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88 records · Page 5

Eighteen Years of Aqua MODIS On-orbit Operation, Calibration, and Performance

Aqua MODIS has successfully operated for more than 18 years since its launch in May 2002and has generated numerous science products in support of studies of the Earth’s system and its changes via a set of geophysical and environmental parameters. On-orbit calibration and characterization activities have played a vital role in maintaining the quality of MODIS data products. In addition to data collected from sensor on-board calibrators (OBC), near-monthly lunar observations and select earth view targets have been used to monitor and characterize on-orbit changes in sensor responses and to derive and update the calibration look-up tables. In this paper, we provide an overview of Aqua MODIS on-orbit operations, calibration activities and approaches, and algorithm improvements and also illustrate the sensor on-orbit performance using examples derived from various calibration sources and targets. We will focus on issues identified from instrument operations and calibrations, such as solar diffuser degradation, electronic crosstalk, variations in the cold focal plane temperatures, and changes in response versus scan-angle. Also discussed in this paper are remaining challenges and future improvements.

Aqua↗

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗

On-Orbit Performance and Calibration Improvements For the Reflective Solar Bands of Terra and Aqua MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS) is the keystone instrument for NASAs EOS Terra and Aqua missions, designed to extend and improve heritage sensor measurements and data records of the land, oceans and atmosphere. The reflective solar bands (RSB) of MODIS covering wavelengths from 0.41 micrometers to 2.2 micrometers, are calibrated on-orbit using a solar diffuser (SD), with its on-orbit bi-directional reflectance factor (BRF) changes tracked using a solar diffuser stability monitor (SDSM). MODIS is a scanning radiometer using a two-sided paddle-wheel mirror to collect earth view (EV) data over a range of (+/-)55 deg. off instrument nadir. In addition to the solar calibration provided by the SD and SDSM system, lunar observations at nearly constant phase angles are regularly scheduled to monitor the RSB calibration stability. For both Terra and Aqua MODIS, the SD and lunar observations are used together to track the on-orbit changes of RSB response versus scan angle (RVS) as the SD and SV port are viewed at different angles of incidence (AOI) on the scan mirror. The MODIS Level 1B (L1B) Collection 6 (C6) algorithm incorporated several enhancements over its predecessor Collection 5 (C5) algorithm. A notable improvement was the use of the earth-view (EV) response trends from pseudo-invariant desert targets to characterize the on-orbit RVS for select RSB (Terra bands 1-4, 8, 9 and Aqua bands 8, 9) and the time, AOI, and wavelength-dependent uncertainty. The MODIS Characterization Support Team (MCST) has been maintaining and enhancing the C6 algorithm since its first update in November, 2011 for Aqua MODIS, and February, 2012 for Terra MODIS. Several calibration improvements have been incorporated that include extending the EV-based RVS approach to other RSB, additional correction for SD degradation at SWIR wavelengths, and alternative approaches for on-orbit RVS characterization. In addition to the on-orbit performance of the MODIS RSB, this paper also discusses in detail the recent calibration improvements implemented in the MODIS L1B C6.

Angal, Amit↗

STRIPE: Remote Driving Using Limited Image Data

Driving a vehicle, either directly or remotely, is an inherently visual task. When heavy fog limits visibility, we reduce our car's speed to a slow crawl, even along very familiar roads. In teleoperation systems, an operator's view is limited to images provided by one or more cameras mounted on the remote vehicle. Traditional methods of vehicle teleoperation require that a real time stream of images is transmitted from the vehicle camera to the operator control station, and the operator steers the vehicle accordingly. For this type of teleoperation, the transmission link between the vehicle and operator workstation must be very high bandwidth (because of the high volume of images required) and very low latency (because delayed images can cause operators to steer incorrectly). In many situations, such a high-bandwidth, low-latency communication link is unavailable or even technically impossible to provide. Supervised TeleRobotics using Incremental Polyhedral Earth geometry, or STRIPE, is a teleoperation system for a robot vehicle that allows a human operator to accurately control the remote vehicle across very low bandwidth communication links, and communication links with large delays. In STRIPE, a single image from a camera mounted on the vehicle is transmitted to the operator workstation. The operator uses a mouse to pick a series of 'waypoints' in the image that define a path that the vehicle should follow. These 2D waypoints are then transmitted back to the vehicle, where they are used to compute the appropriate steering commands while the next image is being transmitted. STRIPE requires no advance knowledge of the terrain to be traversed, and can be used by novice operators with only minimal training. STRIPE is a unique combination of computer and human control. The computer must determine the 3D world path designated by the 2D waypoints and then accurately control the vehicle over rugged terrain. The human issues involve accurate path selection, and the prevention of disorientation, a common problem across all types of teleoperation systems. STRIPE is the only semi-autonomous teleoperation system that can accurately follow paths designated in monocular images on varying terrain. The thesis describes the STRIPE algorithm for tracking points using the incremental geometry model, insight into the design and redesign of the interface, an analysis of the effects of potential errors, details of the user studies, and hints on how to improve both the algorithm and interface for future designs.

REMOTE CONTROL↗

CropEx Web-Based Agricultural Monitoring and Decision Support

CropEx is a Web-based agricultural Decision Support System (DSS) that monitors changes in crop health over time. It is designed to be used by a wide range of both public and private organizations, including individual producers and regional government offices with a vested interest in tracking vegetation health. The database and data management system automatically retrieve and ingest data for the area of interest. Another stores results of the processing and supports the DSS. The processing engine will allow server-side analysis of imagery with support for image sub-setting and a set of core raster operations for image classification, creation of vegetation indices, and change detection. The system includes the Web-based (CropEx) interface, data ingestion system, server-side processing engine, and a database processing engine. It contains a Web-based interface that has multi-tiered security profiles for multiple users. The interface provides the ability to identify areas of interest to specific users, user profiles, and methods of processing and data types for selected or created areas of interest. A compilation of programs is used to ingest available data into the system, classify that data, profile that data for quality, and make data available for the processing engine immediately upon the data s availability to the system (near real time). The processing engine consists of methods and algorithms used to process the data in a real-time fashion without copying, storing, or moving the raw data. The engine makes results available to the database processing engine for storage and further manipulation. The database processing engine ingests data from the image processing engine, distills those results into numerical indices, and stores each index for an area of interest. This process happens each time new data is ingested and processed for the area of interest, and upon subsequent database entries, the database processing engine qualifies each value for each area of interest and conducts a logical processing of results indicating when and where thresholds are exceeded. Reports are provided at regular, operator-determined intervals that include variances from thresholds and links to view raw data for verification, if necessary. The technology and method of development allow the code base to easily be modified for varied use in the real-time and near-real-time processing environments. In addition, the final product will be demonstrated as a means for rapid draft assessment of imagery.

Harvey. Craig↗

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States

A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.

Manning, Robert M.↗

The CCD/Transit Instrument (CTI) data-analysis system

The automated software system for archiving, analyzing, and interrogating data from the CCD/Transit Instrument (CTI) is described. The CTI collects up to 450 Mbytes of image-data each clear night in the form of a narrow strip of sky observed in two colors. The large data-volumes and the scientific aims of the project make it imperative that the data are analyzed within the 24-hour period following the observations. To this end a fully automatic and self evaluating software system has been developed. The data are collected from the telescope in real-time and then transported to Tucson for analysis. Verification is performed by visual inspection of random subsets of the data and obvious cosmic rays are detected and removed before permanent archival is made to the optical disc. The analysis phase is performed by a pair of linked algorithms, one operating on the absolute pixel-values and the other on the spatial derivative of the data. In this way both isolated and merged images are reliably detected in a single pass. In order to isolate the latter algorithm from the effects of noise spikes a 3x3 Hanning filter is applied to the raw data before the analysis is run. The algorithms reduce the input pixel-data to a database of measured parameters for each image which has been found. A contrast filter is applied in order to assign a detection-probability to each image and then x-y calibration and intensity calibration are performed using known reference stars in the strip. These are added to as necessary by secondary standards boot-strapped from the CTI data itself. The final stages involve merging the new data into the CTI Master-list and History-list and the automatic comparison of each new detection with a set of pre-defined templates in parameter-space to find interesting objects such as supernovae, quasars and variable stars. Each stage of the processing from verification to interesting image selection is performed under a data-logging system which both controls the pipe-lining of data through the system and records key performance monitor parameters which are built into the software. Furthermore, the data from each stage are stored in databases to facilitate evaluation, and all stages offer the facility to enter keyword-indexed free-format text into the data-logging system. In this way a large measure of certification is built into the system to provide the necessary confidence in the end results.

Cawson, M. G. M.↗

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

17 WIND ENERGY↗

Heuristics for Relevancy Ranking of Earth Dataset Search Results

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

science data management↗

Relevancy Ranking of Satellite Dataset Search Results

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

science data management↗

Aqua MODIS: 20 Years of On-orbit Calibration and Performance

Since its launch in May 2002, Aqua MODIS has successfully operated for more than 20 years and has continuously generated a wide range of data products that have enabled and supported the remote sensing community and users worldwide for their studies of the Earth’s system by monitoring changes in its key environmental parameters. Although Aqua MODIS, designed with a lifetime requirement of 6 years, is currently operated in its extended mission phase, it continues to make high quality global observations of the Earth’s surface via its 36 spectral bands that cover wavelengths from visible to long-wave infrared. To date, all instrument on-board calibrators (OBC) remain capable of performing their design functions, providing various calibration data sets to help monitor on-orbit changes in sensor responses and performance characteristics. In addition to the OBC, regularly scheduled lunar observations and select Earth-view targets are used extensively to support sensor on-orbit calibration, especially for the calibration of the visible channels (or bands). In this paper, we provide an overview of Aqua MODIS on-orbit calibration activities and methodologies for both reflective solar bands (RSB) and thermal emissive bands (TEB), illustrate its on-orbit performance over the past 20 years using examples derived from OBC measurements, lunar observations, and Earth-view response trends, and describe various calibration improvements made over its entire mission. We focus on key issues identified since launch, such as solar diffuser degradation, electronic crosstalk, and on-orbit changes in sensor response versus scan-angle, along with approaches and strategies developed to mitigate their impact on sensor calibration quality. Also discussed in this paper are some of the key calibration enhancements incorporated recently in the Collection 6.1 and the upcoming Collection 7 Level-1B algorithms.

Polarization↗

NASA Tech Briefs, January 2011

The topics include: 1) Distributed Aerodynamic Sensing and Processing Toolbox; 2) Collaborative Supervised Learning for Sensor Networks; 3) Hazard Detection Software for Lunar Landing; 4) Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme; 5) Network-Capable Application Process and Wireless Intelligent Sensors for ISHM; 6) Interface Supports Multiple Broadcast Transceivers for Flight Applications; 7) FPGA Sequencer for Radar Altimeter Applications; 8) Miniature Sapphire Acoustic Resonator - MSAR; 9) Process-Hardened, Multi-Analyte Sensor for Characterizing Rocket Plume Constituents; 10) SAD5 Stereo Correlation Line-Striping in an FPGA; 11) Hybrid Composite Cryogenic Tank Structure; 12) Nanoscale Deformable Optics; 13) Reliability-Based Design Optimization of a Composite Airframe Component; 14) Zinc Oxide Nanowire Interphase for Enhanced Lightweight Polymer Fiber Composites; 15) Plasma Igniter for Reliable Ignition of Combustion in Rocket Engines; 16) Wire Test Grip Fixture; 17) A Sub-Hertz, Low-Frequency Vibration Isolation Platform; 18) Carbon Nanofibers Synthesized on Selective Substrates for Nonvolatile Memory and 3D Electronics; 19) Nanoparticle/Polymer Nanocomposite Bond Coat or Coating; 20) High-Resolution Wind Measurements for Offshore Wind Energy Development; 21) Spring Tire; 22) Marsviewer 2008; 23) Mission Services Evolution Center Message Bus; 24) Major Constituents Analysis for the Vehicle Cabin Atmosphere Monitor; 25) Astronaut Health Participant Summary Application; 26) Adaption of the AMDIS Method to Flight Status on the VCAM Instrument; 27) Natural Language Interface for Safety Certification of Safety-Critical Software; 28) Cryogenic Caging for Science Instrumentation; 29) Wide-Range Neutron Detector for Space Nuclear Applications; 30) In Situ Guided Wave Structural Health Monitoring System; 31) Multiplexed Energy Coupler for Rotating Equipment; 32) Attitude Estimation in Fractionated Spacecraft Cluster Systems; 33) Full Piezoelectric Multilayer-Stacked Hybrid Actuation/Transduction Systems; 34) Active Flow Effectors for Noise and Separation Control; 35) Method and System for Temporal Filtering in Video Compression Systems; 36) Apparatus for Measuring Total Emissivity of Small, Low-Emissivity Samples; 37) Multiple-Zone Diffractive Optic Element for Laser Ranging Applications; 38) Simplified Architecture for Precise Aiming of a Deep-Space Communication Laser Transceiver; 39) Two-Photon-Absorption Scheme for Optical Beam Tracking; 40) High-Sensitivity, Broad-Range Vacuum Gauge Using Nanotubes for Micromachined Cavities; 41) Wide-Field Optic for Autonomous Acquisition of Laser Link; 42) Extracting Zero-Gravity Surface Figure of a Mirror; 43) Modeling Electromagnetic Scattering From Complex Inhomogeneous Objects; 44) Visual Object Recognition and Tracking of Tools; 45) Method for Implementing Optical Phase Adjustment; 46) Visual SLAM Using Variance Grid Maps; 47) Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration; 48) Efficient Kriging Algorithms; 49) Predicting Spacecraft Trajectories by the WeavEncke Method; 50) An Augmentation of G-Guidance Algorithms; 51) Comparison of Aircraft Icing Growth Assessment Software; 52) Silicon-Germanium Voltage-Controlled Oscillator at 105 GHz; 53) Estimation of Coriolis Force and Torque Acting on Ares-1; 54) Null Lens Assembly for X-Ray Mirror Segments; and 55) High-Precision Pulse Generator.

Source record↗

NASA Tech Briefs, March 2011

Topics covered include: Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation; Airborne Radar Interferometric Repeat-Pass Processing; Plug-and-Play Environmental Monitoring Spacecraft Subsystem; Power-Combined GaN Amplifier with 2.28-W Output Power at 87 GHz; Wallops Ship Surveillance System; Source Lines Counter (SLiC) Version 4.0; Guidance, Navigation, and Control Program; Single-Frame Terrain Mapping Software for Robotic Vehicles; Auto Draw from Excel Input Files; Observation Scheduling System; CFDP for Interplanetary Overlay Network; X-Windows Widget for Image Display; Binary-Signal Recovery; Volumetric 3D Display System with Static Screen; MMIC Replacement for Gunn Diode Oscillators; Feature Acquisition with Imbalanced Training Data; Mount Protects Thin-Walled Glass or Ceramic Tubes from Large Thermal and Vibration Loads; Carbon Nanotube-Based Structural Health Monitoring Sensors; Wireless Inductive Power Device Suppresses Blade Vibrations; Safe, Advanced, Adaptable Isolation System Eliminates the Need for Critical Lifts; Anti-Rotation Device Releasable by Insertion of a Tool; A Magnetically Coupled Cryogenic Pump; Single Piezo-Actuator Rotary-Hammering Drill; Fire-Retardant Polymeric Additives; Catalytic Generation of Lift Gases for Balloons; Ionic Liquids to Replace Hydrazine; Variable Emittance Electrochromics Using Ionic Electrolytes and Low Solar Absorptance Coatings; Spacecraft Radiator Freeze Protection Using a Regenerative Heat Exchanger; Multi-Mission Power Analysis Tool; Correction for Self-Heating When Using Thermometers as Heaters in Precision Control Applications; Gravitational Wave Detection with Single-Laser Atom Interferometers; Titanium Alloy Strong Back for IXO Mirror Segments; Improved Ambient Pressure Pyroelectric Ion Source; Multi-Modal Image Registration and Matching for Localization of a Balloon on Titan; Entanglement in Quantum-Classical Hybrid; Algorithm for Autonomous Landing; Quantum-Classical Hybrid for Information Processing; Small-Scale Dissipation in Binary-Species Transitional Mixing Layers; Superpixel-Augmented Endmember Detection for Hyperspectral Images; Coding for Parallel Links to Maximize the Expected Value of Decodable Messages; and Microwave Tissue Soldering for Immediate Wound Closure.

Source record↗

Use of Generalized Fluid System Simulation Program (GFSSP) for Teaching and Performing Senior Design Projects at the Educational Institutions

This paper describes the experience of the authors in using the Generalized Fluid System Simulation Program (GFSSP) in teaching Design of Thermal Systems class at University of Alabama in Huntsville. GFSSP is a finite volume based thermo-fluid system network analysis code, developed at NASA/Marshall Space Flight Center, and is extensively used in NASA, Department of Defense, and aerospace industries for propulsion system design, analysis, and performance evaluation. The educational version of GFSSP is freely available to all US higher education institutions. The main purpose of the paper is to illustrate the utilization of this user-friendly code for the thermal systems design and fluid engineering courses and to encourage the instructors to utilize the code for the class assignments as well as senior design projects. The need for a generalized computer program for thermofluid analysis in a flow network has been felt for a long time in aerospace industries. Designers of thermofluid systems often need to know pressures, temperatures, flow rates, concentrations, and heat transfer rates at different parts of a flow circuit for steady state or transient conditions. Such applications occur in propulsion systems for tank pressurization, internal flow analysis of rocket engine turbopumps, chilldown of cryogenic tanks and transfer lines, and many other applications of gas-liquid systems involving fluid transients and conjugate heat and mass transfer. Computer resource requirements to perform time-dependent, three-dimensional Navier-Stokes computational fluid dynamic (CFD) analysis of such systems are prohibitive and therefore are not practical. Available commercial codes are generally suitable for steady state, single-phase incompressible flow. Because of the proprietary nature of such codes, it is not possible to extend their capability to satisfy the above-mentioned needs. Therefore, the Generalized Fluid System Simulation Program (GFSSP1) has been developed at NASA Marshall Space Flight Center (MSFC) as a general fluid flow system solver capable of handling phase changes, compressibility, mixture thermodynamics and transient operations. It also includes the capability to model external body forces such as gravity and centrifugal effects in a complex flow network. The objectives of GFSSP development are: a) to develop a robust and efficient numerical algorithm to solve a system of equations describing a flow network containing phase changes, mixing, and rotation; and b) to implement the algorithm in a structured, easy-to-use computer program. The analysis of thermofluid dynamics in a complex network requires resolution of the system into fluid nodes and branches, and solid nodes and conductors as shown in Figure 1. Figure 1 shows a schematic and GFSSP flow circuit of a counter-flow heat exchanger. Hot nitrogen gas is flowing through a pipe, colder nitrogen is flowing counter to the hot stream in the annulus pipe and heat transfer occurs through metal tubes. The problem considered is to calculate flowrates and temperature distributions in both streams. GFSSP has a unique data structure, as shown in Figure 2, that allows constructing all possible arrangements of a flow network with no limit on the number of elements. The elements of a flow network are boundary nodes where pressure and temperature are specified, internal nodes where pressure and temperature are calculated, and branches where flowrates are calculated. For conjugate heat transfer problems, there are three additional elements: solid node, ambient node, and conductor. The solid and fluid nodes are connected with solid-fluid conductors. GFSSP solves the conservation equations of mass and energy, and equation of state in internal nodes to calculate pressure, temperature and resident mass. The momentum conservation equation is solved in branches to calculate flowrate. It also solves for energy conservation equations to calculate temperatures of solid nodes. The equations are coupled and nonlinear; therefore, they are solved by an iterative numerical scheme. GFSSP employs a unique numerical scheme known as simultaneous adjustment with successive substitution (SASS), which is a combination of Newton-Raphson and successive substitution methods. The mass and momentum conservation equations and the equation of state are solved by the Newton-Raphson method while the conservation of energy and species are solved by the successive substitution method. GFSSP is linked with two thermodynamic property programs, GASP2 and WASP3 and GASPAK4, that provide thermodynamic and thermophysical properties of selected fluids. Both programs cover a range of pressure and temperature that allows fluid properties to be evaluated for liquid, liquid-vapor (saturation), and vapor region. GASP and WASP provide properties of 12 fluids. GASPAK includes a library of 36 fluids. GFSSP has three major parts. The first part is the graphical user interface (GUI), visual thermofluid analyzer of systems and components (VTASC). VTASC allows users to create a flow circuit by a 'point and click' paradigm. It creates the GFSSP input file after the completion of the model building process. GFSSP's GUI provides the users a platform to build and run their models. It also allows post-processing of results. The network flow circuit is first built using three basic elements: boundary node, internal node, and branch.

Majumdar, A. K.↗

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗

Mars Global Surveyor Ka-Band Frequency Data Analysis

The Mars Global Surveyor (MGS) spacecraft, launched on November 7, 1996, carries an experimental space-to-ground telecommunications link at Ka-band (32 GHz) along with the primary X-band (8.4 GHz) downlink. The signals are simultaneously transmitted from a 1.5-in diameter parabolic high gain antenna (HGA) on MGS and received by a beam-waveguide (BWG) R&D 34-meter antenna located in NASA's Goldstone Deep Space Network (DSN) complex near Barstow, California. The projected 5-dB link advantage of Ka-band relative to X-band was confirmed in previous reports using measurements of MGS signal strength data acquired during the first two years of the link experiment from December 1996 to December 1998. Analysis of X-band and Ka-band frequency data and difference frequency (f(sub x)-f(sub ka)/3.8) data will be presented here. On board the spacecraft, a low-power sample of the X-band downlink from the transponder is upconverted to 32 GHz, the Ka-band frequency, amplified to I-W using a Solid State Power Amplifier, and radiated from the dual X/Ka HGA. The X-band signal is amplified by one of two 25 W TWTAs. An upconverter first downconverts the 8.42 GHz X-band signal to 8 GHz and then multiplies using a X4 multiplier producing the 32 GHz Ka-band frequency. The frequency source selection is performed by an RF switch which can be commanded to select a VCO (Voltage Controlled Oscillator) or USO (Ultra-Stable Oscillator) reference. The Ka-band frequency can be either coherent with the X-band downlink reference or a hybrid combination of the USO and VCO derived frequencies. The data in this study were chosen such that the Ka-band signal is purely coherent with the X-band signal, that is the downconverter is driven by the same frequency source as the X-band downlink). The ground station used to acquire the data is DSS-13, a 34-meter BWG antenna which incorporates a series of mirrors inside beam waveguide tubes which guide the energy to a subterranean pedestal room, providing a stable environment for the feed and electronics equipment. A dichroic plate is used to reflect the X-band energy and pass the Ka-band energy to another mirror. The RF energy for each band is then focused onto a feed horn and low-noise amplifier package. After amplification and RF/IF downconversion, the IF signals are sent to the Experimental Tone Tracker (ETT), a digital phase-lock-loop receiver, which simultaneously tracks both X-band and Ka-band carrier signals. Once a signal is detected, the ETT outputs estimates of the SNR in a I -Hz bandwidth (Pc/No), baseband phase and frequency of the signals every I -sec. Between December 1996 and December 1998, the Ka-band and X-band signals from MGS were tracked on a regular basis using the ETT. The Ka-band downlink frequencies described here were referenced to the spacecraft's on-board USO which was also the X-band frequency reference (f(sub ka)= 3.8 f(sub x)). The ETT estimates of baseband phase at I -second sampled time tags were converted to sky frequency estimates. Frequency residuals were then generated for each band by removing a model frequency from each observable frequency at each time tag. The model included Doppler and other effects derived from spacecraft trajectory files obtained from the MGS Navigation Team. A simple troposphere correction was applied to the data. In addition to residuals, the USO frequencies emitted by the spacecraft were estimated. For several passes, the USO frequencies were determined from X-band data and from Ka-band data (referred to X-band by dividing by 3.8) and were found to be in good agreement. In addition, X-band USO frequency estimates from MGS Radio Science data acquired from operational DSN stations were available for comparison and were found to agree within the I Hz level. The remaining sub-Hertz differences were attributed to the different models and software algorithms used by MGS Radio Science and KaBLE-11. A summary of the results of a linear fit of the USO frequency versus time (day of year) is presented in Table I for an initial segment of passes.

Morabito, D.↗