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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 235 records · Page 13

Some algorithms for polygons on a sphere.

A limited search for polygon algorithms for use in a new military training simulation that interfaces with several others produced only planar algorithms. To avoid having to implement several different sophisticated map projections to guarantee compatibility with all the other simulations, we opted to develop algorithms that work directly on a sphere. The first is an algorithm to compute the area of a polygon whose edges are segments of great circles. Since our model represents certain object locations as mathematical points, the second topic is whether a specified point is inside a specified polygon. Possibly pathological cases are identified and eliminated. When we realized that most political boundaries are actually rhumb lines, use of the Mercator projection equations seemed unavoidable. We then reasoned that if all the edges were short enough, lat-lon lines, great circle segments, and rhumb lines would be close enough to being identical that we could use whichever was most convenient. Thence, we looked at the relationship between the maximum distances between great circle segments and rhumb lines and between lat-lon lines and rhumb lines as functions of length, azimuth, and latitude. The final algorithm finds the area overlapped by two polygons. Again, potentially pathological cases are identified and eliminated.

Duquette, William H.↗

A two-level trajectory decomposition algorithm featuring optimal intermediate target selection

A decomposition algorithm is presented that optimizes complex missions by partitioning the trajectory into natural segments such as ascent or entry. Each segment defines a full-rank targeting subproblem. These are solved sequentially using the Newton-Raphson algorithm. The master problem, representing the complete mission, is to determine subproblem targets and master-problem controls that optimize the mission objective subject to intersegment constraints. The gradient projection algorithm solves this problem using derivatives obtained analytically from finite-difference subproblem sensitivities. Thus, the mission is optimized by coordinating the solution of tractible subproblems. Computational results for a synchronous equatorial mission are included.

Petersen, F. M.↗

A two-level trajectory decomposition algorithm featuring optimal intermediate target selection

A decomposition algorithm is presented which optimizes complex missions by partitioning the trajectory into natural segments such as ascent or entry. Each segment defines a full-rank targeting subproblem. These are solved sequentially using the Newton-Raphson algorithm. The master problem, representing the complete mission, is to determine subproblem targets and master-problem controls that optimize the mission objective subject to intersegment constraints. The gradient projection algorithm solves this problem using derivatives obtained analytically from finite-difference subproblem sensitivities. Thus, the mission is optimized by coordinating the solution of tractible subproblems. Computational results for a synchronous equatorial mission are included.

Petersen, F. M.↗

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗

Experimental Validation of Advanced Dispersed Fringe Sensing (ADFS) Algorithm Using Advanced Wavefront Sensing and Correction Testbed (AWCT)

Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST.An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.

Dispersed Fringe Sensing (DFS)↗

Improving ICARUS track reconstruction algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab. Its primary objective is to explore the possible existence of sterile neutrinos in the O(1 eV) mass range and to clarify the anomalies observed in the Liquid Scintillator Neutrino Detector and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber, capable of producing high-resolution 3D images and precise calorimetric measurements of ionizing particles. This technology allows for a detailed study of neutrino interactions across a broad energy range, from a few keV to several hundred GeV. The track reconstruction is achieved through a software framework that applies a series of pattern recognition algorithms, transforming raw detector signals into fully reconstructed event topologies. This process involves identifying interaction vertices, particle tracks, and electromagnetic showers within the TPC. However, in certain cases, these algorithms may mistakenly break a single particle track into several shorter segments, interpreting each as a distinct particle. Since track length is used to estimate the particle's energy, such fragmentation can result in an energy underestimation of several hundred MeV. Furthermore, when a track is split into multiple segments, the particle identification (which relies on analyzing the energy loss as a function of the residual range) may fail, potentially leading to the loss of the entire event. To mitigate this problem, we have developed a dedicated algorithm designed to identify and reconnect (“stitch”) the tracks that were erroneously divided into multiple segments.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗

Bayesian Fusion of Color and Texture Segmentations

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)

Manduchi, Roberto↗

LUVOIR Primary Mirror Segment Alignment Control With Joint Laser Metrology and Segment Edge Sensing

An approach is developed for the alignment and stability maintenance of the LUVOIR segmented primary mirror using a segment state estimation and wavefront control method based on a hybrid segment motion sensing architecture of laser truss metrology and segment edge sensors. Our current computer model was generated for LUVOIR Architecture Option A with a 15-meter aperture, 120segment primary mirror. The methodology and simulation results will be presented and analyzed. JPL has a long history of technology development in laser metrology and edge sensors, including work in SIM [7], Keck and TMT [8], CCAT [3] and LUVOIR [1]. We will discuss our current efforts of LUVOIR laser metrology and edge-sensor models development, showing sensitivities of sensor measurements to various mirror eigenmodes, removing global modes and strengthening weak modes by performing joint (hybrid) laser-metrology and edge sensing. We will define and derive an important performance metric called wavefront error multiplier (WEM), and show that WEM provides a simple link between sensor errors and the closed-loop (controlled) system wavefront error. We will show WEM values for several hybrid sensor configuration options studied. We will discuss an algorithm for mirror shape control and maintenance through segment state and wavefront estimations using joint edge-metrology sensing. We will compare simulated performance of mirror state estimation, wavefront estimation and wavefront control based on joint edge-metrology sensing among several sensor configurations, and show the impact of sensor error distributions on the segmented mirror alignment performance. Mirror shape control performance will be also be evaluated in terms of imaging contrast between inner working angles (IWA) and outer working angles (OWA) of a LUVOIR coronagraph. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. LUVOIR is a mission concept under study for the next astronomy Decadal Survey—the information presented in this paper is pre-decisional and is provided for planning and discussion purposes only.

Shelton, Chris↗

A knowledge-based object recognition system for applications in the space station

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

Dhawan, Atam P.↗

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a LANDSAT scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data.

Abotteen, R. A.↗

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a Landsat scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data. The classification performance on the data sets using the algorithms is presented, along with results of statistical tests on the accuracy and proportion estimates. The three algorithms tested on the simulated data produced significant improvements over the results obtained using untransformed signatures. For the consecutive-day data, the tested algorithms produced improvements in most but not all cases. The tests indicated also that no statistically significant differences were noted among the algorithms.

Abotteen, R.↗

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

Some vision algorithms

The ability to determine the contents of a scene by a machine has obvious applications. There are reasons for seeking horizontal, vertical and diagonal line segments of varying lengths in order to interpret the content of a scene. Such line segments aid in determining edges that determine the boundaries of objects within the scene. Algorithms for each of the above as well as for data compacting are presented.

Johnson, Gordon G.↗

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗

Optimum segmented acoustic liners for flow ducts

Analytical and experimental investigations of phased linings for a duct containing a uniform mean flow were conducted as part of a program concerned with the design of more efficient acoustic liners for the reduction of noise emission. An analytical technique was developed which predicts the propagation of sound waves in flow ducts with segmented liners. The calculations for implementing the derived algorithms were conducted with the aid of FORTRAN IV computer programs. Attention is also given to details regarding the experimental program which was designed to supplement the theoretical studies.

Sawdy, D. T.↗

Entry trajectory shaping for Shuttle-deployed experiments

The availability of the Space Transportation System has provided a unique opportunity for deployment of atmospheric entry experiments. These experiments require a trajectory design that provides a well-defined and controlled flight environment. Items addressed in this paper include control of thermal and aerodynamic loading consistent with the test objectives, maintenance of acceptable velocity-altitude conditions, and accommodation of tracking and recovery requirements. Various strategies, including constant-drag flight, equilibrium glide, and controlled linear velocity-altitude flight segments are employed to effect desired trajectory conditions. Algorithms to match thermal constraints to entry conditions and vehicle aerodynamic configuration are also discussed.

Letts, W. R., Jr.↗