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At least 1,351 records · Page 75

A Review of Scientific and Technological Results from the TSS-1R Mission

The Tethered Satellite System (TSS) program was designed to provide a unique opportunity to explore certain space plasma-electrodynamic processes and the orbital mechanics of a gravity-gradient stabilized system of two satellites linked by a long conducting tether. A unique data set was obtained during deployment which has allowed significant science to be accomplished. This paper focuses on results from the TSS-1R mission that are most important to the future technological applications of electrodynamic tethers in space, in particular, the current collection process. Of particular significance is an apparent transition of the physics of current collection when the potential of the collecting body becomes greater than the ram energy of the ionospheric atomic oxygen ions. Previous theoretical models of current collection were electrostatic, assuming that the orbital motion of the system, which is highly subsonic with respect to electron thermal motion, was unimportant. This may still be acceptable for the case of relatively slow-moving sounding rockets. However, the TSS-1R results show that motion relative to the plasma must be accounted for in orbiting systems.

Stone, N. H.↗

Acoustic emission signal processing technique to characterize reactor in-pile phenomena

Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and the signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In the paper, empirical mode decomposition technique is utilized to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal will correspond to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗

Applications of ISES for instrument science

It is often the case that some instruments being used for geophysical measurements cannot measure some parameters that are important for processing the data obtained using the instrument. However, the parameters of interest may be measured by other instruments and these data made available to the operators of the first instrument. Processing the data immediately after it is acquired is useful in directing the operation of the same or different instrument or in providing a quick look data set to users on the ground. The four applications which are considered are: the decision to acquire data due to some important occurrence detected by Eos instruments; the decision not to acquire data at a scheduled time and/or location; the decision to acquire additional data to improve data quality; and combining data from several sources to enhance data quality. General examples are presented, which may or may not apply directly to Eos instruments on the various platforms.

Grant, William B.↗

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the “missing link” needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new “ab initio” organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synchronization of Alternative Models in a Supermodel and the Learning of Critical Behavior

“Supermodeling” climate by allowing different models to assimilate data from one another in run time has been shown to give results superior to those of any one model and superior to any weighted average of model outputs. The only free parameters, connection strengths between corresponding variables in each pair of models, are determined using some form of machine learning. It is demonstrated that supermodeling succeeds because near critical states, interscale interactions are important but unresolved processes cannot be effectively represented diagnostically in any single parameterization scheme. In two examples, a pair of toy quasigeostrophic (QG) channel models of the midlatitudes and a pair of ECHAM5 models of the tropical Pacific atmosphere with a common ocean, supermodels dynamically combine parameterization schemes so as to capture criticality, associated critical structures, and the supporting scale interactions. The QG supermodeling scheme extends a previous configuration in which two such models synchronize with intermodel connections only between medium-scale components of the flow; here the connections are trained against a third “real” model. Intermittent blocking patterns characterize the critical behavior thus obtained, even where such patterns are missing in the constituent models. In the ECHAM-based climate supermodel, the corresponding critical structure is the single ITCZ pattern, a pattern that occurs in neither of the constituent models. In conclusion, for supermodels of both types, power spectra indicate enhanced interscale interactions in frequency or energy ranges of physical interest, in agreement with observed data, and supporting a generalized form of the self-organized criticality hypothesis.

54 ENVIRONMENTAL SCIENCES↗

Radiation Tolerant Electronics and Digital Processing for the Phase-1 Read-out Upgrade of the ATLAS Liquid Argon Calorimeters

The ATLAS Liquid Argon calorimeters are designed and built to study proton-proton collisions produced at the LHC at centre-of-mass energies up to 14 TeV. Liquid argon (LAr) sampling calorimeters are employed for all electromagnetic calorimetry in the pseudorapidity region |η|<3.2, and for hadronic calorimetry in the region from |η|=1.5 to |η|=4.9. Although the nominal LHC experimental programme is still in progress, an upgrade of the read-out electronics is being launched to cope with luminosities of up to 3x10{sup 34} cm{sup -2}s{sup -1}, which are beyond the original design by a factor of 3. An improved spatial granularity of the trigger primitives is therefore proposed in order to improve the identification performance for trigger signatures, like electrons, photons, tau leptons, jets, total and missing energy, at high background rejection rates. For the upgrade Phase-1 in 2018, new LAr Trigger Digitizer Boards (LTDB) are being designed to receive higher granularity signals, digitize them on detector and send them via fast optical links to a new LAr digital processing system (LDPS). The LDPS applies a digital filtering and identifies significant energy depositions in each trigger channel. The refined trigger primitives are then transmitted to the first level trigger system to extract improved trigger signatures. The read-out of the trigger signals will process 34000 so-called Super Cells at every LHC bunch-crossing at a frequency of 40 MHz. The new LTDB on-detector electronics is designed to be radiation tolerant in order to be operated for the remaining live-time of the ATLAS detector up to a total luminosity of 3000 fb{sup -1}. For the analog-to-digital conversion (12-bit ADC at 40 MSPS), the data serialization and the fast optical link (5.44 Gb/s) custom components have been developed. They have been qualified for the expected radiation environment of a total ionization dose of 1.3 kGy and a hadron fluence of 6 x 10{sup 13} h/cm{sup 2} with energies above 20 MeV. For the digital components like the ADC, cross-sections for single event effects have been determined. This talk will present R and D results from tests of the radiation tolerant components, the fast data processing electronics and prototypes of the LTDB and LDPS boards. First experience from a Demonstrator setup will be reported, in which about 1/10 of the full Super Cell readout will be equipped with prototype versions of the LTDB and LDPS boards. The Demonstrator will be operated in parallel to the regular ATLAS trigger read-out during the upcoming LHC run. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA SNPP SIPS - Following in the Path of EOS

NASA's Earth Science Data Information System (ESDIS) Project has been operating NASA's Suomi National Polar-Orbiting Partnership (SNPP) Science Data Segment (SDS) since the launch in October 2011. At launch, the SDS focused primarily on the evaluation of Sensor Data Records (SDRs) and Environmental Data Records (EDRs) produced by the Joint Polar Satellite System (JPSS), a National Oceanic and Atmosphere Administration (NOAA) Program, as to their suitability for Earth system science. During the summer of 2014, NASA transitioned to the production of standard Earth Observing System (EOS)-like science products for all instruments aboard Suomi NPP. The five Science Investigator-led Processing Systems (SIPS): Land, Ocean, Atmosphere, Ozone, and Sounder were established to produce the NASA SNPP standard Level 1, Level 2, and global Level 3 products developed by the SNPP Science Teams and to provide the products to NASA's Distributed Active Archive Centers (DAACs) for archive and distribution to the user community. The processing, archiving and distribution of data from NASA's Clouds and the Earth's Radiant Energy System (CERES) and Ozone Mapper/Profiler Suite (OMPS) Limb instruments will continue. With the implementation of the JPSS Block 2 architecture and the launch of JPSS-1, the SDS will receive SNPP data in near real-time via the JPSS Stored Mission Data Hub (JSH), as well as JPSS-1 and future JPSS-2 data. The SNPP SIPS will ingest EOS compatible Level 0 data from the EOS Data Operations System (EDOS) element for their data processing, enabling the continuous EOS-SNPP-JPSS Satellite Data Record.

near real time↗

Angular Correlation Date Measurements with the GeRMAC system

Advanced modeling and simulation efforts have improved at Idaho National Laboratory in recent years with a solid foundation of experimental results. Current computational methods represent significant modeling capabilities but are limited by the accuracy and availability of nuclear data. The creation of pre- and post-processing software tools to address these limitations is fundamental to the improvement of nuclear science modeling capacities. One aspect of predictive modeling tools deals with gamma-rays emitted from radionuclides, including fissile or fissionable material, fission products, or activation products, produced in reactor experiments or other neutron environments. The resulting radionuclides decay in unique ways, providing complications upon measurement as a result of random and cascade, or true, coincidence summing. These effects are not easily quantified during modeling efforts of gamma-ray source terms., The germanium rotational measurements for angular correlation (GeRMAC) system was built to quantify the relative angles for gamma rays emitted by radionuclides of interest to investigate true coincidence, or cascade, summing as well as the nuclear energy levels of decay schemes of interest. Proof of concept studies utilize a series of laboratory check sources to provide validity, and it will soon be used to perform the same measurements for fission products of interest. The resulting data can be used to implement into a Monte Carlo code, such as Geant4, to provide more precise gamma-ray source terms following irradiations of materials.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Long-Lived In-Situ Solar System Explorer (LLISSE): Potential Contributions to the Next Decade of Solar System Exploration

Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways and holds many scientific mysteries despite many missions that have focused on it in the past. Primary differences include Venus' climate, atmosphere, and perhaps most notably the extreme surface conditions. The layers of sulfuric acid clouds and high pressure CO2 laden atmosphere make remote sensing at Venus much less effective than at other solar system bodies. In addition, surface conditions present formidable engineering challenges due to the high temperature, pressure, and reactive chemistry. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data at the Venus surface. These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other and are transported throughout the atmospheric column. The LLISSE platform, and its variants, are a foundation for future mission concepts based on a core set of long-lived technologies providing significant new science as well as demonstrating new technical capabilities. After completion, LLISSE has the potential to be a complimentary element to missions going to Venus and would provide unique and important science to missions whether they be orbiters or short duration landers.

Kremic, Tibor↗

Long-Lived In-Situ Solar System Explorer (LLISSE)

Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways and holds many scientific mysteries despite many missions that have focused on it in the past. Primary differences include Venus' climate, atmosphere, and perhaps most notably the extreme surface conditions. The layers of sulfuric acid clouds and high pressure CO2 laden atmosphere make remote sensing at Venus much less effective than at other solar system bodies. In addition, surface conditions present formidable engineering challenges due to the high temperature, pressure, and reactive chemistry. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data at the Venus surface. These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other and are transported throughout the atmospheric column. The LLISSE platform, and its variants, are a foundation for future mission concepts based on a core set of long-lived technologies providing significant new science as well as demonstrating new technical capabilities. After completion, LLISSE has the potential to be a complimentary element to missions going to Venus and would provide unique and important science to missions whether they be orbiters or short duration landers.

Kremic, Tibor↗

Deep learning tackles cloud detection

To better understand Earth systems, atmospheric scientists use cloud data collected by a remote sensing tool called lidar. These data are an important part of global forecasting, but analyzing lidar images by hand is time-consuming. Researchers at Pacific Northwest National Laboratory teamed up to see if a deep learning model could process lidar data better than current automatic detection techniques.

54 ENVIRONMENTAL SCIENCES↗

Converging towards atomic and nuclear pressures (Final Report)

his report details the development of transformational tools and techniques, allowing the accurate characterization of matter at the most extreme conditions yet studied in the high energy density (HED) domain. Before this work, most experiments quantitatively mapping the nature of matter in the HED realm were performed using planar geometry, which allows for the isolation and measurement of variables in a single thermodynamic state. Such measurements include equation of state variables, optical conductivity, heat transport and more. However, due to a variety of plasma processes, such experiments are limited to below ~10 TPa (100 Mbar) pressures. To achieve higher pressures, convergent experiments are needed. Historically, convergent experiments have not been used for benchmark data because they are integral measurements. That is each part of a convergent target undergoes a time dependent wide range of states, making it difficult to accurately isolate any particular quantity for a given thermodynamic state. This effort developed innovative convergent HED platforms and techniques enabling the exploration of matter from atomic to nuclear scale pressures. This effort also performed pioneering experiments that yielded the first rigorous benchmark data at these extreme conditions. This funding award began with the overarching goal to understand the behavior of matter at extreme (atomic-to-nuclear scale) pressures. The motivation for these goals lie in the fact that every time scientists explore matter beyond the threshold of an atomic unit, there is a fundamental shift in science. The atomic unit for energy, mass, charge, length, and time have each been explored, and each time such a threshold was crossed, a new sequence of discoveries was made resulting in significant awards such as the Nobel Prize. The only unexplored atomic unit is pressure, and this effort set the course for exploring matter at and beyond such pressures. That most of the recently discovered extrasolar planets and stars, as well as matter in the late implosion stages of inertial fusion targets, have deep internal pressures at and beyond atomic pressures amplifies the importance of this effort. Much of the initial work in developing techniques to create these atomic-to-nuclear pressures already existed within the HED community, predominantly at large scale laser facilities such as the National Ignition Facility (NIF) and the Omega60 laser the University of Rochester and although these experiments were routinely performed the ability to extract information about the underlying physical states and processes remained elusive due to the complexity of the experiments, extreme scales in both time and space, and the integrated nature of all the measurements. This work built a rigorous framework so such measurements can routinely be made. This was done in part through the introduction of Bayesian inference techniques into the field of HED science. These techniques allow for the self-consistent extraction of the relevant variables and their explicit and implicit correlations, so as to make use of integrated experimental data to constrain physical models and provide accurate uncertainty bounds for the data. The techniques developed here are fully transparent and proved immediately useful providing quantitative rigorous benchmarks for physical states and processes at some of the most extreme conditions yet explored on Earth. This funding is directly or partly responsible for 7 publications in major peer-reviewed scientific journals, a doctoral thesis, 3 invited talks at major conferences, and the training of a post-doc, 2 graduate students, and 1 undergraduate student. Beyond the effort supported by this award launched the introduction of Bayesian inference into the HED physics community and helped push the usage if modern data-science techniques within the physics community at large.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Gamma-Rays from Kilonovae and the Cosmic Gamma-Ray Background

The recent detection of the gravitational-wave event GW170817, produced by the coalescence of two neutron stars, and of its optical–infrared counterpart, powered by the radioactive decay of r-process elements, has opened a new window onto gamma-ray astronomy: the direct detection of photons coming from such decays. In this work, we calculate the contribution of kilonovae to the diffuse gamma-ray background in the MeV range, using recent results on the spectra of the gamma-rays emitted in individual events, and we compare it with that from other sources. We find that the contribution from kilonovae is not dominant in such an energy range, but within current uncertainties, and its addition to other sources might help to fit the observational data.

59 BASIC BIOLOGICAL SCIENCES↗