Results for “heterogeneous integration” — Page 9

NASA NTRS2005

Exploring Diverse Data Sets and Developing New Theories and Ideas With Project Integration Architecture

The development of new ideas is the essence of scientific research. This is frequently done by developing models of physical processes and comparing model predictions with results from experiments. With models becoming ever more complex and data acquisition systems becoming more powerful, the researcher is burdened with wading through data ranging in volume up to a level of many terabytes and beyond. These data often come from multiple, heterogeneous sources and usually the methods for searching through it are at or near the manual level. In addition, current documentation methods are generally limited to researchers pen-and-paper style notebooks. Researchers may want to form constraint-based queries on a body of existing knowledge that is, itself, distributed over many different machines and environments and from the results of such queries then spawn additional queries, simulations, and data analyses in order to discover new insights into the problem being investigated. Currently, researchers are restricted to working within the boundaries of tools that are inefficient at probing current and legacy data to extend the knowledge of the problem at hand and reveal innovative and efficient solutions. A framework called the Project Integration Architecture is discussed that can address these desired functionalities.

Benyo, Theresa L.↗

NASA NTRS2015

Lidar Observations of Atmospheric CO2 Column During 2014 Summer Flight Campaigns

Advanced knowledge in atmospheric CO2 is critical in reducing large uncertainties in predictions of the Earth' future climate. Thus, Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) from space was recommended by the U.S. National Research Council to NASA. As part of the preparation for the ASCENDS mission, NASA Langley Research Center (LaRC) and Exelis, Inc. have been collaborating in development and demonstration of the Intensity-Modulated Continuous-Wave (IM-CW) lidar approach for measuring atmospheric CO2 column from space. Airborne laser absorption lidars such as the Multi-Functional Fiber Laser Lidar (MFLL) and ASCENDS CarbonHawk Experiment Simulator (ACES) operating in the 1.57 micron CO2 absorption band have been developed and tested to obtain precise atmospheric CO2 column measurements using integrated path differential absorption technique and to evaluate the potential of the space ASCENDS mission. This presentation reports the results of our lidar atmospheric CO2 column measurements from 2014 summer flight campaign. Analysis shows that for the 27 Aug OCO-2 under flight over northern California forest regions, significant variations of CO2 column approximately 2 ppm) in the lower troposphere have been observed, which may be a challenge for space measurements owing to complicated topographic condition, heterogeneity of surface reflection and difference in vegetation evapotranspiration. Compared to the observed 2011 summer CO2 drawdown (about 8 ppm) over mid-west, 2014 summer drawdown in the same region measured was much weak (approximately 3 ppm). The observed drawdown difference could be the results of the changes in both meteorological states and the phases of growing seasons. Individual lidar CO2 column measurements of 0.1-s integration were within 1-2 ppm of the CO2 estimates obtained from on-board in-situ sensors. For weak surface reflection conditions such as ocean surfaces, the 1- s integrated signal-to-noise ratio (SNR) of lidar measurements at 11 km altitude reached 376, which was equivalent to a 10-s CO2 error 0.33 ppm. For the entire processed 2014 summer flight campaign data, the mean differences between lidar remote sensed and in-situ estimated CO2 values were about -0.013 ppm. These results indicate that current laser absorption lidar approach could meet space measurement requirements for CO2 science goals.

Lin, Bing↗

NASA NTRS1998

A Monte Carlo Uncertainty Analysis of Ozone Trend Predictions in a Two Dimensional Model

We use Monte Carlo analysis to estimate the uncertainty in predictions of total O3 trends between 1979 and 1995 made by the Goddard Space Flight Center (GSFC) two-dimensional (2D) model of stratospheric photochemistry and dynamics. The uncertainty is caused by gas-phase chemical reaction rates, photolysis coefficients, and heterogeneous reaction parameters which are model inputs. The uncertainty represents a lower bound to the total model uncertainty assuming the input parameter uncertainties are characterized correctly. Each of the Monte Carlo runs was initialized in 1970 and integrated for 26 model years through the end of 1995. This was repeated 419 times using input parameter sets generated by Latin Hypercube Sampling. The standard deviation (a) of the Monte Carlo ensemble of total 03 trend predictions is used to quantify the model uncertainty. The 34% difference between the model trend in globally and annually averaged total O3 using nominal inputs and atmospheric trends calculated from Nimbus 7 and Meteor 3 total ozone mapping spectrometer (TOMS) version 7 data is less than the 46% calculated 1 (sigma), model uncertainty, so there is no significant difference between the modeled and observed trends. In the northern hemisphere midlatitude spring the modeled and observed total 03 trends differ by more than 1(sigma) but less than 2(sigma), which we refer to as marginal significance. We perform a multiple linear regression analysis of the runs which suggests that only a few of the model reactions contribute significantly to the variance in the model predictions. The lack of significance in these comparisons suggests that they are of questionable use as guides for continuing model development. Large model/measurement differences which are many multiples of the input parameter uncertainty are seen in the meridional gradients of the trend and the peak-to-peak variations in the trends over an annual cycle. These discrepancies unambiguously indicate model formulation problems and provide a measure of model performance which can be used in attempts to improve such models.

Considine, D. B.↗

NASA NTRS2023

Distributed Quantum Processing via Integrated Regional Quantum Networks Using Free-Space Optical Links

The landscape for quantum computing is rapidly evolving with the development of quantum computers and the emergence of future science enabling applications being pursued in many areas, such as distributed quantum computing, hybrid computing scenarios that require cooperatively networked quantum and classical computing assets and more. Quantum networking is also advancing with multiple regional quantum networks and testbeds maturing across the nation. These quantum networking testbeds typically include multiple access points with a heterogeneous mixture of capabilities interconnected by fiber spanning anywhere from tens to hundreds of kilometers; many such networks incorporate a classical networking framework. Alongside these connectivity advancements in the infrastructure, there is quantum sensing device and component technology maturation that is serving to expand the body of quantum networking scenarios that are presently possible and redefining the quantum- enabled future. Bridging the present collective progress in quantum computing and quantum networking will enable the next era of purposeful advancements for national priority use-cases for the benefit of science. One such use-case is enabling future quantum computing applications via integrated regional quantum networking infrastructure in neighboring locations like New York and Maryland, both states are home to robust classical and quantum networking capabilities. To enable near-term distributed quantum computing applications via the practical integration of regional quantum networking infrastructure, free-space optical links via satellite communications will be essential to avoid the exponential loss commonly observed with fiber implementations. There is both an urgent need and a practical justification for pursuing such space-based integration scope now.

Harry Shaw↗

NASA NTRS2008

Sensor-Web Operations Explorer

Understanding the atmospheric state and its impact on air quality requires observations of trace gases, aerosols, clouds, and physical parameters across temporal and spatial scales that range from minutes to days and from meters to more than 10,000 kilometers. Observations include continuous local monitoring for particle formation; field campaigns for emissions, local transport, and chemistry; and periodic global measurements for continental transport and chemistry. Understanding includes global data assimilation framework capable of hierarchical coupling, dynamic integration of chemical data and atmospheric models, and feedback loops between models and observations. The objective of the sensor-web system is to observe trace gases, aerosols, clouds, and physical parameters, an integrated observation infrastructure composed of space-borne, air-borne, and in-situ sensors will be simulated based on their measurement physics properties. The objective of the sensor-web operation is to optimally plan for heterogeneous multiple sensors, the sampling strategies will be explored and science impact will be analyzed based on comprehensive modeling of atmospheric phenomena including convection, transport, and chemical process. Topics include system architecture, software architecture, hardware architecture, process flow, technology infusion, challenges, and future direction.

observation↗

NASA NTRS2013

Estimating the Concentration of Large Raindrops from Polarimetric Radar and Disdrometer Observations

Estimation of rainfall integral parameters, including radar observables, and empirical relations between them are sensitive to the truncation of the drop size distribution (DSD), particularly at the large drop end. The sensitivity of rainfall integral parameters to the maximum drop diameter (D(sub max)) is exacerbated at C‐band since resonance effects are pronounced for large drops in excess of 5 mm diameter (D). Due to sampling limitations, it is often difficult to reliably estimate D(sub max) with disdrometers. The resulting uncertainties in D(sub max0 potentially increase errors in radar retrieval methods, particularly at C‐band, that rely on disdrometer observations for DSD input to radar models. In fact, D(sub max) is typically an assumed DSD parameter in the development of radar retrieval methods. Because of these very uncertainties, it is difficult to independently confirm disdrometer estimates of D(sub max) with polarimetric radar observations. A couple of approaches can be taken to reduce uncertainty in large drop measurement. Longer integration times can be used for the collection of larger disdrometer samples. However, integration periods must be consistent with a radar resolution volume (RRV) and the temporal and spatial scales of the physical processes affecting the DSD therein. Multiple co‐located disdrometers can be combined into a network to increase the sample size within a RRV. However, over a reasonable integration period, a single disdrometer sample volume is many orders of magnitudes less than a RRV so it is not practical to devise a network of disdrometers that has an equivalent volume to a typical RRV. Since knowledge of DSD heterogeneity and large drop occurrence in time and space is lacking, the specific accuracy or even general representativeness of disdrometer based D(sub max) and large drop concentration estimates within a RRV are currently unknown. To address this complex issue, we begin with a simpler question. Is the frequency of occurrence of large rain drops (D > 5 mm) in disdrometer observations, either stand alone or networked, generally representative and consistent with polarimetric radar observations? We first show from simulations that the concentration of large (D > 5 mm) rain drops (N(sub T5)) can be estimated from polarimetric observations of specific differential phase (K(sub dp)) and differential reflectivity (Z(sub dr)), N(sub T5)=F(K(sub dp),Z(sub dr)), or horizontal reflectivity (Z(sub h)) and Z(sub dr), N(sub T5)=(Z(sub h),Z(sub dr)). We assess the error associated with polarimetric retrieval of N(sub T5), including sensitivity to D(sub max) parameterization assumptions and measurement error in the radar simulations. Polarimetric measurements at S‐band and C‐band will then be used to retrieve estimates of N(sub T5) and compared to disdrometer estimates of N(sub T5). After careful consideration of retrieval error, we will check consistency between disdrometer and polarimetric radar estimates of N(sub T5) and the frequency of occurrence of large rain drops in a variety of precipitating regimes using data from NASA's Global Precipitation Measurement (GPM) Ground Validation (GV) program, including field campaigns such as MC3E (Oklahoma) and IFloodS (Iowa) and extended measurements over Huntsville, Alabama and NASA Wallops Flight Facility in coastal Virginia.

Carey, Lawrence D.↗

NASA NTRS1993

Study of atmospheric and bidirectional effects on surface reflectance and vegetation index time series: Application to NOAA AVHRR and preparation for future space missions

The objectives of the investigation, namely 'to characterize the atmospheric and directional effects on surface reflectance and vegetation index using the First International Satellite Cloud Climatology Project (ISLCSP) Field Experiment (FIFE) data set, develop new algorithms to obtain better Advanced Very High Resolution Radiometer (AVHRR) indices, and define possible improvements for future satellite missions', were addressed in three separate, yet complementary studies. First, it was shown, from theoretical calculations, that visible and near infrared reflectances combined linearly at optimum (one or two) viewing angles relate linearly to the fraction of photosynthetically available radiation absorbed by plants, f(sub par), can be used independently of the type of foliage and substrate, eliminate the effects of sub-pixel spatial heterogeneity, and improve the accuracy of the f(sub par) estimates when compared to the Normalized Difference Vegetation Index, NDVI. Second, it was demonstrated that NDVI, even though it is not a linear combination of radiances or reflectances, can be spatially integrated without significant loss of information from scales of 300 to 1000 m. Third, AVHRR visible and near-infrared reflectances over the FIFE site, separating temporal and bidirectional components and determining the model parameters through an original iterative scheme was successfully modeled. It appears that NDVI generated from the top-of-atmosphere reflectances normalized by the bidirectional effects (as determined in the scheme) is a better vegetation index than maximum NDVI. Details about the three studies are presented.

Frouin, Robert↗

NASA NTRS1992

Catalytic methods using molecular oxygen for treatment of PMMS and ECLSS waste streams, volume 2

Catalytic oxidation has proven to be an effective addition to the baseline sorption, ion exchange water reclamation technology which will be used on Space Station Freedom (SSF). Low molecular weight, polar organics such as alcohols, aldehydes, ketones, amides, and thiocarbamides which are poorly removed by the baseline multifiltration (MF) technology can be oxidized to carbon dioxide at low temperature (121 C). The catalytic oxidation process by itself can reduce the Total Organic Carbon (TOC) to below 500 ppb for solutions designed to model these waste waters. Individual challenges by selected contaminants have shown only moderate selectivity towards particular organic species. The combined technology is applicable to the more complex waste water generated in the Process Materials Management System (PMMS) and Environmental Control and Life Support System (ECLSS) aboard SSF. During the phase 3 Core Module Integrated Facility (CMIF) water recovery tests at NASA MSFC, real hygiene waste water and humidity condensate were processed to meet potable specifications by the combined technology. A kinetic study of catalytic oxidation demonstrates that the Langmuir-Hinshelwood rate equation for heterogeneous catalysts accurately represent the kinetic behavior. From this relationship, activation energy and rate constants for acetone were determined.

Akse, James R.↗

NASA NTRS1988

OSSA's Telescience concept for the Space Station era

NASA's Office of Space Science and Applications has formulated an operational concept, 'telescience', for Space Station-related missions; telescience encompasses teledesign, teleoperations, teleanalysis, and an overall integrating structure that defines and delineates the system's architectural infrastructure and networking for implementation. Attention is here given to the development status of data interchange standards, interoperable networks, automated data-ingest mechanisms and access directories, means to the accessing of heterogeneous data bases, and user-support environments.

Weiss, James R.↗

NASA NTRS2011

Allowing for Horizontally Heterogeneous Clouds and Generalized Overlap in an Atmospheric GCM

While fully accounting for 3D effects in Global Climate Models (GCMs) appears not realistic at the present time for a variety of reasons such as computational cost and unavailability of 3D cloud structure in the models, incorporation in radiation schemes of subgrid cloud variability described by one-point statistics is now considered feasible and is being actively pursued. This development has gained momentum once it was demonstrated that CPU-intensive spectrally explicit Independent Column Approximation (lCA) can be substituted by stochastic Monte Carlo ICA (McICA) calculations where spectral integration is accomplished in a manner that produces relatively benign random noise. The McICA approach has been implemented in Goddard's GEOS-5 atmospheric GCM as part of the implementation of the RRTMG radiation package. GEOS-5 with McICA and RRTMG can handle horizontally variable clouds which can be set via a cloud generator to arbitrarily overlap within the full spectrum of maximum and random both in terms of cloud fraction and layer condensate distributions. In our presentation we will show radiative and other impacts of the combined horizontal and vertical cloud variability on multi-year simulations of an otherwise untuned GEOS-5 with fixed SSTs. Introducing cloud horizontal heterogeneity without changing the mean amounts of condensate reduces reflected solar and increases thermal radiation to space, but disproportionate changes may increase the radiative imbalance at TOA. The net radiation at TOA can be modulated by allowing the parameters of the generalized overlap and heterogeneity scheme to vary, a dependence whose behavior we will discuss. The sensitivity of the cloud radiative forcing to the parameters of cloud horizontal heterogeneity and comparisons of CERES-derived forcing will be shown.

Lee, D.↗

NASA NTRS2011

The Necessity of Functional Analysis for Space Exploration Programs

As NASA moves toward expanded commercial spaceflight within its human exploration capability, there is increased emphasis on how to allocate responsibilities between government and commercial organizations to achieve coordinated program objectives. The practice of program-level functional analysis offers an opportunity for improved understanding of collaborative functions among heterogeneous partners. Functional analysis is contrasted with the physical analysis more commonly done at the program level, and is shown to provide theoretical performance, risk, and safety advantages beneficial to a government-commercial partnership. Performance advantages include faster convergence to acceptable system solutions; discovery of superior solutions with higher commonality, greater simplicity and greater parallelism by substituting functional for physical redundancy to achieve robustness and safety goals; and greater organizational cohesion around program objectives. Risk advantages include avoidance of rework by revelation of some kinds of architectural and contractual mismatches before systems are specified, designed, constructed, or integrated; avoidance of cost and schedule growth by more complete and precise specifications of cost and schedule estimates; and higher likelihood of successful integration on the first try. Safety advantages include effective delineation of must-work and must-not-work functions for integrated hazard analysis, the ability to formally demonstrate completeness of safety analyses, and provably correct logic for certification of flight readiness. The key mechanism for realizing these benefits is the development of an inter-functional architecture at the program level, which reveals relationships between top-level system requirements that would otherwise be invisible using only a physical architecture. This paper describes the advantages and pitfalls of functional analysis as a means of coordinating the actions of large heterogeneous organizations for space exploration programs.

program management↗

NASA NTRS2001

Middleware as a mission enabler for Mars exploration

Effective midleware can improve the capability of business and science applications in several ways, e.g., by hiding platform heterogeneity or by providing standard shared services which reduce the complexity or increase the capability of every application. Recent successes in midleware, such as multi-tier client/server and web-based architectures, have fueled phenomenal growth in enterprise level applications, which provide better integration and more rapid adaptability of business in many fields.

middleware software architecture services data com↗

NASA NTRS2021

Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions

The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.

Manuel Helbig↗

NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗

NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

NASA NTRSDate not supplied

Benchmarking and Performance of the NASA Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) is as a “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling of hierarchical materials and structures subjected to thermomechanical. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described. The intended application for NASMAT is massively multiscale modeling on high performance computing systems. As such, results benchmarking the performance of the integration of NASMAT with the Abaqus commercial finite element method software are also presented.

Multiscale Modeling↗

NASA NTRS1994

Two-dimensional simulation of Pinatubo aerosol and its effect on stratospheric ozone

This paper presents time-dependent simulations of the response of the stratosphere to the injection into the atmosphere of massive amounts of sulfur during the eruption of Mt. Pinatubo (The Philippines) in June 1991. The study is based on a coupled two-dimensional chemical-dynamical-radiative model to which a microphysical model for sulfate aerosol formation and fate has been added. The study suggests that, during the first year (July 1991 to June 1992) following the volcanic eruption, the observed changes in the ozone amount integrated between 65 deg S and 65 deg N were caused primarily by changes in the meridional circulation (associated with heating by the volcanic cloud in the tropics) and in the photolysis rate of molecules such as ozone (associated with backscattering of light by the cloud). During the second year after the eruption, as the aerosol was dispersed at all latitudes and, in particular, reached the polar region, the largest contribution to ozone reduction resulted from the heterogeneous chemical conversion of N2O5 and ClONO2 on the surface of the aerosol particles. The conversion of the latter compound, and hence the magnitude of the calculated ozone depletion, is highly dependent on the temperature in the lower stratosphere. Despite the fact that the surface area provided by aerosol particles decreased during the second year following the eruption, the calculated ozone depletion remained significant because the conversion of N2O5 is insensitive to the aerosol surface area density for values larger than 1-10 sq microns/cu cm (depending on latitude). The predicted reduction in ozone at 20 km in March during the third year (July 1993 to June 1994) of the model integration is smaller by a factor of 2 than it was during the second year.

Tie, Xuexi↗