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Functions and capabilities of large capacity structural analysis and matrix interpretive system digital computer program to analyze frame and shell structures
Generation and interpretation of input and output data for Structural Analysis and Matrix Interpretive System /SAMIS/
Anomalous transport in the H-mode pedestal region of five Alcator C-Mod discharges, representing a collisionality scan is analyzed. The understanding of anomalous transport in the pedestal region is important for the development of a comprehensive model for the H-mode pedestal slope. In this research, a possible role of the drift resistive inertial ballooning modes (Rafiq et al 2010 Phys. Plasmas 17 082511) in the edge of Alcator C-Mod discharges is analyzed. The stability analysis, carried out using the TRANSP code, indicates that the DRIBM modes are strongly unstable in Alcator C-Mod discharges with large electron collisionality. An improved interpretive analysis of H-mode pedestal experimental data is carried out utilizing the additive flux minimization technique (Pankin et al 2013 Phys. Plasmas 20 102501) together with the guiding-center neoclassical kinetic XGC0 code. The neoclassical and neutral physics are simulated in the XGC0 code and the anomalous fluxes are computed using the additive flux minimization technique. The anomalous fluxes are reconstructed and compared with each other for the collisionality scan Alcator C-Mod discharges. It is found that the electron thermal anomalous diffusivities at the pedestal top increase with the electron collisionality. This dependence can also point to the drift resistive inertial ballooning modes as the modes that drive the anomalous transport in the plasma edge of highly collisional discharges.
JET experiments have shown that the three-ion scenarios using waves in the ion cyclotron range of frequencies (ICRF) is an efficient way to build fast ion population through beam ion acceleration by radio frequency (RF) waves. Here, such a heating scheme is applied to plasmas with at least two thermal ion species. Analysis of mixed discharges with complex heating schemes requires a workflow that allows to model thermal and fast ion transport consistently. This paper is dedicated to modelling of a mixed plasma discharge with significant fraction of fast ions and contributes to development of fast ion transport models. For interpretive analysis with the TRANSP code a JET hydrogen-deuterium (HD) plasma discharge with neutral beam injection (NBI) and ICRF heating has been chosen. The task is complicated by NBI-ICRF synergy and plasma magnetohydrodynamic activity, like sawtooth crashes. D beam ions accelerated by RF waves form a high energy tail in fast ion distribution. Significant difference between the neutron rate computed by TRANSP and measured one is observed if the same diffusivity for electrons and ions is assumed. Sensitivity studies show that uncertainties in input plasma parameters and thermal ion transport models are crucial for modelling mixed plasma discharges and increased D transport is required to reach the plasma composition consistent with diagnostic measurements at the plasma edge. Fast ion redistribution by a sawtooth instability is characterised by non-resonant transport due to reconnection of magnetic field lines and resonant transport caused by resonance interaction between the instability and fast ions. With ORBIT simulations it has been shown that resonant interaction strongly affects fast ions of high energies, like beam ions accelerated by RF waves and fusion products. For the considered case, fast ion profiles simulated by ORBIT remain peaked after the sawtooth crashes.
We received funding for the acquisition, statistical analysis, interpretation, and publication of atmospheric methane data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) mission, flown February through April of 2001. These high precision measurements were made aboard the NASA DC-8 and P-3B aircraft using a fast response, tunable diode laser system, the DACOM instrument. Our analysis focused on the vertical and geographical distribution of this key trace gas, particularly in the context of other data available from the region such as those from the Pacific NOAA CMDL surface sites, and earlier NASA aircraft missions (such as PEM-West A and B). The paper reporting these results, "Large-Scale Distribution of CH4 in the Western Pacific: Sources and Transport from the Asian Continent" by authors K.B. Bartlett, G.W. Sachse, T. Slate, C. Harward, and D.R. Blake, is attached. The dataset of roughly 13,800 CH4 measurements ranged between 1602 ppb in stratospherically impacted air and 2149 ppb in highly polluted air. Overall, we found that CH4 concentrations were highly correlated with trace gases characteristic of a mix of anthropogenic industrial and combustion sources. They were strikingly correlated with ethane (C2H6) in particular, whose major sources are natural gas leakage and biomass burning. Correlation between the two hydrocarbons indicated that these sources may dominate for CH4 as well at this time.
One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.
Variations in cloud condensation nuclei number concentration (N CCN ) significantly influence cloud microphysics, yet direct N CCN measurements remain challenging. Here, we present an N CCN ensemble learning (NEL) model utilizing ensemble learning and interpretability analysis on aerosol optical parameters. Validated at two land sites, two ocean sites and one polar site within the Atmospheric Radiation Measurement program, the mean absolute percentage error range of the NEL model across different environments is from 12% to 36%, demonstrating high accuracy. Key findings reveal that aerosol optical parameters can serve as predictors for N CCN . Aerosol scattering and backscattering coefficients, absorption coefficient, backscatter fraction (BSF), and Ångström exponent (AE) are positively correlated with N CCN , while single scattering albedo shows negative correlations. N CCN prediction at land sites is highly sensitive to BSF, largely driven by the backscattering coefficient, as fine particles dominate in these sites. At ocean sites, N CCN prediction is more sensitive to AE, primarily influenced by the scattering coefficient, due to the higher proportion of larger particles. At the polar site, N CCN prediction shows sensitivity to both BSF and AE, mainly driven by the scattering coefficient, as polar sites are cleaner and contain larger particles. These differences reflect the variation in particle size and number concentration across different environments.
Methods for modeling the uncertainty in laser tracker angle measurements vary within the metrology industry, leading to confusion and questionable stochastic modeling for survey network adjustments and error propagation analysis. Interpreting the published laser tracker manufacturer performance specifications to determine an a priori sigma value for weighting azimuth and zenith angle measurements can be confusing and has led to differing implementations. Furthermore, this paper proposes a unique way to model survey network a priori laser tracker angular uncertainties based on laser tracker manufacturers’ published maximum permissible error (MPE) values referenced to current standards for weighting survey network measurements. This paper’s proposed model takes into account the disparate effects that pointing errors, target centering errors, and leveling errors have on azimuth and zenith angular uncertainties for measurements with steep sightings and at near ranges.
Finite-temperature quantum field theories are formulated in terms of Green's functions and self-energies on the Matsubara axis. In multiorbital systems, these quantities are related to positive semidefinite matrix-valued functions of the Carathéodory and Schur class. Analysis, interpretation, and evaluation of derived quantities such as real-frequency response functions requires analytic continuation of the off-diagonal elements to the real axis. Here we derive the criteria under which such functions exist for given Matsubara data and present an interpolation algorithm that intrinsically respects their mathematical properties. For small systems with precise Matsubara data, we find that the continuation exactly recovers all off-diagonal and diagonal elements. In real-materials systems, we show that the precision of the continuation is sufficient for the analytic continuation to commute with the Dyson equation, and we show that the truncation of the off-diagonal self-energy elements leads to considerable approximation artifacts.
Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.
The DECOVALEX Project is an on-going international research collaboration, established in 1992, to advance the understanding and modeling of coupled Thermal (T), Hydrological (H), Mechanical (M) and Chemical (C) processes in geological in geological systems. DECOVALEX was initially motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. Later it was realized that these processes also play a critical role in other subsurface engineering activities, such as subsurface CO 2 storage, enhanced geothermal systems, and unconventional oil and gas production through hydraulic fracturing. Research teams from many countries (e.g., Canada, China, Czech Republic, Finland, France, Germany, Japan, Republic of Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom, and the United States) various institutions have participated in the DECOVALEX Project over the years, providing a wide range of perspectives and solutions to these complex problems. These institutions represent radioactive waste management organizations, national research institutes, regulatory agencies, universities, as well as industry and consulting groups. This document is the final report of Task E which was proposed and coordinated by Andra, the National Radioactive Waste Management Agency in France, presenting the technical definitions of the problems studied, approaches applied, achievements made and outstanding issues for future research. The purpose of Task E of the DECOVALEX-2019 project is to investigate upscaling THM modelling from small-scale experiments (some cubic meters) to full-scale experiments (some ten cubic meters) and finally to the scale of the waste repository (cubic kilometers). To achieve this aim, the data of two in-situ heating experiments performed by Andra (the French National Radioactive Waste Management Agency) in the Meuse/Haute-Marne Underground Research Laboratory (MHM URL) have formed the basis for the understanding of the THM behavior of the COx at different scales. The first experiment provided the reference values of the THM parameters by means of a calibration exercise and they were used for a blind prediction and an interpretative analysis of the second one.
The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.
Structural Analysis Interpretative Routine /STAIR/ 7094 program for advanced antenna system reflector assembly
Satellite detection of energetic particles in outer space and interpretive analysis of relationships to planetary fields and interplanetary medium
Interpretation, analysis, and development of algorithms have provided the necessary computational programming tools for soil data processing, data handling and analysis. Algorithms that have been developed thus far, are adequate and have been proven successful for several preliminary and fundamental applications such as software interfacing capabilities, probability distributions, grey level print plotting, contour plotting, isometric data displays, joint probability distributions, boundary mapping, channel registration and ground scene classification. A description of an Earth Resources Flight Data Processor, (ERFDP), which handles and processes earth resources data under a users control is provided.
An urban area was studied through the use of the inventory-by-surrogate method rather than by direct interpretation of photographic imagery. Prior uses of remote sensing in urban and public research are examined. The effects of crowding, poor housing conditions, air pollution, and street conditions on public health are considered. Color infrared photography was used to categorize land use features and the grid method was used in photo interpretation analysis. The incidence of shigella and salmonella, hepatitis, meningitis, tuberculosis, myocardial infarction and veneral disease were studied, together with mortality and morbidity rates. Sample census data were randomly collected and validated. The hypothesis that land use and residential quality are associated with and act as an influence upon health and physical well-being was studied and confirmed.
The system is designed for manipulation, analysis, interpretation, and processing of a wide variety of image data. LANDSAT (ERTS) and other data in digital form can be input directly into the system. Photographic prints and transparencies are first converted to digital form with an on-line high-resolution microdensitometer. The system is implemented on a Hewlett-Packard 3000 computer with 128 K bytes of core memory and a 47.5 megabyte disk. It includes a true color display monitor, with processing memories, graphics overlays, and a movable cursor. Image data formats are flexible so that there is no restriction to a given set of remote sensors. Conversion between data types is available to provide a basis for comparison of the various data. Multispectral data is fully supported, and there is no restriction on the number of dimensions. In this way multispectral data collected at more than one point in time may simply be treated as a data collected with twice (three times, etc.) the number of sensors. There are various libraries of functions available to the user: processing functions, display functions, system functions, and earth resources applications functions.
Possibilities are explored for applying estimation theory to the analysis, interpretation, and use of air quality measurements in conjunction with simulation models to provide a cost effective method of obtaining reliable air quality estimates for wide urban areas. The physical phenomenology of real atmospheric plumes from elevated localized sources is discussed. A fluctuating plume dispersion model is derived. Individual plume parameter formulations are developed along with associated a priori information. Individual measurement models are developed.