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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Pre-Hardware Optimization and Implementation Of Fast Optics Closed Control Loop Algorithms

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). The FFT is particularly useful in two-dimensional (2-D) image processing (FFT2) within optical systems control. However, timing constraints of a fast optics closed control loop would require a supercomputer to run the software implementation of the FFT2 and its inverse, as well as other image processing representative algorithm, such as numerical image folding and fringe feature extraction. A laboratory supercomputer is not always available even for ground operations and is not feasible for a night project. However, the computationally intensive algorithms still warrant alternative implementation using reconfigurable computing technologies (RC) such as Digital Signal Processors (DSP) and Field Programmable Gate Arrays (FPGA), which provide low cost compact super-computing capabilities. We present a new RC hardware implementation and utilization architecture that significantly reduces the computational complexity of a few basic image-processing algorithm, such as FFT2, image folding and phase diversity for the NASA Solar Viewing Interferometer Prototype (SVIP) using a cluster of DSPs and FPGAs. The DSP cluster utilization architecture also assures avoidance of a single point of failure, while using commercially available hardware. This, combined with the control algorithms pre-hardware optimization, or the first time allows construction of image-based 800 Hertz (Hz) optics closed control loops on-board a spacecraft, based on the SVIP ground instrument. That spacecraft is the proposed Earth Atmosphere Solar Occultation Imager (EASI) to study greenhouse gases CO2, C2H, H2O, O3, O2, N2O from Lagrange-2 point in space. This paper provides an advanced insight into a new type of science capabilities for future space exploration missions based on on-board image processing for control and for robotics missions using vision sensors. It presents a top-level description of technologies required for the design and construction of SVIP and EASI and to advance the spatial-spectral imaging and large-scale space interferometry science and engineering.

Kizhner, Semion↗
NASA NTRS1986

Nearly-grazing optimal trajectories for aeroassisted orbital transfer

In the present treatment of optimal control problems arising in the study of coplanar aeroassisted orbital transfer, the hybrid combination of propulsive parameters in space and aerodynamic maneuvers employing lift modulation in the sensible atmosphere indicates that the optimal energy-viewpoint solution is the grazing trajectory; this trajectory is characterized by favorable values of the peak heating rate and the peak dynamic pressure. Numerical solutions are obtained by means of the sequential gradient restoration algorithm for optimal control problems. It is found that nearly-grazing trajectories yielding the least-square value of the path inclination have desirable characteristics from the standpoints of energy, heating rate, and dynamic pressure.

Miele, A.↗
NASA NTRS1989

Parallel dynamic programming for on-line flight path optimization

Parallel systolic algorithms for dynamic programming(DP) and their respective hardware implementations are presented for a problem in on-line trajectory optimization. The method is applied to a model for helicopter flight path optimization through a complex constraint region. This problem has application to an air traffic control problem and also to a terrain following/threat avoidance problem.

Slater, G. L.↗
NASA NTRSDate not supplied

Smart Crop Farming Systems for Artemis Exploration Missions

Space crop production systems that mitigate risks of crew poor performance or illness due to inadequate food and nutrition are needed during manned Artemis exploration missions beyond LEO. Prototype farms must be designed for deployment on ISS and tested in manned platforms: Gateway, lunar habitats, and Mars trans-hab spacecraft in preparation for human missions to Mars. Food production must be optimal and safe for human consumption. Thus, plant growth facilities (i.e. Veggie and APH) can be enhanced with imaging systems (including hyperspectral, multispectral, lidar, and fluorescence imaging systems) for nondestructive monitoring of plant health, stress and assessing food safety. Databases of crop responses to stress obtained during ground studies can be used to develop novel artificial intelligence (AI) algorithms for optimizing crop production (i.e. environmental settings during growth) and for detecting crop indices that ensure food safety. Future farming systems should be sustainable and smart. Novel adaptive AI algorithms requiring limited data sets for calibration are needed for reducing crew intervention during plant cultivation except for maintenance and harvesting events. Eventually, AI driven control systems that include autonomous planting, growing, and harvesting as well as periodic sanitization need evaluation for supplementing crew diets with fresh produce during future Mars exploration missions.

O Monje↗
NASA NTRS1974

Gust alleviation for a STOL transport by using elevator, spoilers, and flaps

Control laws were developed to investigate methods of alleviating the response of a STOL transport to gusty air. The transport considered in the study had triple-slotted, externally blown jet flaps and a large T-tail. The control devices used were the elevator, spoilers, and flaps. A hybrid computing system was used to simulate linearized longitudinal dynamics of the aircraft and to implement a conjugate gradient optimal search algorithm. The aircraft was simulated in the low-speed approach condition only. Feedback control matrices were found which minimized the average of a quadratic functional involving passenger compartment accelerations, pitch angle and rate, flight path angle and speed variations. The optimization was performed for artificially designed gust inputs in the form of predetermined rectangular waveforms. Results were obtained for elevator, spoilers, and flaps acting singly and in combination. Additional results were obtained for unit sinusoidal gust inputs by using the gain matrices computed for the artificial test gusts. Various sensor configurations were also investigated.

Lallman, F. J.↗
NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Suarez, Max J.↗
NASA NTRS2004

A Most Probable Point-Based Method for Reliability Analysis, Sensitivity Analysis and Design Optimization

A major step in a most probable point (MPP)-based method for reliability analysis is to determine the MPP. This is usually accomplished by using an optimization search algorithm. The minimum distance associated with the MPP provides a measurement of safety probability, which can be obtained by approximate probability integration methods such as FORM or SORM. The reliability sensitivity equations are derived first in this paper, based on the derivatives of the optimal solution. Examples are provided later to demonstrate the use of these derivatives for better reliability analysis and reliability-based design optimization (RBDO).

Hou, Gene J.-W↗
NASA NTRS1979

Evaluating and minimizing noise impact due to aircraft flyover

Existing techniques were used to assess the noise impact on a community due to aircraft operation and to optimize the flight paths of an approaching aircraft with respect to the annoyance produced. Major achievements are: (1) the development of a population model suitable for determining the noise impact, (2) generation of a numerical computer code which uses this population model along with the steepest descent algorithm to optimize approach/landing trajectories, (3) implementation of this optimization code in several fictitious cases as well as for the community surrounding Patrick Henry International Airport, Virginia.

Jacobson, I. D.↗
NASA NTRS1992

Tracing the efficient curve for multi-objective control-structure optimization

A recently developed active set algorithm for tracing parameterized optima is adapted to multiobjective optimization. The algorithm traces a path of Kuhn-Tucker points using homotopy curve tracking techniques, and is based on identifying and maintaining the set of active constraints. Second order necessary optimality conditions are used to determine nonoptimal stationary points on the path. In the bi-objective optimization case the algorithm is used to trace the curve of efficient solution (Pareto optima). As an example, the algorithm is applied to the simultaneous minimization of the weight and control force of a ten-bar truss with two collocated sensors and actuators, with some interesting results.

Rakowska, J.↗
NASA NTRS2000

Algorithmic Perspectives on Problem Formulations in MDO

This work is concerned with an approach to formulating the multidisciplinary optimization (MDO) problem that reflects an algorithmic perspective on MDO problem solution. The algorithmic perspective focuses on formulating the problem in light of the abilities and inabilities of optimization algorithms, so that the resulting nonlinear programming problem can be solved reliably and efficiently by conventional optimization techniques. We propose a modular approach to formulating MDO problems that takes advantage of the problem structure, maximizes the autonomy of implementation, and allows for multiple easily interchangeable problem statements to be used depending on the available resources and the characteristics of the application problem.

Alexandrov, Natalia M.↗
NASA NTRS1990

Active rejection of persistent disturbances in flexible space structures

A dynamic compensator for active rejection of persistent disturbances in flexible space structures is designed on the principle of the H(infinity)-optimization of the sensitivity transfer function matrix. A general state space solution is formulated to the multiinput multioutput H(infinity)-optimal control problem, allowing the use of the H(infinity)-optimal synthesis algorithm for the state-space models of space structures that result from model order reduction. Disturbances encountered in flexible space structures, such as shuttle docking, are investigated using the high-mode and the reduced-order models of a cantilevered two-bay truss, demonstrating the applicability of the H(infinity)-optimal approach.

Hwang, Cheng-Neng↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2002

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Sobieszczanski-Sobieski, Jaroslaw↗
NASA NTRSDate not supplied

ISRU Pilot Excavator - Development of Autonomous Excavation Algorithms

The ISRU Pilot Excavator (IPEx) is a Space Technology Mission Directorate (STMD) Game Changing Development (GCD) project to develop a robotic excavator to demonstrate excavation of up to 10 metric tons of lunar regolith. IPEx is based on the Regolith Advanced Surface Systems Operations Robot (RASSOR) excavator developed at NASA Kennedy Space Center (KSC) and utilizes a counter rotating bucket drum concept to balance excavation forces for use on reduced gravity planetary bodies. To take advantage of the counter rotating bucket drum mechanism, work is being done to develop new autonomous excavation strategies and algorithms. Referred to as “Auto-dig”, these algorithms will allow IPEx to excavate, drive, and deliver its target mass of 10 metric tons of lunar regolith during an 11-day mission semi-autonomously. Due to bandwidth and latency constraints teleoperation will be kept to a minimum, with operators periodically confirming and verifying the excavator’s high-level tasks and operations. While the work to develop optimized autodig solutions is ongoing at KSC, early tests have yielded interesting results that have led to the discovery of additional risks that need to be mitigated in autonomous excavation. Testing has also encouraged the development of new software and visualization tools that provide real-time insight into excavation loads during operation, allowing faster development and helping build better intuition to the excavation process. These tools will help in the pursuit to develop fully optimized digging algorithms that are robust enough to handle hazards such as rocks or irregular terrain.

B. C. Buckles↗
NASA NTRS1997

Biweekly Maps of Wind Stress for the North Pacific from the ERS-1 Scatterometer

The European Remote-sensing Satellite (ERS-1) was launched in July 1991 and contained several instruments for observing the Earth's ocean including a wind scatterometer. The scatterometer measurements were processed by the European Space Agency (ESA) and the Jet Propulsion Laboratory (JPL). JPL reprocessed (Freilich and Dunbar, 1992) the ERS-1 backscatter measurements to produced a 'value added' data set that contained the ESA wind vector as well as a set of up to four ambiguities. These ambiguities were further processed using a maximum-likelihood estimation (MLE) and a median filter to produce a 'selected vector.' This report describes a technique developed to produce time-averaged wind field estimates with their expected errors using only scatterometer wind vectors. The processing described in this report involved extracting regions of interest from the data tapes, checking the quality and creating the wind field estimate. This analysis also includes the derivation of biweekly average wind vectors over the North Pacific Ocean at a resolution of 0.50 x 0.50. This was done with an optimal average algorithm temporally and an over-determined biharmonic spline spatially. There have been other attempts at creating gridded wind files from ERS-1 winds, e.g., kriging techniques (Bentamy et al., 1996) and successive corrections schemes (Tang and Liu, 1996). There are several inherent problems with the ERS-1 scatterometer. Since this is a multidisciplinary mission, the satellite is flown in different orbits optimized for each phase of the mission. The scatterometer also shares several sub-systems with the Synthetic Aperture Radar (SAR) and cannot be operated while the SAR is in operation. The scatterometer is also a single-sided instrument and only measures backscatter along the right side of the satellite. The processing described here generates biweekly wind maps during the wktwo years analysis period regardless of the satellite orbit or missing data.

SYNTHETIC APERTURE RADAR↗
NASA NTRS2012

SBUV version 8.6 Retrieval Algorithm: Error Analysis and Validation Technique

SBUV version 8.6 algorithm was used to reprocess data from the Back Scattered Ultra Violet (BUV), the Solar Back Scattered Ultra Violet (SBUV) and a number of SBUV/2 instruments, which 'span a 41-year period from 1970 to 2011 (except a 5-year gap in the 1970s)[see Bhartia et al, 2012]. In the new version Daumont et al. [1992] ozone cross section were used, and new ozone [McPeters et ai, 2007] and cloud climatologies Doiner and Bhartia, 1995] were implemented. The algorithm uses the Optimum Estimation technique [Rodgers, 2000] to retrieve ozone profiles as ozone layer (partial column, DU) on 21 pressure layers. The corresponding total ozone values are calculated by summing ozone columns at individual layers. The algorithm is optimized to accurately retrieve monthly zonal mean (mzm) profiles rather than an individual profile, since it uses monthly zonal mean ozone climatology as the A Priori. Thus, the SBUV version 8.6 ozone dataset is better suited for long-term trend analysis and monitoring ozone changes rather than for studying short-term ozone variability. Here we discuss some characteristics of the SBUV algorithm and sources of error in the SBUV profile and total ozone retrievals. For the first time the Averaging Kernels, smoothing errors and weighting functions (or Jacobians) are included in the SBUV metadata. The Averaging Kernels (AK) represent the sensitivity of the retrieved profile to the true state and contain valuable information about the retrieval algorithm, such as Vertical Resolution, Degrees of Freedom for Signals (DFS) and Retrieval Efficiency [Rodgers, 2000]. Analysis of AK for mzm ozone profiles shows that the total number of DFS for ozone profiles varies from 4.4 to 5.5 out of 6-9 wavelengths used for retrieval. The number of wavelengths in turn depends on solar zenith angles. Between 25 and 0.5 hPa, where SBUV vertical resolution is the highest, DFS for individual layers are about 0.5.

Kramarova, N. A.↗
NASA NTRS2004

Evolutionary computing for the design search and optimization of space vehicle power subsystems

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

evolutionary computing genetic algorithms optimiza↗