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NASA NTRS2000

Total Ozone Mapping Spectrometer (TOMS) Level-3 Data Products User's Guide

Data from the TOMS series of instruments span the time period from November 1978, through the present with about a one and a-half year gap from January 1994 through July 1996. A set of four parameters derived from the TOMS measurements have been archived in the form of daily global maps or Level-3 data products. These products are total column ozone, effective surface reflectivity, aerosol index, and erythermal ultraviolet estimated at the Earth surface. A common fixed grid of I degree latitude by 1.25 degree longitude cells over the entire globe is provided daily for each parameter. These data are archived at the Goddard Space Flight Center Distributed Active Archive Center (DAAQ in Hierarchical Data Format (HDF). They are also available in a character format through the TOMS web site at http://toms.gsfc.nasa.gov. The derivations of the parameters, the mapping algorithm, and the data formats are described. The trend uncertainty for individual TOMS instruments is about 1% decade, but additional uncertainty exists in the combined data record due to uncertainty in the relative calibrations of the various TOMS.

McPeters, Richard D.↗
NASA NTRS2000

Effects of Space Environment on Flow and Concentration During Directional Solidification

A study of directional solidification of a weak binary alloy (specifically, Bi - 1 at% Sn) based on the fixed grid single domain approach is being undertaken. The enthalpy method is used to solve for the temperature field over the computational domain including both the solid and liquid phases; latent heat evolution is treated with the aid of an effective specific heat coefficient. A source term accounting for the release of solute into the liquid during solidification has been incorporated into the solute transport equation. The vorticity-stream function formulation is used to describe thermosolutal convection in the liquid region. In this paper we numerically investigate the effects of g-jitter on directional solidification. A background gravity of 1 micro-g has been assumed, and new results for the effects of periodic disturbances over a range of amplitudes and frequencies on solute field and segregation have been presented.

Benjapiyaporn, C.↗
NASA NTRS2000

Effect of g-jitter on Directional Solidification of a Binary Alloy

A study of directional solidification of a weak binary alloy (specifically, Bi - 1 at% Sn) based on the fixed grid single domain approach is being undertaken. The enthalpy method is used to solve for the temperature field over the computational domain including both the solid and liquid phases; latent heat evolution is treated with the aid of an effective specific heat coefficient. A source term accounting for the release of solute into the liquid during solidification has been incorporated into the solute transport equation. The vorticity-stream function formulation is used to describe thermosolutal convection in the liquid region. In this paper we present a numerical simulation of g-jitter: the small, rapid fluctuations in gravitational acceleration which may be experienced in an orbiting space vehicle. A background gravity of 1 micro-g has been assumed, and new results for the effects of orientation angle of the periodic disturbances over a range of amplitudes and frequencies on solute field and segregation have been presented.

Santiviriyapanich, P.↗
NASA NTRS2005

Variable specific impulse high power ion thruster

The power, Isp and thrust of ion thrusters are constrained by ther fixed grid gap in the ion accellerator, which limits performance and life to a limited range in Isp and thrust.

electric propulsion↗
NASA NTRS2010

Methods and Results for a Global Precipitation Measurement (GPM) Validation Network Prototype

As one component of a ground validation system to meet requirements for the upcoming Global Precipitation Measurement (GPM) mission, a quasi-operational prototype a system to compare satellite- and ground-based radar measurements has been developed. This prototype, the GPM Validation Network (VN), acquires data from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite and from ground radar (GR) networks in the continental U.S. and participating international sites. PR data serve as a surrogate for similar observations from the Dual-frequency Precipitation Radar (DPR) to be present on GPM. Primary goals of the VN prototype are to understand and characterize the variability and bias of precipitation retrievals between the PR and GR in various precipitation regimes at large scales, and to improve precipitation retrieval algorithms for the GPM instruments. The current VN capabilities concentrate on comparisons of the base reflectivity observations between the PR and GR, and include support for rain rate comparisons. The VN algorithm resamples PR and GR reflectivity and other 2-D and 3-D data fields to irregular common volumes defined by the geometric intersection of the instrument observations, and performs statistical comparisons of PR and GR reflectivity and estimated rain rates. Algorithmic biases and uncertainties introduced by traditional data analysis techniques are minimized by not performing interpolation or extrapolation of data to a fixed grid. The core VN dataset consists of WSR-88D GR data and matching PR orbit subset data covering 21 sites in the southeastern U. S., from August, 2006 to the present. On average, about 3.5 overpass events per month for these WSR-88D sites meet VN criteria for significant precipitation, and have matching PR and GR data available. This large statistical sample has allowed the relative calibration accuracy and stability of the individual ground radars, and the quality of the PR reflectivity attenuation correction in convective and stratiform precipitation to be evaluated. We will present results of PR-GR reflectivity and rain rate bias comparisons for each OR site, and for different rain types, for the full data set and as time series. The capabilities of the statistical analysis and vertical cross section tools for display and analysis of individual site overpass event data will be described, and examples of the tools' outputs will be shown.

Morris, Kenneth R.↗
NASA NTRS2009

REFAME: Rain Estimation Using Forward Adjusted-Advection of Microwave Estimates

Sensors flying on satellites provide the only practical means of estimating the precipitation that falls over the entire globe, particularly across the vast unpopulated expanses of Earth s oceans. The sensors that observe the Earth using microwave frequencies provide the best data, but currently these are mounted only on satellites flying in "low Earth orbit". Such satellites constantly move across the Earth s surface, providing snapshots of any given location every 12-36 hours. The entire constellation of low-orbit satellites numbers less than a dozen, and their orbits are not coordinated, so a location will frequently go two or more hours between snapshots. "Geosynchronous Earth orbit" (GEO) satellites continuously observe the same region of the globe, allowing them to provide very frequent pictures. For example, the "satellite movies" shown on television come from GEO satellites. However, the sensors available on GEO satellites cannot match the skill of the low-orbit microwave sensors in estimating precipitation. It is perhaps obvious that scientists should try to combine these very different kinds of data, taking advantage of the strengths of each, but this simple concept has proved to be a huge challenge. The scheme in this paper is "Lagrangian", meaning we follow the storm systems, rather than being tied to a fixed grid of boxes on the Earth s surface. Whenever a microwave snapshot occurs, we gladly use the resulting precipitation estimate. Then at all the times between the microwave snapshots we force the storm system to make a smooth transition from one snapshot s values to the next. We know that a lot more changes occur between the snapshots, but this smooth transition the best we can do with the microwave data alone. The key new contribution in this paper is that we also look at the relative variations in the GEO estimates during these in-between times and force the estimated changes in the precipitation to have similar variations. Preliminary testing shows that this approach has enough promise that it should be developed and studied.

Behrangi, Ali↗
NASA NTRS2011

One Dimensional Capacitive Loading in a Frequency Selective Surface for Low Profile Antenna Applications

In this paper, the impact of adding discrete capacitive loading along one dimension of a frequency selective surface for low profile antenna applications is presented for the first time. The measured data demonstrates comparable performance between a non-loaded and a capacitively-loaded FSS with a significant reduction in the number of cells and/or cell geometry size. Additionally, the provision of discrete capacitive loads reduces the FSS susceptibility to fabrication tolerances based on placement of a fixed grid capacitance. The bandwidth increased from 1.8% to 7.3% for a total antenna thickness of approx. lambda/22, and from 1.5% to 9.2% for a thickness of approx. lambda/40. The total antenna area for each case was reduced by 55% and 12%, respectively.

Cure, David↗
NASA NTRS2016

Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Navigation↗
NASA NTRS2016

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Registration↗
NASA NTRS2016

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational Level 2 soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 kilometer Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.

passive↗
NASA NTRS2019

Geometry Modeling for Unstructured Mesh Adaptation

The quantification and control of discretization error is critical to obtaining reliable simulation results. Adaptive mesh techniques have the potential to automate discretization error control, but have made limited impact on production analysis workflow. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic mesh adaptation mechanics. However, the poor integration of initial mesh generation and adaptive mesh mechanics to typical sources of geometry has hindered adoption of adaptive mesh techniques, where these geometries are often created in Mechanical Computer- Aided Design (MCAD) systems. The difficulty of this coupling is compounded by two factors: the inherent complexity of the model (e.g., large range of scales, bodies in proximity, details not required for analysis) and unintended geometry construction artifacts (e.g., translation, uneven parameterization, degeneracy, self-intersection, sliver faces, gaps, large tolerances be- tween topological elements, local high curvature to enforce continuity). Manual preparation of geometry is commonly employed to enable fixed-grid and adaptive-grid workflows by reducing the severity and negative impacts of these construction artifacts, but manual process interaction inhibits workflow automation. Techniques to permit the use of complex geometry models and reduce the impact of geometry construction artifacts on unstructured grid workflows are models from the AIAA Sonic Boom and High Lift Prediction are shown to demonstrate the utility of the current approach.

Park, Michael A.↗
NASA NTRS2010

TPSAS-NF1676L-10980-DND

We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code

Elizabeth M Lee-Rausch↗
NASA NTRS2010

TPSAS-NF1676L-10454-DND

We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code

C L Rumsey↗
NASA NTRSDate not supplied

HLPW-4/GMGW-3: Overview and Workshop Summary

The Fourth AIAA CFD High Lift Prediction Workshop and the Third Geometry and Mesh Generation Workshop were held collaboratively with the common goal of assessing the numerical prediction capability of current-generation computational fluid dynamics (CFD) technology for swept, medium/high-aspect-ratio wings in high-lift configurations. A key aspect of this joint endeavor was the use of Technology Focus Groups, an innovative new approach for workshops involving close collaboration between participants. These groups, which included both mesh generation and flow solver experts, worked to accelerate advancements for their particular methodologies by addressing key questions of importance {\em prior} to the workshop. The high-lift version of the NASA Common Research Model (CRM-HL) configuration was the focus of this workshop. Measured experimental wind tunnel data were available for comparison. The workshop also included a two-dimensional turbulence model verification exercise based on the CRM-HL wing shape. Altogether, 44 participants submitted a total of 184 data sets of CFD results. This paper provides a high-level summary of the results and conclusions from the workshop. Like at past workshops, fixed-grid Reynolds-averaged Navier-Stokes continued to be inaccurate and inconsistent for high lift. However, mesh adaptation definitively brought more consistency. Scale-resolving methods appeared most promising for predicting high-lift flow physics.

Christopher L Rumsey↗
NASA NTRSDate not supplied

HLPW-5: Overview and Workshop Summary

The Fifth AIAA CFD High-Lift Prediction Workshop was held with the goal of assessing the numerical prediction capability of current-generation computational fluid dynamics (CFD) technology for swept, medium/high-aspect-ratio wings in high-lift configurations. A key aspect of this endeavor was the use of Technology Focus Groups (TFG), which included both mesh generation and flow-solver experts working together to accelerate advancements for their particular CFD methodology, by addressing key questions of importance prior to the workshop. The high-lift version of the NASA Common Research Model (CRM-HL) configuration was the focus of this workshop, and was used for three unique test cases. Wind-tunnel data were available for comparison for one of the test cases. Altogether, 365 datasets of CFD results were submitted by 47 teams, with 41 teams contributing to the multiple configurations of Case 1, 40 to Case 2, and 18 to Case 3. This paper provides a high-level summary of the results and conclusions from the workshop. As concluded from past workshops, fixed-grid Reynolds-averaged Navier-Stokes methods continued to be inaccurate and inconsistent for high-lift flows near maximum lift. However, application of mesh-adaptation technology helped to achieve improved consistency. Scale-resolving methods appeared most promising for predicting high-lift flow physics, particularly at maximum lift. Best practices for these methods were refined over the course of the workshop and new challenges were identified.

Adam M Clark↗
NASA NTRSDate not supplied

High-Lift Prediction Workshop 5: Summary of Reynolds-Averaged Navier–Stokes Technology Focus Group Summary

The fifth High-Lift Prediction Workshop (HLPW-5), which involved the high-lift version of the NASA common research model in several configurations, assessed various computational fluid dynamics methods, including Reynolds-averaged Navier-Stokes (RANS) and hybrid large-eddy simulations. This paper summarizes RANS solutions computed on fixed grids. Case 1, a verification case, considered a simple wing-body configuration and focused on grid convergence of lift, drag, and pitching moment coefficients. Case 2 is a configuration buildup case that focused on predicting the effects of increasing geometric complexity. For buildup configurations with slats, flaps, and a nacelle/pylon (configurations 2.2, 2.3, and 2.4), wind-tunnel data were provided by ONERA. Case 3 focused on the reference landing configuration at four Reynolds-number conditions. For Case 1, grid-converged RANS solutions were achieved using the standard Spalart-Allmaras (SA) turbulence model and the SA model with a quadratic constitutive relation and a rotation correction. Agreement between RANS solutions was observed for the simplest configuration 2.1 of Case 2 with the standard SA model. For other configurations, agreement between RANS solutions was hampered by insufficient iterative and grid convergence, especially at high angles of attack. In comparison with the experiment, RANS solutions qualitatively showed the correct configuration buildup trend but underpredicted lift and overpredicted both drag and pitching moment at high angles of attack.

Boris Diskin↗