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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
NASA NTRS1980

Evaluation of the cyclic behavior of aircraft turbine disk alloys, part 2

Several nickel-base aircraft turbine disk superalloys were evaluated at 650 C for resistance to fatigue crack initiation and propagation under cyclic and cyclic/dwell conditions. Controlled strain low cycle fatigue (LCF) and controlled load crack propagation tests were performed and results utilized to provide a direct comparison among the alloys. Tests were performed on selected alloys to evaluate the effects of hold times, mean stresses, stress-dwell cycle types, inert environment, and contractor test methods. At the lower total strain ranges of interest, the alloys exhibited generally increasing initiation life with increasing tensile strength for both cyclic (0.33 Hz) and cyclic/dwell (900-sec hold per cycle) conditions. Rank order of the alloys by LCF initiation life changed substantially at higher strain ranges, approaching the rank order expected from monotonic tensile ductilities. The effect of the 900 sec (15 min) hold time fatigue life varied significantly from alloy to alloy. Generally, the higher-strength, finer-grained alloys exhibited more significant reductions in fatigue life due to the dwell. The effects of mean strain were found to be negligible and the effects of mean stress were pronounced. At high strain ranges the mean stress was near zero and did not contribute to reduction in life. At low strain ranges, however, mean stresses were large and significant reductions in LCF lives occurred.

Cowles, B. A.↗
NASA NTRS2007

The Business Case for Automated Software Engineering

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

model evaluation↗
DOE OSTI2016

A Scaling-based Applicability Assessment of the CAER Integrated Systems Test Facility

This paper presents a scaling-based applicability assessment of the Integrated Systems Test (IST) facility located at the Center of Advanced Engineering Research in Forest, Virginia. The assessment draws upon both traditional and contemporary scaling methods, seeking an optimal representation of light water reactor (LWR) and small modular reactor (SMR) characteristics during design-basis events. Based on published LWR loss-of-coolant-accident phenomena identification and ranking tables compiled with a similar assessment completed by BWXT mPower, Inc., for the mPower{sup TM} SMR, the phenomena considered in the IST design include break flow, decay heat, loop thermal-hydraulics, and primary-to-secondary heat transfer. Among these, the single- and two-phase natural circulation and system depressurization, by association to loop thermal-hydraulics, were the principal challenges. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Dynamic Mode Decomposition for Compressive System Identification

Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation [Proctor et al., 2016] and systems with heavily subsampled measurements [Brunton et al., 2015]. When combined, these methods yield a novel framework for compressive system identification [code is publicly available at: https://github.com/zhbai/cDMDc]. It is possible to identify a low-order model from limited input-output data and reconstruct the associated full-state dynamic modes with compressed sensing, adding interpretability to the state of the reduced-order model. Moreover, when full-state data is available, it is possible to dramatically accelerate downstream computations by first compressing the data. We demonstrate this unified framework on two model systems, investigating the effects of sensor noise, different types of measurements (e.g., point sensors, Gaussian random projections, etc.), compression ratios, and different choices of actuation (e.g., localized, broadband, etc.). In the first example, we explore this architecture on a test system with known low-rank dynamics and an artificially inflated state dimension. The second example consists of a real-world engineering application given by the fluid flow past a pitching airfoil at low Reynolds number. This example provides a challenging and realistic test-case for the proposed method, and results demonstrate that the dominant coherent structures are well characterized despite actuation and heavily subsampled data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗
DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Effect of Microwave and Thermal Co-pyrolysis of Low-Rank Coal and Pine Wood on Product Distributions and Char Structure

Direct conversion of a low-rank coal into valuable chemicals or improving its char’s coking value became very demanding aims in coal utilization strategies. Here, the co-pyrolysis of a low-rank lignite coal and pine wood sawdust biomass blended at a 3:1 coal-to-biomass ratio was investigated along with original coal and biomass samples by microwave-assisted and conventional thermal methods at 550 °C under nitrogen and ambient pressure. The carbon structure and its reactivity in generated chars and the product distributions were greatly affected by the applied heating mechanism and the presence of biomass during coal pyrolysis. High gas and low tar yields were observed for all microwave chars in comparison to thermal chars, regardless of composition. The addition of biomass to coal increased the tar yield under both methods and to a higher extent under the microwave. This agrees with the high gas yield and high aromatic-to-aliphatic fraction observed under the microwave and the presence of biomass. The high O/C ratio and low fixed carbon content in a biomass structure relative to coal affect the product distribution during microwave pyrolysis. This could selectively heat the biomass in the sample, remove its polar groups, and convert it into an efficient microwave absorber biochar that can decompose coal efficiently during co-pyrolysis. The aromatic carbon stacking and its ordering in the generated chars were investigated by powder X-ray diffraction, Raman spectroscopy, dielectric property measurements, and electron spin resonance techniques. A synergistic effect was observed upon biomass addition during microwave coal pyrolysis. Electron spin resonance spectroscopy revealed that the microwave coal/biomass char is the most stable char with the lowest free radical concentration. Furthermore, this agrees with the highest I G /I all band area ratio calculated from Raman analysis revealing a more graphitic nature for carbon in this char. Similarly, the dielectric properties confirmed that the addition of biomass to coal under the microwave has the highest loss tangent, indicating a high graphitic nature compared to pure biochar or coal char.

09 BIOMASS FUELS↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kincher-Winoto, Kina↗
DOE OSTI2019

Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates

Introduction: Leading cancer hospitals have increasingly shared their ‘brand’ with smaller hospitals through affiliations. Because each brand evokes a distinct reputation for the care provided, ‘brand-sharing’ has the potential to impact the public’s ability to differentiate the safety and quality within hospital networks. The general public was surveyed to determine the perceived similarities and differences in the safety and quality of complex cancer surgery performed at top cancer hospitals and their smaller affiliate hospitals. Methods: A national, web-based KnowledgePanel (GfK) survey of American adults was conducted. Respondents were asked about their beliefs regarding the quality and safety of complex cancer surgery at a large, top-ranked cancer hospital and a smaller, local hospital, both in the presence and absence of an affiliation between the hospitals. Results: A total of 1010 surveys were completed (58.1% response rate). Overall, 85% of respondents felt ‘motivated’ to travel an hour for complex surgery at a larger hospital specializing in cancer, over a smaller local hospital. However, if the smaller hospital was affiliated with a top-ranked cancer hospital, 31% of the motivated respondents changed their preference to the smaller hospital. When asked to compare leading cancer hospitals and their smaller affiliates, 47% of respondents felt that surgical safety, 66% felt guideline compliance, and 53% felt cure rates would be the same at both hospitals. Conclusions: Approximately half of surveyed Americans did not distinguish the quality and safety of surgical care at top-ranked cancer hospitals from their smaller affiliates, potentially decreasing their motivation to travel to top centers for complex surgical care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

4th Penn State Bioinorganic Workshop

The research area of biological inorganic chemistry encompasses a wide variety of subfields, including molecular biology, biochemistry, biophysics, inorganic chemistry, analytical chemistry, physical chemistry, and theoretical chemistry, as well as many different methods, such as biochemical characterization of enzymes, reaction kinetics, a plethora of spectroscopic techniques, and computational methods. The above methods are combined to understand the formation, function, and regulation of the many metallo-cofactors found in Nature as well as to identify novel metallo-cofactors. Many metalloenzyme-catalyzed reactions are extremely complex, but are of fundamental importance to science and society. Examples include (i) the reduction of the chemically inert molecule, dinitrogen, to ammonia by the enzyme nitrogenase (this reaction is fundamental for the production of nitrogen fertilizers); (ii) the oxidation of water to dioxygen by the Mn4Ca cluster found in photosystem II; and (iii) myriad reactions in which aliphatic, inert C-H bonds are cleaved for subsequent functionalization of the carbon atoms (the latter reactions are important in the biosynthesis of many natural products). Because of the broad range of areas and techniques employed in this field, research in bioinorganic chemistry is typically carried out collaboratively between two or more research groups. It is of paramount importance that researchers working in this field have a good, basic, working knowledge of many methods and approaches employed in the field, in order to design and discuss experiments with collaborators. Therefore, the training of students working in bioinorganic chemistry is an important aspect of this field. Hugely successful “bioinorganic workshops” were offered in the 1990s at The University of Georgia. These workshops laid the foundation for many of the extant collaborative research efforts in this area today. The large and diverse group of bioinorganic chemists at The Pennsylvania State University and our unique laboratory space are well suited for the continuation of such training workshops. The co-principal investigators of this award lead these efforts. After a smaller “trial workshop” in 2010, the Penn State bioinorganic group, led by the co-PIs, offers these workshops biennially. The 2012, 2014, and 2016 workshops provided training to 123, 162, and 153 participants, respectively, by offering (i) a series of lectures given by faculty experts on the given topic, (ii) hands-on training in small groups by experts in the various methods, and (iii) sharing research results of the participants by oral and poster presentations. The centerpiece of the workshops is the hands-on training, in which approximately half of the participants from all ranks (undergraduate students to faculty) served as teachers. In this section, the traditional roles of teachers and students were sometimes reversed to the extent that undergraduate students taught faculty in the students' areas of specialty. We anticipate that these workshops will facilitate research in bioinorganic chemistry and will help establish future collaborations among “workshop alumni” to carry out cutting-edge research in bioinorganic chemistry that will address many important topics relevant to our society.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Sequential design of discrete linear quadratic regulators via optimal root-locus techniques

A sequential method employing classical root-locus techniques has been developed in order to determine the quadratic weighting matrices and discrete linear quadratic regulators of multivariable control systems. At each recursive step, an intermediate unity rank state-weighting matrix that contains some invariant eigenvectors of that open-loop matrix is assigned, and an intermediate characteristic equation of the closed-loop system containing the invariant eigenvalues is created.

Shieh, Leang S.↗
NASA NTRS1991

Application of multiple criteria decision methods in space exploration initiative design and planning

Fellowship activities were directed towards the identification of opportunities for application of the Multiple Criteria Decision Making (MCDM) techniques in the Space Exploration Initiative (SEI) domain. I identified several application possibilities and proposed demonstration application in these three areas: evaluation and ranking of SEI architectures, space mission planning and selection, and space system design. Here, only the first problem is discussed. The most meaningful result of the analysis is the wide separation between the two top ranked architectures, indicating a significant preference difference between them. It must also be noted that the final ranking reflects, to some extent, the biases of the evaluators and their understanding of the architecture.

Masud, Abu S. M.↗
DOE OSTI2014

Parameterization and sensitivity analysis of a process‐based terrestrial ecosystem model using adjoint method

Abstract We developed an adjoint version of a process‐based biogeochemistry model, the Terrestrial Ecosystem Model (TEM). The adjoint model of TEM was then used to: (1) conduct sensitivity studies of net ecosystem production (NEP) for three terrestrial ecosystems: grassland, deciduous broadleaf forest and evergreen needle‐leaf forest; (2) rank the importance of parameters in controlling NEP; (3) optimize the model parameters by assimilating eddy flux data of NEP; and (4) evaluate parameterization by extrapolating optimal parameters to other sites that have the same plant functional type as the calibration sites. We found that: (1) the maximum rate of photosynthesis ( C MAX ) was the most important parameter in determining NEP, while the importance of the remaining parameters varied depending on plant functional type, suggesting that the same ecosystem process has different degrees of importance in modeling carbon fluxes; (2) the sensitivity of NEP to C MAX had a significant seasonal variability and the control of C MAX on NEP was much larger in growing season (defined as from April to October) than that in nongrowing season (defined as from December to March); and (3) after parameterization, TEM could reasonably reproduce carbon fluxes observed at eddy flux tower sites. This study provided an effective model‐data fusion framework of TEM, which could improve the future quantification of terrestrial ecosystem carbon fluxes at both site and regional levels.

Zhu, Qing↗
DOE OSTI2024

Dataset of mechanically induced thermal runaway measurement and severity level on Li-ion batteries

The deployment of Li-ion batteries covers a wide range of energy storage applications, from mobile phones, e-bikes, electric vehicles (EV) and stationary energy storage systems. However, safety issue such as thermal runaway is always one of the most important concerns to prevent Li-ion batteries from further market penetration. A standardized single-side indentation test protocol was developed to mechanically induce an internal short-circuit. The cell voltage, compressive load, indenter stroke, and temperature at the indentation point are measured in time series. The test data of each cell, along with cell parameters such as dimensions, mass, chemistry, state of charge (SOC), capacity, are integrated together to calculate a thermal runaway severity score from 0 to100. Complete data collection process including the original measured record, test method, severity score calculation scheme is presented in this article. The thermal runaway severity analysis and the more than 100 tested Li-ion battery records provide a good data source for further comparison and ranking of thermal runaway risks.

25 ENERGY STORAGE↗
DOE OSTI2014

DOC-2/DAB2 Interacting Protein Status in High-Risk Prostate Cancer Correlates With Outcome for Patients Treated With Radiation Therapy

Purpose: This pilot study investigates the role of DOC-2/DAB2 Interacting Protein (DAB2IP) and enhancer of zeste homolog 2 (EZH2) as prognostic biomarkers in high-risk prostate cancer patients receiving definitive radiation therapy. Methods and Materials: Immunohistochemistry was performed and scored by an expert genitourinary pathologist. Clinical endpoints evaluated were freedom from biochemical failure (FFBF), castration resistance–free survival (CRFS), and distant metastasis–free survival (DMFS). Log-rank test and Cox regression were used to determine significance of biomarker levels with clinical outcome. Results: Fifty-four patients with high-risk prostate cancer (stage ≥T3a, or Gleason score ≥8, or prostate-specific antigen level ≥20 ng/mL) treated with radiation therapy from 2005 to 2012 at our institution were evaluated. Nearly all patients expressed EZH2 (98%), whereas 28% of patients revealed DAB2IP reduction and 72% retained DAB2IP. Median follow-up was 34.0 months for DAB2IP-reduced patients, 29.9 months for DAB2IP-retained patients, and 32.6 months in the EZH2 study. Reduction in DAB2IP portended worse outcome compared with DAB2IP-retained patients, including FFBF (4-year: 37% vs 89%, P=.04), CRFS (4-year: 50% vs 90%, P=.02), and DMFS (4-year: 36% vs 97%, P=.05). Stratified EZH2 expression trended toward significance for worse FFBF and CRFS (P=.07). Patients with reduced DAB2IP or highest-intensity EZH2 expression exhibited worse FFBF (4-year: 32% vs 95%, P=.02), CRFS (4-year: 28% vs 100%, P<.01), and DMFS (4-year: 39% vs 100%, P=.04) compared with the control group. Conclusion: Loss of DAB2IP is a potent biomarker that portends worse outcome despite definitive radiation therapy for patients with high-risk prostate cancer. Enhancer of zeste homolog 2 is expressed in most high-risk tumors and is a less potent discriminator of outcome in this study. The DAB2IP status in combination with degree of EZH2 expression may be useful for determining patients with worse outcome within the high-risk prostate cancer population.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Global solutions of restricted open-shell Hartree-Fock theory from semidefinite programming with applications to strongly correlated quantum systems

We present a density matrix approach for computing global solutions of restricted open-shell Hartree-Fock theory, based on semidefinite programming (SDP), that gives upper and lower bounds on the Hartree-Fock energy of quantum systems. While wave function approaches to Hartree-Fock theory yield an upper bound to the Hartree-Fock energy, we derive a semidefinite relaxation of Hartree-Fock theory that yields a rigorous lower bound on the Hartree-Fock energy. We also develop an upper-bound algorithm in which Hartree-Fock theory is cast as a SDP with a nonconvex constraint on the rank of the matrix variable. Equality of the upper- and lower-bound energies guarantees that the computed solution is the globally optimal solution of Hartree-Fock theory. The work extends a previously presented method for closed-shell systems [S. Veeraraghavan and D. A. Mazziotti, Phys. Rev. A 89, 010502–R (2014)]. For strongly correlated systems the SDP approach provides an alternative to the locally optimized Hartree-Fock energies and densities with a certificate of global optimality. Applications are made to the potential energy curves of C{sub 2}, CN, Cr {sub 2}, and NO {sub 2}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints

In this work, we consider the problem of low-rank approximation of massive dense nonnegative tensor data, for example, to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes, and performing efficient and scalable parallel algorithms to compute the low-rank approximation. We present a software package called Parallel Low-rank Approximation with Nonnegativity Constraints, which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work). We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets.

97 MATHEMATICS AND COMPUTING↗