’s model is commonly used to predict efficiency; however, it exhibits poor performance with new non-fullerene acceptor (NFA) devices, since it was designed for fullerene-based devices. In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using semiempirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yield a similar accuracy. The models presented in this paper, termed organic photovoltaic efficiency predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD- DFT of 1.55%. As a result, the proposed computational models can be used for the fast and accurate screening of new high-efficiency NFAs/donor pairs.

14 SOLAR ENERGY↗
DOE OSTI2021

Quantitative Proteomics and Quantitative PCR as Predictors of cis -1,2-Dichlorethene and Vinyl Chloride Reductive Dechlorination Rates in Bioaugmented Aquifer Microcosms

Quantitative measurement of process-specific biomarker genes of Dehalococcoides mccartyi (Dhc) supports monitoring at chlorinated ethene contaminated sites. In this study, we varied Dhc cell abundances from ~103 to 108 cells/mL in aquifer microcosms and correlated the corresponding reductive dehalogenase (RDase) gene and RDase protein abundances with measured reductive dechlorination (RD) rates of cis-1,2-dichloroethene (cDCE) and vinyl chloride (VC). An additional set of microcosms tested the RD rate-predictive power of the regression analyses. These efforts revealed (1) that targeted proteomics quantifies Dhc biomarker proteins (e.g., TceA and VcrA, OmeA) over a relevant range of Dhc cell densities, and (2) that protein and gene abundances can predict RD rates. Protein detection limits translated to a rate coefficient of 10 –4 day –1 (0.04 year –1 ) for both k cDCE and k vc , which is within the range observed at sites undergoing monitored natural attenuation (MNA) (i.e., without the implementation of enhanced bioremediation treatment). Rates predicted using a combination of quantitative biomarker gene and protein measurements generally resulted in the best match with experimentally determined rate constants. These new findings provide evidence that quantitative biomarker measurements may be useful predictors of in situ RD rates, which would constitute a major advance for the cost-effective management of contaminated sites.

42 ENGINEERING↗
DOE OSTI2023

Functional Predictor Variables for the Leaching Potential of Arsenic and Selenium from Coal Fly Ash

The release of leachates from intact coal ash impoundments is a concern due to the enrichment and mobilization of toxic elements such as arsenic (As) and selenium (Se). This study aims to explore the intrinsic properties of coal fly ash that correlate with the relative leachability of As and Se. We performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic regions. We assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition, and pH. The results of regression models indicated that major elements (Fe, Ca, and Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Using Heat as a Predictor of CO 2 Breakthrough in Highly Heterogeneous Reservoirs

Injecting supercritical CO 2 into the subsurface changes the temperature, pressure, and geochemistry of the storage reservoir. Understanding these perturbations within the reservoir may be used to monitor the CO 2 plume during a carbon capture and sequestration (CCS) project. Here we analyze results from 1-D, 2-D, and 3-D numerical modeling studies to investigate how the thermal signature of the CO 2 -water system evolves during CCS. These models show that the thermodynamic processes of the CO 2 -water system results in a characteristic thermal profile within a homogeneous storage reservoir during a CO 2 injection. This thermal signature is characterized by warming front of up to 4 °C, which is caused by CO 2 dissolution and migrates contemporaneously with free-phase CO 2 migration. When reservoir properties are highly heterogeneous, this thermal front travels well ahead of free-phase CO 2 , thus implying that thermal monitoring may be an effective predictor of CO 2 breakthrough.

58 GEOSCIENCES↗
DOE OSTI2024

Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors

Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha −1 ) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha −1 ), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha −1 ) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha −1 ) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha −1 ), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha −1 ). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha −1 ) and 75.3% (7.0 ± 0.38 Mg ha −1 ) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Environmental Sciences & Ecology↗
DOE OSTI2025

A Novel Thermodynamical Predictor of Tropical High‐Cloud Area Coverage: Estimated Anvil‐Outflow Stability

The stability at the lapse-rate tropopause (LRT) was previously defined as the upper-tropospheric stability (UTS and SUT) but might underestimate the stability control on high-level clouds, since there is little direct connection between convective processes and the thermal stratification at the LRT. Here, a novel estimated anvil-outflow stability (EAS) based on the minimum stability in the upper troposphere is proposed. At the Manus site, the results show that small and large values of the LRT-based UTS and SUT both correspond to strong divergence and frequent occurrence of high-level ice clouds. In contrast, smaller EAS corresponds to stronger convective outflows to produce likely more high-level ice clouds, with a strong negative correlation. In tropics, EAS better explains the geographic distribution of high-level cloud coverage (HCC) and its temporal variations than UTS and SUT. With a strong linear correlation, EAS is likely a simple useful predictor of HCC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm –2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hurricane-Induced Rainfall is a Stronger Predictor of Tropical Forest Damage in Puerto Rico Than Maximum Wind Speeds

Projected increases in cyclonic storm intensity under a warming climate will have profound effects on forests, potentially changing these ecosystems from carbon sinks to sources. Forecasting storm impacts on these ecosystems requires consideration of risk factors associated with storm meteorology, landscape structure, and forest attributes. Here we evaluate risk factors associated with damage severity caused by Hurricanes María and Irma across Puerto Rican forests. Using field and remote sensing data, total forest aboveground biomass (AGB) lost to the storms was estimated at 10.44 (±2.33) Tg, ca. 23% of island-wide pre-hurricane forest AGB. Storm-related rainfall was a stronger predictor of forest damage than maximum wind speeds. Soil water storage capacity was also an important risk factor, corroborating the influence of rainfall on forest damage. Expected increases of 20% in hurricane-associated rainfall in the North Atlantic highlight the need to consider how such shifts, together with high speed winds, will affect terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2015

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and a local nonlinear predictor. We compare the performances of these three methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks

Here in this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

A real-time machine learning-based disruption predictor in DIII-D

A disruption prediction algorithm, named DPRF (Disruption Prediction using Random Forests), has run in real-time in the DIII-D plasma control system (PCS) over more than 900 discharges. DPRF naturally provides a probability mapping associated to its predictions, i.e. the disruptivity signal, now incorporated in the DIII-D PCS. Here, we report on disruption prediction accomplishments in terms of shot-by-shot performances, by simulating alarms on each discharge as in the PCS framework. Due to the optimised performance metric chosen to evaluate DPRF, we find that almost all disruptive discharges are detected in average with few hundred of milliseconds of warning time, but this comes at a high cost of false alarms produced. Performances are not satisfying ITER requirements, where the success rate has to be higher than 95%, but this is not completely unexpected. DPRF is trained on many years of major disruptions occurring during the flattop phase of the plasma current on DIII-D, but without any differentiation by cause. Furthermore, we find that DPRF is affected by a relatively high fraction of false alarms occurring during the first 500 milliseconds from the flattop onset. This subtle effect, more evident on the discharges where DPRF ran in real-time, can be marginalized by taking specific precautions on the validity range of the predictions, and performances do improve. Even if presently burdened by some limitations, DPRF provides an incredible and novel advantage. Thanks to the feature contribution analysis (e.g., the identification of which signals contributed to triggering an alarm), it is possible to interpret and explain DPRF predictions. It is the first time that such interpretability features is exploited by a disruption predictor: by uncovering the disruption events' causes, a better understanding of disruption dynamics is achieved, and a clear path toward the design of disruption avoidance strategies can be provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Is the dark-matter halo spin a predictor of galaxy spin and size?

ABSTRACT The similarity between the distributions of spins for galaxies (λgal) and for dark-matter haloes (λhalo), indicated both by simulations and observations, is naively interpreted as a one-to-one correlation between the spins of a galaxy and its host halo. This is used to predict galaxy sizes in semi-analytic models via Re ≃ fjλhaloRvir, where Re is the half-mass radius of the galaxy, fj is the angular momentum retention factor, and Rvir is the halo radius. Using two suites of zoom-in cosmological simulations, we find that λgal and the λhalo of its host halo are in fact barely correlated, especially at z ≥ 1, in line with previous indications. Since the spins of baryons and dark matter are correlated at accretion into Rvir, the null correlation in the end reflects an anticorrelation between fj and λhalo, which can arise from mergers and a ‘wet compaction’ phase that many high-redshift galaxies undergo. It may also reflect that unrepresentative small fractions of baryons are tapped to the galaxies. The galaxy spin is better correlated with the spin of the inner halo, but this largely reflects the effect of the baryons on the halo. While λhalo is not a useful predictor for Re, our simulations reproduce a general relation of the form of Re = ARvir, in agreement with observational estimates. We find that the relation becomes tighter with A = 0.02(c/10)−0.7, where c is the halo concentration, which in turn introduces a dependence on mass and redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Observations on using inside air concentrations as a predictor of outside air concentrations

Here, excavations of radiological material were performed within confined structures with known operational parameters, such as a filtered exhaust system with known filtration efficiency. Given the known efficiency, the assumption could be made that the air concentrations of radioactivity measured outside the structure would be proportional to the air concentrations measured inside the structure. To investigate this assumption, the inside concentration data was compared with the outside concentration data. The correlation of the data suggested that the inside concentrations were not a good predictor of the outside concentrations. This poor correlation was deemed to be a result of operational unknowns within the structures.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Choice reaction time and grip strength as predictors of cardiovascular mortality in middle‐aged and elderly Japanese: from the Radiation Effects Research Foundation Adult Health study

Abstract Background Cognitive function and physical function are important predictors of mortality. Aim To investigate whether or not reaction time (RT) as a cognitive function and grip strength (GS) as a physical function were associated, alone or in combination, with mortality from heart disease or stroke. Methods The subjects included 4901 Adult Health Study participants in Hiroshima who had undergone RT and GS measurements, were 35–74 years old at baseline (1970–1972) and were followed until the end of 2007. Results After adjustment for other potential risk factors, RT was positively and GS was negatively associated with mortality from both heart disease and stroke. These associations were persistent in the model when adjusting simultaneously for RT, GS and other factors, but hazard ratios were attenuated. When we evaluated the associations by baseline age and gender, we found the greater hazard ratios for RT in the younger cohort, but no clear modification by age for GS. The interaction between RT and GS was statistically significant ( P = 0.012) for stroke mortality. In the stratified analyses divided using the age‐specific median value of RT or GS, the estimated hazard ratio of stroke mortality for RT was significant in participants with weak or strong GS but greater in the former, and for GS, it was only significant in participants with slow RT. Conclusion RT and GS, alone and in combination, predicted heart disease and stroke mortalities. Interventions for both cognitive function and physical function may be beneficial for the prevention of cardiovascular disease mortality.

Shimizu, Masaki↗