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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Deep Learning Approach for TNC Trip Demand Prediction Considering Spatial-Temporal Features: Preprint

Ride-hailing or transportation network companies (TNCs), such as Uber, Lyft, DiDi Chuxing, or RideAustin, are emerging as a new and disruptive on-demand mobility service in recent years. However, the methods for developing predictive analytics to explore the nature and dynamics of TNCs across cities in the United States are still nascent due to the lack of publicly available data. Recently available public datasets on TNCs by RideAustin offer a unique opportunity to examine spatial, temporal, environmental, and special event factors associated with TNC trip demand. This study explores the use of a deep learning approach - Long Short-Term Memory (LSTM) - to predict TNC trip demand at the ZIP Code level using data from Austin, Texas. The analysis includes key predictive factors such as time of day, day of week, precipitation, and temperature, indicating their corresponding associations with TNC trip demand. Results from initial analysis show that LSTM is able to predict the TNC trip demand for the upcoming hour accurately. LSTM, when compared to other prediction methods, such as historical average and instantaneous trip demand, reduces the mean absolute error (MAE) of the model predictions by 37% and 24%, respectively. This novel method offers significant potential for scaling up and/or replicability across cities where data are available for understanding TNC trip demand to inform emerging mobility system operators. Predicting trip demand can help TNC drivers make informed decisions on how to be more efficient by maximizing passenger pickups and minimizing wait times and deadheading.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Efficient Computation of Free Energy Surfaces of Diels–Alder Reactions in Explicit Solvent at Ab Initio QM/MM Level

For Diels–Alder (DA) reactions in solution, an accurate and converged free energy (FE) surface at ab initio (ai) quantum mechanical/molecular mechanical (QM/MM) level is imperative for the understanding of reaction mechanism. However, this computation is still far too expensive. In a previous work, we proposed a new method termed MBAR+wTP, with which the computation of the ai FE profile can be accelerated by several orders of magnitude via a three-step procedure: (I) an umbrella sampling (US) using a semi-empirical (SE) QM/MM Hamiltonian is performed; (II) the FE profile is generated using the Multistate Bennett Acceptance Ratio (MBAR) analysis; and (III) a weighted Thermodynamic Perturbation (wTP) from the SE Hamiltonian to the ai Hamiltonian is performed to obtain the ai QM/MM FE profile using weight factors from the MBAR analysis. In this work, this method is extended to the calculations of two-dimensional FE surfaces of two Diels–Alder reactions of cyclopentadiene with either acrylonitrile or 1-4-naphthoquinone at ai QM/MM level. The accurate activation free energies at the ai QM/MM level, which are much closer to the experimental measurements than those calculated by other methods, indicate that this MBAR+wTP method can be applied in the studies of complex reactions in condensed phase with much-enhanced efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluating Contaminant Transport in the Vadose Zone to Support Remedy Decisions at Aqueous-Waste Disposal Sites - 16195

Inorganic contaminants in aqueous waste disposed at land surface have a potential to contaminate groundwater. However, contaminant concentrations are attenuated during transport through the vadose zone. Quantifying contaminant attenuation and the resulting temporal concentration profile of in underlying groundwater is important for assessing the need for, and type of, remediation in the vadose zone and groundwater. An approach was developed for evaluating vadose zone transport and attenuation of aqueous wastes containing inorganic (non-volatile) contaminants that were disposed at land surface (i.e., directly to the ground in cribs, trenches, tile fields, etc.) and their effect on underlying groundwater. The approach provides a structured method to estimate transport of contaminants through the vadose zone and the resulting temporal profile of groundwater contaminant concentrations. A series of numerical model simulations was applied to investigate how different factors affect contaminant transport in the vadose zone and the conditions for which individual factors are important. This analysis demonstrated that, while water flow and contaminant transport can be complex during aqueous-waste disposal, conditions will change over time after disposal ceases and fewer factors will control the contaminant transport rate and resulting temporal profile of groundwater contaminant concentrations. Data verifying that these conditions have been reached can be provided by a few key metrics such as the vertical profile of moisture content, the temporal profile of recharge at the site, and basic information about waste disposal and subsurface properties. Under these conditions, and with information about specific contaminant transport parameters (e.g., for partitioning), estimates of contaminant transport can be made and used to support remedy decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Identifying key factors of the seawater intrusion model of Dagu river basin, Jiaozhou Bay

Highlights: • The key factors of seawater intrusion model is identified by mutual entropy analysis. • Precipitation rate and groundwater pumping are the key factors for seawater intrusion. • The hydraulic conductivity of zone 1 has significant influence on seawater intrusion. Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users’ personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs. Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl{sup -} concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can’t handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Factor of spontaneous emission into the optical dielectric waveguide mode

Within the framework of a complete vector analysis, an expression for the factor of spontaneous emission into the transverse mode of a dielectric waveguide is found. The results are compared with those previously obtained in the framework of the scalar approximation. It is shown that their most significant quantitative difference is possible for waveguides used in diode lasers and amplifiers in which the transverse beam size is commensurable with the wavelength. The conditions for the applicability of the scalar approximation for calculating the spontaneous emission factor are obtained. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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.↗