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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Experiments in robotic catching

Real-time coordination of visual information with high-speed manipulator control is studied in the context of three-dimensional robotic catching. All path planning for the catch occurs in real-time during the half-second that the targeted object is airborne. A trajectory-matching algorithm is used that combines an observer with a varying-strength filter, an error estimator, and an initial motion algorithm. The results are demonstrated experimentally using a real-time vision system and a four-degree-of-freedom, cable-driven arm with a workspace of 4.2 m3 and speed capabilities of up to 2.0 m/s.

Hove, Barbara↗
NASA NTRS2014

Three-Dimensional Imaging and Numerical Reconstruction of Graphite/Epoxy Composite Microstructure Based on Ultra-High Resolution X-Ray Computed Tomography

A combined experimental and computational study aimed at high-resolution 3D imaging, visualization, and numerical reconstruction of fiber-reinforced polymer microstructures at the fiber length scale is presented. To this end, a sample of graphite/epoxy composite was imaged at sub-micron resolution using a 3D X-ray computed tomography microscope. Next, a novel segmentation algorithm was developed, based on concepts adopted from computer vision and multi-target tracking, to detect and estimate, with high accuracy, the position of individual fibers in a volume of the imaged composite. In the current implementation, the segmentation algorithm was based on Global Nearest Neighbor data-association architecture, a Kalman filter estimator, and several novel algorithms for virtualfiber stitching, smoothing, and overlap removal. The segmentation algorithm was used on a sub-volume of the imaged composite, detecting 508 individual fibers. The segmentation data were qualitatively compared to the tomographic data, demonstrating high accuracy of the numerical reconstruction. Moreover, the data were used to quantify a) the relative distribution of individual-fiber cross sections within the imaged sub-volume, and b) the local fiber misorientation relative to the global fiber axis. Finally, the segmentation data were converted using commercially available finite element (FE) software to generate a detailed FE mesh of the composite volume. The methodology described herein demonstrates the feasibility of realizing an FE-based, virtual-testing framework for graphite/fiber composites at the constituent level.

Czabaj, M. W.↗
NASA NTRSDate not supplied

Fluid for Thought: SLS Rocket Aerodynamics featuring Pressure Sensitive Paint

Combining computer vision algorithms, state-of-the-art high-speed cameras, pressure sensitive paint (PSP) and transonic wind tunnel facilities, teams at NASA and Boeing study unsteady pressure over the Space Launch System (SLS) rocket model with unprecedented temporal and spatial data resolution. Post-processing visualizes pressure fluctuations over a 3D model by converting intensity data from videos of the wind tunnel test. Additionally, data analysis creates predictions of buffet forcing and aeroacoustic activity over the rocket body with the accuracy of a CFD model! The development of the technique will revolutionize aerodynamics research and reveal uncharted insights into fluid behavior and vehicle performance, enhancing aerospace technology and design as they advance. The talk will focus on the impact of the PSP technique on the SLS design and other potential aerospace applications, as well as lay out the experimental process, from wind tunnel test to insights from the data analysis.

Lucy Zhonghui Tang↗
NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗
DOE OSTI2018

Enhancing Clean Energy Innovation Ecosystem Discovery Tool

During March 2016, the DOE’s Office of Energy Policy and Systems Analysis (EPSA) sought a methodology to identify and quantify the strength of the existing clean energy innovation ecosystems (IE) in the U.S. and their characteristics. Oak Ridge National Lab (ORNL) did a pilot study (Phase 1) and demonstrated the feasibility of an application comprised of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The data-collection, ingest and analytics pipelines were combined with an advanced user interface, together known as the Ecosystem Discovery Tool, to enhance DOE’s understanding of existing geographic innovation clusters. During Phase 2 of this project, this tool has been further enhanced by integrating new data sets and through backend software architecture changes, validation and bug fixing, making it a much more robust and powerful application. The tool’s user interface is a lot more intuitive, which enables a user to visualize the IE rankings for various geographic regions(CBSA, state level) for different clean energy technologies and seek automated insights. ORNL also created a DOE-specific dashboard that allows the user to visualize and analyze federal funding from various offices including Energy Efficiency and Renewable Energy(EERE), Office of Electricity(OE), Fossil Energy(FE), and Nuclear Energy(NE). During Phase 1 of this project, EPSA defined a clean energy innovation ecosystem as the overlap of five Ecosystem Components: 1) nascent clean energy Indicators, 2) investors, 3) enabling environment, 4) networking assets and 5) large companies. EPSA and ORNL worked together to collect data for each component: 1) small and medium companies, ARPA-E awardees, SBIR awardees, patents, publications, ERCs for Nascent Clean Energy Indicators; 2) qualified investors; 3) the Clean Edge Policy Index/DSIRE for the Enabling Environment; 4) universities, national laboratories, ERCs and incubators for Networking Assets; and 5) large companies and a subset of the Russell 1000 list for Large Companies. The ORNL team created a visual tool based on Tableau that integrated ecosystem component data to score, rank and map IEs. The tool was created with the flexibility to allow the user to choose the weights of each of the five ecosystem components and the subcomponents. This flexibility allows the user to visualize different subsets and to use the underlying data for different types of analysis. During Phase 2 of this project, the backend database and computation process have undergone major changes to provide a much more refined user-interface and added new features into the scoring algorithm. This phase also involved addition of newer databases such as a list of Energy Research Centers (ERC) ,DSIRE, and funding data from EERE, FE, NE, and OE to provide more granular information and more accurate/better quantification.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Introduction to ALERT: A User-Interface Tool for Resilient Optimization

As the cyber-physical systems grow in complexity, there is a need for proactive resilience strategies that involve online, adaptive control actions to best prepare for any impending adversarial events. In this technical effort, supported by RD2C LDRD initiative, the project team designed and demonstrated online strategies – referred to as ALERT controls – for proactive and adaptive tuning of existing optimal controls in a microgrid, with quantifiably assured margins of resilience to various cyber-physical adversarial events. This ALERT functionality is made available to the end-users, e.g., the system operators, via an interactive user-interface. The end-users will not only be able to use the interface to visualize the system’s operation under various cyber-physical adversarial scenarios, but also evaluate the amount of tolerance the system has against selected adversarial perturbations of interest (e.g., malfunctioning sensors, suspected attacked measurements) via the adversarial plots. In this technical report, we briefly outline the algorithmic modules of the developed ALERT control technology, and introduce the user-interface tool that allows end-users (e.g., microgrid operators) to enter their system description, specify various operational and resilience requirements, and evaluate the impact of the control decisions via illustrative plots.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Block adaptive rate controlled image data compression

A block adaptive rate controlled (BARC) image data compression algorithm is described. It is noted that in the algorithm's principal rate controlled mode, image lines can be coded at selected rates by combining practical universal noiseless coding techniques with block adaptive adjustments in linear quantization. Compression of any source data at chosen rates of 3.0 bits/sample and above can be expected to yield visual image quality with imperceptible degradation. Exact reconstruction will be obtained if the one-dimensional difference entropy is below the selected compression rate. It is noted that the compressor can also be operated as a floating rate noiseless coder by simply not altering the input data quantization. Here, the universal noiseless coder ensures that the code rate is always close to the entropy. Application of BARC image data compression to the Galileo orbiter mission of Jupiter is considered.

Rice, R. F.↗
NASA NTRS1987

Strake-generated vortex interactions for a fighter-like configuration

A combination of the Euler, and thin-layer Navier-Stokes equations were solved for the flowfield around a wing-strake-fuselage configuration similar to the F-16A aircraft. The flowfield was divided into multiple blocks using a zoning program, and the TNSWF solution program, based on an implicit solution algorithm, updates each subregion of the flowfield sequentially. The total grid contained approximately 300,000 grid points, and clustering was effected normal to all aircraft surfaces. The computed solutions agreed well quantitatively with wind-tunnel pressure distribution data, and they agreed well qualitatively with flow visualization from wind- and water-tunnel tests.

Reznick, Steven G.↗
NASA NTRS1990

A computational study of the impingement region of an unsteady subsonic jet

Vertical/Short Take-Off and Landing (V/STOL) aircraft may encounter operating difficulties while hovering near the ground. Lift loss, hot gas and debris ingestion, and landing surface damage are some of the potential problems which can result from the interaction of the propulsive jets with the airframe and ground. The purpose of the present work is to develop a detailed understanding of a flowfield, a jet exiting from a flat plate impacting a ground plane, that contains much of the essential physics of the complex flows encountered by V/STOL aircraft in hover. To achieve this, a high-resolution time-accurate numerical simulation of the impinging jet flow was performed. The numerical simulation is based on the solution of the laminar compressible Navier-Stokes equations using the Beam-Warming algorithm. Complex start-up and periodic jet and jet-induced vortex structures were captured, including the formation of primary and secondary jet-induced vortices and wall-jet separation. The formation and development of these phenomena are supported by experimental measurements and smoke visualization. Results are compared with appropriate experiments.

Jalamani, Zakaria A.↗
NASA NTRS1993

Automated propellant leak detection

An automated hydrogen leak detection system is being developed for earth-to-orbit rocket engine applications. The system consists of three elements, a sensor array, a signal processing unit, and a diagnostic processor. The sensor array consists of discrete solid state sensors which are located at specific potential leak sites and in potential leak zones. The signal processing unit provides excitations power for the sensors and provides analog to digital data conversion of the sensor signals. The diagnostic computer analyzes the sensor outputs to determine leak sources and magnitude. Leak data from the sensor network is interpreted using knowledge based software and displayed on-line through a graphical user interface including 3-D leak visualization. The system requirements have been developed assuming eventual application to the Space Shuttle Main Engine which requires approximately 72 measurement locations. A prototype system has been constructed to demonstrate the operational features of the system. This system includes both prototype electronics and data processing software algorithms. Experiments are in progress to evaluate system operation at conditions which simulate prelaunch and flight. The prototype system consists of a network using 16 sensors within a testbed which simulates engine components. Sensor response, orientation, and data analysis algorithms are being evaluated using calibrated leaks produced within the testbed. A prototype flight system is also under development consisting of 8 sensors and flight capable electronics with autonomous control and data recording.

Makel, D. B.↗
NASA NTRS1993

A study of the compressible flow through a diffusing S-duct

Benchmark aerodynamic data are presented for compressible flow through a representative S-duct configuration. A numerical prediction of the S-duct flow field, obtained from a subsonic parabolized Navier-Stokes algorithm, is also shown. The experimental and numerical results are compared. Measurements of the three-dimensional velocity field, total pressures, and static pressures were obtained at five cross-sectional planes. Aerodynamic data were gathered with calibrated pneumatic probes. Surface static pressure and surface flow visualization data were also acquired. All reported tests were conducted with an inlet centerline Mach number of 0.6. The Reynolds number, based on the inlet centerline velocity and duct inlet diameter, was 2.6 x 10(exp 6). Thin inlet turbulent boundary layers existed. The collected data should be beneficial to aircraft inlet designers and the measurements are suitable for the validation of computational codes. The results show that a region of streamwise flow separation occurred within the duct. Details about the separated flow region, including mechanisms which drive this complicated flow phenomenon, are discussed. Results also indicate that the duct curvature induces strong pressure driven secondary flows. The cross flows evolve into counter-rotating vortices. These vortices convect low momentum fluid of the boundary layer toward the center of the duct, degrading both the uniformity and magnitude of the total pressure profile.

Wellborn, Steven R.↗
NASA NTRS1994

A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm

Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data.

Matic, Roy M.↗
DOE OSTI2024

GLIMPSE of Future Power Grid Models

Power grid is one of the critical national infrastructures with social, economics, and national security impacts. Particularly, power distribution systems represent part of the infrastructure between power distribution substations and customers such as residential, commercial, and industrial. To address various grid modernization challenges, state-of-the-art algorithms and methodologies have been developed to deploy, operate, and expand a secure and resilient power grid. At the same time, there is a need to develop capabilities to assist power grid stakeholders to quickly get insight into the design and structure of the grid. Data visualization is a key approach to comprehend and understand complex systems such as the power grid. We present GLIMPSE Grid Layout Interface for Model Preview and System Exploration), a graph-based semantic-aware application to visualize and update distribution power grid models. The GLIMPSE can be used with standard IEEE models to search and highlight power grid objects such as generators, loads, overhead lines, etc. Additionally, it supports updating attributes and model export to integrate with GridLAB-D simulations.

Cybersecurity, power grid, visualization↗
NASA NTRS2000

Prediction of thrombus-related mechanical prosthetic valve dysfunction using transesophageal echocardiography

Identification of thrombus-related mechanical prosthetic valve dysfunction (MPVD) has important therapeutic implications. We sought to develop an algorithm, combining clinical and echocardiographic parameters, for prediction of thrombus-related MPVD in a series of 53 patients (24 men, age 52 +/- 16 years) who had intraoperative diagnosis of thrombus or pannus from 1992 to 1997. Clinical and echocardiographic parameters were analyzed to identify predictors of thrombus and pannus. Prevalence of thrombus and diagnostic yields relative to the number of predictors were determined. There were 22 patients with thrombus, 19 patients with pannus, and 12 patients with both. Forty-two of 53 masses were visualized using transesophageal echocardiography (TEE), including 29 of 34 thrombi or both thrombi and panni and 13 of 19 isolated panni. Predictors of thrombus or mixed presentation include mobile mass (p = 0.009), attachment to occluder (p = 0.02), elevated gradients (p = 0.04), and an international normalized ratio of < or = 2.5 (p = 0.03). All 34 patients with thrombus or mixed presentation had > or = 1 predictor. The prevalence of thrombus in the presence of < or = 1, 2, and > or = 3 predictors is 14%, 69%, and 91%, respectively. Thus, TEE is sensitive in the identification of abnormal mass in the setting of MPVD. An algorithm based on clinical and transesophageal echocardiographic predictors may be useful to estimate the likelihood of thrombus in the setting of MPVD. In the presence of > or = 3 predictors, the probability of thrombus is high.

NASA Discipline Cardiopulmonary↗
DOE OSTI2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc . An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage

Purpose: Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images. Methods: The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers. Results: Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage. Conclusions: The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Importance of Scientific Visualization on Novel Hardware

Innovation in HPC hardware and adoption of heterogeneous systems has led to a variety of unique programming models. This has led to a challenge for scientific visualization software (and indeed all HPC software) to take full advantage of recent generations of supercomputing. Edge computing, where hardware is specialized for the needs of the particular application, exacerbates the problem. VTK-m has had many successes on this front by providing device-agnostic algorithms that compare favorably to implementations written to specific devices as shown in Table 1. However, VTK-m has focused mostly on GPU and traditional CPU multicore technology. There are numerous processor technologies, both existing and potential future, that are not being addressed by current R&D efforts.

97 MATHEMATICS AND COMPUTING↗