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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

End-to-end online performance data capture and analysis for scientific workflows

With the increased prevalence of employing workflows for scientific computing and a push towards exascale computing, it has become paramount that we are able to analyze characteristics of scientific applications to better understand their impact on the underlying infrastructure and vice-versa. Such analysis can help drive the design, development, and optimization of these next generation systems and solutions. Here, we present the architecture, integrated with existing well-established and newly developed tools, to collect online performance statistics of workflow executions from various, heterogeneous sources and publish them in a distributed database (Elasticsearch). Using this architecture, we are able to correlate online workflow performance data, with data from the underlying infrastructure, and present them in a useful and intuitive way via an online dashboard. We have validated our approach by executing two classes of real-world workflows, both under normal and anomalous conditions. The first is an I/O-intensive genome analysis workflow; the second, a CPU- and memory-intensive material science workflow. Based on the data collected in Elasticsearch, we are able to demonstrate that we can correctly identify anomalies that we injected. The resulting end-to-end data collection of workflow performance data is an important resource of training data for automated machine learning analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Energy-consumption and carbon-emission analysis of vehicle and component manufacturing.

A model is presented for calculating the environmental burdens of the part manufacturing and vehicle assembly (VMA) stage of the vehicle life cycle. The approach is bottom-up, with a special focus on energy consumption and CO{sub 2} emissions. The model is applied to both conventional and advanced vehicles, the latter of which include aluminum-intensive, hybrid electric, plug-in hybrid electric and all-electric vehicles. An important component of the model, a weight-based distribution function of materials and associated transformation processes (casting, stamping, etc.), is developed from the United States Council for Automotive Research Generic Vehicle Life Cycle Inventory Study. As the approach is bottom-up, numerous transformation process data and plant operational data were extracted from the literature for use in representing the many operations included in the model. When the model was applied to conventional vehicles, reliable estimates of cumulative energy consumption (34 GJ/vehicle) and CO{sub 2} emission (2 tonnes/vehicle) were computed for the VMA life-cycle stage. The numerous data sets taken from the literature permitted the development of some statistics on model results. Because the model explicitly includes a greater coverage of relevant manufacturing processes than many earlier studies, our energy estimates are on the higher end of previously published values. Limitations of the model are also discussed. Because the material compositions of conventional vehicles within specific classes (cars, light duty trucks, etc.) are sensibly constant on a percent-by-weight basis, the model can be reduced to a simple linear form for each class dependent only on vehicle weight. For advanced vehicles, the material/transformation process distribution developed above needs to be adjusted for different materials and components. This is particularly so for aluminum-intensive and electric-drive vehicles. In fact, because of their comparatively high manufacturing energy, batteries required for an electric vehicle can significantly add to the energy burden of the VMA stage. Overall, for conventional vehicles, energy use and CO{sub 2} emissions from the VMA stage are about 4% of their total life-cycle values. They are expected to be somewhat higher for advanced vehicles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Inversion of coherent surface scattering images via deep learning network

Coherent surface scattering imaging is a powerful tool for imaging a surface/interface of a thin nanostructure deposited on an opaque substrate. A mathematical conversion of an object image from a scattering pattern is essential for coherent surface scattering imaging to visualize structures of specimens. It has been achieved by using phase retrieval algorithms requiring oversampling in scattering patterns and employing alternating projection approaches. It is a computationally challenging and time-consuming process. In this paper, we demonstrate CSSI-NN, which is a deep learning neural network model to predict images of objects from scattering intensities in coherent surface scattering imaging. This model allowing for the instant outcome from scattering patterns would be tremendously beneficial not only for effective experiments but also for data analysis of phase retrieval.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

A Lagged Diffusivity Method for Computing Total Variation Regularized Fluid Flow

There is a great deal of recent work using optical flow methods for analyzing the dynamics of fluids, and much attention has been paid to developing regularization schemes for variational approaches that are consistent with the physics of fluid flow. In this work we show that using total variation to regularize two different kinds of optical flow functionals leads to very good flow field reconstructions for the kinds of dynamical structures that appear in fluid flow. The first optical flow functional is the classical component-based conservation of intensity, and the second approach is to reconstruct the potential of the flow, rather than the flow components. In the two cases, total variation regularization corresponds to imposing different scientific priors on the solution, which we compute using a variation of the Lagged Diffusivity Fixed Point Iteration. Numerical details are presented, and the results are demonstrated on synthetic data and on a data-driven oceanic flow model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2010

Four-dimensional intensity-modulated radiation therapy planning for dynamic tracking using a direct aperture deformation (DAD) method

Purpose: Planning for the delivery of intensity-modulated radiation therapy (IMRT) to a moving target, referred to as four-dimensional (4D) IMRT planning, is a crucial step for achieving the treatment objectives for sites that move during treatment delivery. The authors proposed a simplistic method that accounts for both rigid and nonrigid respiration-induced target motion based on 4D computed tomography (4DCT) data sets. Methods: A set of MLC apertures and weights was first optimized on a reference phase of a 4DCT data set. At each beam angle, the apertures were morphed from the reference phase to each of the remaining phases according to the relative shape changes in the beam's eye view of the target. Three different planning schemes were evaluated for two lung cases and one pancreas patient: (1) Individually optimizing each breathing phase; (2) optimizing the reference phase and shifting the optimized apertures to other breathing phases based on a rigid-body image registration; and (3) optimizing the reference phase and deforming the optimized apertures to the other phases based on the deformation and translation of target contours. Planning results using scheme 1 serves as the ''gold standard'' for plan quality assessment; scheme 2 is the method previously proposed in the literature; and scheme 3 is the method the authors proposed in this article. The optimization results were compared between the three schemes for all three cases. Results: The proposed scheme 3 is comparable to scheme 1 in plan quality, and provides improved target coverage and conformity with similar normal tissue dose compared with scheme 2. Conclusions: Direct aperture deformation method for 4D IMRT planning improves upon methods that only consider rigid-body motion and achieves a plan quality close to that optimized for each of the phases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Mie Scattering Analysis

Mie scattering theory describes how light scatters on a particle cloud when the particle size is approximately the same as the wavelength of scattered light. Assuming the particles generated in an ejecta experiment are spherical, one goal of such experiments is to determine the particle radii distribution, which in theory can be done by measuring intensities of scattered light at several different angles. The purpose of this report is the present an assessment of how closely the analysis of Mie scattering data in practice rejects the theoretical possibilities.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2010

Application of the rigorous method to x-ray and neutron beam scattering on rough surfaces

The paper presents a comprehensive numerical analysis of x-ray and neutron scattering from finite-conducting rough surfaces which is performed in the frame of the boundary integral equation method in a rigorous formulation for high ratios of characteristic dimension to wavelength. The single integral equation obtained involves boundary integrals of the single and double layer potentials. A more general treatment of the energy conservation law applicable to absorption gratings and rough mirrors is considered. In order to compute the scattering intensity of rough surfaces using the forward electromagnetic solver, Monte Carlo simulation is employed to average the deterministic diffraction grating efficiency due to individual surfaces over an ensemble of realizations. Some rules appropriate for numerical implementation of the theory at small wavelength-to-period ratios are presented. The difference between the rigorous approach and approximations can be clearly seen in specular reflectances of Au mirrors with different roughness parameters at wavelengths where grazing incidence occurs at close to or larger than the critical angle. This difference may give rise to wrong estimates of rms roughness and correlation length if they are obtained by comparing experimental data with calculations. Besides, the rigorous approach permits taking into account any known roughness statistics and allows exact computation of diffuse scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Data-Intensive Visual Analysis for Cyber Security

Protecting communications networks against attacks where the aim is to steal information, disrupt order, or harm critical infrastructure can require the collection and analysis of staggering amounts of data. The ability to detect and respond to threats quickly is a paramount concern across sectors, and especially for critical government, utility and financial networks. Yet detecting emerging or incipient threats in immense volumes of network traffic requires new computational and analytic approaches. Network security increasingly requires cooperation between human analysts able to spot suspicious events through means such as data visualization and automated systems that process streaming network data in near real-time to triage events so that human analysts are best able to focus their work.

Pike, William A.↗
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 OSTI2010

Theoretical investigation of the spectrum and conversion efficiency of short wavelength extreme-ultraviolet light sources based on terbium plasmas

The emission spectrum and conversion efficiency of laser-produced terbium plasmas are theoretically investigated on the basis of computational atomic data. The theoretically calculated spectrum reproduces the main peak of observed spectrum at {lambda}=6.5 nm, which originates from 4d-4f transitions of near palladiumlike ions (Tb{sup 19+}). A simple model of the isothermal expansion of terbium plasma suggests that efficient emission can be achieved by pumping the plasma with a laser pulse at an intensity of approximately one order of magnitude greater than that used with tin sources at {lambda}=13.5 nm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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↗