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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
NASA NTRS1994

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

Morelli, Eugene A.↗
DOE OSTI2022

Micro-strains, local stresses, and coherently diffracting domain size in shock compressed Al(100) single crystals

In situ x-ray diffraction (XRD) measurements and their analysis in Al single crystals shock compressed along the [100]-direction were utilized to examine shock wave induced microstructural heterogeneities. High-resolution XRD line profiles for the 200, 400, and 600 Al peaks were measured in uniaxial strain compression states to either 5.6 or 11.7 GPa and partial stress release to 3.5 or 6.6 GPa, respectively. Broadening of the XRD line profiles was analyzed to determine the magnitude of the longitudinal micro-strain distribution (0.195% and 0.28% full width at half maximum for 3.5 and 6.6 GPa stresses, respectively) and the size of coherently diffracting domains (CDDs) (0.125 and 0.07 μm for 3.5 and 6.6 GPa stresses, respectively). From the longitudinal micro-strain distributions, the distribution of local stress differences (or stress deviators) was obtained in the shocked state. The full width at half maximum of this distribution, a measure of the local stress inhomogeneities, is greater than half of the macroscopic stress difference for both 3.5 and 6.6 GPa peak stresses, suggesting considerable variation in local stress deviators. The CDD sizes determined here are comparable to characteristic length scales for defect-free regions determined from defect density measurements in post-shock recovery experiments. As a result, the present work represents an important step in understanding material microstructure and inhomogeneities in the shock-compressed state.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Valorizing the carbon byproduct of methane pyrolysis in batteries

While low-cost natural gas remains abundant, the energy content of this fuel can be utilized without greenhouse gas emissions through the production of molecular hydrogen and solid carbon via methane pyrolysis. In the absence of a carbon tax, methane pyrolysis is not economically competitive with current hydrogen production methods unless the carbon byproducts can be valorized. In this work, we assess the viability of the carbon byproduct produced from methane pyrolysis in molten salts as high-value-added anode or conductive additive for secondary Li-ion and Na-ion batteries. Raman characterization and electrochemical differential capacity analysis demonstrate that the use of molten salt mixtures with catalytically-active FeCl 3 - or MnCl 2 result in more graphitic carbon co-products. These graphitic carbons exhibit the best electrochemical performance (up to 272 mAh/g of reversible capacity) when used as Li-ion anodes. For all carbon samples studied here, disordered carbon domains and retained salt species trapped and/or intercalated into the carbon structure were identified by X-ray photoelectron and multinuclear solid-state nuclear magnetic resonance spectroscopy. The latter lead to reduced electrochemical activity and reversibility, and poorer rate performance compared to commercial carbon anodes. The electronic conductivity of the pyrolyzed carbons is found to be highly dependent on their purity, with the purest carbon exhibiting an electronic conductivity nearly on par with that of commercial carbon additives. These findings suggest that more effective removal of the salt catalyst could enable applications of these carbons in secondary batteries, providing a financial incentive for the large-scale implementation of methane pyrolysis for “low-carbon” hydrogen production.

08 HYDROGEN↗
DOE OSTI2017

Hanford Site Composite Analysis Technical Approach Description: Groundwater

The groundwater facet of the revised CA is responsible for generating predicted contaminant concentration values over the entire analysis spatial and temporal domain. These estimates will be used as part of the groundwater pathway dose calculation facet to estimate dose for exposure scenarios. Based on the analysis of existing models and available information, the P2R Model was selected as the numerical simulator to provide these estimates over the 10,000-year temporal domain of the CA. The P2R Model will use inputs from initial plume distributions, updated for a start date of 1/1/2017, and inputs from the vadose zone facet, created by a tool under development as part of the ICF, to produce estimates of hydraulic head, transmissivity, and contaminant concentration over time. A recommendation of acquiring 12 computer processors and 2 TB of hard drive space is made to ensure that the work can be completed within the anticipated schedule of the revised CA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Updated SAM Model for the Molten Salt Reactor Experiment (MSRE)

The development of reference standard problems based on prototypical reactor designs is of particular importance to verify the adequacy of computer codes and evaluation models for specific reactor types. To support the multiphysics coupled simulation of molten-salt-fueled reactor (MSR) using SAM and Griffin computer codes for safety and licensing analysis, much efforts have been put into enhancing code capabilities and developing reference models for the MSR primary loop in SAM. In this work, a previously developed Molten Salt Reactor Experiment (MSRE) primary loop model was updated to include a two-dimensional (2-D) core region and external core components in one-dimension (1-D) or zero-dimension (0-D). To ensure accurate feedback calculation in multi-physics simulations, the delayed neutron precursor tracking model and solid graphite model were added in the SAM model. In addition, the 2-D and 1-D domains are tightly coupled using the recently developed single-solve approach in SAM. The updated model has been tested under both steady-state and transient scenarios to demonstrate its potential for the multi-physics simulation of MSRE with coupled SAM and Griffin.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1994

Boundary Conditions for Unsteady Compressible Flows

This paper explores solutions to the spherically symmetric Euler equations. Motivated by the work of Hagstrom and Hariharan and Geer and Pope, we modeled the effect of a pulsating sphere in a compressible medium. The literature available on this suggests that an accurate numerical solution requires artificial boundary conditions which simulate the propagation of nonlinear waves in open domains. Until recently, the boundary conditions available were in general linear and based on nonreflection. Exceptions to this are the nonlinear nonreflective conditions of Thompson, and the nonlinear reflective conditions of Hagstrom and Hariharan. The former are based on the rate of change of the incoming characteristics; the latter rely on asymptotic analysis and the method of characteristics and account for the coupling of incoming and outgoing characteristics. Furthermore, Hagstrom and Hariharan have shown that, in a test situation in which the flow would reach a steady state over a long time, Thompson's method could lead to an incorrect steady state. The current study considers periodic flows and includes all possible types and techniques of boundary conditions. The technique recommended by Hagstrom and Hariharan proved superior to all others considered and matched the results of asymptotic methods that are valid for low subsonic Mach numbers.

Hariharan, S. I.↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

HEPnOS: a Specialized Data Service for High Energy Physics Analysis

In this paper, we present HEPnOS, a distributed data service for managing data produced by high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC resources more effciently than traditional fle-based applications. The fle-based model leads to a rigid, chunk-based allocation of computational resources and limits the number of cores that can be used concurrently by an HEP application. The fundamental problem is that organizing domain-specifc data into fles inadvertently introduces a single, artifcial, confated tuning parameter that puts key optimization goals into confict: larger fle sizes reduce metadata overhead and thus improve I/O effciency, but smaller fle sizes provide more opportunity for workfow parallelism and load balancing. In this work, we introduce a domain-specifc data service that decouples that constraint so that data can be accessed and processed in its natural granularity while still maintaining I/O effciency. By removing the constraints introduced by fle handling we are able to obtain better scaling and make effcient use of more cores for processing a fxed-sized data sample. We demonstrate the improved scalability by using an application developed in the fle-based paradigm and comparing it to a version modifed to use HEPnOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

PMR5, an acetylation protein at the intersection of pectin biosynthesis and defense against fungal pathogens

Powdery mildew ( Golovinomyces cichoracearum ), one of the most prolific obligate biotrophic fungal pathogens worldwide, infects its host by penetrating the plant cell wall without activating the plant's innate immune system. The Arabidopsis mutant powdery mildew resistant 5 (pmr5) carries a mutation in a putative pectin acetyltransferase gene that confers enhanced resistance to powdery mildew. In this work, we show that heterologously expressed PMR5 protein transfers acetyl groups from [ 14 C]-acetyl-CoA to oligogalacturonides. Through site-directed mutagenesis, we show that three amino acids within a highly conserved esterase domain in putative PMR5 orthologs are necessary for PMR5 function. A suppressor screen of mutagenized pmr5 seed selecting for increased powdery mildew susceptibility identified two previously characterized genes affecting the acetylation of plant cell wall polysaccharides, RWA2 and TBR. The rwa2 and tbr mutants also suppress powdery mildew disease resistance in pmr6, a mutant defective in a putative pectate lyase gene. Cell wall analysis of pmr5 and pmr6, and their rwa2 and tbr suppressor mutants, demonstrates minor shifts in cellulose and pectin composition. In direct contrast to their increased powdery mildew resistance, both pmr5 and pmr6 plants are highly susceptibile to multiple strains of the generalist necrotroph Botrytis cinerea , and have decreased camalexin production upon infection with B. cinerea . These results illustrate that cell wall composition is intimately connected to fungal disease resistance and outline a potential route for engineering powdery mildew resistance into susceptible crop species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Predicting departure from nucleate boiling with advanced data- and physics-driven approaches

The subcooled and low-quality saturated flow boiling critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) is one of the major limiting factors in the design and operation of pressurized water reactors (PWRs). Due to lack of agreement on its physical mechanisms, development of an accurate DNB-type CHF model has been elusive. The various steady-state (SS) predictive tools that have been proposed can be grouped into two categories: (1) data-driven methods—namely best-fit empirical correlations and look-up tables that result in relatively good agreement with specific experimental datasets but often fail to extend beyond their ranges of validity, and (2) the physics-based mechanistic models that rely on reasonable yet limited understanding of the underlying physics and apply constitutive relations to close the conservation equations. On one hand, with recent advances in computational capabilities and optimization techniques, machine learning (ML)-based methods constitute an alternative and a more advanced, data-driven approach. With enough data, they can be particularly useful in engineering fields where the physical phenomena are complex and challenging to model. On the other hand, a state-of-the-art physics-driven approach would not only provide more accurate prediction of SS CHF, but it would also help better understand and model transient applications. The data-driven regression models use deep feed-forward neural networks (NNs) and random forests to cross-validate with 1,865 CHF test cases, covering a wide range of flow conditions and channel geometries. The best-estimate ML-based predictors compare favorably with the widely used look-up table for annulus and plate, and sensitivity analysis has confirmed their effectiveness. The key advantage of ML-based methods is their online extensibility of applicability domain. The proposed physics-driven approach combines key assumptions and parameters in the relatively well-accepted theories of liquid sublayer dryout and near-wall bubble crowding. A more realistic understanding of local mechanisms has been modeled. The new model has been optimized and validated against 1,439 tube data, and it shows considerably improved performance when compared to a recent mechanistic model, demonstrating unbiased close agreement with measurements over a wide range of operating conditions. Future work will extend the improved physics-driven model to non-tube geometry applications and will couple the model with ML via a hybrid approach. The mechanistic SS work will also serve to improve understanding and to model transient CHF scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Structure and reconstitution of yeast Mpp6-nuclear exosome complexes reveals that Mpp6 stimulates RNA decay and recruits the Mtr4 helicase

Nuclear RNA exosomes catalyze a range of RNA processing and decay activities that are coordinated in part by cofactors, including Mpp6, Rrp47, and the Mtr4 RNA helicase. Mpp6 interacts with the nine-subunit exosome core, while Rrp47 stabilizes the exoribonuclease Rrp6 and recruits Mtr4, but it is less clear if these cofactors work together. Using biochemistry with Saccharomyces cerevisiae proteins, we show that Rrp47 and Mpp6 stimulate exosome-mediated RNA decay, albeit with unique dependencies on elements within the nuclear exosome. Mpp6-exosomes can recruit Mtr4, while Mpp6 and Rrp47 each contribute to Mtr4-dependent RNA decay, with maximal Mtr4-dependent decay observed with both cofactors. The 3.3 Å structure of a twelve-subunit nuclear Mpp6 exosome bound to RNA shows the central region of Mpp6 bound to the exosome core, positioning its Mtr4 recruitment domain next to Rrp6 and the exosome central channel. Genetic analysis reveals interactions that are largely consistent with our model.

Wasmuth, Elizabeth V.↗
NASA NTRS1992

A study of mapping exogenous knowledge representations into CONFIG

Qualitative reasoning is reasoning with a small set of qualitative values that is an abstraction of a larger and perhaps infinite set of quantitative values. The use of qualitative and quantitative reasoning together holds great promise for performance improvement in applications that suffer from large and/or imprecise knowledge domains. Included among these applications are the modeling, simulation, analysis, and fault diagnosis of physical systems. Several research groups continue to discover and experiment with new qualitative representations and reasoning techniques. However, due to the diversity of these techniques, it is difficult for the programs produced to exchange system models easily. The availability of mappings to transform knowledge from the form used by one of these programs to that used by another would open the doors for comparative analysis of these programs in areas such as completeness, correctness, and performance. A group at the Johnson Space Center (JSC) is working to develop CONFIG, a prototype qualitative modeling, simulation, and analysis tool for fault diagnosis applications in the U.S. space program. The availability of knowledge mappings from the programs produced by other research groups to CONFIG may provide savings in CONFIG's development costs and time, and may improve CONFIG's performance. The study of such mappings is the purpose of the research described in this paper. Two other research groups that have worked with the JSC group in the past are the Northwest University Group and the University of Texas at Austin Group. The former has produced a qualitative reasoning tool named SIMGEN, and the latter has produced one named QSIM. Another program produced by the Austin group is CC, a preprocessor that permits users to develop input for eventual use by QSIM, but in a more natural format. CONFIG and CC are both based on a component-connection ontology, so a mapping from CC's knowledge representation to CONFIG's knowledge representation was chosen as the focus of this study. A mapping from CC to CONFIG was developed. Due to differences between the two programs, however, the mapping transforms some of the CC knowledge to CONFIG as documentation rather than as knowledge in a form useful to computation. The study suggests that it may be worthwhile to pursue the mappings further. By implementing the mapping as a program, actual comparisons of computational efficiency and quality of results can be made between the QSIM and CONFIG programs. A secondary study may reveal that the results of the two programs augment one another, contradict one another, or differ only slightly. If the latter, the qualitative reasoning techniques may be compared in other areas, such as computational efficiency.

Mayfield, Blayne E.↗
NASA NTRS2015

Robust Stability Analysis of the Space Launch System Control Design: A Singular Value Approach

Classical stability analysis consists of breaking the feedback loops one at a time and determining separately how much gain or phase variations would destabilize the stable nominal feedback system. For typical launch vehicle control design, classical control techniques are generally employed. In addition to stability margins, frequency domain Monte Carlo methods are used to evaluate the robustness of the design. However, such techniques were developed for Single-Input-Single-Output (SISO) systems and do not take into consideration the off-diagonal terms in the transfer function matrix of Multi-Input-Multi-Output (MIMO) systems. Robust stability analysis techniques such as H(sub infinity) and mu are applicable to MIMO systems but have not been adopted as standard practices within the launch vehicle controls community. This paper took advantage of a simple singular-value-based MIMO stability margin evaluation method based on work done by Mukhopadhyay and Newsom and applied it to the SLS high-fidelity dynamics model. The method computes a simultaneous multi-loop gain and phase margin that could be related back to classical margins. The results presented in this paper suggest that for the SLS system, traditional SISO stability margins are similar to the MIMO margins. This additional level of verification provides confidence in the robustness of the control design.

Pei, Jing↗
DOE OSTI2017

A geometric projection method for designing three-dimensional open lattices with inverse homogenization

Topology optimization is a methodology for assigning material or void to each point in a design domain in a way that extremizes some objective function, such as the compliance of a structure under given loads, subject to various imposed constraints, such as an upper bound on the mass of the structure. Geometry projection is a means to parameterize the topology optimization problem, by describing the design in a way that is independent of the mesh used for analysis of the design's performance; it results in many fewer design parameters, necessarily resolves the ill-posed nature of the topology optimization problem, and provides sharp descriptions of the material interfaces. We extend previous geometric projection work to 3 dimensions and design unit cells for lattice materials using inverse homogenization. We perform a sensitivity analysis of the geometric projection and show it has smooth derivatives, making it suitable for use with gradient-based optimization algorithms. The technique is demonstrated by designing unit cells comprised of a single constituent material plus void space to obtain light, stiff materials with cubic and isotropic material symmetry. Here, we also design a single-constituent isotropic material with negative Poisson's ratio and a light, stiff material comprised of 2 constituent solids plus void space.

42 ENGINEERING↗
DOE OSTI2016

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this article, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Software Development Standard Processes (SDSP)

A JPL-created set of standard processes is to be used throughout the lifecycle of software development. These SDSPs cover a range of activities, from management and engineering activities, to assurance and support activities. These processes must be applied to software tasks per a prescribed set of procedures. JPL s Software Quality Improvement Project is currently working at the behest of the JPL Software Process Owner to ensure that all applicable software tasks follow these procedures. The SDSPs are captured as a set of 22 standards in JPL s software process domain. They were developed in-house at JPL by a number of Subject Matter Experts (SMEs) residing primarily within the Engineering and Science Directorate, but also from the Business Operations Directorate and Safety and Mission Success Directorate. These practices include not only currently performed best practices, but also JPL-desired future practices in key thrust areas like software architecting and software reuse analysis. Additionally, these SDSPs conform to many standards and requirements to which JPL projects are beholden.

Lavin, Milton L.↗