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NASA NTRS2003

HEASARC Software Archive

(1) Chandra Archive: SAO has maintained the interfaces through which HEASARC gains access to the Chandra Data Archive. At HEASARC's request, we have implemented an anonymous ftp copy of a major part of the public archive and we keep that archive up-to- date. SAO has participated in the ADEC interoperability working group, establishing guidelines or interoperability standards and prototyping such interfaces. We have provided an NVO-based prototype interface, intending to serve the HEASARC-led NVO demo project. HEASARC's Astrobrowse interface was maintained and updated. In addition, we have participated in design discussions surrounding HEASARC's Caldb project. We have attended the HEASARC Users Group meeting and presented CDA status and developments. (2) Chandra CALDB: SA0 has maintained and expanded the Chandra CALDB by including four new data file types, defining the corresponding CALDB keyword/identification structures. We have provided CALDB upgrades for the public (CIAO) and for Standard Data Processing. Approximately 40 new files have been added to the CALDB in these version releases. There have been in the past year ten of these CALDB upgrades, each with unique index configurations. In addition, with the inputs from software, archive, and calibration scientists, as well as CIAO/SDP software developers, we have defined a generalized expansion of the existing CALDB interface and indexing structure. The purpose of this is to make the CALDB more generally applicable and useful in new and future missions that will be supported archivally by HEASARC. The generalized interface will identify additional configurational keywords and permit more extensive calibration parameter and boundary condition specifications for unique file selection. HEASARC scientists and developers from SAO and GSFC have become involved in this work, which is expected to produce a new interface for general use within the current year. (3) DS9: One of the decisions that came from last year's HEADCC meeting was to make the ds9 image display program the primary vehicle for displaying line graphics (as well as images). The first step required to make this possible was to enhance the line graphics capabilities of ds9. SAO therefore spent considerable effort upgrading ds9 to use Tcl 8.4 so that the BLT line graphics package could be built and imported into ds9 from source code, rather than from a pre-built (and generally outdated) shared library. This task, which is nearly complete, allows us to extend BLT as needed for the HEAD community. Following HEADCC discussion concerning archiving and the display of archived data, we extended ds9 to support full access to many astronomical Web-based archives sites, including HEASARC, MAST, CHANDRA, SKYVIEW, ADS, NED, SIMBAD, IRAS, NVRO, SAO TDC, and FIRST. Using ds9's new internal Web access capabilities, these archives can be accessed via their Web page. FITS images, plots, spectra, and journal abstracts can be referenced, down-loaded, and displayed directly and easily in ds9. For more information, see: http://hea-www.harvard.edu/saord/ds9. Also after the HEADCC discussion concerning region filtering, we extended the Funtools sample implementation of region filtering as described in: http://hea-www.harvard.edu/saord/funtools/regions.html. In particular, we added several new composite regions for event and image filtering, including elliptical and box annuli. We also extended the panda (Pie AND Annulus) region support to include box pandas and elliptical pandas. These new composite regions are especially useful in programs that need to count photons in each separate region using only a single pass through the data. Support for these new regions was added to ds9. In the same vein, we developed new region support for filtering images using simple FITS image masks, i.e. 8-bit or 16-bit FITS images where the value of a pixel is the region id number for that pixel. Other important enhancements to DS9 this year, include supporor multiple world coordinate systems, three dimensional event file binning, image smoothing, region groups and tags, the ability to save images in a number of image formats (such as JPEG, TIFF, PNG, FITS), improvements in support for integrating external analysis tools, and support for the virtual observatory. In particular, a full-featured web browser has been implemented within D S 9 . This provides support for full access to HEASARC archive sites such as SKYVIEW and W3BROWSE, in addition to other astronomical archives sites such as MAST, CHANDRA, ADS, NED, SIMBAD, IRAS, NVRO, SA0 TDC, and FIRST. From within DS9, the archives can be searched, and FITS images, plots, spectra, and journal abstracts can be referenced, downloaded and displayed The web browser provides the basis for the built-in help facility. All DS9 documentation, including the reference manual, FAQ, Know Features, and contact information is now available to the user without the need for external display applications. New versions of DS9 maybe downloaded and installed using this facility. Two important features used in the analysis of high energy astronomical data have been implemented in the past year. The first is support for binning photon event data in three dimensions. By binning the third dimension in time or energy, users are easily able to detect variable x-ray sources and identify other physical properties of their data. Second, a number of fast smoothing algorithms have been implemented in DS9, which allow users to smooth their data in real time. Algorithms for boxcar, tophat, and gaussian smoothing are supported.

White, Nicholas↗

NASA NTRS2005

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Cloud microphysics are inevitable affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds, Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effect of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bim microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.

Tao, Wei-Kuo↗

NASA NTRS2007

Influences of Local Sea-Surface Temperatures and Large-scale Dynamics on Monthly Precipitation Inferred from Two 10-year GCM-Simulations

Two parallel sets of 10-year long: January 1, 1982 to December 31, 1991, simulations were made with the finite volume General Circulation Model (fvGCM) in which the model integrations were forced with prescribed sea-surface temperature fields (SSTs) available as two separate SST-datasets. One dataset contained naturally varying monthly SSTs for the chosen period, and the oth& had the 12-monthly mean SSTs for the same period. Plots of evaporation, precipitation, and atmosphere-column moisture convergence, binned by l C SST intervals show that except for the tropics, the precipitation is more strongly constrained by large-scale dynamics as opposed to local SST. Binning data by SST naturally provided an ensemble average of data contributed from disparate locations with same SST; such averages could be expected to mitigate all location related influences. However, the plots revealed: i) evaporation, vertical velocity, and precipitation are very robust and remarkably similar for each of the two simulations and even for the data from 1987-ENSO-year simulation; ii) while the evaporation increased monotonically with SST up to about 27 C, the precipitation did not; iii) precipitation correlated much better with the column vertical velocity as opposed to SST suggesting that the influence of dynamical circulation including non-local SSTs is stronger than local-SSTs. The precipitation fields were doubly binned with respect to SST and boundary-layer mass and/or moisture convergence. The analysis discerned the rate of change of precipitation with local SST as a sum of partial derivative of precipitation with local SST plus partial derivative of precipitation with boundary layer moisture convergence multiplied by the rate of change of boundary-layer moisture convergence with SST (see Eqn. 3 of Section 4.5). This analysis is mathematically rigorous as well as provides a quantitative measure of the influence of local SST on the local precipitation. The results were recast to examine the dependence of local rainfall on local SSTs; it was discernible only in the tropics. Our methodology can be used for computing relationship between any forcing function and its effect(s) on a chosen field.

Sud, Y. C.↗

NASA NTRS2011

Evaluation of Pulse Counting for the Mars Organic Mass Analyzer (MOMA) Ion Trap Detection Scheme

The Mars Organic Mass Analyzer is being developed at Goddard Space Flight Center to identify organics and possible biological compounds on Mars. In the process of characterizing mass spectrometer size, weight, and power consumption, the use of pulse counting was considered for ion detection. Pulse counting has advantages over analog-mode amplification of the electron multiplier signal. Some advantages are reduced size of electronic components, low power consumption, ability to remotely characterize detector performance, and avoidance of analog circuit noise. The use of pulse counting as a detection method with ion trap instruments is relatively rare. However, with the recent development of high performance electrical components, this detection method is quite suitable and can demonstrate significant advantages over analog methods. Methods A prototype quadrupole ion trap mass spectrometer with an internal electron ionization source was used as a test setup to develop and evaluate the pulse-counting method. The anode signal from the electron multiplier was preamplified. The an1plified signal was fed into a fast comparator for pulse-level discrimination. The output of the comparator was fed directly into a Xilinx FPGA development board. Verilog HDL software was written to bin the counts at user-selectable intervals. This system was able to count pulses at rates in the GHz range. The stored ion count nun1ber per bin was transferred to custom ion trap control software. Pulse-counting mass spectra were compared with mass spectra obtained using the standard analog-mode ion detection. Prelin1inary Data Preliminary mass spectra have been obtained for both analog mode and pulse-counting mode under several sets of instrument operating conditions. Comparison of the spectra revealed better peak shapes for pulse-counting mode. Noise levels are as good as, or better than, analog-mode detection noise levels. To artificially force ion pile-up conditions, the ion trap was overfilled and ions were ejected at very high scan rates. Pile-up of ions was not significant for the ion trap under investigation even though the ions are ejected in so-called 'ion-micro packets'. It was found that pulse counting mode had higher dynamic range than analog mode, and that the first amplification stage in analog mode can distort mass peaks. The inherent speed of the pulse counting method also proved to be beneficial to ion trap operation and ion ejection characterization. Very high scan rates were possible with pulse counting since the digital circuitry response time is so much smaller than with the analog method. Careful investigation of the pulse-counting data also allowed observation of the applied resonant ejection frequency during mass analysis. Ejection of ion micro packets could be clearly observed in the binned data. A second oscillation frequency, much lower than the secular frequency, was also observed. Such an effect was earlier attributed to the oscillation of the total plasma cloud in the ion trap. While the components used to implement pulse counting are quite advanced, due to their prevalence in consumer electronics, the cost of this detection system is no more than that of an analog mode system. Total pulse-counting detection system electronics cost is under $250

Van Amerom, Friso H.↗

NASA NTRS2012

Calculating Soil Wetness, Evapotranspiration and Carbon Cycle Processes Over Large Grid Areas Using a New Scaling Technique

Soil wetness typically shows great spatial variability over the length scales of general circulation model (GCM) grid areas (approx 100 km ), and the functions relating evapotranspiration and photosynthetic rate to local-scale (approx 1 m) soil wetness are highly non-linear. Soil respiration is also highly dependent on very small-scale variations in soil wetness. We therefore expect significant inaccuracies whenever we insert a single grid area-average soil wetness value into a function to calculate any of these rates for the grid area. For the particular case of evapotranspiration., this method - use of a grid-averaged soil wetness value - can also provoke severe oscillations in the evapotranspiration rate and soil wetness under some conditions. A method is presented whereby the probability distribution timction(pdf) for soil wetness within a grid area is represented by binning. and numerical integration of the binned pdf is performed to provide a spatially-integrated wetness stress term for the whole grid area, which then permits calculation of grid area fluxes in a single operation. The method is very accurate when 10 or more bins are used, can deal realistically with spatially variable precipitation, conserves moisture exactly and allows for precise modification of the soil wetness pdf after every time step. The method could also be applied to other ecological problems where small-scale processes must be area-integrated, or upscaled, to estimate fluxes over large areas, for example in treatments of the terrestrial carbon budget or trace gas generation.

Sellers, Piers↗

NASA NTRS2016

Extraction and Capture of Water from Martian Regolith Experimental Proof-of-Concept

A novel concept for extraction of water from the Mars soil in a real-time, open-air process was demonstrated in a Mars environment chamber. The concept breadboard uses radiative heating to bake off water from exposed soil contained in a bin. An enclosure, intended to mimic the bottom of a rover, covers the bin. A fan continuously blows the Mars atmospheric gases through the enclosure to collect the evolved water while a tiller was used to churn up moist subsurface soil. These initial tests verified concept feasibility. The sweep gas generated by commercially available muffin fans at 7 Torr was sufficient to transfer water vapor into a condenser flow loop. The radiative heating, while non-optimized, heated the soil surface to 60 C to generate water vapor. A rototiller working through the soil bin brought sufficient amounts of new moist soil to the heated surface to show an increase in rate of water extraction.

Mars Exploration↗

NASA NTRS2015

Intensity Conserving Spectral Fitting

The detailed shapes of spectral line profiles provide valuable information about the emitting plasma, especially when the plasma contains an unresolved mixture of velocities, temperatures, and densities. As a result of finite spectral resolution, the intensity measured by a spectrometer is the average intensity across a wavelength bin of non-zero size. It is assigned to the wavelength position at the center of the bin. However, the actual intensity at that discrete position will be different if the profile is curved, as it invariably is. Standard fitting routines (spline, Gaussian, etc.) do not account for this difference, and this can result in significant errors when making sensitive measurements. Detection of asymmetries in solar coronal emission lines is one example. Removal of line blends is another. We have developed an iterative procedure that corrects for this effect. It can be used with any fitting function, but we employ a cubic spline in a new analysis routine called Intensity Conserving Spline Interpolation (ICSI). As the name implies, it conserves the observed intensity within each wavelength bin, which ordinary fits do not. Given the rapid convergence, speed of computation, and ease of use, we suggest that ICSI be made a standard component of the processing pipeline for spectroscopic data.

Klimchuk, J. A.↗

NASA NTRS2018

Towards an Enhanced Droplet Activation Scheme for Multi-Moment Bulk Microphysics Schemes

Initial droplet spectra produced upon activation impact the ensuing chain of microphysical processes andtherefore play a crucial role in cloud evolution. This work re-examines dependencies of newly formed clouddroplet size distribution (CDSD) characteristics on environmental and aerosol properties via parcel model simulationsthat serve as the basis for a multi-moment bulk microphysics droplet activation scheme suitable for acloud-resolving model (CRM). It is found that applying a fixed size threshold to define activated droplets versusemploying physical considerations can lead to erroneous activation and overly broad CDSDs for high aerosolconcentration and weak updraft conditions. Aerosol distributions characterized by larger median sizes and/orincreased solubility can result in greater activated droplet numbers, whereas impacts of these parameters onCDSD spectral width depend on both aerosol number concentration and updraft velocity. An expansion of theactivation scheme to include CDSD spectral width is proposed to aid efforts to extend high-order momentprediction to cloud droplet categories in CRMs as well as better represent variability in the activation process onthe cloud scale.simulations to investigate the regime dependence of the relative dispersion(d)1 of newly activated CDSDs, where d is the ratio of dropletradius standard deviation (σ) to the mean radius (r ). C16 demonstratedthat increasing Na resulted in increasing (decreasing) d values via reducedcondensational narrowing (spectral broadening) rates within theAL (UL) regime, with d values peaking in the TR regime. Their findingssuggest a similar regime dependence for d as R09 noted for Nc and helpexplain reportedly conflicting relationships between Na and CDSDspectral characteristics (cf. Hudson and Noble, 2014; Liu et al., 2014),although the applicability of these results within bulk microphysicalschemes was not addressed.Simulating aerosol-cloud interactions with CRMs employing bulkmicrophysics requires that the scheme minimally predict two CDSDparameters, namely mass and number concentrations, and represent thedroplet activation process. Various activation schemes aim to determineNc from aerosol and environmental properties and include analyticalexpressions (e.g., Abdul-Razzak et al., 1998; Morrison et al., 2005) aswell as lookup tables (LUTs) based on detailed parcel model calculations(e.g., Saleeby and Cotton, 2004, hereafter SC04; Segal and Khain,2006; Thompson and Eidhammer, 2014). Expressions to diagnose CDSDspectral width from Nc (Grabowski, 1998; Liu et al., 2006; Morrison andGrabowski, 2007) or cloud water content (Geoffroy et al., 2010) havealso been developed, although more robust methods to obtain CDSDspectral width upon activation are presently lacking. This latter point isrelevant for triple-moment (3 M) bulk microphysics that aim to predictdistribution spectral width alongside number and mass concentrations(e.g., Loftus et al., 2014; Milbrandt and Yau, 2005).The current work extends the findings of C16 to the current LUTbasedaerosol activation scheme used in the Regional AtmosphericModeling System (RAMS) (Cotton et al., 2003; SC04; Saleeby and vanden Heever, 2013, hereafter SvdH13) and additionally examinesaerosol size and solubility impacts on newly activated CDSD properties.Because early cloud development processes such as condensationalgrowth, evaporation, and droplet self-collection depend on and impactCDSD spectral width (Hudson and Yum, 1997; Seifert and Beheng 2001;Lu and Seinfeld, 2006; Igel and van den Heever, 2017), an expansion ofthe activation LUTs to include CDSD spectral width is proposed as apreliminary step for extending 3M prediction to CDSDs in CRMs forimproved simulations of aerosol-cloud interactions.2. MethodologyThe current RAMS two-moment microphysics module determinesthe fractional number of aerosol particles that activate to cloud dropletsfrom five-dimensional LUTs based on model predicted air temperature(T), w, Na, and the geometric median radius (rg) and soluble fraction (ε)of the aerosol size distribution (SvdH13). These LUTs are created offlineusing a one-dimensional Lagrangian adiabatic parcel model (Feingoldand Heymsfield, 1992; Heymsfield and Sabin, 1989; SC04) to simulateexplicit droplet activation and initial CDSD growth for a range of ambientatmospheric conditions [T, w] and binned lognormal aerosol sizedistributions given by= ⎡⎣ ⎢− ⎤⎦ ⎥N r Nr π σr rσ( )2 lnexp[ln( / )]2(ln )aggg22 (1)where r is the dry aerosol particle bin radius and σg is the geometricstandard deviation of the distribution. As the parcel model simulationsfocus on the activation process, other processes such as coalescence,sedimentation, and mixing are not considered. Details of the parcelmodel can be found in SC04 and SvdH13, and only a brief description isprovided here. At the onset of parcel model calculations, the initiallydry aerosol particles in all bins first deliquesce and reach theirequilibrium diameters in a sub-saturated environment based on theKöhler equation for solution droplets. The parcel is then lifted at a fixedupward velocity w, and particle growth by vapor diffusion, along withconcurrent changes in the ambient environment, are iteratively computedusing the Variable-coefficient Ordinary Differential Equation(VODE) solver (Brown et al., 1989). The time resolution of these calculationsis determined within the VODE solver, and the frequency atwhich the solver is called is controlled by a longer model time stepbased on fixed upward parcel displacement increments (Δz) at thespecified w (Δt=Δz/w). Model calculations proceed until the parcelreaches a height 50m beyond the level of maximum saturation ratio(Smax) or total parcel displacement exceeds 2 km. Upon model termination,Smax and the fractional number of aerosols (factv) resulting innewly formed cloud droplets, defined as particles having diameters of atleast 2 μm, are cataloged in the LUTs according to the specified T, w, Na,rg, and ε parameter values.A critical point regarding the creation of these LUTs is the use of afixed minimum diameter (Dmin) to define cloud droplets in the parcelmodel, which can produce erroneous CDSD characteristics, particularlywithin the UL regime. For aerosol distributions with large rg valuesunder low SS conditions, for example, deliquesced aerosols within thelarge tail of the distribution can exceed 2 μm in diameter yet remainunactivated as ‘haze’ particles (Levin and Cotton, 2009; McFigganset al., 2006). For this study, aerosol particles activate to cloud dropletsbased on the critical diameter Dcrit as a function of parcel supersaturationratio (Sr) as in R09:D = σ MS RTρ83 ln( ) critsol wr w (2)where σsol is the surface tension of a solution droplet, Mw and ρw are themolar mass and density of liquid water, respectively, and R is theuniversal gas constant. Additionally, at relatively large w values withinthe AL regime, Nc stabilizes shortly after reaching supersaturation.However, parcel ascent and condensational growth continue beyondthe level of Smax, potentially causing additional narrowing of the CDSD.In the current work, model calculations terminate upon reaching Smaxas changes in Nc are negligible with continued ascent (Peng et al., 2007;R09).Parcel model simulations are performed to examine the sensitivitiesof CDSD characteristics to w, Na, rg, and ε, with the ranges for theseparameters listed in Table 1. Aerosols are assumed to be a mix of solubleand insoluble material of equal density, specified by ε, where fullysoluble aerosols correspond to ammonium sulfate with hygroscopicityparameter κ=0.61 (Petters and Kreidenweis, 2007). FollowingSvdH13, aerosol geometric standard deviation is fixed at σg=1.8, andaerosol distributions (Eq. 1) are partitioned into 100 logarithmicallyspacedbins spanning a size range specific to each rg value. For all simulations,Δz=1 m, and initial values of relative humidity, air temperatureand pressure are set to RH=0.99, T=10 °C and p=900 hPa,respectively.

Loftus, Adrian M.↗

NASA NTRSDate not supplied

Monitoring Airspace Complexity and Determining Contributing Factors

The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.

complexity↗

NASA NTRSDate not supplied

Examining the Feasibility of Detailed Decomposition of Radiative Flux Anomalies to Cloud Changes

This presentation will explore the degree to which radiative flux anomalies in the last two decades seen by CERES can be interpreted as the result of changes in particular cloud types. A study of this kind is potentially enabled by the new CERES FlxByCldTyp (FBCT) product which provides combined Terra and Aqua daytime 1°-regional gridded daily and monthly top-of-the-atmosphere radiative fluxes and associated MODIS-derived cloud properties stratified by Cloud Top Pressure (CTP) and Cloud Optical Thickness (TAU), i.e., arrays of cloud properties and radiative fluxes resolved in CTP-TAU bins. It has been shown (Sun et al. 2022) that the FBCT time series of global flux anomalies tracks closely its SSF1deg and EBAF counterparts, indicating that these three products provide fluxes of consistent stability for interannual variability studies. We are thus in position to examine whether CERES regional or global radiative fluxes anomalies and trends are driven by changes in certain elements of FBCT flux arrays corresponding to particular cloud types. Anomalies in flux array elements will in turn be investigated in terms of cloud property variations within the CTP-TAU bins where the biggest flux anomalies are encountered. We will essentially seek to obtain the relative contributions to the anomalies of binned all-sky fluxes of changes in cloud fraction within cloud types and of changes in their overcast fluxes modulated by other cloud properties. Such an analysis holds great promise of unveiling the root causes of cloud-driven changes in the planetary radiation budget of the last two decades.

cloud↗

NASA NTRS2023

X-Ray Polarimetry of the Accreting Pulsar GX 301-2

The phase- and energy-resolved polarization measurements of accreting X-ray pulsars (XRPs) allow us to test different theoretical models of their emission, and they also provide an avenue to determine the emission region geometry. We present the results of the observations of the XRPGX 301−2 performed with the Imaging X-ray Polarimetry Explorer (IXPE). A persistent XRP, GX 301−2 has one of the longest spin periods known:∼680 s. A massive hyper-giant companion star Wray 977 supplies mass to the neutron star via powerful stellar winds. We did not detect significant polarization in the phase-averaged data when using spectropolarimetric analysis, with the upper limit on the polarization degree (PD)of 2.3% (99% confidence level). Using the phase-resolved spectropolarimetric analysis, we obtained a significant detection of polarization (above99% confidence level) in two out of nine phase bins and a marginal detection in three bins, with a PD ranging between∼3% and∼10% and a polarization angle varying in a very wide range from∼0◦to∼160◦. Using the rotating vector model, we obtained constraints on the pulsar geometry using both phase-binned and unbinned analyses, finding excellent agreement. Finally, we discuss possible reasons for a low observed polarization in GX 301−2.

GX 301-2, polarization, X-ray, X-ray binary, pulsa↗

NASA NTRSDate not supplied

GlennICE Simulation of 24, 28 and 36 Inch Diameter eVTOL Propellers in Forward Flight

Flow solutions and water collection results are presented for three different propeller sizes. Flow solutions were generated using the NASA FUN3D software, and water collection was simulated using NASA GlennICE software. The geometries considered represent experimental articles that have been tested in the NASA Icing Research Tunnel. Each configuration has four blades. For the experiment, the same spinner centerbody is used for each propeller size. The different sized blades are similar for radii larger than about 40% of total span. Operating conditions were chosen to match operating points where experimental data was collected. Results are presented for both single bin simulations, as well as 7-bin Langmuir D distributions. For 80 microns MVD the Langmuir D distribution showed small differences compared to the single bin. For 15 microns MVD the Langmuir D distribution had a larger impact, especially on the spinner. In the experiment, the centerbody was unchanged as propeller size was changed. In this paper, additional simulations were completed with the 36 inch case perfectly scaled down to 24 and 28 inch. The perfect geometric scaling allows for an assessment of the effect of constant centerbody size on the results. While slight differences in pressure distribution on the propeller are visible between scaled and non-scaled centerbody, the effect seems quite minimal regarding water collection. All simulations were run very cold to ensure rime ice, since handling runback water in the non-inertial frame is beyond the scope of the present work. Ice shapes were generated using a single time step growth. The total mass on the blade and spinner was generally underpredicted for 15 microns MVD, and overpredicted for 80 microns MVD. When comparing ice shapes at operating conditions the numerical results produce very similar ice shapes.

Aircraft Icing↗

NASA NTRSDate not supplied

GlennICE Simulation of 24, 28 and 36 Inch Diameter eVTOL Propellers in Forward Flight

Aircraft Icing↗

DOE OSTI2020

Measurement of 3-Flavour Neutrino Oscillation Parameters in the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment consisting of two functionally identical tracking calorimeters, a Near and Far Detector, that measure neutrino interactions induced by the Fermi National Accelerator Laboratory’s NuMI beam at baselines of 1 km and 810 km, respectively. The NuMI beam can be configured to produce either a primary ν µ neutrino or $\bar{ν}$ µ anti-neutrino beam. Neutrino oscillations are observed and measured by the analysis of ν µ + $\bar{ν}$ µ disappearance and νe + $\bar{ν}$ e appearance in the beam, comparing the neutrino energy spectra in the Near and Far detectors means that neutrino oscillation parameters sin 2 θ 23 , |Δ$m^2_{32}$|, and δ CP can be constrained. This thesis presents the 2018 NOvA ν µ +$\bar{ν}$ µ disappearance, ν e +$\bar{ν}$ e appearance, and combined analyses using both neutrino and anti-neutrino data, where oscillation fits have been performed, where possible, on an event-by-event basis rather than on a bin-by-bin basis as has conventionally been used in NOvA oscillation analyses. This allows for better precision in applying both neutrino oscillation probabilities and systematic uncertainties. Furthermore, oscillation analyses for the disappearance, appearance, and combined channels are presented using an unbinned likelihood fit and compared with the equivalent binned χ 2 likelihood fit used in the standard analysis. The 14 ktonne detector equivalent beam exposures used for this thesis are 8.85 × 10 20 and 6.91 × 10 20 protons on target for neutrino and antineutrino data respectively, corresponding to 5 years of NOvA data taking. A combined ν µ + $\bar{ν}$ µ disappearance and ν e + $\bar{ν}$ e appearance fit to the Far Detector data, assuming normal mass ordering and using the event-by-event oscillation and unbinned fitting methodologies, produces oscillation parameter constraints of Δ$m^2_{32}$ = (2.50$^{+0.08}_{-0.06}$) × 10 -3 eV 2 , sin 2 θ 23 = 0.59$^{+0.02}_{-0.04}$, and δ CP = 0.72$^{+0.5}_{-0.9}$π.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Examination of Digital-Delay Rossi-$\alpha$ for 252 Cf-Driven Highly Enriched Uranium Using Organic Scintillators

Neutron noise techniques constitute several analysis methods applicable to non-destructive assay. One technique is the Rossi-α method to calculate the prompt neutron decay constant (α) or its inverse, the prompt neutron period (1/α), for assemblies of fissionable material. This work evaluates a high-data-throughput measurement at the National Criticality Experiments Research Center (NCERC) with organic scintillators measuring a subcritical assembly of highly enriched uranium (HEU) metal (93% 235 U). The assembly comprises hemi shells stacked together to form fully closed shells and is driven by a Cf source at the center. The assembly is a total of 59.85 kg HEU and a k eff of 0.99, according to MCNP6.2 KCODE simulations. Measurements were acquired with a three-by-four array of 5.08-cm-diameter by 5.08-cm-length trans-stilbene crystals 166 cm from the assembly center. Two types of coincident binning methods are used to build the Rossi-α distribution of coincident neutron detections: A) type 1 binning, also known as any-and-all forward time differences, and B) type 1 binning with a digital-delay technique that is analogous to Orndoff’s use of delay cabling in early Rossi-alpha measurements. Method B) disregards same-detector coincidences and once all nearest time coincidences are collected between all detectors after a trigger, a time delay of 0.75 µs is implemented. The measured prompt neutron decay constants for both techniques are calculated from single exponential fits and the two methods are compared. Method A) shows an apparent timing discontinuity near 500 ns time differences. This timing discontinuity interferes with the fitting method used to calculate the prompt neutron decay constant. Method B) mitigates the artificial timing discontinuity and removes the disagreement of the fit. Future work will model the time-dependent detector response to discern why this timing discontinuity occurs, discern how Method B) reduces the amplitude of this timing discontinuity, and apply this method to critical assembly measurements to ultimately confirm the recommendation to use Method B) for high-data-throughput measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

A Custom Machine to Convey Radiologically Impacted Cohesive Soils to the Orion ScanSortSM System - 20382

An established method to reduce the volume of material required to be disposed of as radioactive waste is conveyor-based sorting of potentially radiologically impacted soils. With the Orion ScanSortSM system, potentially impacted soils are conveyed beneath radiation detectors and sorted into above and below criteria bins based on the detectors' response. Confident measurements can only be efficiently achieved when the soil column being conveyed has a reasonably consistent geometry. Highly cohesive soils present a very significant challenge as they tend to clump and adhere to the conveyor hopper, strike-off bar, and belt skirting. This causes voids and valleys in the soil column that affect the measurement geometry and reduce the confidence in the measurement. The reduction in confidence necessitates a longer residence time (i.e. reduction in conveyor belt speed and processing rates) or that the soils in highly unfavorable geometries be dispositioned as impacted or segregated for resurvey (should site logistics support that option). These outcomes may increase the volume of material required to be dispositioned as radiological waste and have a negative impact on project cost and schedule. To minimize impact to sorting operations, Wood designed and built a customized Extruder, to optimize the soil column prior to assay. The design was developed and refined to minimize the volume of highly cohesive soils presented in highly unfavorable geometries in the soil column in order to confidently assess and disposition such soils in a high-production environment. The Extruder is a customized 90 cm wide flat conveyor that has an oversized hopper with a pair of motor-driven rollers that force the soils in the hopper through an opening of adjustable height. The soils are extruded through the opening to produce a soil column that is ∼75 cm wide and ∼8-18 cm deep with a design rate of 225 metric tons/hr. The Extruder was recently deployed with the Orion ScanSort{sup SM} System to a site in northeast Ohio. The site has highly cohesive soils that result from the weathering of glacial sediments and consist of clay and clay loam, resulting in poor drainage and high moisture content. The project infrastructure only supported two bins of material (above and below criteria), which necessitated that soils with unfavorable geometries be discharged into the above criteria bin, along with material determined to have been contaminated. The Extruder was extremely successful in producing a stable soil column with favorable geometries for radiological assay. During production, less than 0.5% (by mass) of the soils processed were presented in unsatisfactory geometries requiring disposition as radioactive waste. The Extruder did not get clogged due to the cohesive soils (as is typical with conventional conveyors) and required very little maintenance, again minimizing impacts to sorting operations. The conveyor belt speed was operated at 14 cm/s with a typical belt loading of ∼109 kg/m resulting in a mean process rate of ∼49 metric tons/hr. The process rate was constrained by other project logistics, rather than by the capabilities of the Extruder, which was operated at less than 20% of the maximum design speed (75 cm/s). It is concluded that the Extruder is highly likely to produce a stable highly cohesive soil column suitable for efficient and confident radiological assay in production environments of 250 short tons/hr or more. The Extruder has the capability to drastically reduce the duration and costs of projects with radiologically impacted highly cohesive soils. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Timelike Compton Scattering from a longitudinally polarised target with CLAS12 at Jefferson Lab

Explorations into the internal dynamics of hadrons are constantly evolving, and the requirement for experimental results to verify theoretical models of hadron structure is paramount. A key area in this field is the study of Generalised Parton Distributions (GPDs), which are functions used to model the momenta of quarks and gluons within hadrons, and the methods to access GPDs experimentally. One such scattering process that allows access to these is Timelike Compton Scattering. TCS complements existing Deeply Virtual Compton Scattering experiments and allows investigation into the universality of GPDs through access to the real and imaginary parts of the parton helicity independent GPD Hq via beam spin asymmetries (BSA), and it provides novel access to the real and imaginary parts of the parton helicity dependent GPD ˜Hq through target polarisation asymmetries (TSA). This thesis work presents a comparative study with the first published BSA for TCS at the Thomas Jefferson National Accelerator Facility (JLab), alongside a first time extraction of a Target Spin Asymmetry with the Summer 2022 data taking run. JLab hosts the Continuous Electron Beam Accelerator Facility (CEBAF) which provides a 12 GeV electron beam to four experimental halls. Hall-B contains the CEBAF Large Acceptance Spectrometer, which took data across three run periods on a longitudinally polarised NH3 and ND3 fixed target from 2022-2023, to extract measurements of electron-proton scattering, from which a TCS signal could be extracted. The thesis discusses work done to understand and eliminate contributions from the non-/low-polarised nuclear background, testing pre-established cuts to eliminate pion background from a dilepton (e+e-) final state and modifying them as needed for the new experimental run, and attempts to hone in on a clean TCS signal from which to extract the two asymmetry observables. A comparison with existing BSA results was performed; however, the statistical errors are too large to draw a significant conclusion as to whether there is agreement across each bin. More data is needed for a multidimensionally binned extraction. A proof of principle was achieved in the TSA measurements, with two out of four kinematic bins showing preliminary agreement in shape with theoretical values. Again, the errors are significant due to the contributions from the nuclear background. To support these conclusions, a further study was done, which takes into account an estimate of the asymmetries with the full available dataset (this thesis is based only on data taken in the summer set; at the time of writing processing was still being conducted for the final two datasets), as well as an estimate including additional future experiment days that were awarded in July 2024. Additional work was done on a secondary project exploring the feasibility of measuring TCS at the upcoming Electron Ion Collider, supporting the design proposal for the detector for the first interaction region and giving a positive outlook for the future of these types of measurements beyond JLab.

Gates, Kayleigh [Univ. of Glasgow, Scotland (Unite↗

DOE OSTI2026

Clustering of high-redshift quasars with DESI DR2

We present clustering measurements for high-redshift quasars using data from the Dark Energy Spectroscopic Instrument Data Release 2. Our sample consists of quasars with $2.0 < z < 3.5$ in the luminosity range $M_{1450} \leq -19.94$ mag. We measure the mean quasar bias $b_Q(\bar{z} = 2.48) = 3.61 \pm 0.01$ for the full sample of $\sim 715,000$ quasars and quantify the redshift evolution of quasar bias by dividing the sample into four equal redshift bins. There is strong evolution of the quasar bias with redshift that is well fit by the function $b_Q(z) = a [(1 + z)^2 - 6.565] + b$ with $a=0.230 \pm 0.007$ and $b=2.394 \pm 0.035$, and this fit is also a good match to lower redshift measurements in the literature. This bias evolution is consistent with a characteristic halo mass of $\bar{M}_{\mathrm{h}} \sim 10^{12}\,\mathrm{M_\odot}$ that does not vary significantly with redshift. The inferred duty cycles for quasars in our sample are $f_{\mathrm{duty}} \sim 10^{-2}$, staying mostly constant over redshifts. We investigate the luminosity dependence of quasar clustering by dividing each of our four redshift bins into three luminosity bins. The size of our quasar sample permits the first statistically significant measurement of the luminosity dependence of quasar bias at these redshifts. We measure weak dependence of quasar bias on luminosity at fixed redshift, inconsistent with no dependence, but weaker than predicted by a model in which quasar luminosity is tightly correlated with halo mass. These clustering measurements provide a stringent test for models of active black hole light curves and the black hole-halo connection at high redshift.