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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of the isotope effect on divertor detachment and pedestal structure in DIII-D

Dedicated H-mode hydrogen experiments have been conducted at DIII-D and are compared to similar deuterium plasmas under a range of divertor conditions, revealing significant effects of the isotope mass on divertor and pedestal conditions. H-mode confinement is found to be systematically lower in hydrogen and experiences a worse degradation with increased fueling. This coincides with a higher ne,sep in hydrogen for similar fueling rates. When ne,ped is matched in hydrogen and deuterium as are the ne and Te pedestal structures, the target conditions deviate strongly from each other. An analysis of two major factors in the pedestal structure, pedestal stability and neutral fueling, shows they are not sufficient to explain either the pedestal differences between isotopes or the hydrogen pedestal structure respectively. An analysis of hydrogen and deuterium density ramps demonstrates a 20% higher ion current and a 30% higher upstream density at detachment onset in hydrogen. The characteristics of target heat flux and divertor radiation are shown to change significantly with the isotope mass in each divertor operational regime. The results shown here indicate divertor conditions trend with ion mass in a manner beneficial for divertor performance in future devices.

Mattes, Ray [Univ. of Tennessee, Knoxville, TN (Un↗
DOE OSTI2015

Common Cause Analysis Report for 2011 Fueled Vessel Drain, 2015 Defueled Vessel Drain, and 2015 EFIS Switch Misalignment

This causal analysis was initiated by ATR Management and committed to in INR 5238 for the August 24, 2015 primary coolant system (PCS) leak. This analysis sought common causal factors between the August 24, 2015 PCS leak, the July 23, 2015 emergency firewater injection system (EFIS) switch mispositioning, and the 2011 ATR fueled vessel level loss. This common cause analysis was conducted at the level of effort of an apparent cause.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2017

Immersive Simulations and Engineering Environment (iSEE)

In January 2017, the Immersive Simulations and Engineering Environment (iSEE), formally known as the Human Factor Tool (HFT), was brought back online. Since August, the lab had been operating at a nominal status, but with dated hardware. An evaluation was done on the present system so that the improvements and capabilities brought about by the upgrades can be quantified. Immediately following the analysis, evaluations with human factor engineers were performed and some upgrades were implemented. Due to a malfunction with a crucial piece of hardware, the lab was reconfigured temporarily. This allowed easy analysis and integration of various other components which improved the laboratories overall performance. Soon after, work began on creating a video to showcase the capabilities of the lab. This video not only served to show the actual functions of the lab, but also show the expanded capabilities the new hardware and software made available. Skills were acquired throughout the renovation process in various fields, all of which were crucial to not only implementing the updates to the lab, but understanding how they will improve it.

Motion Capture↗
DOE OSTI2023

Spin cutoff factor and level density for Ni 59 from an analysis of compound nuclear reactions

The spin cutoff parameter for 59 Ni has been studied from different types of experimental data, including neutron angular distributions from the 56 Fe(α,n) 59 Ni reaction, spin of discrete levels, the level density from the proton evaporation spectrum of the 54 Fe( 6 Li,p) 59 Ni reaction and neutron resonance spacing. Experimental data points were compared with calculations using models widely used in literature. It was found that the available empirical models overestimate data points in the energy region below the neutron separation energy, while microscopic calculations which take into account pairing correlations within Hartree-Fock +Bardeen-Cooper-Schrieffer approach are consistent with data. As a result, it confirmed earlier findings that pairing correlations play an important role and need to be taken into account when the spin cutoff parameter is calculated below the neutron separation energy.

(a,n) reaction↗
NASA NTRS2008

Tool Support for Parametric Analysis of Large Software Simulation Systems

The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.

Schumann, Johann↗
DOE OSTI2021

Trust in Artificial Intelligence: Meta-Analytic Findings

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Behavioral Sciences↗
DOE OSTI2020

Coffea -- Columnar Object Framework For Effective Analysis

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1996

Independent Verification and Validation of Complex User Interfaces: A Human Factors Approach

The Usability Testing and Analysis Facility (UTAF) at the NASA Johnson Space Center has identified and evaluated a potential automated software interface inspection tool capable of assessing the degree to which space-related critical and high-risk software system user interfaces meet objective human factors standards across each NASA program and project. Testing consisted of two distinct phases. Phase 1 compared analysis times and similarity of results for the automated tool and for human-computer interface (HCI) experts. In Phase 2, HCI experts critiqued the prototype tool's user interface. Based on this evaluation, it appears that a more fully developed version of the tool will be a promising complement to a human factors-oriented independent verification and validation (IV&V) process.

Whitmore, Mihriban↗
DOE OSTI2022

Dynamic Approach to Dependency Analysis in Human Reliability Analysis: Application in a Stream Generator Tube Rupture Scenario

Dependency analysis in human reliability analysis (HRA) is a method of adjusting the failure probability of a given action by considering the impact of the action preceding it. It plays a role in reasonably accounting for human actions in the context of probabilistic safety assessments (PSAs), preventing PSA results from being estimated too optimistically based on the HRA results. Nevertheless, the existing dependency methods present a couple of challenges in that the quantification approaches rarely explain the adjustment of human error probabilities (HEPs). For this reason, the authors’ previous research has pointed out challenges of the existing dependency approaches and conceptually, theoretically proposed a performance shaping factor (PSF)-based dynamic dependency analysis method for HRA in order to complement the existing dependency methods. The current paper explores the latest version of the method and guidance for applying it to a steam generator tube rupture (SGTR) scenario.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

COFFEA - Columnar Object Framework For Effective Analysis [Slides]

The COFFEA Framework provides a new approach to HEP analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language and commodity big data technologies such as Apache Spark and NoSQL databases. To achieve this suite of improvements across many use cases, COFFEA takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will present published results from analysis of CMS data using the COFFEA framework along with a discussion of metrics and the user experience of arriving at those results with columnar analysis.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Determination of stress intensity factors for interface cracks under mixed-mode loading

A simple technique was developed using conventional finite element analysis to determine stress intensity factors, K1 and K2, for interface cracks under mixed-mode loading. This technique involves the calculation of crack tip stresses using non-singular finite elements. These stresses are then combined and used in a linear regression procedure to calculate K1 and K2. The technique was demonstrated by calculating three different bimaterial combinations. For the normal loading case, the K's were within 2.6 percent of an exact solution. The normalized K's under shear loading were shown to be related to the normalized K's under normal loading. Based on these relations, a simple equation was derived for calculating K1 and K2 for mixed-mode loading from knowledge of the K's under normal loading. The equation was verified by computing the K's for a mixed-mode case with equal and normal shear loading. The correlation between exact and finite element solutions is within 3.7 percent. This study provides a simple procedure to compute K2/K1 ratio which has been used to characterize the stress state at the crack tip for various combinations of materials and loadings. Tests conducted over a range of K2/K1 ratios could be used to fully characterize interface fracture toughness.

Naik, Rajiv A.↗
DOE OSTI2025

Identification and overexpression of endogenous transcription factors to enhance lipid accumulation in the biotechnologically relevant species Chlamydomonas pacifica

Sustainable low-carbon energy solutions are critical to mitigating global carbon emissions. Algae-based platforms offer potential by converting carbon dioxide into valuable products while aiding carbon sequestration. However, scaling algae cultivation faces challenges like contamination in outdoor systems. Previously, our lab evolved Chlamydomonas pacifica, an extremophile green alga, which tolerates high temperature, pH, salinity, and light, making it ideal for large-scale bioproduct production, including biodiesel. Here, we enhanced lipid accumulation in evolved C. pacifica by identifying and overexpressing key endogenous transcription factors through genome-wide in-silico analysis and in-vivo testing. These factors include Lipid Remodeling Regulator 1 (CpaLRL1), Nitrogen Response Regulator 1 (CpaNRR1), Compromised Hydrolysis of Triacylglycerols 7 (CpaCHT7), and Phosphorus Starvation Response 1 (CpaPSR1). Under nitrogen deprivation, CpaLRL1, CpaNRR1, and CpaCHT7 overexpression enhanced lipid accumulation compared to wild-type. However, CpaPSR1 increased lipid accumulation compared to wild-type in normal media and did not increase further under nitrogen deprivation, highlighting the difference in function based on media conditions. Notably, lipid analysis of CpaPSR1 under normal media conditions revealed a 2.4-fold increase in triglycerides (TAGs) compared to the wild-type, highlighting its potential for biodiesel production. This approach provides a framework for transcription factor-focused metabolic engineering in algae, advancing bioenergy and biomaterial production.

Biofuels↗
DOE OSTI2019

Immunohistochemical analysis of cyclin A expression in Wilms tumor

Background: Cyclin A overexpression is found in a variety of human tumors and correlates with unfavorable outcome. We analyzed immunohistochemical expression of cyclin A in Wilms tumor (WT) in relation to clinicopathological characteristics, preoperative chemotherapy (PrOpChTh), and overall survival (OS). Methods: This retrospective study involved 43 patients who underwent nephrectomy from January 1996 to October 2010. Tumor stage and histological subtype were determined by revised Societé International d’Oncologie Pediatrique protocol, based on histological components/alterations caused by PrOpChTh, within the prognostic group of low, intermediate and high risk, and with criteria for anaplasia. The regressive/necrotic changes in total tumor mass of primary tumor and the proportion of epithelial, blastemal, and stromal components in the remaining viable tumor tissue were also determined. Cyclin A expression was evaluated by immunohistochemistry using a polyclonal rabbit, antihuman antibody (H-432). Results: Cyclin A overexpression was found in 34.3% of WTs, with higher frequency in tumors with epithelial (31.3%) and blastemal (37.1%) components than those with stromal component (17.7%). Regarding histological type, cyclin A overexpression was found most often in focal anaplasia (100%), stromal (60%), and diffuse anaplastic (66.7) WTs. The overexpression was also more frequent in stages 3 and 4 (77.8% and 66.7%, respectively) compared to tumors in stages 1 and 2 (13.3% and 12.5%, respectively; p = 0.004) in all components, as well as in blastemal component in stages 3 and 4 (77.8% and 66.7%, respectively) vs. stages 1 and 2 (13.3% and 25%, respectively, p = 0.009). Cyclin A overexpression in all components was 66.7% in WTs with metastasis and 31.3% in WTs without metastasis (p = 0.265, Fisher test). Log-rank testing revealed differences of OS regarding stage (p = 0.000), prognostic groups (p = 0.001), and cyclin A expression in blastemal component (p = 0.025). After univariate analysis, tumor stage (p = 0.001), prognostic group (p = 0.004), and cyclin A expression in blastemal component (p = 0.042) were significant prognostic factors for OS; however, after multivariate analysis, none of these factors were confirmed as independent predictors of survival. Discussion: This study showed that cyclin A overexpression might be associated with the development and progression of WT with anaplasia. Also, cyclin A overexpression was more often observed in advanced stages (3 and 4) of WT, in the group of high-risk WTs, and in focal and diffuse anaplasia WTs. There was no relation of cyclin A overexpression and metastatic ability of WT. Although this study has not confirmed the prognostic value of cyclin A overexpression, its association with unfavorable prognosis should be further evaluated. Copyright 2019 Radojevic-Škodric et al.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Analysis of Radiation-natural Convection Interactions in 1-g and low-g Environments using the Discrete Exchange Factor Method

A new numerical method is presented for the analysis of combined natural convection and radiation heat transfer with applications in many engineering situations such as materials processing, combustion and fire research. Because of the recent interest in the low gravity environment of space, attention is devoted to both 1-g and low-g applications. The two-dimensional mathematical model is represented by a set of coupled nonlinear integro-partial differential equations. Radiative exchange is formulated using the Discrete Exchange Factor method (DEF). This method considers point to point exchange and provides accurate results over a wide range of radiation parameters. Numerical results show that radiation significantly influences the flow and heat transfer in both low-g and 1-g applications. In the low-g environment, convection is weak, and radiation can easily become the dominant heat transfer mode. It is also shown that volumetric heating by radiation gives rise to an intricate cell pattern in the top heated enclosure.

Kassemi, M.↗
DOE OSTI2022

The impacts of COVID-19 on clean energy labor markets: Evidence from multifaceted analysis of public health interventions and COVID-health factors

COVID-19 pandemic has affected clean energy labor market. Using real-time job vacancy data, this study analyzes the impacts of the pandemic on the U.S. clean energy labor market in 2020, including biomass, energy efficiency (EE), electric vehicle (EV), power/microgrid, solar, and wind industries. This study identifies how COVID-health factors and public health interventions influence clean energy job availability during the early COVID pandemic. Overall, California had the most energy jobs and experienced a significant decrease in April 2020. EV and solar had the highest percentages of job vacancies during the pandemic in general. Still, lockdowns had the most severe influence on EE and wind jobs. Stay-at-home orders negatively affected clean energy job vacancies in biomass, EV, power/microgrid, and wind. Social-gathering restrictions, however, did not have much influence. Increased COVID tests at the state level had the strongest and most positive influence on clean energy job postings, indicating the importance of a state's ability to manage public health infrastructure or crisis issues. COVID hospitalizations negatively influenced the job vacancies in biomass and wind but did not affect the other four sectors; conversely, as COVID death numbers increased, the number of jobs in biomass, EV, power grid, solar, and wind decreased, but not in EE jobs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data

The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline. In addition, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist's daily distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3-18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. However, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗