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NASA System-Level Design, Analysis and Simulation Tools Research on NextGen

A review of the research accomplished in 2009 in the System-Level Design, Analysis and Simulation Tools (SLDAST) of the NASA's Airspace Systems Program is presented. This research thrust focuses on the integrated system-level assessment of component level innovations, concepts and technologies of the Next Generation Air Traffic System (NextGen) under research in the ASP program to enable the development of revolutionary improvements and modernization of the National Airspace System. The review includes the accomplishments on baseline research and the advancements on design studies and system-level assessment, including the cluster analysis as an annualization standard of the air traffic in the U.S. National Airspace, and the ACES-Air MIDAS integration for human-in-the-loop analyzes within the NAS air traffic simulation.

Bardina, Jorge

Korean coastal water depth/sediment and land cover mapping /1:25,000/ by computer analysis of Landsat imagery

Computer analysis was applied to single data Landsat MSS imagery of a coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map, and featuring large dynamic sediment transport processes. Supervised image processing yielded a test classification map containing five water depth/sediment classes, two shoreline/tidal classes and five coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%; the training sets were selected by direct examination of the digitally displayed imagery. The unsupervised ISOCLAS (Senkus, 1976) clustering analysis was performed to assess the relative value of this approach to image classification in areas of sparse or nonexistent ground control. Results indicate that it is feasible to produce quantitative maps for detailed study of dynamic coastal processes given a Landsat image data base at sufficiently frequent time intervals.

Park, K. Y.

The Hydrological Cycle and Ocean Circulation of the Maritime Continent in the Mid-Pliocene: Results From PlioMIP2

The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land–sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.

Hydrological cycle

Kinematic evidence of satellite galaxy populations in the potential wells of first-ranked cluster galaxies

The velocities of 38 centrally positioned galaxies (r much less than 100 kpc) were measured relative to the velocity of the first-ranked galaxy in 14 rich clusters. Analysis of the velocity distribution function of this sample and of previous data shows that the population cannot be fit by a single Gaussian. An adequate fit is obtained if 60 percent of the objects lie in a Gaussian with sigma = 250 km/s and the remainder in a population with sigma = 1400 km/s. All previous data sets are individually consistent with this conclusion. This suggests that there is a bound population of galaxies in the potential well of the central galaxy in addition to the normal population of the cluster core. This is taken as supporting evidence for the galactic cannibalism model of cD galaxy formation.

Cowie, L. L.

The Contribution of Ionizing Stars to the Far-Infrared and Radio Emission in the Galaxy

A summary of research activities carried out in this eighth and final progress report. The final report includes: this summary document, copies of three published research papers, plus a draft manuscript of a fourth research paper entitled "The Contribution of Ionizing Stars to the FarInfrared and Radio Emission in the Milky Way; Evidence for a Swept-up Shell and Diffuse Ionized Halo around the W4 Chimney/Supershell." The main activity during the final quarterly reporting period was research on W4, including analysis of the radio and far-infrared images, generation of shell models, a literature search, and preparation of a research manuscript. There will be additional consultation with co-authors prior to submission of the paper to the Astrophysical Journal. The results will be presented at the 4th Tetons Summer Conference on "Galactic Structure, Stars, and the ISM" in May 2000. In this fourth and last paper we show W4 has a swept-up partially ionized shell of gas and dust which is powered by the OCl 352 star cluster. Analysis shows there is dense interstellar material directly below the shell, evidence that that the lower W4 shell "ran into a brick wall" and stalled, whereas the upper W4 shell achieved "breakout" to form a Galactic chimney. An ionized halo is evidence of Lyman continuum leakage which ionizes the WIM (warm ionized medium). It has long been postulated that the strong winds and abundant ionizing photons from massive stars are responsible for much of the large scale structure in the interstellar medium (ISM), including the ISM in other galaxies. However standard HII region theory predicts few photons will escape the local HII region. The significance of W4 and this work is it provides a direct example of how stellar winds power a galactic chimney, which in turn leads to a low density cavity from which ionizing photons can escape to large distances to ionize the WIM.

Terebey, S.

Venus global radar reflectivity and correlations with elevation

A geological analysis of the global radar reflectivity data for Venus is presented. The statistical and map pattern distribution of the radar reflectivity rho is first analyzed in the context of the porosity structure of the surface. Rho is then correlated with elevation in order to examine the degree of topographic control on material properties which could depend on PT-sensitive chemical weathering phenomena. Comparison of the rms slope alpha and rho correlations with elevation demonstrates that there are distinct topographic zones defined on the basis of the rho and alpha trends. Finally, hierarchical clustering analysis is applied to the global rho, alpha, and 100-km baseline regional slope data sets in order to define radar property-related subregions.

Garvin, J. B.

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation

Assessing Dynamic Time Warping Techniques for Discriminating Seismic Sources at Local and Regional Distances

Effective monitoring of seismic explosions and hazard assessment relies heavily on the accurate discrimination of underground seismic sources. This study investigates the application of novel nonlinear alignment techniques, specifically Dynamic Time Warping (DTW), for event-type discrimination at regional and local distances. Building on prior research that used DTW and Elastic Shape Analysis (ESA) in discrimination at regional distances, we evaluate the performance of recently developed variants of DTW, including a method that employs Pearson cross-correlation as a measure of warping distance and a time distortion coefficient that quantifies the type and degree of time distortion between signals. By analyzing observational datasets that include different source types, we assess the performance of these approaches for realistic monitoring scenarios. Specifically, we consider a dataset recorded at regional distances in the Korean Peninsula and a local-distance subset from the Unconstrained Utah Event Bulletin catalog to evaluate DTW-based discrimination across multiple distance scales. Additionally, we introduce the maximum cross-correlations of warped waveforms as a similarity metric for event classification. Through hierarchical cluster analysis and dendrogram interpretation, we present our findings, highlighting the strengths and limitations of these techniques in seismic event classification.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF

Leonid Storm Flux Analysis From One Leonid MAC Video AL50R

A detailed meteor flux analysis is presented of a seventeen-minute portion of one videotape, collected on November 18, 1999, during the Leonid Multi-instrument Aircraft Campaign. The data was recorded around the peak of the Leonid meteor storm using an intensified CCD camera pointed towards the low southern horizon. Positions of meteors on the sky were measured. These measured meteor distributions were compared to a Monte Carlo simulation, which is a new approach to parameter estimation for mass ratio and flux. Comparison of simulated flux versus observed flux levels, seen between 1:50:00 and 2:06:41 UT, indicate a magnitude population index of r = 1.8 +/- 0.1 and mass ratio of s = 1.64 +/- 0.06. The average spatial density of the material contributing to the Leonid storm peak is measured at 0.82 +/- 0.19 particles per square kilometer per hour for particles of at least absolute visual magnitude +6.5. Clustering analysis of the arrival times of Leonids impacting the earth's atmosphere over the total observing interval shows no enhancement or clumping down to time scales of the video frame rate. This indicates a uniformly random temporal distribution of particles in the stream encountered during the 1999 epoch. Based on the observed distribution of meteors on the sky and the model distribution, recommendations am made for the optimal pointing directions for video camera meteor counts during future ground and airborne missions.

Gural, Peter S.

Aerosol Seasonal Variations over Urban-Industrial Regions in Ukraine According to AERONET and POLDER Measurements

The paper presents an investigation of aerosol seasonal variations in several urban-industrial regions in Ukraine. Our analysis of seasonal variations of optical and physical aerosol parameters is based on the sun-photometer 2008-2013 data from two urban ground-based AERONET (AErosol RObotic NETwork) sites in Ukraine (Kyiv, Lugansk) as well as on satellite POLDER instrument data for urban-industrial areas in Ukraine. We also analyzed the data from one AERONET site in Belarus (Minsk) in order to compare with the Ukrainian sites. Aerosol amount and optical depth (AOD) values in the atmosphere columns over the large urbanized areas like Kyiv and Minsk have maximum values in the spring (April-May) and late summer (August), whereas minimum values are observed in late autumn. The results show that fine-mode particles are most frequently detected during the spring and late summer seasons. The analysis of the seasonal AOD variations over the urban-industrial areas in the eastern and central parts of Ukraine according to both ground-based and POLDER data exhibits the similar traits. The seasonal variation similarity in the regions denotes the resemblance in basic aerosol sources that are closely related to properties of aerosol particles. The behavior of basic aerosol parameters in the western part of Ukraine is different from eastern and central regions and shows an earlier appearance of the spring and summer AOD maxima. Spectral single-scattering albedo, complex refractive index and size distribution of aerosol particles in the atmosphere column over Kyiv have different behavior for warm (April-October) and cold seasons. The seasonal features of fine and coarse aerosol particle behavior over the Kyiv site were analyzed. A prevailing influence of the fine-mode particles on the optical properties of the aerosol layer over the region has been established. The back-trajectory and cluster analysis techniques were applied to study the seasonal back trajectories and prevailing directions of the arrived air mass for the Kyiv and Minsk sites.

Ukraine

Obtaining Accurate Change Detection Results from High-Resolution Satellite Sensors

Multi-date acquisitions of high-resolution imaging satellites (e.g. GeoEye and WorldView), can display local changes of current economic interest. However, their large data volume precludes effective manual analysis, requiring image co-registration followed by image-to-image change detection, preferably with minimal analyst attention. We have recently developed an automatic change detection procedure that minimizes false-positives. The processing steps include: (a) Conversion of both the pre- and post- images to reflectance values (this step is of critical importance when different sensors are involved); reflectance values can be either top-of-atmosphere units or have full aerosol optical depth calibration applied using bi-directional reflectance knowledge. (b) Panchromatic band image-to-image co-registration, using an orthorectified base reference image (e.g. Digital Orthophoto Quadrangle) and a digital elevation model; this step can be improved if a stereo-pair of images have been acquired on one of the image dates. (c) Pan-sharpening of the multispectral data to assure recognition of change objects at the highest resolution. (d) Characterization of multispectral data in the post-image ( i.e. the background) using unsupervised cluster analysis. (e) Band ratio selection in the post-image to separate surface materials of interest from the background. (f) Preparing a pre-to-post change image. (g) Identifying locations where change has occurred involving materials of interest.

multispectral

Biomass yields, reproductive fertility, compositional analysis, and genetic diversity of newly developed triploid giant miscanthus hybrids

Abstract Miscanthus × giganteus (giant miscanthus), first found as a naturally occurring hybrid, has shown promise as a bioenergy/biomass crop throughout much of the temperate world. This allotriploid (2 n = 3 x = 57) hybrid resulted from a cross between tetraploid Miscanthus sacchariflorus (2 n = 4 x = 76) and diploid Miscanthus sinensis (2 n = 2 x = 38) and is particularly desirable due to its low fertility that minimizes reseeding and potential invasiveness. However, there is limited genetic diversity in commonly grown cultivars of triploid M. × giganteus and breeding and development efforts to improve and domesticate this crop have been minimal. Here, we report on newly developed M. × giganteus hybrids compared with the industry standard M. × giganteus '1993‐1780'. Dry biomass yields of new hybrids ranged from 19.5 to 32.4 Mg/ha/year for the fourth growing season, compared with 21.0 Mg/ha/year for M. × giganteus '1993‐1780'. Plant reproductive fertility remained low for all accessions with overall fertility [(seed set × seed germination)/100] ranging from 0.3% to 4.5% for new hybrids compared to 0.4% for M. × giganteus '1993‐1780'. Culm density and height varied among accessions and were positively correlated with increased biomass. Based on compositional analyses, theoretical ethanol yields ranged from 9, 740 to 16,278 L/ha/year for new hybrids compared to 10,406 L/ha/year for M. × giganteus '1993‐1780'. Relative feed value indices were low overall and ranged between 66.0 and 72.8 for new hybrids compared to M. × giganteus '1993‐1780' with 71.3. The genetic diversity of new hybrids, compared with existing cultivars, was characterized using whole genome sequences. Based on pair‐wise distances, cluster analysis clearly showed increased diversity of new hybrids compared with earlier selections. These results document new triploid hybrids of M. × giganteus with enhanced biomass and theoretical ethanol yields in combination with broader genetic diversity and lowreproductive fertility.

Touchell, Darren H.

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou

Exploring the exact limits of the real-time equation-of-motion coupled cluster cumulant Green’s functions

In this paper, we analyze the properties of the recently proposed real-time equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green’s function approach [Rehr et al., J. Chem. Phys. 152, 174113 (2020)]. We specifically focus on identifying the limitations of the original time-dependent coupled cluster (TDCC) ansatz and propose an enhanced double TDCC ansatz, ensuring the exactness in the expansion limit. In addition, we introduce a practical cluster-analysis-based approach for characterizing the peaks in the computed spectral function from the RT-EOM-CC cumulant Green’s function approach, which is particularly useful for the assignments of satellite peaks when many-body effects dominate the spectra. Our preliminary numerical tests focus on reproducing, approximating, and characterizing the exact impurity Green’s function of the three-site and four-site single impurity Anderson models using the RT-EOM-CC cumulant Green’s function approach. The numerical tests allow us to have a direct comparison between the RT-EOM-CC cumulant Green’s function approach and other Green’s function approaches in the numerical exact limit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Temporal and Spatial Evolution of Non-Elastic Strain Accumulation in Stanstead Granite During Brittle Creep

Understanding the long-term behavior of brittle rocks requires fundamental consideration of time-dependent strain evolution and brittle creep processes. Previous studies have evaluated sub-critical crack growth during time-dependent deformation and damage evolution in brittle rocks; however, there is an incomplete knowledge of how damage evolves spatially and temporally within the body of intact rocks, where distributed regions of damage interact and coalesce during creep. This paper presents laboratory research focusing on evaluating brittle creep damage processes in Stanstead granite (SG) using 2-dimensional digital image correlation (2D-DIC). In the laboratory, the prismatic SG specimens were loaded beyond an estimated Crack Damage stress threshold (CD) level and then maintained a constant stress to initiate the creep process. DIC was used to characterize full-field spatiotemporal strain evolution, which was then interpreted in the context of local regions of “damage”, determined according to a strain-based criterion. Here, a method was proposed for identifying “existing” and “new” damage regions over specified intervals during the test, followed by spatial clustering of these regions to assess their spatiotemporal evolution. The clustering analysis results demonstrated the extension of existing damage regions was the main damage process during brittle creep, which is consistent with existing models of sub-critical crack growth. In addition, temporal analysis of tensile and shear strains on a point-by-point basis revealed both new damage formation and the strain concentration within existing damaged regions significantly contribute to overall specimen strain during primary creep. In contrast, during secondary creep, increases in specimen deformation are influenced by the accumulation of strains within already damaged regions.

58 GEOSCIENCES

Investigating the Determinants of Household Capabilities Burden During Power Outages: The Case of Winter Storm Uri

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION