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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Prediction of long-term (> 10 year) cardiovascular outcomes in heart transplant recipients: Value of stress technetium-99m tetrofosmin myocardial perfusion imaging

Background: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is useful in the evaluation of cardiac allograft vasculopathy (CAV) in heart transplant (HTx) recipients. The current study evaluated the long-term prognostic value of stress SPECT MPI for predicting all-cause mortality and cardiac events in HTx recipients. Methods: The study population consisted of 166 HTx recipients (mean age 54 ± 10 years, 84% male) who underwent exercise or dobutamine stress {sup 99m}Tc-tetrofosmin SPECT MPI for the assessment of CAV. An abnormal SPECT MPI was defined as the presence of a fixed or a reversible perfusion defect. Endpoints were all-cause mortality, cardiac mortality, and non-fatal myocardial infarction (MI). Results: MPI abnormalities were detected in 55 patients (33%), including fixed defects in 28 patients (17%), partially reversible in 17 patients (10%), and completely reversible defects in 10 patients (6%). During a median follow-up of 12.8 years (range 0-15, mean follow-up 9.5 years), 109 (66%) patients died (all-cause mortality), of which 67 (40%) were due to cardiac causes. A total of 5 (3%) patients experienced a non-fatal MI. HTx recipients with a normal stress {sup 99m}Tc-tetrofosmin SPECT MPI had a significantly better prognosis as compared with those with an abnormal study, up to 5 years after the initial test. The presence of a reversible perfusion defect was a significant predictor of all-cause mortality, cardiac mortality, and major cardiac events, during the entire follow-up period. Conclusions: Stress {sup 99m}Tc-tetrofosmin SPECT MPI provides valuable prognostic information for the prediction of long-term outcome in HTx recipients. Patients with a normal stress {sup 99m}Tc-tetrofosmin SPECT MPI have a significantly better prognosis as compared with those with an abnormal study, up to 5 years after initial testing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Added prognostic value of left ventricular shape by gated SPECT imaging in patients with suspected coronary artery disease and normal myocardial perfusion

Background: Left ventricular (LV) remodeling is associated with adverse cardiovascular events. We evaluated the added prognostic value of LV shape index (SI) assessed by gated single-photon emission tomography (SPECT) in patients without known coronary artery disease (CAD). Methods and results: We studied 674 patients with normal myocardial perfusion and normal LV ejection fraction (EF) on stress gated SPECT imaging. An automated software program was used to calculate end-diastolic and end-systolic LVSI. An LVSI ≤ 0.54 at end-systole was considered normal. Follow-up was 96% complete with a median follow-up of 37 months. During follow-up, 25 events occurred (3.8% cumulative event rate). Event-free survival was lower in patients with abnormal end-systolic LVSI (P < .001). Age (P = .021), diabetes (P = .048), and end-systolic LVSI (P < .001) were independent predictors of events. LVSI added prognostic information increasing the global chi-square of the model including age and diabetes from 15.15 to 25.97 (P < .001). The effect of diabetes on hazard ratio increased with increasing values of end-systolic LVSI. The probability of events at 48 months predicted by Weibull analysis progressively increased with increasing values of end-systolic LVSI and was higher in patients with diabetes as compared to those without. Decision curve analyses indicate that the model including end-systolic LVSI resulted in an increased net benefit between 5% and 30% threshold probability, indicating superior estimation of outcomes at low threshold probability levels. Conclusions: The evaluation of LVSI may identify patients with early-stage LV remodeling and at higher risk of adverse cardiac events, even in the presence of normal myocardial perfusion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Complete revascularization determined by myocardial perfusion imaging could improve the outcomes of patients with stable coronary artery disease, compared with incomplete revascularization and no revascularization

Objectives: To compare the outcomes among patients treated by complete coronary revascularization (CCR) or incomplete coronary revascularization (ICR) and no coronary revascularization (NCR) by myocardial perfusion imaging (MPI), as well as to evaluate the impact of severity of ischemia on patients with coronary artery disease (CAD) by different therapy strategies. Background: Using myocardial ischemia severity determined by MPI guiding treatment strategies for CAD patients still lacks strong clinical evidences. Methods: Consecutive patients (N = 286) underwent clinical stress-rest SPECT MPI and were retrospectively followed-up. For assessment of outcome of treatment, all patients were classified into three groups (CCR, ICR, and NCR), and further divided into two subgroups as mild ischemia (< 10% ischemic myocardium) and moderate-severe ischemia (≥ 10% ischemic myocardium). All-cause death was defined as the primary endpoint, and the composite of deaths, nonfatal myocardial infarction, and repeat revascularization (MACE) as the secondary endpoint. Results: Two-hundred eighty-six patients were followed-up for 46 ± 21 months. Thirty deaths and 65 MACEs were recorded. Patients treated by revascularization had significantly lower MACE (P < .001) but not mortality (P = .158) than patients treated by NCR. Outcomes of CCR related to mortality rate were greater than ICR and NCR (death: P = .019, MACE: P < .001). In patients with moderate-severe ischemia, CCR showed improved outcomes than ICR and NCR (death: P = .034; and MACE: P < .001). In patients with mild ischemia, the outcomes of CCR, ICR, and NCR had no significant difference (P > .05). Multivariate regression Cox analysis revealed that summed difference score [death: HR 1.09 (1.03, 1.15), P = .004] was an independent risk factor and CCR was an independent negative predictor [death: HR 0.31 (0.12, 0.81), P = .017; MACE: HR 0.30 (0.16, 0.57), P < .001]. Conclusions: Outcomes of patients treated by CCR were most likely more promising in comparison with treatment of ICR and NCR, especially when patients had over 10% ischemic myocardium.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Reliability of the {sup 123}I-mIBG heart/mediastinum ratio: Results of a multicenter test–retest reproducibility study

A quantitative measurement, the Heart-to-Mediastinum (H/M) ratio of counts derived from a planar acquisition approximately 4 hours after injection of {sup 123}I-mIBG, is a strong predictor of outcomes in patients with stable class II-III heart failure and LVEF ≤ 35%. This study assessed the test–retest reproducibility of the H/M ratio in such patients. 47 subjects with class II-III systolic heart failure and LVEF ≤ 35% were tested at two time intervals separated by 5 to 14 days. Subjects were imaged twice on the same camera using the same radionuclide dose. Images were sent to a core analysis lab, where three nuclear technologists independently determined the H/M ratios. The primary endpoint was test–retest H/M ratio reproducibility calculated as the absolute difference in mean value determined by the three readers. Mean subject age was 65 ± 12 years, 85% were male, and mean BMI was 29 ± 6 kg/m{sup 2}. Mean injected activity was 10.18 ± 0.43 mCi for first dose and 10.09 ± 0.52 mCi for the second dose. The mean and SD values for first and repeat studies were almost identical: the 95% confidence interval of the mean test–retest difference was 0.055 to 0.076. Bland–Altman plots showed no systematic effect of the H/M ratio on the magnitude of the difference between replicate measurements. Inter-reader measurements were nearly identical. There were no serious adverse events despite exposure to {sup 123}I-mIBG on 2 occasions in a short time period. The Heart-to-Mediastinum ratio of {sup 123}I-mIBG is a consistent and highly reproducible measurement in stable Class II to III heart failure patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning

Background: We developed machine-learning (ML) models to estimate a patient’s risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient’s risk to provide insight to clinicians beyond that of a “black box.” Methods: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model’s rationale to facilitate clinical interpretation. Results: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. Conclusions: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2018

Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system

The use of adaptive neuro-fuzzy inference system (ANFIS) has been reported for predicting the volume fractions in a gas–oil–water multiphase system. In fact, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of {sup 152}Eu and {sup 137}Cs and one NaI detector using ANFIS. Since the summation of volume fractions is constant, therefore ANFIS must predict only two volume fractions. In this study, three ANFIS networks are applied. The first is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil, and the last one is used to predict the water and oil volume fractions. In the next step, ANFIS networks must be trained based on numerically obtained data from MCNP-X code. Then, the average testing errors of these three networks are computed and compared. The network with the least error has been selected as the best predictor model.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Three-step LES-C models for flows at high Reynolds numbers

Here, we investigate the need for the second correction step in the recently proposed LES-C models for fluid flows at high Reynolds numbers. These models use a predictor-corrector idea to enhance the efficiency of the existing Large Eddy Simulation models. Different three-step(one defect step, two corrections) LES-C models, based on the Leray-α, ADM and NS-ω LES models, are tested in three different situations. The new Leray-α-C2 model (C2 stands for two correction steps) is applied to the Navier–Stokes equations; the ADC2 is applied to the MagnetoHydroDynamic flow; and the NS-ω-C2 is used in the fluid-fluid interaction problem. We evaluate the effectiveness of the second correction step in all these settings, using qualitative and quantitative numerical tests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.

02 PETROLEUM↗
DOE OSTI2023

A ductility metric for refractory-based multi-principal-element alloys

We propose a quantum-mechanical dimensionless metric, the local-lattice distortion (LLD), as a reliable predictor of ductility in refractory multi-principal-element alloys (RMPEAs). The LLD metric is based on electronegativity differences in localized chemical environments and combines atomic-scale displacements due to local lattice distortions with a weighted average of valence-electron count. To evaluate the effectiveness of this metric, we examined body-centered cubic (bcc) refractory alloys that exhibit ductile-to-brittle behavior. Our findings demonstrate that local-charge behavior can be tuned via composition to enhance ductility in RMPEAs. With finite-sized cell effects eliminated, the LLD metric accurately predicted the ductility of arbitrary alloys, which compares well with existing tensile-elongation experiments. To validate further, we qualitatively evaluated the ductility of two refractory RMPEAs, i.e., NbTaMoW and Mo 72 W 13 Ta 10 Ti 2.5 Zr 2.5 , through the observation of crack formation under indentation, again showing excellent agreement with LLD predictions. Additionally, a comparative study of three refractory alloys provides further insights into the electronic-structure origin of ductility in refractory RMPEAs. This proposed metric enables rapid and accurate assessment of ductility behavior in the vast RMPEA composition space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An energetic link between order and strength in metals: A nanocrystalline strength limit in high-entropy alloys and intermetallic compounds

The metallurgy and materials communities have long understood and exploited fundamental links between chemical and structural ordering in metallic solids to tailor their mechanical properties. We extend these ideas to include prediction of the nanocrystalline strength limit in high-entropy alloys and intermetallic compounds, where a breakdown occurs in the classical Hall-Petch strengthening behavior. The highest reported strength achievable through alloying has rapidly climbed and given rise to new classifications of materials with extraordinary properties, with a notable case being nanocrystalline metals. High-entropy alloys (chemically disordered, concentrated solid solutions) and intermetallic compounds are two boundary cases of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically meaningful parameters based on their atomic arrangements and used – with no fitting parameters – to predict the maximum strength of these materials. This includes a generalized energy-based accounting for the degree of structural and chemical ordering that allows for rapid and reasonably accurate prediction of peak strength (validated in the nanocrystalline limit) as a function of temperature. Predictions of maximum strength based on the activation energy (with all materials properties derived from DFT calculations or experiments) for a stress-driven transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, high-entropy alloys, and metallic glasses. Further, this activation energy is also shown to be directly proportional to interstitial electronic charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys and solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties, such as through correlations between the activation energies for amorphization with stacking-fault and grain boundary energies. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Linking pyrometry to porosity in additively manufactured metals

Porosity in additively manufactured metals can reduce material strength which is generally considered undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A mass-transfer particle-tracking method for simulating transport with discontinuous diffusion coefficients

The problem of a spatially discontinuous diffusion coefficient (D(x)) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow properties. To date, mass-transfer particle-tracking (MTPT) methods, a family of Lagrangian methods in which diffusion is jointly simulated by random walk and diffusive mass transfers, have been unable to solve this problem. This manuscript presents a new mass-transfer (MT) algorithm that enables MTPT methods to accurately solve the problem of discontinuous D(x). To achieve this, we derive a semi-analytical solution to the discontinuous D(x) problem by employing a predictor-corrector approach, and we use this semi-analytical solution as the weighting function in a reformulated MT algorithm. Furthermore, this semi-analytical solution is generalized for cases with multiple 1D interfaces as well as for 2D cases, including a 2 × 2 tiling of 4 subdomains that corresponds to a numerically-generated diffusion field. The solutions generated by this new mass-transfer algorithm closely agree with an analytical 1D solution or, in more complicated cases, trusted numerical results, demonstrating the success of our proposed approach.

42 ENGINEERING↗
DOE OSTI2025

Bayesian modeling of traffic-related air pollutants: A case study of urban transportation and air quality dynamics in Columbia, South Carolina

Traffic emissions significantly impact near-road air quality and public health. This research applies a Bayesian modeling framework to investigate these impacts using high-resolution traffic and air pollutant data from an urban corridor in Columbia, South Carolina. Despite a data collection period truncated by the COVID-19 lockdown, the Bayesian approach successfully identified significant predictors and quantified model uncertainty. Employing Bayesian Model Selection and Averaging enhanced prediction accuracy and evaluated model uncertainty. Findings indicate that higher temperatures and increased moisture levels elevate particulate matter (PM 1.0 , PM 2.5 , PM 10 ) concentrations, while traffic speed significantly affects nitrogen dioxide (NO 2 ) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO 2 concentrations, suggesting that less congested conditions reduce NO 2 emissions. This study highlights the robustness of Bayesian methods for generating reliable air quality insights even under data-constrained conditions. The findings underscore the importance of traffic flow management (e.g., reducing congestion) for mitigating near-road NO 2 exposure and provide a basis for developing targeted public health strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis

Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multiphysics analysis of fuel fragmentation, relocation, and dispersal susceptibility–Part 3: Thermal hydraulic evaluation of large break LOCA under high-burnup conditions

Increasing the peak rod average burnup of pressurized water reactor (PWR) fuel beyond 62 GWd/tU may increase fuel fragmentation, relocation, and dispersal (FFRD) susceptibility during a large break loss of coolant accident (LBLOCA). TRACE thermal hydraulic (TH) LBLOCA analyses were performed for a realistic 24-month high-burnup PWR equilibrium cycle, to inform subsequent transient BISON high-burnup FFRD susceptibility evaluations. Realistic LBLOCA systems behavior was first established by configuring to and comparing with the BEMUSE OECD LBLOCA benchmark. Fuel and operating conditions were then applied from high-burnup VERA depletion calculations. LBLOCA simulations were performed for 281 selected high-burnup rods, for which transient TH boundary conditions were collected for later use in BISON. The TRACE results indicated that rod linear heat rate (rather than burnup) is the main predictor of peak cladding temperature (PCT) during the event. PCT typically occurred at a local burnup lower than the rod-average burnup, especially for twice-burned fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗