Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “dynamic data collection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Track-train dynamic analysis and test program, truck static test

A series of tests were conducted to define the characteristics of an ASF 11 Ride Truck Assembly including joint slop, friction and stiffness. Loading to the truck assembly included vertical load to simulate the car/pool loading combined with lateral or moment loading that resulted in desired truck deflections for the various phases of testing. All seven test conditions were successfully completed with load and deflection data being collected. No attempt is made to reduce the applicable data other than to provide computer plots.

Nemes, A. G.↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2024

Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

Yoseline Angel↗
DOE OSTI2021

The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems

We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Molecular Dynamics Simulations of the Adsorption of Phthalate Esters on Smectite Clay Surfaces

The partitioning of organic contaminants between water and solid surfaces is a key process controlling their fate and transport in natural environments. A novel methodology was developed to predict the adsorption of organic contaminants by smectite clay minerals (high specific surface area adsorbents abundant in natural soils) using molecular dynamics (MD) simulations. The methodology models a stack of flexible Ca- montmorillonite lamellae in direct contact with a bulk aqueous reservoir and uses the metadynamics technique to facilitate the exploration of the free energy landscape. The methodology was tested and validated in the case of six phthalate esters, widely used chemical plasticizers with endocrine disrupting properties. Simulation predictions reveal strong phthalate adsorption, especially for the larger and more hydrophobic phthalates. Predicted partition coefficients (K d values) are consistent with collected batch experimental data. Adsorption was observed on both the exterior basal surfaces and within the interlayer nanopore, with phthalate molecules predominately adopting a flat orientation on the clay surface. Intercalation was also detected in complementary X-ray diffraction (XRD) experiments. A strong inverse relationship between extent of adsorption and clay surface charge density was observed, as phthalate molecules preferentially occupied the more hydrophobic uncharged patches on each surface. Detailed analysis of the free energy of adsorption revealed that phthalate affinity for the clay surface results from a small favorable van der Waals contribution and a large favorable entropic contribution. Overall, this research demonstrates the substantial affinity of smectite clays for phthalate esters and establishes a computational methodology capable of predicting the water-clay partition coefficients of organic contaminants, a key parameter in environmental fate and transport models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2013

Analysis of High-Penetration Levels of Photovoltaics into the Distribution Grid on Oahu, Hawaii: Detailed Analysis of HECO Feeder WF1

Renewable generation is growing at a rapid rate due to the incentives available and the aggressive renewable portfolio standard (RPS) targets implemented by state governments. Distributed generation in particular is seeing the fastest growth among renewable energy projects, and is directly related to the incentives. Hawaii has the highest electricity costs in the country due to the high percentage of oil burning steam generation, and therefore has some of the highest penetration of distributed PV in the nation. The High Penetration PV (HiP-PV) project on Oahu aims to understand the effects of high penetration PV on the distribution level, to identify penetration levels creating disturbances on the circuit, and to offer mitigating solutions based on model results. Power flow models are validated using data collected from solar resources and load monitors deployed throughout the circuit. Existing interconnection methods and standards such as IEEE 1547, Hawaii Rule 14H and California Rule 21 are evaluated in these emerging high penetration scenarios. A key finding is a shift in the level of detail to be considered and moving away from steady-state peak time analysis towards dynamic and time varying simulations. Each level of normal interconnection study is evaluated and enhanced to a new level of detail, allowing full understanding of each issue.

14 SOLAR ENERGY↗
DOE OSTI2015

Strategy and gaps for modeling, simulation, and control of hybrid systems

The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled, dynamic energy systems requires multiple simulation tools, potentially developed in several programming languages and resolved on separate time scales. Whereas further investigation and development of hybrid concepts will provide a more complete understanding of the joint computational and physical modeling needs, this report highlights areas in which co-simulation capabilities are warranted. The current development status, quality assurance, availability and maintainability of simulation tools that are currently available for hybrid systems modeling is presented. Existing gaps in the modeling and simulation toolsets and development needs are subsequently discussed. This effort will feed into a broader Roadmap activity for designing, developing, and demonstrating hybrid energy systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

GD SDR Automatic Gain Control Characterization Testing

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) will provide experimenters an opportunity to develop and demonstrate experimental waveforms in space. The GD SDR platform and initial waveform were characterized on the ground before launch and the data will be compared to the data that will be collected during on-orbit operations. A desired function of the SDR is to estimate the received signal to noise ratio (SNR), which would enable experimenters to better determine on-orbit link conditions. The GD SDR does not have an SNR estimator, but it does have an analog and a digital automatic gain control (AGC). The AGCs can be used to estimate the SDR input power which can be converted into a SNR. Tests were conducted to characterize the AGC response to changes in SDR input power and temperature. This purpose of this paper is to describe the tests that were conducted, discuss the results showi ng how the AGCs relate to the SDR input power, and provide recommendations for AGC testing and characterization.

Nappier, Jennifer M.↗
NASA NTRS2013

GD SDR Automatic Gain Control Characterization Testing

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) will provide experimenters an opportunity to develop and demonstrate experimental waveforms in space. The GD SDR platform and initial waveform were characterized on the ground before launch and the data will be compared to the data that will be collected during on-orbit operations. A desired function of the SDR is to estimate the received signal to noise ratio (SNR), which would enable experimenters to better determine on-orbit link conditions. The GD SDR does not have an SNR estimator, but it does have an analog and a digital automatic gain control (AGC). The AGCs can be used to estimate the SDR input power which can be converted into a SNR. Tests were conducted to characterize the AGC response to changes in SDR input power and temperature. This purpose of this paper is to describe the tests that were conducted, discuss the results showing how the AGCs relate to the SDR input power, and provide recommendations for AGC testing and characterization.

Nappier, Jennifer M.↗
NASA NTRS2017

Temporal Consistency Between Gross Primary Production and Solar-Induced Chlorophyll Fluorescence in the Ten Most Populous Megacity Areas over Years

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5deg x 0.5deg resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

urban↗
NASA NTRS2021

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗
DOE OSTI2024

Theory of capillary tension and interfacial dynamics of motility-induced phases

The statistical mechanics of equilibrium interfaces has been well-established for over a half century. In the past decade, a wealth of observations have made increasingly clear that a new perspective is required to describe interfaces arbitrarily far from equilibrium. In this work, beginning from microscopic particle dynamics that break time-reversal symmetry, we derive the linear interfacial dynamics of coexisting motility-induced phases. Doing so allows us to identify the athermal energy scale that excites interfacial fluctuations and the nonequilibrium surface tension that resists these excitations. Our theory identifies that, in contrast to equilibrium fluids, this active surface tension contains contributions arising from nonconservative forces which act to suppress interfacial fluctuations and, crucially, is distinct from the mechanical surface tension of Kirkwood and Buff. Here we find that the interfacial stiffness scales linearly with the intrinsic persistence length of the constituent active particle trajectories, in agreement with simulation data. We demonstrate that at wavelengths much larger than the persistence length, the interface obeys surface-area minimizing Boltzmann statistics with our derived nonequilibrium interfacial stiffness playing a role identical to that of equilibrium systems.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Real-Time Dynamic Brake Assessment Proof of Concept Final Report

This proof-of-concept research was performed to explore the feasibility of using real-world braking data from commercial motor vehicles to make a diagnosis of brake condition similar to that of the performance-based brake tester (PBBT). This was done by determining the relationship between pressure and brake force (P-BF), compensating for the gross vehicle weight (GVW). The nature of this P-BF relationship (e.g., low braking force for a given brake application pressure) may indicate brake system problems. In order to determine the relationship between brake force and brake application pressure, a few key parameters of duty cycle information were collected. Because braking events are often brief, spanning only a few seconds, a sample rate of 10 Hz was needed. The algorithm under development required brake application pressure and speed (from which deceleration was calculated). Accurate weight estimation was also needed to properly derive the braking force from the deceleration. In order to ensure that braking force was the predominant factor in deceleration for the segments of data used in analysis, the data was screened for grade as well. Also, the analysis needed to be based on pressures above the crack pressure. The crack pressure is the pressure below which the individual brakes are not applied due the nature of the mechanical system. This value, which may vary somewhat from one wheel end to another, is approximately 10 psi. Therefore, only pressures 15 psi and above were used in the analysis. The Department of Energy s Medium Truck Duty Cycle research has indicated that under the real-world circumstances of the test vehicle brake pressures of up to approximately 30 psi can be expected. Several different types of data were collected during the testing task of this project. Constant-pressure stopping tests were conducted at several combinations of brake application pressure (15, 20, 25, and 30 psi), load conditions (moderately and fully laden), and speeds (20 and 30 mph). Data was collected at 10 Hz. Standard and stepped-pressure performance-based brake tests with brake pressure transducers were performed for each loading condition. The stepped-pressure test included the constant-pressure intervals of brake application at 15, 20, 25, and 30 psi. The PBBT data files included 10 Hz streaming data collected during the testing of each axle. Two weeks of real-world duty cycle (driving and braking) data was also collected at 10 Hz. Initial analysis of the data revealed that the data collected in the field (i.e., day-to-day operations) provided the same information as that obtained from the controlled tests. Analysis of the data collected revealed a strong linear relationship between brake application pressure and deceleration for given GVWs. As anticipated, initial speed was not found to be a significant factor in the deceleration-pressure relationship, unlike GVW. The positive results obtained from this proof of concept test point to the need for further research to expand this concept. A second phase should include testing over a wider range of speeds and include medium brake application pressures in addition to the low pressures tested in this research. Testing on multiple vehicles would also be of value. This future phase should involve testing to determine how degradation of braking performance affects the pressure-deceleration relationship.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Machine-Learning for Excited-State Dynamics

The primary objective of this computational chemistry sciences team is to design a machine learning NAMD environment that will utilize current petascale and future exascale computational capabilities to advance understanding of charge and energy flow in materials. Our machine-learning NAMD environment will 1) integrate advanced NAMD capabilities directly into electronic structure software (e.g., ABINIT, Quantum Espresso, VASP, etc.); 2) merge the preparatory tools of Pychemia into PYXAID and Avogadro environments so that massive data collection from NAMD simulations.

36 MATERIALS SCIENCE↗
NASA NTRS1988

The second generation intelligent user interface for the crustal dynamics data information system

For the past decade, operations and research projects that support a major portion of NASA's overall mission have experienced a dramatic increase in the volume of generated data and resultant information that is unparalleled in the history of the agency. The effect of such an increase is that most of the science and engineering disciplines are undergoing an information glut, which has occurred, not only because of the amount, but also because of the type of data being collected. This information glut is growing exponentially and is expected to grow for the foreseeable future. Consequently, it is becoming physically and intellectually impossible to identify, access, modify, and analyze the most suitable information. Thus, the dilemma arises that the amount and complexity of information has exceeded and will continue to exceed, using present information systems, the ability of all the scientists and engineers to understand and take advantage of this information. As a result of this information problem, NASA has initiated the Intelligent Data Management (IDM) project to design and develop Advanced Information Management Systems (AIMS). The first effort of the Project was the prototyping of an Intelligent User Interface (IUI) to an operational scientific database using expert systems, natural language processing, and graphics technologies. An overview of the IUI formulation and development for the second phase is presented.

Short, Nicholas, Jr.↗