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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Integrating CFD, CAA, and Experiments Towards Benchmark Datasets for Airframe Noise Problems

Airframe noise corresponds to the acoustic radiation due to turbulent flow in the vicinity of airframe components such as high-lift devices and landing gears. The combination of geometric complexity, high Reynolds number turbulence, multiple regions of separation, and a strong coupling with adjacent physical components makes the problem of airframe noise highly challenging. Since 2010, the American Institute of Aeronautics and Astronautics has organized an ongoing series of workshops devoted to Benchmark Problems for Airframe Noise Computations (BANC). The BANC workshops are aimed at enabling a systematic progress in the understanding and high-fidelity predictions of airframe noise via collaborative investigations that integrate state of the art computational fluid dynamics, computational aeroacoustics, and in depth, holistic, and multifacility measurements targeting a selected set of canonical yet realistic configurations. This paper provides a brief summary of the BANC effort, including its technical objectives, strategy, and selective outcomes thus far.

Choudhari, Meelan M.↗
NASA NTRS2014

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Uncertainty↗
NASA NTRS2023

A Blind Convolutional Deep Autoencoder for Spectral Unmixing of Hyperspectral Images Over Waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

hyperspectral imaging↗
NASA NTRS2021

Machine Vision based Sample-Tube Localization for Mars Sample Return

A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detecting and localizing sample tubes deposited on the Martian surface. Towards this end, we study two machine-vision based approaches: First, a geometrydriven approach based on template matching that uses hardcoded filters and a 3D shape model of the tube; and second, a data-driven approach based on convolutional neural networks (CNNs) and learned features. Furthermore, we present a large benchmark dataset of sample-tube images, collected in representative outdoor environments and annotated with ground truth segmentation masks and locations. The dataset was acquired systematically across different terrain, illumination conditions and dust-coverage; and benchmarking was performed to study the feasibility of each approach, their relative strengths and weaknesses, and robustness in the presence of adverse environmental conditions.

Detry, R.↗
NASA NTRSDate not supplied

Benchmark Models for Classification of Radiation Type Induced in Immune Cells

NASA Biological and Physical Sciences and the Science Mission Directorate have published a benchmark dataset of mouse immune cells subjected to radiation-induced DNA damage. The dataset comprises ML-ready microscopic imagery of said cells, including labels indicating radiation type and dose. The machine learning team at NASA Interagency Implementation and Advanced Concept Team (IMPACT) created multiple benchmark models. Initially, we conducted a preliminary analysis using thresholding. The algorithm used thresholds on average brightness of the available images to classify them into their respective radiation type. We also tested machine learning approaches. Convolutional Neural Networks (CNN) emerged as the best-performing model. This poster presents the benchmark scores obtained by the models.

Vishal Perekadan↗
NASA NTRS2014

Long Hole Film Cooling Dataset for CFD Development - Flow and Film Effectiveness

An experiment investigating flow and heat transfer of long (length to diameter ratio of 18) cylindrical film cooling holes has been completed. In this paper, the thermal field in the flow and on the surface of the film cooled flat plate is presented for nominal freestream turbulence intensities of 1.5 and 8 percent. The holes are inclined at 30 deg above the downstream direction, injecting chilled air of density ratio 1.0 onto the surface of a flat plate. The diameter of the hole is 0.75 in. (approx. 0.02 m) with center to center spacing (pitch) of 3 hole diameters. Coolant was injected into the mainstream flow at nominal blowing ratios of 0.5, 1.0, 1.5, and 2.0. The Reynolds number of the freestream was approximately 11,000 based on hole diameter. Thermocouple surveys were used to characterize the thermal field. Infrared thermography was used to determine the adiabatic film effectiveness on the plate. Hotwire anemometry was used to provide flowfield physics and turbulence measurements. The results are compared to existing data in the literature. The aim of this work is to produce a benchmark dataset for Computational Fluid Dynamics (CFD) development to eliminate the effects of hole length to diameter ratio and to improve resolution in the near-hole region. In this report, a Time Filtered Navier Stokes (TFNS), also known as Partially Resolved Navier Stokes (PRNS), method that was implemented in the Glenn-HT code is used to model coolant-mainstream interaction. This method is a high fidelity unsteady method that aims to represent large scale flow features and mixing more accurately.

Shyam, Vikram↗
NASA NTRS2013

Long Hole Film Cooling Dataset for CFD Development : Infrared Thermography and Thermocouple Surveys - Part 1

An experiment investigating flow and heat transfer of long (length to diameter ratio of 18) cylindrical film cooling holes has been completed. In this paper, the thermal field in the flow and on the surface of the film cooled flat plate is presented for nominal freestream turbulence intensities of 1.5 and 8 percent. The holes are inclined at 30deg above the downstream direction, injecting chilled air of density ratio 1.0 onto the surface of a flat plate. The diameter of the hole is 0.75 in. (0.01905 m) with center to center spacing (pitch) of 3 hole diameters. Coolant was injected into the mainstream flow at nominal blowing ratios of 0.5, 1.0, 1.5, and 2.0. The Reynolds number of the freestream was approximately 11,000 based on hole diameter. Thermocouple surveys were used to characterize the thermal field. Infrared thermography was used to determine the adiabatic film effectiveness on the plate. Hotwire anemometry was used to provide flowfield physics and turbulence measurements. The results are compared to existing data in the literature. The aim of this work is to produce a benchmark dataset for Computational Fluid Dynamics (CFD) development to eliminate the effects of hole length to diameter ratio and to improve resolution in the near-hole region. In this report, a Time-Filtered Navier Stokes (TFNS), also known as Partially Resolved Navier Stokes (PRNS), method that was implemented in the Glenn-HT code is used to model coolant-mainstream interaction. This method is a high fidelity unsteady method that aims to represent large scale flow features and mixing more accurately.

Shyam, Vikram↗
NASA NTRS2002

Anytime query-tuned kernel machine classifiers via Cholesky factorization

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

Kernel Cholesky MNIST↗
NASA NTRS2006

Active Learning with Irrelevant Examples

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

machine learning↗
NASA NTRS2011

Slat Noise Simulations: Status and Challenges

Noise radiation from the leading edge slat of a high-lift system is known to be an important component of aircraft noise during approach. NASA's Langley Research Center is engaged in a coordinated series of investigations combining high-fidelity numerical simulations and detailed wind tunnel measurements of a generic, unswept, 3-element, high-lift configuration. The goal of this effort is to provide a validated predictive capability that would enable identification of the dominant noise source mechanisms and, ultimately, help develop physics inspired concepts for reducing the far-field acoustic intensity. This paper provides a brief overview of the current status of the computational effort and describes new findings pertaining to the effects of the angle of attack on the aeroacoustics of the slat cove region. Finally, the interplay of the simulation campaign with the concurrently evolving development of a benchmark dataset for an international workshop on airframe noise is outlined.

Choudhari, Meelan M.↗
NASA NTRS2012

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

Le Moigne, Jacqueline↗
NASA NTRS2017

Experimental Evaluation of Acoustic Engine Liner Models Developed with COMSOL Multiphysics

Accurate modeling tools are needed to design new engine liners capable of reducing aircraft noise. The purpose of this study is to determine if a commercially-available finite element package, COMSOL Multiphysics, can be used to accurately model a range of different acoustic engine liner designs, and in the process, collect and document a benchmark dataset that can be used in both current and future code evaluation activities. To achieve these goals, a variety of liner samples, ranging from conventional perforate-over-honeycomb to extended-reaction designs, were installed in one wall of the grazing flow impedance tube at the NASA Langley Research Center. The liners were exposed to high sound pressure levels and grazing flow, and the effect of the liner on the sound field in the flow duct was measured. These measurements were then compared with predictions. While this report only includes comparisons for a subset of the configurations, the full database of all measurements and predictions is available in electronic format upon request. The results demonstrate that both conventional perforate-over-honeycomb and extended-reaction liners can be accurately modeled using COMSOL. Therefore, this modeling tool can be used with confidence to supplement the current suite of acoustic propagation codes, and ultimately develop new acoustic engine liners designed to reduce aircraft noise.

Schiller, Noah H.↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRSDate not supplied

Validation Experiments of Incipient Turbulent Separation over an Axisymmetric Afterbody

Historically, the flow physics involved with most turbulent separated flows have presented fundamental challenges in validation between experimental and numerical approaches. As recognized by the CFD Vision 2030 study commissioned by NASA, validation of Reynolds-averaged Navier-Stokes (RANS) models and other scale-resolving methods for turbulent separated flow requires the support of advanced, high-fidelity experiments designed specifically for CFD implementation. In accordance with this effort, a new test platform, referred to as the NASA Axisymmetric Afterbody, was designed to obtain detailed information of the flow field over a smooth body, undergoing adverse pressure gradient induced separation for a fixed Reynolds number, Re = 180,000. The parametric body offers a range of flow states from fully attached,incipient separation, to fully separated flow based on variable afterbody geometries. In an initial effort to evaluate RANS turbulence model capabilities, the present configuration of the axisymmetric model features a mild adverse pressure gradient over the contoured boattail section, inducing incipient turbulent separation. Experiments include steady pressure measurements to serve as preliminary comparisons to simulation studies, which examine the effect of variable grid domains and RANS turbulence models, in an effort to understand and evaluate the critical variability between solutions for the present configuration. Results indicate potential discrepancies may be due to the effect of the square tunnel test section walls, relatively large blockage ratio, and inconsistent reference parameters. Ongoing work will focus on the experimental campaigns to obtain surface flow visualizations and high-resolution, off-body flow field measurements using Stereoscopic Particle Image Velocimetry (SPIV) and Laser Doppler Velocimetry (LDV) techniques to provide a detailed benchmark dataset to aid turbulence modelers.

axisymmetric afterbody↗
NASA NTRS2018

Online Self-Supervised Long-Range Scene Segmentation for MAVs

Recently, there have been numerous advances in the development of payload and power constrained lightweight Micro Aerial Vehicles (MAVs). As these robots aspire for high-speed autonomous flights in complex dynamic environments, robust scene understanding at long-range becomes critical. The problem is heavily characterized by either the limitations imposed by sensor capabilities for geometry-based methods, or the need for large-amounts of manually annotated training data required by data-driven methods. This motivates the need to build systems that have the capability to alleviate these problems by exploiting the complimentary strengths of both geometry and data-driven methods. In this paper, we take a step in this direction and propose a generic framework for adaptive scene segmentation using self-supervised online learning. We present this in the context of vision-based autonomous MAV flight, and demonstrate the efficacy of our proposed system through extensive experiments on benchmark datasets and real world field tests.

Matthies, Larry↗
NASA NTRS2021

Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras

Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly translate to the thermal domain due to two major reasons: these algorithms require photometric assumptions to hold, and methods for photometric calibration of RGB cameras cannot be applied to thermal-infrared cameras due to difference in data acquisition and sensor phenomenology. In this paper, we take a step in this direction, and introduce a novel algorithm for online photometric calibration of thermalinfrared cameras. Our proposed method does not require any specific driver/hardware support and hence can be applied to any commercial off-the-shelf thermal IR camera. We present this in the context of visual odometry and SLAM algorithms, and demonstrate the efficacy of our proposed system through extensive experiments for both standard benchmark datasets, and real-world field tests with a thermal-infrared camera in natural outdoor environments.

Daftry, Shreyansh↗
NASA NTRS2023

The Coupling Between Tropical Meteorology, Aerosol Lifecycle, Convection, and Radiation, During the Clouds, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex)

The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent’s complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon Philippines in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical OscillatioNs (PISTON) experiment with its R/VSally Ride stationed in the North Western Tropical Pacific, CAMP2Ex documented diverse biomass burning, industrial and natural aerosol populations and their interactions with small to congestus convection. The 2019 season exhibited El Nino and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP2Ex’s preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP2Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud and radiation processes as well as a crucible for the design of future observing systems.

CAMP2Ex↗