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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Scientific Literature Mining for Experiment Information in Materials Design

Scientific literature is a plethora of documents written in natural language, where the agglomeration of well-cogitated knowledge is inextricably intertwined. The large-scale knowledge source might have the great potential to derive valuable information when text mined and untangled. Typically, scientists obtain preliminary knowledge from research papers prior to beginning a new experiment. Researchers manually harvest experimental information, which is labor-intensive and error prone. To reduce manual effort and human errors, this project aims to build a system for data collection from literature that supplements other data sources so users can look up needed information while preparing experiments or analyzing results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search

The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient --- the sequential version can generate topologies with the same quality while being up to 67% faster. Here, the parallelization schemes can further achieve high speedups compared to the sequential version.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

WE-AB-303-05: Breathing Motion of Liver Segments From Fiducial Tracking During Robotic Radiosurgery and Comparison with 4D-CT-Derived Fiducial Motion

Purpose: To quantify respiratory-induced motion of liver segments using the positions of implanted fiducials during robotic radiosurgery. This study also compared fiducial motion derived from four-dimensional computed tomography (4D-CT) maximum intensity projections (MIP) with motion derived from imaging during treatment. Methods: Forty-two consecutive liver patients treated with liver ablative radiotherapy were accrued to an ethics approved retrospective study. The liver segment in which each fiducial resided was identified. Fiducial positions throughout each treatment fraction were determined using orthogonal kilovoltage images. Any data due to patient repositioning or motion was removed. Mean fiducial positions were calculated. Fiducial positions beyond two standard deviations of the mean were discarded and remaining positions were fit to a line segment using least squares minimization (LSM). For eight patients, fiducial motion was derived from 4D-CT MIPs by calculating the CT number weighted mean position of the fiducial on each slice and fitting a line segment to these points using LSM. Treatment derived fiducial trajectories were corrected for patient rotation and compared to MIP derived trajectories. Results: The mean total magnitude of fiducial motion across all liver segments in left-right, anteroposterior, and superoinferior (SI) directions were 3.0 ± 0.2 mm, 9.3 ± 0.4 mm, and 20.5 ± 0.5 mm, respectively. Differences in per-segment mean fiducial motion were found with SI motion ranging from 12.6 ± 0.8 mm to 22.6 ± 0.9 mm for segments 3 and 8, respectively. Large, varied differences between treatment and MIP derived motion at simulation were found with the mean difference for SI motion being 2.6 mm (10.8 mm standard deviation). Conclusion: The magnitude of liver fiducial motion was found to differ by liver segment. MIP derived liver fiducial motion differed from motion observed during treatment, implying that 4D-CTs may not accurately capture the range of liver motion across fractions and during treatment. Author V. Nair was funded by the Cushing estate for a SABR clinical research fellowship.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Experimental energy levels and partition function of the {sup 12}C{sub 2} molecule

The carbon dimer, the {sup 12}C{sub 2} molecule, is ubiquitous in astronomical environments. Experimental-quality rovibronic energy levels are reported for {sup 12}C{sub 2}, based on rovibronic transitions measured for and among its singlet, triplet, and quintet electronic states, reported in 42 publications. The determination utilizes the Measured Active Rotational-Vibrational Energy Levels (MARVEL) technique. The 23,343 transitions measured experimentally and validated within this study determine 5699 rovibronic energy levels, 1325, 4309, and 65 levels for the singlet, triplet, and quintet states investigated, respectively. The MARVEL analysis provides rovibronic energies for six singlet, six triplet, and two quintet electronic states. For example, the lowest measurable energy level of the a{sup 3}Π{sub u} state, corresponding to the J = 2 total angular momentum quantum number and the F {sub 1} spin-multiplet component, is 603.817(5) cm{sup −1}. This well-determined energy difference should facilitate observations of singlet–triplet intercombination lines, which are thought to occur in the interstellar medium and comets. The large number of highly accurate and clearly labeled transitions that can be derived by combining MARVEL energy levels with computed temperature-dependent intensities should help a number of astrophysical observations as well as corresponding laboratory measurements. The experimental rovibronic energy levels, augmented, where needed, with ab initio variational ones based on empirically adjusted and spin–orbit coupled potential energy curves obtained using the Duo code, are used to obtain a highly accurate partition function, and related thermodynamic data, for {sup 12}C{sub 2} up to 4000 K.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Karhunen-Loeve expansion analysis of uncertainties in cloud microphysical property retrievals

Here, this study proposes a methodology of quantifying uncertainties for cloud retrievals on model resolution to facilitate the comparison with model outputs. Primary component analysis is applied to reduce the dimension of random variables (up to a factor of 50) and reveal the cross correlations in the input data, making large sampling computationally feasible and uncertainty quantification accurate and reliable. Our approach has the capability of parameterizing input uncertainties and attributing the uncertainties in the retrieval output to each individual source, which allows sensitivity analysis of cloud retrieval algorithms and provides directions for improving observation instruments as well as strategies. We applied the method to characterize uncertainties in cloud ice water content (IWC) retrieved from the DOE Atmospheric Radiation Measurement (ARM) programs baseline cloud microphysical retrieval algorithm (MICROBASE). We test it with a selected ice cloud case observed on 9 March 2000 at the ARM Southern Great Plains site during its 2000 cloud intensive observing period. The test results indicate that (1) uncertainties in the output retrieved by MICROBASE are comparable amongst different retrievals; (2) The mean values obtained by our UQ method are closer to the aircraft data with less errors compared to the direct ensemble average; (3) Ice water path (IWP) generally incurred larger uncertainty in optically thin ice clouds and there was more variability in vertical in the retrieved IWC; and (4) Uncertainties in the output are mainly due to the interactions among different modes of ARM radar pro files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Machine learning bandgaps of double perovskites

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

36 MATERIALS SCIENCE↗
DOE OSTI2015

MO-AB-BRA-03: Calorimetry-Based Absorbed Dose to Water Measurements Using Interferometry

Purpose: Interferometry-based calorimetry is a novel technique to measure radiation-induced temperature changes allowing the measurement of absorbed dose to water (ADW). There are no mechanical components in the field. This technique also has the possibility of obtaining 2D dose distributions. The goal of this investigation is to calorimetrically-measure doses between 2.5 and 5 Gy over a single projection in a photon beam using interferometry and compare the results with doses calculated using the TG-51 linac calibration. Methods: ADW was determined by measuring radiation-induced phase shifts (PSs) of light passing through water irradiated with a 6 MV photon beam. A 9×9×9 cm{sup 3} glass phantom filled with water and placed in an arm of a Michelson interferometer was irradiated with 300, 400, 500 and 600 monitor units. The whole system was thermally insulated to achieve sufficient passive temperature control. The depth of measurement was 4.5 cm with a field size of 7×7 cm{sup 2}. The intensity of the fringe pattern was monitored with a photodiode and used to calculate the time-dependent PS curve. Data was acquired 60 s before and after the irradiation. The radiation-induced PS was calculated by taking the difference in the pre- and post-irradiation drifts extrapolated to the midpoint of the irradiation. Results were compared to computed doses. Results: Average comparison of calculated ADW values with interferometry-measured values showed an agreement to within 9.5%. k=1 uncertainties were 4.3% for calculations and 14.7% for measurements. The dominant source of uncertainty for the measurements was a temperature drift of about 30 µK/s caused by heat conduction from the interferometer’s surroundings. Conclusion: This work presented the first absolute ADW measurements using interferometry in the dose range of linac-based radiotherapy. Future work to improve measurements’ reproducibility includes the implementation of active thermal control techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-394: Impact of PTV Margins With Taking Into Account Shape Variation On IMRT Plans For Prostate Cancer

Purpose: The purpose of this study was to investigate the impact of planning target volume (PTV) margins with taking into consideration clinical target volume (CTV) shape variations on treatment plans of intensity modulated radiation therapy (IMRT) for prostate cancer. Methods: The systematic errors and the random errors for patient setup errors in right-left (RL), anterior-posterior (AP), and superior-inferior (SI) directions were obtained from data of 20 patients, and those for CTV shape variations were calculated from 10 patients, who were weekly scanned using cone beam computed tomography (CBCT). The setup error was defined as the difference in prostate centers between planning CT and CBCT images after bone-based registrations. CTV shape variations of high, intermediate and low risk CTVs were calculated for each patient from variances of interfractional shape variations on each vertex of three-dimensional CTV point distributions, which were manually obtained from CTV contours on the CBCT images. PTV margins were calculated using the setup errors with and without CTV shape variations for each risk CTV. Six treatment plans were retrospectively made by using the PTV margins with and without CTV shape variations for the three risk CTVs of 5 test patients. Furthermore, the treatment plans were applied to CBCT images for investigating the impact of shape variations on PTV margins. Results: The percentages of population to cover with the PTV, which satisfies the CTV D98 of 95%, with and without the shape variations were 89.7% and 74.4% for high risk, 89.7% and 76.9% for intermediate risk, 84.6% and 76.9% for low risk, respectively. Conclusion: PTV margins taking into account CTV shape variation provide significant improvement of applicable percentage of population (P < 0.05). This study suggested that CTV shape variation should be taken consideration into determination of the PTV margins.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The Poisson tensor completion non-parametric differential entropy estimator

We introduce the Poisson tensor completion (PTC) estimator, a non-parametric differential entropy estimator. The PTC estimator leverages inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial Poisson process. The Poisson tensor decomposition leads to a completion of the intensity measure over all bins—including those containing few to no samples—and leads to our proposed PTC differential entropy estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values and so can be safely used directly in entropy estimation. Our estimator is the first tensor-based estimator that exploits the underlying spatial Poisson process related to the histogram explicitly when estimating the probability density with low-rank tensor decompositions for the purpose of tensor completion. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

42 ENGINEERING↗
DOE OSTI2021

Subtropical Jet in Reanalysis Data from STJ_PV

Subtropical jet position from a new method for locating the subtropical jet, called the tropopause gradient method. It is based on the peak gradient in potential temperature along the dynamic tropopause. This data has the identified subtropical jet latitude, level, and intensity across four different reanalysis products (CFSR-2, ERA-Interim, JRA-55, and MERRA-2), at both daily and monthly output frequency.

58 GEOSCIENCES↗
DOE OSTI2018

Accelerating Feldman-Cousins on NOvA using NERSC Supercomputers

When fitting to data with low stats and near physical boundaries, extra measures need to be taken to ensure proper statistical coverage. The method NOvA uses is called the Feldman-Cousins procedure, which entails fitting thousands of independent pseudoexperiments to generate acceptance intervals that are then used to correct our fits. The scale required by the Feldman Cousins procedure makes it extremely computationally intensive. In past analyses, it has taken up to several weeks to complete, bottlenecking our final results. Here I present recent work by members of the NOvA experiment and the SciDAC4 collaboration to enable the use of the super-computing facilities at NERSC to process our Feldman-Cousins corrections over 150x faster, allowing us to do more studies, increase the precision of our fits, and produce results quickly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Resource-Adaptive Federated Text Generation with Differential Privacy

In cross-silo federated learning (FL), sensitive text datasets remain confined to local organizations due to privacy regulations, making repeated training for each downstream task both communication-intensive and privacy-demanding. A promising alternative is to generate differentially private (DP) synthetic datasets that approximate the global distribution and can be reused across tasks. However, pretrained large language models (LLMs) often fail under domain shift, and federated finetuning is hindered by computational heterogeneity: only resource-rich clients can update the model, while weaker clients are excluded, amplifying data skew and the adverse effects of DP noise. We propose a flexible participation framework that adapts to client capacities. Strong clients perform DP federated finetuning, while weak clients contribute through a lightweight DP voting mechanism that refines synthetic text. To ensure the synthetic data mirrors the global dataset, we apply control codes (e.g., labels, topics, metadata) that represent each client’s data proportions and constrain voting to semantically coherent subsets. This two-phase approach requires only a single round of communication for weak clients and integrates contributions from all participants. Experiments show that our framework improves distribution alignment and downstream robustness under DP and heterogeneity.

Wang, Jiayi [ORNL]↗
DOE OSTI2017

Scatter Signal Elimination by Localized Primary Modulation in Industrial Computed Radiography

In industrial computed radiography, same as the other projection imaging systems, quantitative integrated information of total beam path including the object is carried by primary part of the beam. It is well known that the accuracy of the image useful data carried by the primary part degrades by scattered part of the beam. In order to have the beam path quantitative information accurately, it is essential to obtain the primary part of the total signal purely. In current study, a practical approach using localized primary modulation has been proposed to extract the primary signal. A semi-transparent beam attenuation grid was used as the beam intensity modulator. Efficiency and accuracy of the approach was practically validated through thickness measurement of metallic planar steps as an illustrative example. Simplified practical implementation and availability of the required accessories are some of advantages of the proposed approach. The results are indicative of the fact that this approach can also be applied as a new technique in thickness measurement, yielding a noticeable accuracy improvement in the most radiography methods. A procedure is included to explain the approach implementation for thickness measurement in computed radiography systems. The procedure overcomes some practical limitations and difficulties of the approach adaptation for computed radiography.

42 ENGINEERING↗
DOE OSTI2017

An Integrated Approach To Offshore Wind Energy Assessment: Great Lakes 3D Wind Experiment

This grant supported fundamental research into the characterization of flow parameters of relevance to the wind energy industry focused on offshore and the coastal zone. A major focus of the project was application of the latest generation of remote sensing instrumentation and also integration of measurements and numerical modeling to optimize characterization of time-evolving atmospheric flow parameters in 3-D. Our research developed a new data-constrained Wind Atlas for the Great Lakes, and developed new insights into flow parameters in heterogeneous environments. Four experiments were conducted during the project: At a large operating onshore wind farm in May 2012; At the National Renewable Energy Laboratory National Wind Technology Center (NREL NWTC) during February 2013; At the shoreline of Lake Erie in May 2013; and At the Wind Energy Institute of Canada on Prince Edward Island in May 2015. The experiment we conducted in the coastal zone of Lake Erie indicated very complex flow fields and the frequent presence of upward momentum fluxes and resulting distortion of the wind speed profile at turbine relevant heights due to swells in the Great Lakes. Additionally, our data (and modeling) indicate the frequent presence of low level jets at 600 m height over the Lake and occasions when the wind speed profile across the rotor plane may be impacted by this phenomenon. Experimental data and modeling of the fourth experiment on Prince Edward Island showed that at 10-14 m escarpment adjacent to long-overseas fetch the zone of wind speed decrease before the terrain feature and the increase at (and slightly downwind of) the escarpment is ~3–5% at turbine hub-heights. Additionally, our measurements were used to improve methods to compute the uncertainty in lidar-derived flow properties and to optimize lidar-scanning strategies. For example, on the basis of the experimental data we collected plus those from one of our research partners we advanced a new methodology to estimate a priori the uncertainty in wind speed retrievals from arc scans based on site characteristics such as wind velocity, turbulence intensity and proposed scan geometry. Insights regarding use of remote sensing technologies deriving from project experiments were used to compile a best practice document http://doi.org/10.7298/X4QV3JGF for measuring wind speeds and turbulence offshore through in-situ and remote sensing technologies. A project-specific web-site was developed and is available at: http://www.geo.cornell.edu/eas/PeoplePlaces/Faculty/spryor/DoE_AIATOWEA/index.html

17 WIND ENERGY↗