Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “pre-processing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

An Automated Marching Scheme for Overset Structured Surface Mesh Generation

Starting with a Boundary Representation (BRep) of the geometry of an aerospace vehicle, an automated marching scheme is presented for generation of structured overset surface meshes. First, a pre-processing step automatically generates discrete representations of the BRep faces and BRep edges by tessellating in parameter space. Topological connectivity between the discretized BRep edges is then established, followed by automatic grid point distribution on these edges based on local turning angle, proximity to sharp geometric features, and prescribed maximum stretching ratio and grid spacing. A set of initial curves for algebraic or hyperbolic marching on a surface is then derived from the redistributed edge curves. A spatially-variable marching distance together with a grid point distribution in the marching direction are automatically determined for each initial curve. A set of overset surface meshes that covers the entire geometry is then obtained by combining the surface meshes around the BRep edges, and the structured meshes derived from the discretized BRep faces

Shishir A Pandya↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27483-DND

scene to retrieve their microphysical properties. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the infrared channels (6.72 Âμm, 7.33 Âμm, 13.3 Âμm,13.6 Âμm, 13.9 Âμm, and 14.2 Âμm) available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) to build its algorithms to retrieval cloud properties. Unfortunately, The Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi-NPP (National Polar-Orbiting Partnership) lacks these particular measurements. While conducting research using VIIRS data, it is desirable to bring these data into the VIIRS resolution. The Cross-track Infrared Sounder (CrIS), which is also on board Suomi-NPP, has these infrared channels. This paper describes a method to map the CrIS spectral radiances of CO2 and water vapor to VIIRS, with the mapping software in ATOVS and AVHRR Pre-processing (AAPP) package provided by Satellite Application Facilities for Numerical Weather Prediction (NWP SAF). This method is validated comparing mapped10.763 Âμm from CrIS with M15 from VIIRS. The mapped CO2 and water vapor channels were then created and combined with the conventional VIIRS product, as well as compared against corresponding channels of Aqua MODIS instrument.

Yan Chen↗
NASA NTRS2020

Validation Practices for Satellite Soil Moisture retrievals: What are (the) errors?

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.

A. Gruber↗
NASA NTRSDate not supplied

Implementing Geometric Surface Imperfections into Sandwich Composite Cylinder Finite Element Method Models

The buckling responses of certain cylindrical shell structures are extremely sensitive to geometric surface imperfections. The NASA Engineering and Safety Center (NESC) Shell Buckling Knockdown Factor Project (SBKF) is conducting research to develop analysis-based buckling design recommendations. Experiments are used to verify the analysis-based factors, but the sensitivity of the test articles to geometric imperfections requires implementing as-manufactured imperfections into high-fidelity finite element method models. Data collection methods such as structured light scanning are used for all geometric surface data used in this work. Common preprocessing and visualization steps used in SBKF are discussed, and steps on how surface scans are prepared for implementation into a finite element model is described. The Python Tool for Implementing Geometric Imperfections in Reduced Structures (Py_TIGIRS), written specifically for the use with SBKF, is briefly described and uses eight functions to extract, modify, and write geometric imperfections into Abaqus input files. Results of the pre-processing methods and results from Py_TIGIRS are provided and compared for Composite Test Article (CTA) 8.2B. Excellent agreement between the visualized scan data and the FEM-extracted geometry is demonstrated. A brief example of why geometric surface imperfections are significant in nonlinear numerical analyses for thin cylinders in axial compression is provided as motivation to use tools such as Py_TIGIRS. Future development of Py_TIGIRS including expansion to structures of arbitrary geometry is planned.

Sandwich structures↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRSDate not supplied

Biological Insights at the Interface of Multiple Arabidopsis Legacy Datasets

The NASA GeneLab database includes an open-access collection of datasets yielded by space biology experiments. Six Gene Lab Data Sets (GLDS’s) performed in Arabidopsis were selected for analysis (7/17/44/121/205/213), all of which included transcriptome data from spaceflight and ground control environments. Hardware, ecotype, environmental conditions, and other experimental conditions varied, allowing the observations of overarching gene expression impacts of microgravity on plant life without focusing on effects of specific experimental conditions. Using GeneLab pre-processed datasets as the basis for the study, RNA microarray data were analyzed to identify genes that showed altered expression in microgravity when compared to control samples for each individual GLDS. All differentially expressed genes were compared to locate differentially expressed genes common between spaceflight experiments. The most noteworthy result is that not one gene shared differential expression among the six GLDS’s. However, gene expression was not influenced randomly by the microgravity environment, as there were several gene ontology terms that were significantly enriched across all experiments. These included 20 significantly enriched biological processes, and although the genes which enriched each term varied, there were many cases of specific genes common to clusters of multiple GLDS’s. Gene expression such as NAC92 and ERF011 or membrane structural element FFP6 provide insight and direction toward understanding the plant response to spaceflight. Characterizing these common processes and the shared differentially expressed genes has demonstrated potential targets for further study to understand and modulate the biological response of plants in microgravity. Life on Earth has never been subjected to the absence of gravity as a selective pressure, so observing how life forms react to a microgravity environment could provide insight to our shared fundamental biological processes. It is also feasible that the genetic modification of specific genes linked to the microgravity response could improve health and yield of space crops.

Joseph Emhof↗
NASA NTRSDate not supplied

MODIS Reflective Solar Band Calibration Improvements using Pseudo-Invariant Desert Targets

To provide the best science data quality, an accurate characterization of the response versus scan angle (RVS) function is critical for the MODIS reflective solar bands (RSB) on-orbit calibration. In every MODIS operational scan, the Earth’s surface, referred to here as Earth view (EV), the space view (SV) port, and the onboard calibrators are viewed via a two-sided scan mirror. The RVS is defined as the sensor’s relative response as a function the angle of incidence (AOI) to the scan mirror. Many different approaches have been developed to derive the time-dependent RVS and its look-up table (LUT) applied to MODIS Level 1B (L1B) products since calibration Collection 4. For most MODIS RSB, the on-board calibrators can reasonably track the RVS change with time. In practice, their RVS is derived using data from on-board calibrators and the EV mirror side ratio (for mirror side 2). For Terra bands 1-4, 8-10 and Aqua bands 1-4, 8-9, an enhancement has been employed in Collections 6 and 6.1 (C6/C6.1) by using Earth scene response trending from pseudo-invariant desert sites in addition to the onboard calibrators. The current C6/C6/1 RVS algorithm is focused on fitting the EV data at each AOI over time and then deriving the relative change at different AOI. The EV response trending is currently fitted with multiple segments over time. Alternatively, the EV responses can be fit first as a function of AOI before fitting temporally in order to reduce the dependence on the stability of the desert site. These pre-treatment methods on the EV data provide improvement in the derived calibration coefficients. However, evidence of insufficient calibration is still observed in the MODIS L1B reflectance data, especially in the form of differences between the mirror sides. In this paper, we review the current methodologies that utilize the EV response trends from the pseudo-invariant Libyan desert targets to supplement the gain derived from the onboard calibrators. An improvement is then proposed and investigated such that a sliding window average (SWA) is used to pre-process the raw EV data. The SWA parameters are carefully selected using trade-off studies to accurately track the Earth scene response trending in multiple cases to overcome the reflectance differences between two mirror sides. Calibration results show improvements for both Aqua and Terra MODIS RSB L1B data products. This new adjustment has been included in the recently delivered Collection 7 LUT that will be evident in the L1B products expected to be released in late 2021.

MODIS↗
NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗
NASA NTRS2021

Data Processing of Miniaturized Laser Heterodyne Radiometer (mini-LHR) Ground Instrument Retrievals

The Miniaturized Laser Heterodyne Radiometer (mini-LHR) is a passive ground instrument that observes the mole fraction of carbon dioxide (CO) and methane (CH)in the atmospheric column by measuring their absorption of sunlight at 1.6 microns. A laser heterodyne radiometer is similar in design to the super heterodyne radio receiver that is well known by ham radio enthusiasts. While not previously a commercial technique, laser heterodyne radiometers have a history of measuring atmospheric trace gases that started in the 1960s. With the commercial availability of inexpensive, low-power, thumbnail-sized lasers developed for the telecommunications industry, it was possible to miniaturize this technique and ultimately commercialize it. The mini-LHR has been under development at NASA GSFC since 2009. During that time, in addition to signicant technical improvements, processing has also evolved and been streamlined. Here we present details of the processing approach for raw mini-LHR data to produce 30- and 60-minute data products of CH and CO column mole fractions. Processing occurs in two general stages: a python-based pre-processing of raw data, followed by ingestion into a Planetary Spectrum Generator (PSG) retrieval algorithm. Raw data processing involves removal of outliers, correcting for changes in air mass throughout the day, averaging scans, and ultimately converting averaged scans into transmittance vs. wavelength. The PSG retrieval simulates a spectra for the time/day/location of the scan with meteorological inputs from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data set and then perturbs the concentrations of CO and CH to obtain a t based on an iterative least-squares curve fitting procedure.

Giancarlo Roberto Zambrano↗
NASA NTRSDate not supplied

Fostering Open Science Inclusiveness for Interdisciplinary Users of Earth Observations

The term Open Science is subject to a variety of interpretations because of a key (and useful) ambiguity in the meaning of “Open”. Open in the sense of Transparency enables more trust in science research by making the details of the scientific process visible and accessible to anyone. “Open” in the sense of Inclusiveness enables more scientists from other disciplines to participate in research in a given discipline, thus producing more interdisciplinary research. Data Systems can play a major role in enabling Open (Inclusive) Science by making it easier for users from other disciplines to work with data within a given discipline. This is challenging for Earth Observation datasets, most of which are the product of advanced instrumentation and sophisticated, specialized variable retrieval algorithms and code. Serving the “extra-disciplinary”communities begins with simple things, like accessible, readable data documentation with adequate scaffolding. But just as important is provisioning Analysis-Ready data that does not require expert pre-processing. Disciplines also often have dominant toolsets, such as R in the biomass community or GIS in many applications communities. Ensuring that EO data are easy to use in the tools favored in other communities will enable more interdisciplinary research. Ideally, interdisciplinary research also benefits from scientists with different domain expertise. Platforms and frameworks that facilitate frictionless collaboration with discipline experts, together with capacity building efforts in those external disciplines also improve the inclusiveness aspect of Open Science. In short, Open Science is at root a way of thinking about how users from diverse discipline can best access and use data and services from a particular discipline.

Christopher Lynnes↗
NASA NTRSDate not supplied

Investigating Waste Preparation Methods for Trash-to-Gas Technologies

Trash-to-Gas technologies show promise in addressing the need for a proper waste management system onboard a long-duration space mission. However, there is a clear need to better understand how the initial waste preparation can affect the overall conversion efficiency. Factors such as the waste size, moisture content, and packing density can have significant impacts on the reactor performance. This paper will focus on the effects of various pre-processing steps on the overall solid-to-gas conversion on the state of the art Trash-to-Gas system developed at NASA Kennedy Space Center. These results will help inform future Trash-to-Gas technologies on what types of supporting subsystems will be necessary to operate effectively for exploration missions.

Trash to Gas↗
NASA NTRS2022

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land cover classifications. However, topographic correction is not routinely implemented in standard data pre-processing chains (e.g., Landsat Analysis Ready Data), and there is a lack of consensus whether topographic correction is necessary, and if so, how to conduct it. Furthermore, methods that correct simultaneously for atmospheric and topographic effects are becoming available, but they have not been compared directly. Our objects were to investigate (1) the effectiveness of two topographic correction approaches that integrate atmospheric and topographic correction, (2) improvements in classification accuracy when analyzing topographically corrected single-date imagery (14 July 2016 and 2 October 2016), versus a full Landsat time series from 2014 to 2016, and 3) improvements in classification accuracy when including additional terrain information (i.e., topographic slope, elevation, and aspect). We developed a physical based model and compared it with an enhanced C-correction, both of which integrate atmospheric and topographic correction. We compared classification accuracies with and without topographic correction using combinations of single-date imagery, image composites and spectral-temporal metrics generated from the full Landsat time series, and additional terrain information in the Caucasus Mountains. We found that both the enhanced C-correction and the physical model performed very well and largely eliminated the correlation (Pearson’s correlation coefficient r ranges from 0.06 to 0.24) between surface reflectance and illumination condition, but the physical model performed best (r ranges from 0.05 to 0.11). Both image composites, and spectral-temporal metrics generated from corrected imagery, resulted in significantly (p ≤ 0.05) higher classification accuracies and better forest classifications, especially for the mixed forests. Adding terrain information reduced classification error significantly, but not as much as topographic correction. In summary, topographic correction remains necessary, even when analyzing a full Landsat time series and including a digital elevation model in the classification. We recommend that topographic correction should be applied when analyzing Landsat satellite imagery in mountainous region for forest cover classification.

Atmospheric correction↗
NASA NTRSDate not supplied

Investigating Waste Preparation Methods for Trash-to-Gas Technologies

Trash-to-Gas technologies show promise in addressing the need for a proper waste management system onboard a long-duration space mission. However, there is a clear need to better understand how the initial waste preparation can affect the overall conversion efficiency. Factors such as the waste size, moisture content, and packing density can have significant impacts on the reactor performance. This paper will focus on the effects of various pre-processing steps on the overall solid-to-gas conversion on the state of the art Trash-to-Gas system developed at NASA Kennedy Space Center. These results will help inform future Trash-to-Gas technologies on what types of supporting subsystems will be necessary to operate effectively for exploration missions.

Trash to Gas↗
NASA NTRSDate not supplied

Spectral Mass-Gauging of Propellant Tanks

An overview of our recent results on the development of Spectral Mass-Gauging (SMG) technology for model-free gauging of propellants in microgravity applications will be presented. The technology is based on application a rigorous result from spectral theory – the Weyl’s Law – which relates the counting function of natural modes in a resonator with its volume. Development of the SMG includes theory of acoustic response of propellant tank, hardware and procedure characterization and optimization, development of data pre-processing approaches and software for automatic mode identification and counting. Main accomplishments in each field of the technology development will be presented. SMG has been tested recently in 1-g on a flight tank filled with water or LN2. We will present results of the tests and discuss their implications for the technology development. The presentation will conclude with a summary of the next steps in the technology maturation.

Mass-gauging↗
NASA NTRS2022

Trash-to-Gas: Trash Preparation and Feed Mechanism

One method of reducing the mass and volume of astronaut waste items during future exploration missions is a process called Trash-to-Gas (TtG), which uses thermal degradation to convert various astronaut waste items into a gas that can either be repurposed onboard or safely vented overboard. This project aims to determine which methods for pre-processing waste may enhance the gasification efficiency within a microgravity TtG reactor. Preparation methods that were investigated include mixing, pre-drying, shredding, compacting, and raw unprocessed waste. Each of these preparation methods was tested within the state-of-the-art subscale TtG system, known as the Orbital Syngas/Commodity Augmentation Reactor (OSCAR), and the resulting solid-to-gas conversion and burn durations were compared. Full-scale CAD models of these various preparation methods in conjunction with a projected full-scale TtG reactor were created using Creo Parametric. An Equivalent System Mass (ESM) analysis was then performed to trade the benefits of improved solid-to-gas performance with the costs associated with implementing the additional components that would increase system mass, power, volume, and design complexity. The results of this ESM analysis will be leveraged for future full-scale TtG system development to help reduce the overall mass, power, and volume of the system while ensuring effective reactor performance.

Ray Pitts↗
NASA NTRS2021

Mars 2020 Radiometric Data and Telemetry Processing, Attitude Estimation, and Thruster Calibration for Orbit Determination

The Mars 2020 spacecraft was spin-stabilized during cruise, just like its predecessor, the Mars Science Laboratory. This spinning motion imparts a signature in the radiometric tracking data that must be dealt with in order to properly model the motion of the spacecraft's center of mass. We discuss how the Orbit Determination team pre-processed the data for efficient computations while also providing other benefits such as high-fidelity attitude modeling and on-board clock verification. Finally, we discuss the analysis and results of the in-flight thruster calibration activity.

Seubert, Jill↗
NASA NTRS2023

Data Mining for Science of the Sun-Earth Connection as a Single System

Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.

Sun-Earth connection↗
NASA NTRSDate not supplied

A Provably Correct Floating-Point Implementation of Well Clear Avionics Concepts

The NASA DAIDALUS library provides formal definitions for Detect-and-Avoid avionics concepts such as when an aircraft is well-clear with respect to the surrounding air traffic, i.e., it does not operate in such proximity to create a collision hazard. While several properties are proven correct for DAIDALUS assuming ideal real number arithmetic, an actual implementation that uses floating-point numbers may behave unexpectedly because of round-off errors and run-time exceptions. This paper presents an experience report on the application of a formal methods toolchain to extract and verify floating-point C code from a real-valued specification of the well-clear module of DAIDALUS. This toolchain comprises the PVS theorem prover, the PRECiSA floating-point analyzer and code generator, and the Frama-C analysis suite. The generated code is automatically instrumented to detect when the control flow of the floating-point program may diverge from the ideal real number specification, and it is annotated with contracts that state the maximum accumulated round-off error. The absence of overflows is also formally verified for the generated code. In order to apply the toolchain to an industrial case study such as DAIDALUS, a formally verified pre-processing of the input specification is performed, which includes a program slicing and several semantic-preserving simplifications.

Program verification↗