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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Analytic Couple Modeling Introducing Device Design Factor, Fin Factor, Thermal Diffusivity Factor, and Inductance Factor

A set of convenient thermoelectric device solutions have been derived in order to capture a number of factors which are previously only resolved with numerical techniques. The concise conversion efficiency equations derived from governing equations provide intuitive and straight-forward design guidelines. These guidelines allow for better device design without requiring detailed numerical modeling. The analytical modeling accounts for factors such as i) variable temperature boundary conditions, ii) lateral heat transfer, iii) temperature variable material properties, and iv) transient operation. New dimensionless parameters, similar to the figure of merit, are introduced including the device design factor, fin factor, thermal diffusivity factor, and inductance factor. These new device factors allow for the straight-forward description of phenomenon generally only captured with numerical work otherwise. As an example a device design factor of 0.38, which accounts for thermal resistance of the hot and cold shoes, can be used to calculate a conversion efficiency of 2.28 while the ideal conversion efficiency based on figure of merit alone would be 6.15. Likewise an ideal couple with efficiency of 6.15 will be reduced to 5.33 when lateral heat is accounted for with a fin factor of 1.0.

Seebeck effect↗
DOE OSTI2022

Choosing the Best Carbon Factor for the Job: Exploring Available Carbon Emissions Factors and the Impact of Factor Selection

Over 600 local governments in the United States, including nearly half of the largest 100 cities, have enacted climate action plans that include carbon reduction goals and greenhouse gas inventories (Markolf et al. 2020). The magnitude of these goals ranges from modest reduction targets to carbon neutrality. None will be met without significant contributions from the buildings sector. Understanding how energy efficiency and building electrification impact greenhouse gas emissions requires local, time-sensitive, and forward-looking carbon emissions factors for electricity use in buildings. There are a variety of emissions factors currently available from various sources, including average emissions factors and historical short-run marginal emissions factors. Long-run marginal emissions factors and future-year short-run marginal emissions factors are also now available from the National Renewable Energy Laboratory's (NREL's) Cambium data sets. In this paper, we describe the different carbon emissions factors available, including both conventional sources and newly available options. We discuss the types of analyses each emissions factor is best suited to support. Then, using residential energy efficiency and electrification load profiles, we demonstrate how different conclusions result from different choices of carbon emissions factors. For two grid regions, we explore takeaways of using current versus future-year emissions, short-run versus long-run, and levelized versus single-year values. We include a framework for selecting the best carbon emissions factor for the job.

carbon emissions factors↗
DOE OSTI2024

Choosing the Best Carbon Factor for the Job: Exploring Available Carbon Emissions Factors and the Impact of Factor Selection: Preprint

Over 600 local governments in the United States, including nearly half of the largest 100 cities, have enacted climate action plans that include carbon reduction goals and greenhouse gas inventories (Markolf et al. 2020). The magnitude of these goals ranges from modest reduction targets to carbon neutrality. None will be met without significant contributions from the buildings sector. Understanding how energy efficiency and building electrification impact greenhouse gas emissions requires local, time-sensitive, and forward-looking carbon emissions factors for electricity use in buildings. There are a variety of emissions factors currently available from various sources, including average emissions factors and historical short-run marginal emissions factors. Long-run marginal emissions factors and future-year short-run marginal emissions factors are also now available from the National Renewable Energy Laboratory's (NREL's) Cambium data sets. In this paper, we describe the different carbon emissions factors available, including both conventional sources and newly available options. We discuss the types of analyses each emissions factor is best suited to support. Then, using residential energy efficiency and electrification load profiles, we demonstrate how different conclusions result from different choices of carbon emissions factors. For two grid regions, we explore takeaways of using current versus future-year emissions, short-run versus long-run, and levelized versus single-year values. We include a framework for selecting the best carbon emissions factor for the job.

carbon emissions factors↗
DOE OSTI2025

Causal Directions Matter: How Environmental Factors Drive Convective Cloud Detrainment Heights

This study investigates how environmental factors influence the level of maximum detrainment (LMD) in deep convective clouds. Through a novel application of the Linear Non‐Gaussian Acyclic Model (LiNGAM), we discover causal structures between environmental variables and LMD, observed at six tropical sites operated by the Atmospheric Radiation Measurement (ARM) user facility. LiNGAM effectively identifies causal directions among variables of interest, revealing robust relationships such as those among the lifting condensation level (LCL), level of free convection (LFC), and convective inhibition (CIN), aligning with prior knowledge. Relative humidity is shown to directly influence LMD; however, this relationship exhibits strong nonlinearity and becomes difficult to detect when the contrast between oceanic and continental environments is excluded from the analysis. This study highlights the importance of establishing causal relationships before performing statistical inference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exploration of Factors That Influence Willingness to Consider Pooled Rideshare

Ridesharing has become an increasingly prevalent form of transportation. Although transportation network companies such as Uber and Lyft initially started as a personal rideshare service where individuals ride alone or with people they know, rideshare services have been expanded to pooled rideshare—a dynamic rideshare system where an individual rides with passengers they do not know. Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S.A. is relatively low compared to other forms of transportation. A national U.S. survey (N = 5385) was conducted to investigate reasons why individuals are willing or unwilling to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed, where the exploratory factor analysis suggests five factors, specifically,service experience,time/cost,traffic/environment,privacy, andsafety. Model fit indices of the confirmatory factor analysis verified that these five factors can represent the factors behind riders’ willingness to consider pooled rideshare. Furthermore, a binomial logistic regression was conducted to explore how the five factors influence riders’ willingness to consider pooled rideshare. The three factors that influence riders’ willingness to consider pooled rideshare wereservice experience(B = 1.05),traffic/environment(B = .38), andtime/cost(B = .26), while a lack ofprivacy(B = −1.46) can be a deterrent for pooled rideshare.Safetyis important for those who are both willing and unwilling to consider the use of pooled rideshare. Understanding these factors is important for the future of pooled rideshare services in the U.S.A.

Engineering↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Willingness to Consider Pooled Rideshare?: An Exploratory Study on Influential Factors

Rideshare use has grown significantly, beginning with solo riders and evolving to pooled rideshare. Pooled rideshare involves sharing a ride with stranger(s). Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S. is relatively low within all rideshare trips and compared to other forms of transportation, e.g., driving one's personal vehicle. A national survey of 5,385 individuals was conducted to identify factors influencing riders' willingness to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed. The survey results indicated five factors: service experience, time/cost, traffic/environment, privacy, and safety. Understanding these factors is crucial for the future of dynamic ridesharing services in the U.S.

Su, Haotian↗
NASA NTRS1985

A Factored Implicit Scheme for Numerical Weather Prediction with Small Factorization Error

Numerical results show that, for large time steps, the factorization error can be significant, even for the slowly propagating Rossby modes. A new scheme is formulated based on a more accurate factorization of the equations. By grouping separately the terms of the equations which give rise to the fast and slow motion, the equations are factored more accurately. The fast-slow factorization eliminated the factorization error. If each of the fast and slow factors are factored again according to spatial components, the resulting scheme only involves the solution of one dimensional linear systems, and computational efficient. It is shown that the factorization error for the slow made component is negligible for this new scheme.

Augenbaum, J. M.↗
DOE OSTI2023

Factors Influencing Adoption of Pooled Rideshare An Explorative Study on User-Centered Design and Services

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals ride alone or with people they know) initially dominated the market, the popularity of pooled ridesharing (individuals share rides with strangers) has grown globally. However, pooled rideshare remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. A national U.S. survey ( N = 5,385) used exploratory and confirmatory factor analyses to identify four key factors influencing riders’ willingness to consider pooled rideshare: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Understanding and implementing these user-centered design principles and service-related factors may be critical for increasing the future use of pooled rideshare services

Gangadharaiah, Rakesh↗
DOE OSTI2021

Developing Generic Prior Distributions for Common Cause Failure Alpha Factors and Causal Alpha Factors

This report is a revision of the original report, INL/LTD-17-43723. Distribution of the original report was to the Nuclear Regulatory Commission (NRC) only, and the report was not made available to the public. The original report was revised as this report for public distribution. This report presents the latest update of generic prior distributions for common cause failure (CCF) alpha factors, as well as the development of new generic prior distributions for CCF causal alpha factors. The history of CCF treatment and parameter estimations is reviewed. The existing process for developing generic prior distributions is reviewed and used to develop new priors for CCF alpha factors and causal alpha factors. For causal alpha factors, different priors are developed for the five different CCF cause groups: Component (GC), Design (GD), Environment (GE), Human (GH), and Other (GO). These generic prior distributions could be used in the Standardized Plant Analysis Risk (SPAR) models for CCF parameter estimation. The issues and preliminary thoughts regarding prior distribution development are documented. Potential future work is then proposed for improving the process of developing priors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1999

Crew Factors in Flight Operations: A Survey of Fatigue Factors in Regional Airline Operations - 11

This report is the eleventh in a series on the physiological effects of flight operations on flight crews. A 119-question survey was completed by 1,424 flight crewmembers from 26 regional carriers to identify factors contributing to fatigue in regional airline operations. Eighty-nine percent of crewmembers identified fatigue as a moderate or serious concern with 88% reporting that it was a common occurrence and 92% reporting that, when it occurs, fatigue represents a moderate or serious safety issue. However, 86% reported they received no company training addressing fatigue issues. Identified fatigue factors included multiple flight segments, scheduling considerations, varying regulations, and others. The two most commonly cited fatigue factors regarded flying multiple (more than four) segments. Scheduling factors accounted for nine of the ten most common recommendations to reduce fatigue in regional operations. Differing requirements among regulations were cited as contributing to fatigue. Other identified factors were the flight deck environment, automation, and diet. The data suggested specific recommendations, including education of industry personnel about fatigue issues and examination of scheduling practices. Education plays a critical role in any effort to address fatigue. Analyzing scheduling practices and identifying potential improvements may result in reduced fatigue as well as other benefits to operations.

Co, Elizabeth L.↗
NASA NTRS2002

A comparison of quality factors and weighting factors for characterizing astronaut radiation exposures

Radiation exposures are typically characterized by two quantities. The first is the absorbed dose, or the energy deposited per unit mass for specific types of radiation passing through specified materials. The same amount of energy deposited in material by two different types of radiation, however, can result in two different levels of risk. Because of this, for the purpose of radiation protection operations, absorbed dose is modified by a second factor intended to normalize the risk associated with a given exposure. We present here an inter-comparison of methods for this modification. First is the radiation quality factor (Q), as defined by ICRP publication 60. This quantity is related functionally to the unrestricted linear energy transfer (LET) of a given radiation, and is multiplied by the absorbed dose to derive the dose equivalent (H). The second method for modifying absorbed dose is the radiation weighting factor, also given in ICRP-60, or as modified in NCRP report 115. To implement the weighting factor, the absorbed dose resulting from incidence of a particular radiation is multiplied by a factor assigned to that type of radiation, giving the equivalent dose. We compare calculations done based on identical fields of radiation representative of that encountered by the MIR space station, applying each of these two methods. c2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved.

NASA Center JSC↗
NASA NTRS2000

Cardiac tissue enriched factors serum response factor and GATA-4 are mutual coregulators

Combinatorial interaction among cardiac tissue-restricted enriched transcription factors may facilitate the expression of cardiac tissue-restricted genes. Here we show that the MADS box factor serum response factor (SRF) cooperates with the zinc finger protein GATA-4 to synergistically activate numerous myogenic and nonmyogenic serum response element (SRE)-dependent promoters in CV1 fibroblasts. In the absence of GATA binding sites, synergistic activation depends on binding of SRF to the proximal CArG box sequence in the cardiac and skeletal alpha-actin promoter. GATA-4's C-terminal activation domain is obligatory for synergistic coactivation with SRF, and its N-terminal domain and first zinc finger are inhibitory. SRF and GATA-4 physically associate both in vivo and in vitro through their MADS box and the second zinc finger domains as determined by protein A pullout assays and by in vivo one-hybrid transfection assays using Gal4 fusion proteins. Other cardiovascular tissue-restricted GATA factors, such as GATA-5 and GATA-6, were equivalent to GATA-4 in coactivating SRE-dependent targets. Thus, interaction between the MADS box and C4 zinc finger proteins, a novel regulatory paradigm, mediates activation of SRF-dependent gene expression.

NASA Discipline Musculoskeletal↗
DOE OSTI2022

Transcription Factors Controlling the Rhizobium–Legume Symbiosis: Integrating Infection, Organogenesis and the Abiotic Environment

Abstract Legume roots engage in a symbiotic relationship with rhizobia, leading to the development of nitrogen-fixing nodules. Nodule development is a sophisticated process and is under the tight regulation of the plant. The symbiosis initiates with a signal exchange between the two partners, followed by the development of a new organ colonized by rhizobia. Over two decades of study have shed light on the transcriptional regulation of rhizobium–legume symbiosis. A large number of transcription factors (TFs) have been implicated in one or more stages of this symbiosis. Legumes must monitor nodule development amidst a dynamic physical environment. Some environmental factors are conducive to nodulation, whereas others are stressful. The modulation of rhizobium–legume symbiosis by the abiotic environment adds another layer of complexity and is also transcriptionally regulated. Several symbiotic TFs act as integrators between symbiosis and the response to the abiotic environment. In this review, we trace the role of various TFs involved in rhizobium–legume symbiosis along its developmental route and highlight the ones that also act as communicators between this symbiosis and the response to the abiotic environment. Finally, we discuss contemporary approaches to study TF-target interactions in plants and probe their potential utility in the field of rhizobium–legume symbiosis.

Cell Biology↗
DOE OSTI2020

Two interacting ethylene response factors regulate heat stress response

Abstract The ethylene response factor (ERF) transcription factors are integral components of environmental stress signaling cascades, regulating a wide variety of downstream genes related to stress responses and plant development. However, the mechanisms by which ERF genes regulate the heat stress response are not well understood. Here, we uncover the positive role of ethylene signaling, ERF95 and ERF97 in basal thermotolerance of Arabidopsis thaliana. We demonstrate that ethylene signaling-defective mutants exhibit compromised basal thermotolerance, whereas plants with constitutively activated ethylene response show enhanced basal thermotolerance. EIN3 physically binds to the promoters of ERF95 and ERF97. Ectopic constitutive expression of ERF95 or ERF97 increases the basal thermotolerance of plants. In contrast, erf95 erf96 erf97 erf98 quadruple mutants exhibit decreased basal thermotolerance. ERF95 and ERF97 genetically function downstream of EIN3. ERF95 can physically interact with ERF97, and this interaction is heat inducible. ERF95 and ERF97 regulate a common set of target genes, including known heat-responsive genes and directly bind to the promoter of HSFA2. Thus, our study reveals that the EIN3-ERF95/ERF97-HSFA2 transcriptional cascade may play an important role in the heat stress response, thereby establishing a connection between ethylene and its downstream regulation in basal thermotolerance of plants.

Biochemistry & Molecular Biology↗
DOE OSTI2024

QCD Predictions for Meson Electromagnetic Form Factors at High Momenta: Testing Factorization in Exclusive Processes

We report the first lattice QCD computation of pion and kaon electromagnetic form factors, 𝐹 𝑀⁡ (𝑄 2 ), at large momentum transfer up to 10 and 28 GeV 2 , respectively. Utilizing physical masses and two fine lattices, we achieve good agreement with JLab experimental results at 𝑄 2 ≲ 4 GeV 2 . For 𝑄 2 ≳ 4 GeV 2 , our results provide ab initio QCD benchmarks for the forthcoming experiments at JLab 12 GeV and future electron-ion colliders. We also test the QCD collinear factorization framework utilizing our high-𝑄 2 form factors at next-to-next-to-leading order in perturbation theory, which relates the form factors to the leading Fock-state meson distribution amplitudes. Comparisons with independent lattice QCD calculations using the same framework demonstrate, within estimated uncertainties, the universality of these nonperturbative quantities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗