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At least 181 records · Page 10

Data Production on Past and Future NASA Missions

Data return is a metric that is commonly publicized for all space science missions. In the early days of the Space Program, this figure was small, and could be described in bits or maybe even megabits. But now, missions are capable of returning data volumes two or three orders of magnitude larger. For example, Voyager 1 and 2 combined produced a little over 5 Terabits of data in 39 years of operation. In contrast, the Cassini mission, launched two decades after Voyager, produced about one and a half times those data volumes in half the time. NISAR, an Earth Science Mission currently in implementation, plans to produce over 28 Petabits of raw data in just 3 years. This means that NISAR will produce about as many data in 30 days as the combined data production of nearly all planetary missions to date. These increases in capability are a result of technology enhancements in two main areas: telecommunications architecture (both space and ground segments) and data storage technology. This paper describes the progression of these two technologies over the course of more than three decades of space missions and provides additional insight into the design of the end-to-end NISAR Data System Architecture. Trends in the data are briefly explored and compared to Moore’s Law which provides only a qualitative model for memory growth but not for data production. In summary, early missions are found to be driven by unrefined processes while later missions, having utilized earlier lessons learned, focus more on improvements to flight and ground capabilities. Data return seems to fall into three categories. First, deep space missions are driven by the large distances that limit data return to the Earth. Next, the orbiter infrastructure around Mars helps these missions generate more data than other deep space spacecraft. Finally, near-Earth missions have the greatest capabilities for the studied metrics due to their close proximity to Earth and the ground network availability.

Xaypraseuth, Peter↗

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Jupiter radiation belt engineering model

On the basis of earth observations of the HF and UHF radio emission generated near Jupiter, the presence of energetic charged particles trapped in the planet's dipole magnetic field has been inferred. For electrons, energies of the order of 10 MeV and peak fluxes of the order of 10 to the 7th power per square centimeter per second can be derived from the data for equatorial regions about two planetary radii from the dipole. Energetic protons and lower-energy electrons and protons are also expected, but the limited data require that their fluxes be based on theory or earth analogy. Because descriptions available in the literature suggest large associated uncertainties, both nominal and limiting models for the charged-particle populations of Jupiter's belts are derived. These new engineering models describe electron and proton fluxes and their distributions in energy and position in forms suitable as space vehicle design criteria.

Divine, N.↗

Atmospheric transmission computer program CP

A computer program is described which allows for calculation of the effects of carbon dioxide, water vapor, methane, ozone, carbon monoxide, and nitrous oxide on earth resources remote sensing techniques. A flow chart of the program and operating instructions are provided. Comparisons are made between the atmospheric transmission obtained from laboratory and spacecraft spectrometer data and that obtained from a computer prediction using a model atmosphere and radiosonde data. Limitations of the model atmosphere are discussed. The computer program listings, input card formats, and sample runs for both radiosonde data and laboratory data are included.

Pitts, D. E.↗

The classification of the Arctic Sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

The accurate quantification of new ice and open water areas and surface temperatures within the sea ice packs is a key to the realistic parameterization of heat, moisture, and turbulence fluxes between ocean and atmosphere in the polar regions. Multispectral NOAA advanced very high resolution radiometer/2 (AVHRR/2) satellite images are analyzed to evaluate how effectively the data can be used to characterize sea ice in the Bering and Greenland seas, both in terms of surface type and physical temperature. The basis of the classification algorithm, which is developed using a late wintertime Bering Sea ice cover data, is that frequency distributions of 10.8- micrometers radiances provide four distinct peaks, represeting open water, new ice, young ice, and thick ice with a snow cover. The results are found to be spatially and temporally consistent. Possible sources of ambiguity, especially associated with wider temporal and spatial application of the technique, are discussed. An ice surface temperature algorithm is developed for the same study area by regressing thermal infrared data from 10.8- and 12.0- micrometers channels against station air temperatures, which are assumed to approximate the skin temperatures of adjacent snow and ice. The standard deviations of the results when compared with in situ data are about 0.5 K over leads and polynyas to about 0.5-1.5 K over thick ice. This study is based upon a set of in situ data limited in scope and coverage. Cloud masks are applied using a thresholding technique that utilizes 3.74- and 10.8- micrometers channel data. The temperature maps produced show coherence with surface features like new ice and leads, and consistency with corresponding surface type maps. Further studies are needed to better understand the effects of both the spatial and temporal variability in emissivity, aerosol and precipitable atmospheric ice particle distribution, and atmospheric temperature inversions.

Massom, Robert↗

Understanding Aitken Mode Aerosol Variability over the Southern Ocean and Antarctica: Insights from Cloud Condensation Nuclei Data

Aitken mode aerosol particles play an important role influencing cloud properties and sustenance, acting as a reservoir of potential cloud condensation nuclei against precipitation scavenging. However, there is limited data on Aitken mode aerosols. In this study, we develop a method to estimate Aitken mode aerosol concentrations and size distribution using cloud condensation nuclei measurements (CCN) and κ-Köhler theory. The performance of this method is evaluated using scanning mobility particle sizer (SMPS) data from recent field campaigns to demonstrate its skills and applicability. The method reasonably estimates Aitken- and accumulation-mode aerosol concentrations, achieving correlations of 0.7–0.9 with only modest biases (mean fractional bias within ±23% for Aitken mode and ±34% for accumulation-mode). This method is further applied to measurements collected over the Southern Ocean and Antarctica in recent years from multiple platforms, including ground sites, aircraft, and ships, to derive Aitken and accumulation-mode aerosol concentrations. Using the derived data, we examine the seasonal cycle, latitudinal variations, and vertical distribution of aerosols. Aitken mode aerosol concentrations are elevated over the Southern Ocean and Antarctica during the austral summer similar to the accumulation mode. In the austral summer, the free troposphere has more Aitken mode aerosols and fewer accumulation mode aerosols than the boundary layer, and thus likely serves as an important source of cloud-forming aerosol while also diluting the accumulation mode.

Kang, Litai [University of Washington] (ORCID:0000↗

Development of forming and joining technology for TD-NiCr sheet

Forming joining techniques and properties data were developed for thin-gage TD-NiCr sheet in the recrystallized and unrecrystallized conditions. Theoretical and actual forming limit data are presented for several gages of each type of material for five forming processes: brake forming, corrugation forming, joggling, dimpling and beading. Recrystallized sheet can be best formed at room temperature, but unrecrystallized sheet requires forming at elevated temperature. Formability is satisfactory with most processes for the longitudinal orientation but poor for the transverse orientation. Dimpling techniques require further development for both material conditions. Data on joining techniques and joint properties are presented for four joining processes: resistance seam welding (solid-state), resistance spot welding (solid-state), resistance spot welding (fusion) and brazing. Resistance seam welded (solid-state) joints with 5t overlap were stronger than parent material for both material conditions when tested in tensile-shear and stress-rupture. Brazing studies resulted in development of NASA 18 braze alloy (Ni-16Cr-15Mo-8Al-4Si) with several properties superior to baseline TD-6 braze alloy, including lower brazing temperture, reduced reaction with Td-Ni-Cr, and higher stress-rupture properties.

Torgerson, R. T.↗

Boundary Layer Transition in the NTF: HSR Experience and Plans

Efforts towards understanding boundary layer transition characteristics on a High Speed Civil Transport (HSCT)-class configuration in the National Transonic Facility (NTF) are ongoing. The majority of the High Speed Research (HSR) data base in the NTF has free transition on the wing, even at low Reynolds numbers (Rn) attainable in conventional facilities. Limited data has been obtained and is described herein showing the effects of a conventional, Braslow method based wing boundary-layer trip on drag. Comparisons are made using force data polars and surface flow visualization at selected angles-of-attack and Mach number. Minimum drag data obtained in this study suggest that boundary layer transition occurred very near the wing leading edge by a chord Rn of 30 million. Sublimating chemicals were used in the air mode of operation only at low Rn and low angles-of-attack with no flap deflections; sublimation results suggest that the forebody and outboard wing panel are the only regions with significant laminar flow. The process and issues related to the sublimating chemical technique as applied in the NTF are discussed. Beyond the existing experience, status of efforts to develop a production transition detection system applicable to both air and cryogenic nitrogen environments is presented.

Owens, Lewis R., Jr.↗

The Epic Chronicle of Designing Cassini's Titan Flyby Altitudes

The selection and optimization of Titan flyby altitudes for NASA's Cassini mission at Saturn has traveled a long, fascinating, and often torturous sixteen-year path - starting in 2001, when pre-arrival trajectory design decisions had to be made, through April of 2017 when Cassini's last, and arguably most critical, low flyby takes place. The chronicle of designing and updating the Titan flyby altitudes have twists and turns enough to satisfy a full-length novel or feature film, including: critical design decision-making before arrival with multiple atmospheric models, high uncertainties, and limited data; early flybys that seemed to show trends that weren't there; use and misuse of statistical analysis; unexpected surprises with limited reaction time; navigation of a scientific, engineering, and management community with a wide array of inherent biases; and consideration of a variety of project risk postures in a high-scrutiny, high-impact, high-reward environment.

Bittner, Molly↗

Vaporization and Zonal Mixing in Performance Modeling of Advanced LOX-Methane Rockets

Initial modeling of LOX-Methane reaction control (RCE) 100 lbf thrusters and larger, 5500 lbf thrusters with the TDK/VIPER code has shown good agreement with sea-level and altitude test data. However, the vaporization and zonal mixing upstream of the compressible flow stage of the models leveraged empirical trends to match the sea-level data. This was necessary in part because the codes are designed primarily to handle the compressible part of the flow (i.e. contraction through expansion) and in part because there was limited data on the thrusters themselves on which to base a rigorous model. A more rigorous model has been developed which includes detailed vaporization trends based on element type and geometry, radial variations in mixture ratio within each of the "zones" associated with elements and not just between zones of different element types, and, to the extent possible, updated kinetic rates. The Spray Combustion Analysis Program (SCAP) was leveraged to support assumptions in the vaporization trends. Data of both thrusters is revisited and the model maintains a good predictive capability while addressing some of the major limitations of the previous version.

Williams, George J., Jr.↗

Modeling Electric Vehicle Charging Station Siting Suitability with a Focus on Equity

As adoption of electric vehicles increases, the infrastructure to charge them must keep pace. Determining where to add new charging infrastructure is a complex process subject to many factors, including electrical service availability, vehicle dwell time, the type(s) of drivers and vehicles the stations will serve, traffic levels and timing, and land ownership. In addition, advancing social equity is a current priority of federal efforts to invest in electric vehicle charging infrastructure. Conducting Multi-criteria Decision Analysis (MCDA) within Argonne’s Energy Zones Mapping Tool (EZMT) is a useful method for analyzing many of the factors that influence how suitable a location is for potentially adding new charging infrastructure, and we show how equity metrics can be included in the analysis. However, data limitations impose challenges to using MCDA to evaluate and prioritize locations. We use three examples to demonstrate how to use publicly available data and MDCA to analyze different siting objectives. Each example starts with defining a specific objective and ends with how to use the results to identify specific potential locations that could be investigated further. This analysis demonstrates how interested stakeholders can use the EZMT to run the example MCDA models defined in this study, modify them to suit their needs, or create new MCDA models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Making a Water Data System Responsive to Information Needs of Decision Makers

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems —data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Cantor, Alida↗

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.

deep Q-learning↗

Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

99 GENERAL AND MISCELLANEOUS↗

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗

Analysis of Chromosomal Aberrations in the Blood Lymphocytes of Astronauts after Space Flight

It is a NASA requirement that biodosimetry analysis be performed on all US astronauts who participate in long duration missions of 3 months or more onboard the International Space Station. Cytogenetic analysis of blood lymphocytes is the most sensitive and reliable biodosimetry method available at present, especially if chromosome damage is assessed before as well as after space flight. Results provide a direct measurement of space radiation damage in vivo that takes into account individual radiosensitivity and considers the influence of microgravity and other stress conditions. We present data obtained from all twenty-five of the crewmembers who have participated in the biodosimetry program so far. The yield of chromosome exchanges, measured using fluorescence in situ hybridization (FISH) technique with chromosome painting probes, increased after space flight for all these individuals. In vivo dose was derived from frequencies of chromosome exchanges using preflight calibration curves of in vitro exposed cells from the same individual, and RBE was compared with individually measured physically absorbed dose and projected organ dose equivalents. Biodosimetry estimates using samples collected within a few weeks of return from space lie within the range expected from physical dosimetry. For some of these individuals chromosome aberrations were assessed again several months after their respective missions and a temporal decline in stable exchanges was observed in some cases, suggesting that translocations are unstable with time after whole body exposure to space radiation. This may indicate complications with the use of translocations for retrospective dose reconstruction. Data from one crewmember who has participated in two separate long duration space missions and has been followed up for over 10 years provides limited data on the effect of repeat flights and shows a possible adaptive response to space radiation exposure.

George, K.↗

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗