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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 NTRS1999

[Cyto-embryologic investigation of super dwarf wheat grown on board of the Mir orbital complex]

The cytoembryologic analysis of wet and dry samples of super dwarf wheat cultivated in greenhouse SVET aboard the MIR station over the whole cycle of vegetation was made with the use of light microscopy. Characteristic features of wheat development in space flight are plentiful early tillering, and formation and rapid growth of side shoots. Elementary spikelets in the composite head were more numerous but the top spikelets were rudimentary and, therefore, the ripe head contained less of these spikelets as compared with the ground controls (9-13 and 14, respectively). The number of florets in a spike was also higher reaching 14-16 vs. 7-8 in the control. Typically, no more than 4 to 5 florets vs. 3 in the control were fully differentiated while the others died off earlier in development. The fact that there were no caryopses found in the flight crop is explained by absolute male sterility appearing at different stages of staminal development: before archesporium formation, on the stage of differentiated archesporium, during meiosis, on the stage of microspores or uninucleate "pollen". The female generative system developed mainly without abnormalities. An assumption was made that elevated ethylene concentrations in the MIR atmosphere at the time of the wheat experiment were the cause for abnormal development of the male generative system which led to barrenness of the super dwarf wheat crop.

manned↗
DOE OSTI2023

Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination

Introduction: Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods: To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results: Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties–including data circularity–were demonstrated. The emergent patterns helped identify 11 counties as ‘disseminators’ or ‘facilitators’ (F) and 264 counties as ‘barriers’ (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion: Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

microwave↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2019

Modal Analysis of a Two-Parachute System

The Orion capsule is designed to land under a nominal configuration of three main parachutes; however, the system is required to be fault tolerant and land successfully if one of the main parachutes fails to open. The Capsule Parachute Assembly System (CPAS) Team performed a series of drop tests in order to characterize the performance of the system with two main parachutes. During the series of drop tests, several distinct dynamical modes were observed. The most consequential of these is the pendulum mode. Three other modes are benign: flyout (scissors), maypole, and breathing. The actual multi-body system is nonlinear, flexible, and possesses significant cross-coupling. Rather than perform analysis of this highly complex system directly, we conduct analysis of each dynamical mode observed during flight, based on first principles. This approach is analogous to traditional aircraft flight dynamics analysis in which the full nonlinear behavior of the airframe is decomposed into longitudinal dynamics (phugoid and short-period modes) and lateral dynamics (spiral, roll-subsidence, and dutch-roll modes). This analysis is intended to supplement multi-body nonlinear simulations in order to provide further insight into the system.

Pei, Jing↗
DOE OSTI2021

Safety Analysis Report For Packaging, Y-12 National Security Complex, Model ES-3100 Package with Bulk HEU Contents

This safety analysis report for packaging (SARP) presents the results of the safety analysis prepared in support of the Consolidated Nuclear Security, LLC (CNS) request for licensing of the Model ES-3100 package with bulk highly enriched uranium (HEU) contents and issuance of a Type B(U) Fissile Material Certificate of Compliance. This SARP, published in the format specified in the U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide 7.9 and using information provided in UCID-21218 and NRC Regulatory Guide 7.10, demonstrates that the Y-12 National Security Complex (Y-12) ES-3100 package with bulk HEU contents meets the established NRC regulations for packaging, preparation for shipment, and transportation of radioactive materials given in Title 10, Part 71, of the Code of Federal Regulations (CFR) [10 CFR 71] as well as U.S. Department of Transportation (DOT) regulations for packaging and shipment of hazardous materials given in Title 49 CFR.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Molecular and Electronic Structure, and Hydrolytic Reactivity of a Samarium(II) Crown Ether Complex

The reaction of SmI 2 with dibenzo-30-crown-10 (DB30C10), followed by metathesis with [Bu 4 N][BPh 4 ], allows for the isolation of [Sm II (DB30C10)][BPh 4 ] 2 as bright-red crystals in good yield. Moreover, exposure of [Sm(DB30C10)] 2+ to solvents containing trace water results in the conversion to the dinuclear Sm III complex, Sm 2 (DB30C10)(OH) 2 I 4 . Structural analysis of both complexes shows substantial rearrangement of the crown ether from a folded, Pac-Man form with Sm II to a twisted conformation with Sm III . The optical properties of [Sm II (DB30C10)][BPh 4 ] 2 exhibit a strong temperature dependence and change from broad-band absorption features indicative of domination by 5d states to fine features characteristic of 4f → 4f transitions at low temperatures. Here, exploration of the electronic structure of these complexes via ab initio wave function calculations (SO-CASSCF) reflects that the ground state of Sm II in [Sm II (DB30C10)] 2+ is a 4f 6 state with low-lying 4f 5 5d 1 states, where the latter states have been lowered in energy by ~12 000 cm –1 with respect to the free ion. The decacoordination of the Sm II cation by the crown ether is responsible for this alteration in the energies of the excited state and demonstrates the ability to tune the electronic structure of Sm II .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
DOE OSTI2021

Synthesis, characterization, and theoretical analysis of a plutonyl phosphine oxide complex

The interplay of bond strength and covalency are examined in AnO 2 Cl 2 (OPcy 3 ) 2 (An = Pu, U) complexes. The synthesis of trans-PuO 2 Cl 2 (OPcy 3 ) 2 , 1-Pu, has been carried out and confirmed by single crystal X-ray diffraction along with UV-vis-NIR, and 31 P NMR spectroscopies. Theoretical analysis finds that despite a higher calculated covalency for the Pu–Cl interaction, the Pu–OPcy 3 interaction is stronger due to the accumulation of electron density in the interatomic region. As a result, the coordination of equatorial ligands slightly decreases the strength of the Pu≡O yl interactions relative to the free gas phase (PuO 2 ) 2+ ion.

Windorff, Cory J.↗
NASA NTRS1999

Analysis of the ST-T complex of the electrocardiogram using the Karhunen--Loeve transform: adaptive monitoring and alternans detection

The Karhunen-Loeve transform (KLT) is applied to study the ventricular repolarisation period as reflected in the ST-T complex of the surface ECG. The KLT coefficients provide a sensitive means of quantitating ST-T shapes. A training set of ST-T complexes is used to derive a set of KLT basis vectors that permits representation of 90% of the signal energy using four KLT coefficients. As a truncated KLT expansion tends to favor representation of the signal over any additive noise, a time series of KLT coefficients obtained from successive ST-T complexes is better suited for representation of both medium-term variations (such as ischemic changes) and short-term variations (such as ST-T alternans) than discrete parameters such as the ST level or other local indices. For analysis of ischemic changes, an adaptive filter is described that can be used to estimate the KLT coefficient, yielding an increase in the signal-to-noise ratio of 10 dB (u = 0.1), with a convergence time of about three beats. A beat spectrum of the unfiltered KLT coefficient series is used for detection of ST-T alterans. These methods are illustrated with examples from the European ST-T Database. About 20% of records revealed quasi-periodic salvos of ischemic ST-T change episodes and another 20% exhibit repetitive, but not clearly periodic patterns of ST-T change episodes. About 5% of ischemic episodes were associated with ST-T alterans.

NASA Discipline Cardiopulmonary↗
NASA NTRS2014

Single Event Analysis and Fault Injection Techniques Targeting Complex Designs Implemented in Xilinx-Virtex Family Field Programmable Gate Array (FPGA) Devices

An informative session regarding SRAM FPGA basics. Presenting a framework for fault injection techniques applied to Xilinx Field Programmable Gate Arrays (FPGAs). Introduce an overlooked time component that illustrates fault injection is impractical for most real designs as a stand-alone characterization tool. Demonstrate procedures that benefit from fault injection error analysis.

Single Event Upset Testing↗
DOE OSTI2022

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.

3DRISM↗
NASA NTRS1988

Sensitivity analysis and multidisciplinary optimization for aircraft design: Recent advances and results

Optimization by decomposition, complex system sensitivity analysis, and a rapid growth of disciplinary sensitivity analysis are some of the recent developments that hold promise of a quantum jump in the support engineers receive from computers in the quantitative aspects of design. Review of the salient points of these techniques is given and illustrated by examples from aircraft design as a process that combines the best of human intellect and computer power to manipulate data.

Sobieszczanski-Sobieski, Jaroslaw↗
NASA NTRS1988

Sensitivity analysis and multidisciplinary optimization for aircraft design - Recent advances and results

Optimization by decomposition, complex system sensitivity analysis, and a rapid growth of disciplinary sensitivity analysis are some of the recent developments that hold promise of a quantum jump in the support engineers receive from computers in the quantitative aspects of design. Review of the salient points of these techniques is given and illustrated by examples from aircraft design as a process that combines the best of human intellect and computer power to manipulate data.

Sobieszczanski-Sobieski, Jaroslaw↗