Towards Improved Time-Series InSAR Analysis With A Multi-Depth Multi-Focusing Time-Domain Backprojection SAR Algorithm
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Stacey A Huang↗
SEARCH · Engineering Papers
Results for “multi-”
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.
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Atomistic Multi-Lattice Kinetic Monte Carlo (Kmc) Modeling of Hyperthermal Oxidation of Multi-Layer Graphene
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Sharon Edward↗
The Use of Multi-Mission Datasets in NASA DEVELOP Projects: How the Harmonized Landsat and Sentinel-2 Product and Multi-Source Land Imaging Land Surface Phenology Product Provide Solutions to Temporal Resolution Constraints for Vegetation Monitoring
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Madison Arndt↗
Retinex Preprocessing for Improved Multi-Spectral Image Classification
The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.
Thompson, B.↗
New multi-level codes over GF(q)
Set partitioning to multi-dimensional signal spaces over GF(q), particularly GF sup q-1(q) and GF sup q (q), and show how to construct both multi-level block codes and multi-level trellis codes over GF(q). Two classes of multi-level (n, k, d) block codes over GF(q) with block length n, number of information symbols k, and minimum distance d sub min greater than or = d, are presented. These two classes of codes use Reed-Solomon codes as component codes. They can be easily decoded as block length q-1 Reed-Solomon codes or block length q or q + 1 extended Reed-Solomon codes using multi-stage decoding. Many of these codes have larger distances than comparable q-ary block codes, as component codes. Low rate q-ary convolutional codes, work error correcting convolutional codes, and binary-to-q-ary convolutional codes can also be used to construct multi-level trellis codes over GF(q) or binary-to-q-ary trellis codes, some of which have better performance than the above block codes. All of the new codes have simple decoding algorithms based on hard decision multi-stage decoding.
Wu, Jiantian↗
Progress in multi-dimensional upwind differencing
Multi-dimensional upwind-differencing schemes for the Euler equations are reviewed. On the basis of the first-order upwind scheme for a one-dimensional convection equation, the two approaches to upwind differencing are discussed: the fluctuation approach and the finite-volume approach. The usual extension of the finite-volume method to the multi-dimensional Euler equations is not entirely satisfactory, because the direction of wave propagation is always assumed to be normal to the cell faces. This leads to smearing of shock and shear waves when these are not grid-aligned. Multi-directional methods, in which upwind-biased fluxes are computed in a frame aligned with a dominant wave, overcome this problem, but at the expense of robustness. The same is true for the schemes incorporating a multi-dimensional wave model not based on multi-dimensional data but on an 'educated guess' of what they could be. The fluctuation approach offers the best possibilities for the development of genuinely multi-dimensional upwind schemes. Three building blocks are needed for such schemes: a wave model, a way to achieve conservation, and a compact convection scheme. Recent advances in each of these components are discussed; putting them all together is the present focus of a worldwide research effort. Some numerical results are presented, illustrating the potential of the new multi-dimensional schemes.
Vanleer, Bram↗
Advanced Multi-Phase Flow CFD Model Development for Solid Rocket Motor Flowfield Analysis
It is known that the simulations of solid rocket motor internal flow field with AL-based propellants require complex multi-phase turbulent flow model. The objective of this study is to develop an advanced particulate multi-phase flow model which includes the effects of particle dynamics, chemical reaction and hot gas flow turbulence. The inclusion of particle agglomeration, particle/gas reaction and mass transfer, particle collision, coalescence and breakup mechanisms in modeling the particle dynamics will allow the proposed model to realistically simulate the flowfield inside a solid rocket motor. The Finite Difference Navier-Stokes numerical code FDNS is used to simulate the steady-state multi-phase particulate flow field for a 3-zone 2-D axisymmetric ASRM model and a 6-zone 3-D ASRM model at launch conditions. The 2-D model includes aft-end cavity and submerged nozzle. The 3-D model represents the whole ASRM geometry, including additional grain port area in the gas cavity and two inhibitors. FDNS is a pressure based finite difference Navier-Stokes flow solver with time-accurate adaptive second-order upwind schemes, standard and extended k-epsilon models with compressibility corrections, multi zone body-fitted formulations, and turbulence particle interaction model. Eulerian/Lagrangian multi-phase solution method is applied for multi-zone mesh. To simulate the chemical reaction, penalty function corrected efficient finite-rate chemistry integration method is used in FDNS. For the AL particle combustion rate, the Hermsen correlation is employed. To simulate the turbulent dispersion of particles, the Gaussian probability distribution with standard deviation equal to (2k/3)(exp 1/2) is used for the random turbulent velocity components. The computational results reveal that the flow field near the juncture of aft-end cavity and the submerged nozzle is very complex. The effects of the turbulent particles affect the flow field significantly and provide better prediction of the ASRM performance. The multi-phase flow analysis using the FDNS code in the present research can be used as a design tool for solid rocket motor applications.
Liaw, Paul↗
Laboratory instrumentation and techniques for characterizing multi-junction solar cells for space applications
Multi-junction solar cells are attractive for space applications because they can be designed to convert a larger fraction of AMO into electrical power at a lower cost than single-junction cells. The performance of multi-junction cells is much more sensitive to the spectral irradiance of the illuminating source than single-junction cells. The design of high efficiency multi-junction cells for space applications requires matching the optoelectronic properties of the junctions to AMO spectral irradiance. Unlike single-junction cells, it is not possible to carry out quantum efficiency measurements using only a monochromatic probe beam and determining the cell short-circuit current assuming linearity of the quantum efficiency. Additionally, current-voltage characteristics can not be calculated from measurements under non-AMO light sources using spectral-correction methods. There are reports in the literature on characterizing the performance of multi junction cells by measuring and convoluting the quantum efficiency of each junction with the spectral irradiance; the technique is of limited value for the characterization of cell performance under AMO power-generating conditions. We report the results of research to develop instrumentation and techniques for characterizing multi junction solar cells for space . An integrated system is described which consists of a standard lamp, spectral radiometer, dual-source solar simulator, and personal computer based current-voltage and quantum efficiency equipment. The spectral radiometer is calibrated regularly using the tungsten-halogen standard lamp which has a calibration based on NIST scales. The solar simulator produces the light bias beam for current-voltage and cell quantum efficiency measurements. The calibrated spectral radiometer is used to 'fit' the spectral irradiance of the dual-source solar simulator to WRL AMO data. The quantum efficiency apparatus includes a monochromatic probe beam for measuring the absolute cell quantum efficiency at various voltage biases, including the voltage bias corresponding to the maximum-power point under AMO light bias. The details of the procedures to 'fit' the spectral irradiance to AMO will be discussed. An assessment of the role of the accuracy of the 'fit' of the spectral irradiance and probe beam intensity on measured cell characteristics will be presented. quantum efficiencies were measured with both spectral light bias and AMO light bias; the measurements show striking differences. Spectral irradiances were convoluted with cell quantum efficiencies to calculate cell currents as function of voltage. The calculated currents compare with measured currents at the 1% level. Measurements on a variety of multi-junction cells will be presented. The dependence of defects in junctions on cell quantum efficiencies measured under light and voltage bias conditions will be presented. Comments will be made on issues related to standards for calibration, and limitations of the instrumentation and techniques. Expeditious development of multi-junction solar cell technology for space presents challenges for cell characterization in the laboratory.
Woodyard, James R.↗
Using Multi-Scale Modeling Systems to Study the Precipitation Processes
In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.
Tao, Wei-Kuo↗
Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes
In recent years, exponentially increasing computer power extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale models can be run in grid size similar to cloud resolving models through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model). (2) a regional scale model (a NASA unified weather research and forecast, W8F). (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling systems to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use the multi-satellite simulator to improve precipitation processes will be discussed.
Tao, Wei--Kuo↗
Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes
In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the recent developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitating systems and hurricanes/typhoons will be presented. The high-resolution spatial and temporal visualization will be utilized to show the evolution of precipitation processes. Also how to use of the multi-satellite simulator tqimproy~ precipitation processes will be discussed.
Tao, Wei-Kuo↗
Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements
The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.
CAR↗
A Multi-Satellite Assimilation and Modeling Platform to Construct a Global and Complete view of the Hydrologic Cycle
What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.
Girotto, Manuela↗
An Assessment of the Icing Blade and the SEA Multi-Element Sensor for Liquid Water Content Calibration of the NASA GRC Icing Research Tunnel
The Icing Research Tunnel at NASA Glenn has recently switched from using the Icing Blade to using the SEA Multi-Element Sensor (also known as the multi-wire) for its calibration of cloud liquid water content. In order to peform this transition, tests were completed to compare the Multi-Element Sensor to the Icing Blade, particularly with respect to liquid water content, airspeed, and drop size. The two instruments were found to compare well for the majority of Appendix C conditions. However, it was discovered that the Icing Blade under-measures when the conditions approach the Ludlam Limit. This paper also describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, mounting underneath a splitter plate, and correcting for a jump in the compensation wire power. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor, health-monitoring checks for the instrument, and a sensing-element configuration comparison. Ultimately these tests showed that in the IRT, the multi-wire is a better instrument for measuring cloud liquid water content than the blade.
aircraft icing↗
Structural Analysis, Modeling, and Testing of Multi-nut Joint for TriTruss Structure
The Multi-nut joint, developed at NASA Langley Research Center (LaRC), was designed to assemble TriTruss modules for a doubly curved 65.6-feet telescope support structure. Axial and bending tests for a multi-nut joint in the TriTruss structure were conducted to characterize the structural performance of the multi-nut joint and to obtain the installation torque value of the attachment fastener. This paper describes the summary of the axial and bending tests of the multi-nut joint, along with the test results. The equivalent axial stiffness (EA) and equivalent bending stiffness (EI) of the multi-nut joint were characterized based on these test results and are presented in this report. Additionally, finite element models (FEMs) of the axial and bending test specimens were developed, and the predicted axial and bending responses of the multi-nut joint were compared with the test results. Test-analysis comparison results show that the predicted axial load-displacement response of the test specimen was within 5 percent of the test results, while the predicted load-bending response of the test specimen exhibited a consistent trend with the test data.
TriTruss↗
Structural Analysis, Modeling, and Testing of Multi-nut Joint for TriTruss Structure
The Multi-nut joint, developed at NASA Langley Research Center (LaRC), was designed to assemble TriTruss modules for a doubly curved 65.6-feet telescope support structure. Axial and bending tests for a multi-nut joint in the TriTruss structure were conducted to characterize the structural performance of the multi-nut joint and to obtain the installation torque value of the attachment fastener. This paper describes the summary of the axial and bending tests of the multi-nut joint, along with the test results. The equivalent axial stiffness (EA) and equivalent bending stiffness (EI) of the multi-nut joint were characterized based on these test results and are presented in this report. Additionally, finite element models (FEMs) of the axial and bending test specimens were developed, and the predicted axial and bending responses of the multi-nut joint were compared with the test results. Test-analysis comparison results show that the predicted axial load-displacement response of the test specimen was within 5 percent of the test results, while the predicted load-bending response of the test specimen exhibited a consistent trend with the test data.
Structural analysis↗
Multi-Agent Swarm State of the Art Report
The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.
agent↗
Multi-disciplinary coupling effects for integrated design of propulsion systems
Effective computational simulation procedures are described for modeling the inherent multi-disciplinary interactions which govern the accurate response of propulsion systems. Results are presented for propulsion system responses including multi-disciplinary coupling effects using coupled multi-discipline thermal, structural, and acoustic tailoring; an integrated system of multi-disciplinary simulators; coupled material behavior/fabrication process tailoring; sensitivities using a probabilistic simulator; and coupled materials, structures, fracture, and probabilistic behavior simulator. The results demonstrate that superior designs can be achieved if the analysis/tailoring methods account for the multi-disciplinary coupling effects. The coupling across disciplines can be used to develop an integrated coupled multi-discipline numerical propulsion system simulator.
Chamis, C. C.↗