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NASA NTRS1992

User's manual for Interactive Data Display System (IDDS)

A computer graphics package for the visualization of three-dimensional flow in turbomachinery has been developed and tested. This graphics package, called IDDS (Interactive Data Display System), is able to 'unwrap' the volumetric data cone associated with a centrifugal compressor and display the results in an easy to understand two-dimensional manner. IDDS will provide the majority of the visualization and analysis capability for the ICE (Integrated CFD and Experiment) system. This document is intended to serve as a user's manual for IDDS in a stand-alone mode. Currently, IDDS is capable of plotting two- or three-dimensional simulation data, but work is under way to expand IDDS so that experimental data can be accepted, plotted, and compared with a simulation dataset of the actual hardware being tested.

Stegeman, James D.↗

NASA NTRS2000

Data Retrieval Algorithms for Validating the Optical Transient Detector and the Lightning Imaging Sensor

A linear algebraic solution is provided for the problem of retrieving the location and time of occurrence of lightning ground strikes from an Advanced Lightning Direction Finder (ALDF) network. The ALDF network measures field strength, magnetic bearing, and arrival time of lightning radio emissions. Solutions for the plane (i.e., no earth curvature) are provided that implement all of these measurements. The accuracy of the retrieval method is tested using computer-simulated datasets, and the relative influence of bearing and arrival time data an the outcome of the final solution is formally demonstrated. The algorithm is sufficiently accurate to validate NASA:s Optical Transient Detector and Lightning Imaging Sensor. A quadratic planar solution that is useful when only three arrival time measurements are available is also introduced. The algebra of the quadratic root results are examined in detail to clarify what portions of the analysis region lead to fundamental ambiguities in sc)iirce location, Complex root results are shown to be associated with the presence of measurement errors when the lightning source lies near an outer sensor baseline of the ALDF network. For arbitrary noncollinear network geometries and in the absence of measurement errors, it is shown that the two quadratic roots are equivalent (no source location ambiguity) on the outer sensor baselines. The accuracy of the quadratic planar method is tested with computer-generated datasets, and the results are generally better than those obtained from the three-station linear planar method when bearing errors are about 2 deg.

Koshak, W. J.↗

NASA NTRS2012

Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis

MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.

Duffy, Daniel Q.↗

NASA NTRS2013

Hadoop for High-Performance Climate Analytics: Use Cases and Lessons Learned

Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.

analytics↗

NASA NTRS2018

Reynolds-Stress Budgets in an Impinging Shock Wave/Boundary-Layer Interaction

Implicit large-eddy simulation (ILES) of a shock wave/boundary-layer interaction (SBLI) was performed. Comparisons with experimental data showed a sensitivity of the current prediction to the modeling of the sidewalls. This was found to be common among various computational studies in the literature where periodic boundary conditions were used in the spanwise direction, as was the case in the present work. Thus, although the experiment was quasi-two-dimensional, the present simulation was determined to be two-dimensional. Quantities present in the exact equation of the Reynolds-stress transport, i.e., production, molecular diffusion, turbulent transport, pressure diffusion, pressure strain, dissipation, and turbulent mass flux were calculated. Reynolds-stress budgets were compared with past large-eddy simulation and direct numerical simulation datasets in the undisturbed portion of the turbulent boundary layer to validate the current approach. The budgets in SBLI showed the growth in the production term for the primary normal stress and energy transfer mechanism was led by the pressure strain term in the secondary normal stresses. The pressure diffusion term, commonly assumed as negligible by turbulence model developers, was shown to be small but non-zero in the normal stress budgets, however it played a key role in the primary shear stress budget.

shock wave/boundary-layer interactio↗

NASA NTRS2019

Cellular Statistical Models of Broken Cloud Fields. Part IV: Effects of Pixel Size on Idealized Satellite Observations

In the fourth part of our “Cellular Statistical Models of Broken Cloud Fields” series we use the binary Markov processes framework for quantitative investigation of the effects of low resolution of idealized satellite observations on the statistics of the retrieved cloud masks. We assume that the cloud fields are Markovian and are characterized by the “actual” cloud fraction (CF) and scale length. We use two different models of observations: a simple discrete-point sampling and a more realistic “pixel” protocol. The latter is characterized by a state attribution function (SAF) which has the meaning of the probability that the pixel with a certain CF is declared cloudy in the observed cloud mask. The stochasticity of the SAF means that the cloud/clear attribution is not ideal and can be affected by external or unknown factors. We show that the observed cloud masks can be accurately described as Markov chains of pixels and use the master-matrix formalism (introduced in Part III of the series) for analytical computation of their parameters: the “observed” CF and scale length. This procedure allows us to establish a quantitative relationship (which is pixel-size dependent) between the actual and the observed cloud-field statistics. The feasibility of restoring the former from the latter is considered. The adequacy of our analytical approach to idealized observations is evaluated using numerical simulations. Comparison of the observed parameters of the simulated datasets with their theoretical expectations showed an agreement within 0.005 for the CF, while for the scale length it is within 1% in the sampling case and within 4% in the pixel case.

Mikhail D. Alexandrov↗

NASA NTRS2011

TPSAS-NF1676L-12839-DND

Hyper-spectral remote sensors, such as Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI), provides top of atmospheric radiances with high information content on atmospheric and surface properties. In order to analyze these data in real time, fast, and accurate forward and inverse models are needed. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The method has been successfully applied to AIRS, IASI, and NAST-I data. By applying the same method to the current and future hyperspectral sounders, the derived products will have less errors due to biases introduced by different retrieval methods. We have compared the retrieved products to radiosondes, aircraft measurements and other validation dataset. Simulations have done to quantify retrieval errors associated with the retrievals. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the hyper-spectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a subset of the channels. The PCRTM forward model has also been used to performance end-to-end sensor performance simulations of the Climate Absolute Radiance and Refractivity Observatory (CLARREO).

Xu Liu↗

NASA NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perserverance rover to the surface of Mars at Jezero Crater. The entry capsule carried a set of instrumentation installed on the heat shield and backshell, named the Mars 2020 Entry, Descent, and Landing Instrumentation. The instruments include pressure transducers, thermocouples, heat flux gauges, and radiometers to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, are located in close proximity on the backshell. Each sensor is exposed to roughly the same environment, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that location on the backshell. The approach makes use of the Kalman filter methodology for solving state estimation problems. The filter has a predictor/corrector structure in which simplified process models are used to propagate the aeroheating states forward through time, which are then updated based on the measurement data. The method has been tested on simulated datasets.

Christopher D. Karlgaard↗

NASA NTRS2020

Automatic Feature Tracking on Small Bodies for Autonomous Approach

Abstract—The autonomous approach of a spacecraft to an asteroid or comet (a small body) relies heavily on visual feature tracking to aid in estimating relative trajectories and the properties of the small body. Feature tracking for small bodies brings several challenges, including changing lighting, poor visual texture, and a concentration of features in a small part of an image. Six existing, open-source algorithms for feature tracking were tested on a simulated dataset and compared to the ground truth in the path of features. The main finding is that none of the algorithms provide all of the desired characteristics of long feature tracks with low errors and few outliers. Instead, there is a trade-off between long feature tracks and low error. The feature-matching algorithms SIFT, and BRISK provide good error characteristics, but short feature tracks, whereas the optical flow algorithm KLT provides long feature tracks, but with many features of large error. Given the challenges in feature tracking, it is recommended to focus development on each component of a feature tracking system: detection, description, and outlier rejection.

Morrell, Benjamin J↗

NASA NTRSDate not supplied

Review of Noise Metric Sensitivities for Analysis of Quiet Supersonic Overflight

Six different noise metrics (PL, ASEL, BSEL, DSEL, ESEL, and ISBAP) are currently used to quantify sonic boom levels from overflight of supersonic aircraft. A previous meta-analysis using laboratory subjective data identified these metrics, each of which correlated well with human perception of low-boom sounds both indoors and outdoors. Because the analysis did not identify a single metric that was significantly superior, no internationally agreed-upon metric has been chosen for the quiet supersonic aircraft noise certification procedures currently under development. Other analyses of metric sensitivities using existing empirical and simulation datasets, however, have shown significant differences between metrics. Variability of metrics due to macro-atmospheric effects and atmospheric turbulence perturbations have been explored, as well as variability in measurements due to microphone setup configurations, array layouts, and ambient noise effects. This paper reviews these prior studies, summarizes the current understanding of metric sensitivities, and discusses how they may impact future downselection of metrics for certification procedures.

sonic boom↗

NASA NTRSDate not supplied

Application of an Affine Nonlinear Galerkin Reduced-order Model to Compressible Fluid Flows

Galerkin reduced-order models (ROMs) often struggle to accurately capture multiscale fluid physics in challenging flow regimes such as flows experiencing compressibility effects. This in part stems from the global nature of both the basis construction problem and the spectral formulation itself. In this work, a multi-basis ROM is developed in an affine space based on proper orthogonal decomposition (POD) by projecting the full Navier-Stokes equations expressed in terms of the specific volume, velocity, and pressure primitive variables. The model is applied to high-fidelity numerical simulation datasets obtained for a canonical compressible flow configuration: the flow over a backward facing step at different subsonic Mach numbers. It is observed that application of an eigenvalue reassignment (ER) stabilization method is required to avoid early divergence of the ROM predictions for this configuration in the three Mach numbers tested. The sensitivity of the POD-ROM results to the choice of parameters in the stabilization algorithm is discussed.

reduced-order model↗

NASA NTRSDate not supplied

Application of an Affine Nonlinear Galerkin Reduced-order Model to Compressible Fluid Flows

reduced-order model↗

NASA NTRS2023

Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach

Inland and coastal waters provide key ecosystem services and are closely linked to human well-being. In this study, we propose a semi-analytical method, which can be applied to Sentinel-2 MultiSpectral Instrument (MSI) images to retrieve high spatial-resolution total suspended solids (TSS) concentration in a broad spectrum of aquatic ecosystems ranging from clear to extremely turbid waters. The presented approach has four main steps. First, the remote sensing reflectance ( R rs ) at a band lacking in MSI (620 nm) is estimated through an empirical relationship from R rs at 665 nm. Second, waters are classified into four types (clear, moderately turbid, highly turbid, and extremely turbid). Third, semi-analytical algorithms are used to estimate the particulate backscattering coefficient ( b bp ) at a reference band depending on the water types. Last, TSS is estimated from bbp at the reference band. Validation and comparison of the proposed method with three existing methods are performed using a simulated dataset ( N = 1000), an in situ dataset collected from global inland and coastal waters ( N = 1265) and satellite matchups ( N = 40). Results indicate that the proposed method can improve TSS estimation and provide accurate retrievals of TSS from all three datasets, with a median absolute percentage error (MAPE) of 14.88 %, 31.50 % and 41.69 % respectively. We also present comparisons of TSS mapping between the Sentinel-3 Ocean and Land Colour Instrument (OLCI) and MSI in Lake Kasumigaura, Japan and the Tagus Estuary, Portugal. Results clearly demonstrate the advantages of using MSI for TSS monitoring in small water bodies such as rivers, river mouths and other nearshore waters. MSI can provide more detailed and realistic TSS estimates than OLCI in these water bodies. The proposed TSS estimation method was applied to MSI images to produce TSS time-series in Lake Kasumigaura, which showed good agreements with in situ and OLCI-derived TSS time-series.

Sentinel-2↗

NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗

DOE OSTI2013

Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both contexts. One of the most interesting models we propose attempt to marry the two by obtaining a coarse global representation using prediction models, and a detailed local representation based on topology. Our methods are validated on several analytical test functions as well as a small nuclear simulation dataset modeled after a simplified Pressurized Water Reactor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Large-Scale Daylighting Analysis for Buildings, Products, and Policy

Daylighting in buildings has come under increased scorn in the wake of ubiquitous solid-state lighting (SSL), as installed lighting power densities in buildings have been reduced to the point where designers and owners question the benefits of properly-designed daylighting and electric lighting control. At the same time the very process of demonstrating well-executed daylighting to satisfy new building rating systems has become burdensome. This paper demonstrates the utility of illuminance as an input to simple, existing qualitative and quantitative metrics for daylighting performance in buildings, at scale. This assertion is supported by sensitivity analysis of a large-scale daylighting simulation dataset. Uses of this data for quantifying non-energy benefits (NEBs) of daylighting are also explored. We do not eschew the use of newer, more detailed daylight metrics in design, but we do encourage the use of simple daylight metrics for a broader range of new and retrofit building projects toward a net-zero future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Searches for New Physics With Muon Conversion at Fermilab and Triboson Production at the LHC

We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.

Kampa, Cole Erik [Northwestern U. (main)] (ORCID:0↗

DOE OSTI2023

Projections of Hourly Meteorology by Balancing Authority Based on the IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulations

This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for 54 Balancing Authorities (BAs) in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). To go from the TGW data to these BA-level aggregated data we first averaged the raw gridded data by county in the United States. That intermediate data step is also stored in MSD-LIVE (https://doi.org/10.57931/1960548). We then population-weight the county-level hourly data in order to create population-weighted meteorology time series for each BA. Historical populations are from the United States Census Bureau and the evolving future populations are based on Shared Socioeconomic Pathways (SSPs) 3 and 5. The four climate scenarios crossed with the two SSPs yield eight different future projections for each BA: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotterssp3, rcp85hotterssp5. For the historical period and each of the eight future scenarios the dataset has hourly estimates of the population-weighted average of five meteorological variables: Temperature, specific humidity, shortwave radiation, longwave radiation, and wind speed. All times are in Coordinated Universal Time (UTC). The code to go from the raw TGW data to county-level and then BA-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.

Balancing Authority↗