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A combined electron beam/optical lithography process step for the fabrication of sub-half-micron-gate-length MMIC chips

Advanced radar and communication systems rely heavily on state-of-the-art microelectronics. Systems such as the phased-array radar require many transmit/receive (T/R) modules which are made up of many millimeter wave - microwave integrated circuits (MMIC's). The heart of a MMIC chip is the Gallium Arsenide (GaAs) field-effect transistor (FET). The transistor gate length is the critical feature that determines the operating frequency of the radar system. A smaller gate length will typically result in a higher frequency. In order to make a phased array radar system economically feasible, manufacturers must be capable of producing very large quantities of small-gate-length MMIC chips at a relatively low cost per chip. This requires the processing of a large number of wafers with a large number of chips per wafer, minimum processing time, and a very high chip yield. One of the bottlenecks in the fabrication of MIMIC chips is the transistor gate definition. The definition of sub-half-micron gates for GaAs-based field-effect transistors is generally performed by direct-write electron beam lithography (EBL). Because of the throughput limitations of EBL, the gate-layer fabrication is conventionally divided into two lithographic processes where EBL is used to generate the gate fingers and optical lithography is used to generate the large-area gate pads and interconnects. As a result, two complete sequences of resist application, exposure, development, metallization and lift-off are required for the entire gate structure. We have baselined a hybrid process, referred to as EBOL (electron beam/optical lithography), in which a single application of a multi-level resist is used for both exposures. The entire gate structure, (gate fingers, interconnects and pads), is then formed with a single metallization and lift-off process. The EBOL process thus retains the advantages of the high-resolution E-beam lithography and the high throughput of optical lithography while essentially eliminating an entire lithography/metallization/lift-off process sequence. This technique has been proven to be reliable for both trapezoidal and mushroom gates and has been successfully applied to metal-semiconductor and high-electron-mobility field-effect transistor (MESFET and HEMT) wafers containing devices with gate lengths down to 0.10 micron and 75 x 75 micron gate pads. The yields and throughput of these wafers have been very high with no loss in device performance. We will discuss the entire EBOL process technology including the multilayer resist structure, exposure conditions, process sensitivities, metal edge definition, device results, comparison to the standard gate-layer process, and its suitability for manufacturing.

Sewell, James S.↗

Persistent Homology Metrics Reveal Quantum Fluctuations and Reactive Atoms in Path Integral Dynamics

Nuclear quantum effects (NQEs) are known to impact a number of features associated with chemical reactivity and physicochemical properties, particularly for light atoms and at low temperatures. In the imaginary time path integral formalism, each atom is mapped onto a “ring polymer” whose spread is related to the quantum mechanical uncertainty in the particle’s position, i.e., its thermal wavelength. A number of metrics have previously been used to investigate and characterize this spread and explain effects arising from quantum delocalization, zero-point energy, and tunneling. Many of these shape metrics consider just the instantaneous structure of the ring polymers. However, given the significant interest in methods such as centroid molecular dynamics and ring polymer molecular dynamics that link the molecular dynamics of these ring polymers to real time properties, there exists significant opportunity to exploit metrics that also allow for the study of the fluctuations of the atom delocalization in time. Here we consider the ring polymer delocalization from the perspective of computational topology, specifically persistent homology, which describes the 3-dimensional arrangement of point cloud data, (i.e. atomic positions). We employ the Betti sequence probability distribution to define the ensemble of shapes adopted by the ring polymer. The Wasserstein distances of Betti sequences adjacent in time are used to characterize fluctuations in shape, where the Fourier transform and associated principal components provides added information differentiating atoms with different NQEs based on their dynamic properties. We demonstrate this methodology on two representative systems, a glassy system consisting of two atom types with dramatically different de Broglie thermal wavelengths, and ab initio molecular dynamics simulation of an aqueous 4 M HCl solution where the H-atoms are differentiated based on their participation in proton transfer reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

42 ENGINEERING↗

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗

Structural Studies of Medicago truncatula Histidinol Phosphate Phosphatase from Inositol Monophosphatase Superfamily Reveal Details of Penultimate Step of Histidine Biosynthesis in Plants

The penultimate enzyme in the histidine biosynthetic pathway catalyzes dephosphorylation of l-histidinol 1-phosphate (HOLP) into l-histidinol. The recently discovered in Arabidopsis thaliana plant-type histidinol phosphate phosphatase (HPP) shares no homology with the two other HPP superfamilies known previously in prokaryotes and resembles myo-inositol monophosphatases (IMPases). In this study, identification of an HPP enzyme from a model legume, Medicago truncatula (MtHPP) was based on the highest sequence identity to A. thaliana enzyme. Biochemical assays confirmed that MtHPP was able to cleave inorganic phosphate from HOLP but not from d-myo-inositol-1-phosphate, the main substrate of IMPases. Dimers of MtHPP, determined by size exclusion chromatography, in the presence of CO2 or formaldehyde form mutual, methylene-bridged cross-links between Lys 158 and Cys 245 residues. Four high resolution crystal structures, namely complexes with HOLP (substrate), l-histidinol (product), and PO 4 3– (by-product) as well as the structure showing the cross-linking between two MtHPP molecules, provide detailed structural information on the enzyme. Based on the crystal structures, the enzymatic reaction mechanism of IMPases is accustomed to fit the data for MtHPP. The enzymatic reaction, which requires Mg 2+ cations, is catalyzed mainly by amino acid residues from the N-terminal domain. The C-terminal domain, sharing little identity with IMPases, is responsible for the substrate specificity (i.e. allows the enzyme to distinguish between HOLP and d-myo-inositol-1-phosphate). Structural features, mainly the presence of a conserved Asp 246 , allow MtHPP to bind HOLP specifically.

59 BASIC BIOLOGICAL SCIENCES↗

Short-period post-common envelope binaries with Balmer emission from SDSS and LAMOST based on ZTF photometric data

ABSTRACT We present here 55 short-period post-common envelope binaries (PCEBs) containing a hot white dwarf (WD) and a low-mass main sequence (MS). Based on the photometric data from Zwicky Transient Facility survey data Release 19 (ZTF DR19), the light curves are analysed for about 200 WDMS binaries with emission line(s) identified from the Sloan Digital Sky Survey (SDSS) or the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) spectra, in which 55 WDMS binaries are found to exhibit variability in their luminosities with a short period and are thus short-period binaries (i.e. PCEBs). In addition, it is found that the orbital periods of these PCEBs locate in a range from 2.2643 to 81.1526 h. However, only six short-period PCEBs are newly discovered and the orbital periods of 19 PCEBs are improved in this work. Meanwhile, it is found that three objects are newly discovered eclipsing PCEBs, and a object (i.e. SDSS J1541) might be the short-period PCEB with a late M-type star or a brown dwarf companion based on the analysis of its spectral energy distribution. At last, the mechanism(s) being responsible for the emission features in the spectra of these PCEBs are discussed, the emission features arising in their optical spectra might be caused by the stellar activity or an irradiated component owing to a hot WD companion because most of them contain a WD with an effective temperature higher than $\sim$10 000 K.

Li, Lifang↗

Molecular Basis of Unusually High Neutralization Resistance in Tier 3 HIV-1 Strain 253-11

Understanding the mechanisms used by HIV-1 to evade antibody neutralization may contribute to the design of a high-coverage vaccine. The tier 3 virus 253-11 is poorly neutralized by subtype-matched and subtype C sera, even compared to other tier 3 viruses, and is also recognized poorly by V3/glycan-targeting monoclonal antibodies (MAbs). We found that sequence polymorphisms in the V3 loop and N-linked glycosylation sites contribute only minimally to the high neutralization resistance of 253-11. Interestingly, the 253-11 membrane-proximal external region (MPER) is rarely recognized by sera in the context of the wild-type virus but is commonly recognized in the context of an HIV-2 chimera, suggesting steric or kinetic hindrance of binding to MPER in the native envelope (Env). Mutations in the 253-11 MPER, which were previously reported to increase the lifetime of the prefusion Env conformation, affected the resistance of 253-11 to antibodies targeting various epitopes on HIV-1 Env, presumably destabilizing its otherwise stable, closed trimer structure. To gain insight into the structure of 253-11, we constructed and crystallized a recombinant 253-11 SOSIP trimer. The resulting structure revealed that the heptad repeat helices in gp41 are drawn in close proximity to the trimer axis and that gp120 protomers also showed a relatively compact disposition around the trimer axis. Furthermore, these observations give substantial insight into the molecular features of an envelope spike from a tier 3 virus and into possible mechanisms that may contribute to its unusually high neutralization resistance.

59 BASIC BIOLOGICAL SCIENCES↗

Time-series spectroscopy of the pulsating eclipsing binary XX Cephei

Oscillating Algol-type eclipsing binaries (oEA) are very interesting objects that have three observational features of eclipse, pulsation, and mass transfer. Direct measurement of their masses and radii from the double-lined radial velocity data and photometric light curves would be the most essential for understanding their evolutionary process and for performing the asteroseismological study. We present the physical properties of the oEA star XX Cep from high-resolution time-series spectroscopic data. The effective temperature of the primary star was determined to be 7946 ± 240 K by comparing the observed spectra and the Kurucz models. We detected the absorption lines of the secondary star, which had never been detected in previous studies, and obtained the radial velocities for both components. With the published BVRI light curves, we determined the absolute parameters for the binary via Wilson–Devinney modeling. The masses and radii are M{sub 1}=2.49±0.06 M{sub ⊙}, M{sub 2}=0.38±0.01 M{sub ⊙}, R{sub 1}=2.27±0.02 R{sub ⊙}, and R{sub 2}=2.43±0.02 R{sub ⊙}, respectively. The primary star is about 45% more massive and 60% larger than the zero-age main sequence stars with the same effective temperature. It is probably because XX Cep has experienced a very different evolutionary process due to mass transfer, contrasting with the normal main sequence stars. The primary star is located inside the theoretical instability strip of δ Sct-type stars on the HR diagram. We demonstrated that XX Cep is an oEA star, consisting of a δ Sct-type pulsating primary component and an evolved secondary companion.

79 ASTRONOMY AND ASTROPHYSICS↗

A Satellite-Derived Upper-Tropospheric Water Vapor Transport Index for Climate Studies

A new approach is presented to quantify upper-level moisture transport from geostationary satellite data. Daily time sequences of GOES-7 water vapor imagery were used to produce estimates of winds and water vapor mixing ratio in the upper-troposphere sensed by the 6.7-microns water vapor channel. The winds and mixing ratio values were gridded and then combined to produce a parameter called the Water Vapor Transport Index (WVTI) which represents the magnitude of the two dimensional transport of water vapor in the upper troposphere. Daily grids of WVTI, meridional moisture transport, mixing ratio, pressure and other associated parameters were averaged to produce monthly fields for June, July and August of 1987 and 1988 over the Americas and surrounding oceanic regions. The WVTI was used to compare upper-tropospheric moisture transport between the summers of 1987 and 1988, contrasting the latter part of the 1986/87 El Nino event and the La Nina period of 1988. A similar product derived from the NCEP reanalysis was used to help validate the index and understand interannual variability of moisture transport from the modeling perspective. Both datasets showed large upper-level water vapor transport associated with synoptic features over the Americas and with outflow from tropical convective systems. Minimal transport occurred over tropical and subtropical high pressure regions where winds were light. Index values from NCEP were 2-3 times larger than that determined from the GOES satellite This difference resulted from an over estimate of the zonal winds and upper-tropospheric moisture in the reanalysis model. A comparison of the satellite-derived monthly values between the summers of 1987 and 1988 provided some insight into the impact of the ENSO event on upper-level moisture and its transport during the period. During July 1987, a large portion of the tropics in the eastern Pacific Ocean and Caribbean Sea was dominated by strong vapor transport in excess of 5 g/kg/ms with relatively small amounts in the other months. In contrast, JJA 1988 showed an opposite trend with July 1988 being less dominated by tropical water vapor transport. The meridional transport of upper-level water vapor indicated significant poleward transport from the tropics to the higher latitudes. This transport favored the Southern Hemisphere with large transport occurring south of the ITCZ which extended across the eastern Pacific and northern South America. Zonally-averaged monthly transport values were shown to provide a simple way to quantify the monthly and interannual changes in water vapor transport. Zonally-averaged WVTI values peaked in the Southern Hemisphere subtropics during both Austral winters. In the tropics a single more pronounced peak located over the equator and south latitudes occurred in 1988 as opposed to a dual peak in 1987. The second peak around 20 deg.N latitude is consistent with findings of others where upper-tropospheric winds were noted to be stronger in this region during warm ENSO events. Zonally-averaged meridional transport was southward for all summer months and stronger in 1988. The asymmetric nature of the zonally-averaged meridional transport (more southerly water vapor transport) was enhanced during JJA 1998 thus indicating a stronger upper-level branch of the Hadley circulation during this notable strong La Nina period.

Jedlovec, Gary J.↗

Quantitative proteome profile of water deficit stress responses in eastern cottonwood ( Populus deltoides ) leaves

Drought stress is a recurring feature of world climate and the single most important factor influencing agricultural yield worldwide. Plants display highly variable, species-specific responses to drought and these responses are multifaceted, requiring physiological and morphological changes influenced by genetic and molecular mechanisms. Moreover, the reproducibility of water deficit studies is very cumbersome, which significantly impedes research on drought tolerance, because how a plant responds is highly influenced by the timing, duration, and intensity of the water deficit. Despite progress in the identification of drought-related mechanisms in many plants, the molecular basis of drought resistance remains to be fully understood in trees, particularly in poplar species because their wide geographic distribution results in varying tolerances to drought. Herein, we aimed to better understand this complex phenomenon in eastern cottonwood ( Populus deltoides ) by performing a detailed contrast of the proteome changes between two different water deficit experiments to identify functional intersections and divergences in proteome responses. We investigated plants subjected to cyclic water deficit and compared these responses to plants subjected to prolonged acute water deficit. In total, we identified 108,012 peptide sequences across both experiments that provided insight into the quantitative state of 22,737 Populus gene models and 8,199 functional protein groups in response to drought. Together, these datasets provide the most comprehensive insight into proteome drought responses in poplar to date and a direct proteome comparison between short period dehydration shock and cyclic, post-drought re-watering. Altogether, this investigation provides novel insights into drought avoidance mechanisms that are distinct from progressive drought stress. Additionally, we identified proteins that have been associated as drought-relevant in previous studies. Importantly, we highlight the RD26 transcription factor as a gene regulated at both the transcript and protein level, regardless of species and drought condition, and, thus, represents a key, universal drought marker for Populus species.

59 BASIC BIOLOGICAL SCIENCES↗

Beamline Electrostatic Levitator (BESL) for in-situ High Energy K-Ray Diffraction Studies of Levitated Solids and Liquids at High Temperature

Determinations of the phase formation sequence, the crystal structures and the thermodynamic properties of materials at high temperatures are difficult because of contamination from the sample container and environment. Containerless processing techniques, such as electrostatic (ESL), electromagnetic (EML), aerodynamic, and acoustic levitation, are most suitable these studies. An adaptation of ESL for in-situ structural studies of a wide range of materials, including metals, semiconductors, insulators using high energy (125 keV) synchrotron x-rays is described here. This beamline ESL (BESL) allows the in-situ determination of the atomic structures of equilibrium solid and liquid phases, including undercooled liquids, as well as real-time studies of solid-solid and liquid-solid phase transformations. The use of image plate (MAR345) or GE-Angio detectors enables fast (30 ms - 1s) acquisition of complete diffraction patterns over a wide q-range (4 - 140/mm). The wide temperature range (300 - 2500 K), containerless processing under high vacuum (10(exp -7) - 10(exp -8) torr), and fast data acquisition, make BESL particularly suitable for phase diagram studies of high temperature materials. An additional, critically important, feature of BESL is the ability to also make simultaneous measurement of a host of thermo-physical properties, including the specific heat, enthalpy of transformation, solidus and liquidus temperatures, density, viscosity, and surface tension; all on the same sample and simultaneous with the structural measurements.

Gangopadhyay, A. K.↗

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes

Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. Unfortunately, an efficient parallel implementation is difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. To address this problem, we have developed PLUM, an automatic portable framework for performing adaptive large-scale numerical computations in a message-passing environment. First, we present an efficient parallel implementation of a tetrahedral mesh adaption scheme. Extremely promising parallel performance is achieved for various refinement and coarsening strategies on a realistic-sized domain. Next we describe PLUM, a novel method for dynamically balancing the processor workloads in adaptive grid computations. This research includes interfacing the parallel mesh adaption procedure based on actual flow solutions to a data remapping module, and incorporating an efficient parallel mesh repartitioner. A significant runtime improvement is achieved by observing that data movement for a refinement step should be performed after the edge-marking phase but before the actual subdivision. We also present optimal and heuristic remapping cost metrics that can accurately predict the total overhead for data redistribution. Several experiments are performed to verify the effectiveness of PLUM on sequences of dynamically adapted unstructured grids. Portability is demonstrated by presenting results on the two vastly different architectures of the SP2 and the Origin2OOO. Additionally, we evaluate the performance of five state-of-the-art partitioning algorithms that can be used within PLUM. It is shown that for certain classes of unsteady adaption, globally repartitioning the computational mesh produces higher quality results than diffusive repartitioning schemes. We also demonstrate that a coarse starting mesh produces high quality load balancing, at a fraction of the cost required a fine initial mesh. Results indicate that our parallel load balancing strategy will remain viable on large numbers of processors.

Oliker, Leonid↗

High-Resolution UV/Optical/IR Imaging of Jupiter in 2016–2019

Imaging observations of Jupiter with high spatial resolution were acquired beginning in 2016, with a cadence of 53 days to coincide with atmospheric observations of the Juno spacecraft during each perijove pass. The Wide Field Camera 3 (WFC3) aboard the Hubble Space Telescope (HST) collected Jupiter images from 236 to 925 nm in 14 filters. The Near-Infrared Imager (NIRI) at Gemini North imaged Jovian thermal emission using a lucky-imaging approach (co-adding the sharpest frames taken from a sequence of short exposures), using the M' filter at 4.7 μm. We discuss the data acquisition and processing and an archive collection that contains the processed WFC3 and NIRI data (doi:10.17909/T94T1H). Zonal winds remain steady over time at most latitudes, but significant evolution of the wind profile near 24°N in 2016 and near 15°S in 2017 was linked with convective superstorm eruptions. Persistent mesoscale waves were seen throughout the 2016–2019 period. We link groups of lightning flashes observed by the Juno team with water clouds in a large convective plume near 15°S and in cyclones near 35°N–55°N. Thermal infrared maps at the 10.8 micron wavelength obtained at the Very Large Telescope show consistent high brightness temperature anomalies, despite a diversity of aerosol properties seen in the HST data. Both WFC3 and NIRI imaging reveal depleted aerosols consistent with downwelling around the periphery of the 15°S storm, which was also observed by the Atacama Large Millimeter/submillimeter Array. NIRI imaging of the Great Red Spot shows that locally reduced cloud opacity is responsible for dark features within the vortex. The HST data maps multiple concentric polar hoods of high-latitude hazes.

Michael H. Wong↗

Mineralogical Analysis of Calcium-Aluminum-Rich Inclusions Provides Insight Into Post-Formation Processes

Introduction: Calcium-aluminum inclusions (CAIs) are cm- to mm-sized intergrowths of refractory phases found in chondritic meteorites [1]. Their mineral compositions closely match the compositions of the solids thought to condense from an extremely hot (>1500K) gas with a bulk solar composition [2-4], suggesting that CAIs were the earliest solids within the Solar System [2-4]. These inclusions provide invaluable insights into the conditions and dynamics of the early Solar System. CAIs must be transported from their formation region near the protosun to the chondrite parent-body accretion region. The nature of their journey could affect how, when, and where the secondary processes recorded in these CAIs occurred [4]. Did these secondary processes occur in the solar nebula or during accretion with the parent body, or both? How were the CAIs affected by varying thermochemical processes? To better answer these questions, we have undertaken an in-depth, textural study of the secondary alteration of select CAI samples. Methods: We chose three CAIs that experienced a range of post-formation processing to gain better understanding of secondary alteration based on previous preliminary examination [5]. Representative CAIs were chosen from NWA 5508 (CV3), NWA 12772 (CV3), and Coolidge (CL4) carbonaceous chondrites. We used scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) techniques for detailed chemical, mineralogical, and textural analysis of the CAIs. Backscattered electron (BSE) images and X-ray elemental maps were taken of Coolidge and NWA 5508 using the Lunar and Planetary Institute (LPI) Phenom SEM. The JEOL 7900F SEM at NASA Johnson Space Center (JSC) was used to obtain energy dispersive spectroscopy (EDS) chemical maps of all samples. High resolution EBSD analysis identified the mineral phases and textures, providing key information on their nature. Based on the SEM and EBSD data, minerals of interest were selected for quantitative chemical electron probe micro-analysis (EPMA) using the JEOL JXA-8530F at NASA JSC. Results and Discussion: The EDS maps show that the NWA 5508 and NWA 12772 CAIs designated “Saguaro” and “Hoopoe” respectively [5] are enriched in calcium while the Coolidge CAI designated “Cottonwood” is aluminum and magnesium rich. Saguaro. A ~1.5cm diameter igneous Type B CAI with a rounded shape. The dominant phases are melilite, spinel, and Al-Ti pyroxene with minor anorthite. Spinel is subhedral and occurs in clusters. Some of these clusters have a circular geometry which encloses other minerals, known as a palisade structure [6] (Fig. 1). Some palisades form near perfect circles while others are more irregular in shape. Melilite in Saguaro ranges in size from coarse (>250m) to fine-grained (5-7m) and forms intergrown laths. A third of the melilite grains exhibit simple twinning about their <001> axis. The spinel palisades and twinned melilite in Saguaro suggest an igneous history, and the lack of secondary minerals suggests minimal aqueous alteration. At some point in time after the initial condensation of the minerals and formation of the inclusion, the sample was remelted and quickly solidified. The formation of the palisades is still heavily debated. One hypothesis is that the palisades are the rims of smaller CAIs that accreted early on, essentially acting as xenoliths within the larger CAIs [7]. Another hypothesis suggests an igneous origin for palisade structures [6-8] wherein the melt traps gas bubbles, and the spinel nucleates on the surface of this bubble. Based on the WDS spot analyses of 41 melilite grains using EPMA, the data suggest that the composition inside and outside the palisades is nearly identical. This finding indicates that these palisades are likely not exogenous but rather formed from melt-vapor reactions. Our results are consistent with studies by Simon and Grossman,1997 [6] and Zhang et al. (2019) [8]. Hoopoe. A ~0.5cm compact Type A CAI with an irregular shape. The dominant mineral phases are melilite, spinel, and hibonite with minor amounts of anorthite, augite, and perovskite. The melilite ranges in size from ~500 to 50µm. The larger melilite grains have simple twinning along the <001> axis like melilite in Saguaro. Melilite in Hoopoe exhibits crystal-plastic strain with misorientation dominantly about the <010> and <110> axes. Spinel shows subhedral to euhedral morphology and appears in clusters. Hibonite grains are similar to spinel in habit and size but show more plastic strain. The two minerals are often found together with one appearing to replace the other. Perovskite appears in fine grained recrystallized regions alongside fine augite and spinel. The abundant strain and deformation features in the melilite and hibonite suggest that Hoopoe experienced shock. This shock could have occurred in the nebula [9] or from an impact of another body on the parent body asteroid. The appearance of fine-grained (<10m) areas of augite, perovskite, and spinel in the dominantly coarse-grained inclusion suggest recrystallization, possibly due to the sudden increase in pressure and temperature. Cottonwood. This ~0.5cm CAI exhibits distinct mineralogy and textures suggesting a high degree of alteration. It is irregular in shape. The dominant mineral phases are spinel and anorthite with minor amounts of augite and rutile. The two main texture types can be seen in Fig. 2. The first type consists of coarse euhedral to subhedral spinel and anorthite. The second includes fine grained spinel, anorthite, rutile, and iron sulfides. Within these fine-grained regions, the anorthite grains are clustered into domains exhibiting the same crystallographic orientation. Rutile exclusively occurs with fine anorthite indicating a potential relationship between the two. Cottonwood has a clear and unbroken Wark-Lovering [10] rim (Fig. 2) on one side that consists of a sequence of spinel followed by anorthite and an outer layer of augite. The abundance of the fine-grained regions containing iron oxides and iron sulfides, secondary phases such as rutile, and oriented anorthite grains is evidence for recrystallization associated with a high degree of thermal metamorphism. The EPMA analyses on both coarse- and fine-grained spinel show that the fine spinel grains are more enriched in Cr (1-2 wt. %) than their coarse counterparts (0.1-0.5 wt. %). The data suggest thermal metamorphism drove chemical exchange of the previously refractory inclusion, introducing chromium and iron as well as sulfur, which is moderately volatile. Conclusions: The three CAIs analyzed record distinct secondary nebular and parent body processes. Saguaro melted in the nebula as seen by the twinned melilite and spinel palisades. Hoopoe experienced intense shock which deformed its melilite and recrystallized perovskite, augite, and spinel in fine-grained regions. Cottonwood shows evidence of recrystallization and chemical changes consistent with thermal metamorphism occurring after accretion into the parent-body.

V E Burnette↗

Digitalization, Management and Synthesis of Thermal Hydraulic Legacy Data Using Dynamical System Scaling

A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. The safety case for nuclear systems is a written, traceable expression of safety, demonstrating the system’s protective measures against uncontrolled radiological releases. The nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments are typically conducted in sub-scale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically but subject to proven similarity criteria, often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena. As we move into the digital world, capturing this multi-decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years FPoliSolutions has been actively developing an enterprise digital data ontology platform (FPoliDON) intended to do just that. The concept was introduced in a paper at the previous NURETH edition and has been significantly enhanced since. With the vision of helping designers use the data to assess their safety case evaluation models, the platform was designed not only to ontologically organize the experimental data, but also to guide the user in the complex analytics associated with the interpretation and use of such test results. A new feature which has been recently added is the ability for the user to access a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the Dynamical System Scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform. These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across SETs and IETs which has the promise to yield efficiency in the evaluation code assessment activities. This is often a daunting and expensive task to set up evaluation models that support the safety case of current and future nuclear power plants.

Reyes, Jose↗

Recent SCALE Activities Within the Nuclear Criticality Safety Program

The role of SCALE in the Department of Energy’s Nuclear Criticality Safety Program (NCSP) is defined according to the Five-Year Plan, and summarized as follows. 1. Sustain/improve SCALE NCS features through user driven enhancements, software quality assurance (SQA) and validation and verification (V&V). 2. Assure adaptability to various computing platforms and compilers. 3. Improve user interfaces/documentation consistent with modern engineering software. 4. Support responsive communication to SCALE criticality safety users through SCALE Newsletters, email notices, and updates on the SCALE website. 5. Modernize software infrastructure and capabilities to improve quality and reliability and to ensure long term sustainability of the NCS capabilities. This paper describes the specific activities which have been performed within each area above for the NCSP in the fiscal year (FY) 2018, from October 1, 2017, to September 30, 2018.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗

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