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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Construction of Supersonic Flight Dynamics Test (SFDT-2) Vehicle Monte Carlo Splashdown Footprints for Use in Range Safety and Recovery Planning

Overview of Low Density Supersonic Decelerator (LDSD) SFDT-2 ( Supersonic Flight Dynamics Test-2) flight profile; Description of Monte Carlo flight dynamics simulations performed; Modeling and uncertainties; Results with statistical prediction of test vehicle splashdown location; Use of Monte Carlo results for range safety and recovery planning.

Strauss, Bill↗
NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗
NASA NTRS2019

Predictive Thermal Control (PTC) Technology to Enable Thermally Stable Telescopes: First Two Year Status

Predictive Thermal Control Technology (PTCT) development project is a multiyear effort initiated in Fiscal Year (FY) 2017, to mature the Technology Readiness Level (TRL) of critical technologies required to enable ultra-thermally-stable ultraviolet/optical/infrared (UVOIR) space telescope primary-mirror assemblies for ultra-high-contrast observations of exoplanets. Key accomplishments of 2017 to 2019 include: creating a high-fidelity STOP (Structural, Thermal, and Optical Performance) model of the AMTD-2 (Advance Mirror Technology Development) 1.5-meter Ultra-Low Expansion (ULE®) mirror (manufactured by Harris Corp) by merging 3D X-Ray computed tomography data of the ‘as-built’ mirror and coefficient of thermal expansion (CTE) data maps for each of the 18 core elements; partially validating this model by measuring the mirror’s response to bulk temperature changes and lateral thermal gradients; designed and built (with PTC partner Harris Corp) a 1.5-meter enclosure with 26 actively-control thermal zones; and defined specifications for a potential 4-meter primary mirror thermal enclosure for the Habitable Exoplanet (HabEx) Imager mission.

Astrophysics↗
NASA NTRS2020

Airborne Science Mission Capabilities of the NASA DC-8 and Possible Alternative Aircraft

Age-related concerns associated with operating the 50-year-old NASA Airborne Science Program DC-8 Flying Laboratory aircraft are raising concerns about the prospects of someday retiring the NASA DC-8. Given that eventuality, this study examines how current and anticipated science requirements can be incorporated with aeronautical performance and cost metrics for assessing, comparing, and down-selecting candidate replacement aircraft. A literature review of NASA DC-8 missions and interviews with current DC-8 science users provides context for the initial phase of this study and offers a pathway for collecting aircraft specifications. Performance models are developed and calibrated against published range-payload data for various mission fuel and payload weight combinations. The models are extended to three airborne science missions, with payload weights ranging from 30,000-52,000 lb and mission ranges up to 5,050 nmi. Results point towards the 767-200ER aircraft as a top contender offering a bit more experimenter floor-space, payload capacity, and range than the NASA DC-8, and to the U.S. Navy P-8 aircraft as a viable option offering perhaps not too much less of those items, with the relative benefit of using less fuel per mission.

Thomas A Ozoroski↗
NASA NTRSDate not supplied

WFIRST CGI: Prism and Polarizer Characterization Modes

As part of its technology demonstration, the WFIRST coronagraph instrument (CGI) will demonstrate point source spectroscopy and polarization measurements of disks. The specific implementation of spectroscopy is a zero-deviation Amici prism and slit to be placed on the planet after high contrast has been achieved by CGI. The polarization optics are a set of Wollaston prisms so that orthogonal polarization states can be measured simultaneously. The CGI spectral characterization mode, being designed and built and Goddard Space Flight Center (GSFC), has a spectral resolution of R50 and is designed to accommodate a 15% bandpass spanning 610-785nm. In order to recover Stokes information, there are two sets of Wollaston prisms clocked 45 degrees with respect to one another with each measurement taken in series. The Wollaston design and optical elements are a contribution by the Japanese Aerospace Exploration Agency, with final alignment and testing being done at GSFC. The spectroscopy mode is designed to target Methane absorption features around 730nm, keeping the spectral resolution as low as possible to improve the signal-to-noise ratio and hence reduce detection time. We highlight the requirements for these modes and address the challenge of on-orbit spectral calibration for a deployable slit in the presence of pointing drifts. Of unique interest is how the observatory error budget couples into good stellar spectrum calibration and subsequent speckle subtraction. We also provide further detail on the optomechanical design, its stability based on thermal and structural predictions, modeled performance, and operations concept. These performance metrics are simulated to demonstrate how a slit located at an arbitrary field point is homed onto the planet and converted to a calibrated spectrum.

Tyler D Groff↗
NASA NTRSDate not supplied

Predictive Thermal Control (PTC) Technology to Enable Thermally Stable Telescopes: Year Three Status

The Predictive Thermal Control (PTC) project is a multiyear effort initiated in Fiscal Year 2017, to mature the Technology Readiness Level (TRL) of technologies required to enable ultra-thermally-stable ultraviolet/optical/infrared (UVOIR) space telescope primary-mirror assemblies for ultra-high-contrast observations of exoplanets. PTC has three objectives: validate thermal optical performance models, derive thermal system stability specifications, and demonstrate predictive thermal control. This paper reviews recent and previous accomplishments

H Philip Stahl↗
NASA NTRS2021

Mathematical Characterization of Battery Models

The purpose of this document is to demonstrate the use of the Extended Kalman Filter as a tool for battery state estimation and the estimation of battery state of charge. The mathematical details based on the equivalent circuit model are presented followed by an electrochemical engineering model. A simplified first-order model is used to demonstrate the procedure followed by second and third-order models. Next a simplified electrochemistry model is presented along with observer development. State observability is calculated for the simpler equivalent circuit models and the simplified electrochemistry model. An outline of the battery model parameter identification method is presented, and model performance based on experimental and flight data is demonstrated.

Battery↗
NASA NTRS2018

In-Flight Verification and Validation of Colloid Microthruster Performance

Colloid Micronewton Thrusters (CMNTs) use an electrospray to provide precision spacecraft position and pointing control. They were demonstrated in space for the first time as part of NASA’s Space Technology 7 (ST7) payload hosted by the European Space Agency’s (ESA’s) LISA Pathfinder (LPF) technology demonstration mission in January, 2016. CMNTs were the actuator in the disturbance reduction system (DRS) that provided drag-free operation of the LPF spacecraft, which will be necessary for future gravity wave observatories such as ESA’s Laser Interferometer Space Antenna (LISA) mission, currently in Phase A and scheduled for launch in 2034. The CMNT technology met performance requirements operating at 5-30 µN of thrust with 0.1 µN resolution and ≤0.1 µN/ÖHz thrust noise to deliver the required nanometer-level precision spacecraft control measured by the gravitational reference sensor (GRS) in the ESA LISA Technology Package (LTP). The performance of seven of the eight CMNTs in flight was consistent with ground test results, and as a system, all eight thrusters met mission-level performance requirements. The colloid microthruster performance model of thrust and thrust noise as a function of operational parameters (i.e. beam current, voltage, temperature, etc.) was validated in flight over a wide range of conditions. A model and simulation of the thruster control algorithm was developed and validated with flight data to predict thrust noise. This capability is important for future missions because it relates directly to the acceleration noise on the test masses, which provide the gravity wave measurements. The CMNT thruster model data and validation with LISA Pathfinder/ST7-DRS flight experiments are presented in this paper.

"O'Donnell, James"↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗
NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗
NASA NTRS2022

Western Montana Ecological Forecasting II: Enhancing Habitat Suitability Modeling of Mustelid Species and Contaminant Monitoring in Northern Montana Using NASA Earth Observations

Environmental contaminants in aquatic ecosystems threaten both human and ecosystem health. Western Montana’s rivers possess great economic and ecological value, yet the status of contaminants in these systems can be unclear. Contaminants such as brominated flame-retardants, heavy metals, and pharmaceuticals negatively affect ecosystem health by traveling through the food chain and bioaccumulating in apex predators. Exposure to these contaminants can result in neurological, endocrine, and reproductive impairments in wildlife and humans. American mink (Neovison vison) and North American river otter (Lontra canadensis) have a predominantly aquatic diet and serve as reliable indicator species of environmental health. Working Dogs for Conservation (WD4C) uses detection dogs to locate scat samples of these species which are assessed for contaminants by the Virginia Institute of Marine Science (VIMS). With Software for Assisted Habitat Modeling (SAHM), the team generated current (2013-2020) and projected (2021-2040) habitat suitability models for mink and otter utilizing NASA Earth observations from Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-Satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). The habitat suitability maps’ study region encompassed Flathead National Forest and Blackfeet Indian Reservation. Additionally, the team created site accessibility and precipitation anomaly maps that display the viability of survey locations. The current habitat suitability models performed well (AUC-PR=0.88). The most important predictor variables for suitable habitat were distance to rivers, elevation, and land cover. These end products will further inform WD4C survey site selection and contaminant monitoring.

Chelsea Morton↗
NASA NTRS2020

Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

Accurate real-time pose estimation of spacecraft or object in space is a key capability necessary for on orbit spacecraft servicing and assembly tasks. Pose estimation of objects in space is more challenging than for objects on Earth due to space images containing widely varying illumination conditions, high contrast, and poor resolution in addition to power and mass constraints. In this paper, a convolutional neural network is leveraged to uniquely determine the translation and rotation of an object of interest relative to the camera. The main idea of using CNN model is to assist object tracker used in on space assembly tasks where only feature based method is always not sufficient. The simulation framework designed for assembly task is used to generate dataset for training the modified CNN models and, then results of different models are compared with measure of how accurately models are predicting the pose. Unlike many current approaches for spacecraft or object in space pose estimation, the model does not rely on hand-crafted object-specific features which makes this model more robust and easier to apply to other types of spacecraft. It is shown that the model performs comparable to the current feature-selection methods and can therefore be used in conjunction with them to provide more reliable estimates.

Sonawani, Shubham↗
NASA NTRS2020

Names Don't Fly: Smart Filters for Profanity Detection and Classification in User-Generated Content

Generally, names associate with a person’s identity. But what if in the pretext of a legitimate name and given the opportunity, users of software provide names to online web forms that carry along offensive language, slurs, and other profanity that is then sent to Mars ? The answer is simple: they don’t fly. In this paper,we perform model explorations to detect and classify inappropriate content in the names submitted from people across the world to ‘Send Your Names to MARS’ public engagement campaign.We propose a novel pipeline approach, that can effectively overcome the issues of lack of negative samples, noisy labels by gathering expert knowledge over time with human(s) in the loop and data augmentation, and achieve high accuracy in classifying inappropriate names with very little or no context. We describe cloud-based infrastructure to deploy our application and run predictions on large-scale data through our pipeline and achieve significant speedup over offline processes, with enhanced reliability and security.

Soderstrom, Tomas↗
NASA NTRS2020

Prediction and Evaluation of the Image of the WFIRST Coronagraph Pupil at the Shaped-Pupil Mask

To create a high contrast field of view with a shaped pupil coronagraph (SPC), the telescope pupil is imaged onto the shaped pupil mask, the shaped pupil mask is designed and fabricated through a design optimization based on a given pupil wavefront, and deformable mirrors (DM) in the coronagraph are modulated according to a nonlinear optimization procedure. Both the mask design optimization and the DM optimization require accurate knowledge of the image of the pupil at the shaped pupil plane for the calculation of Jacobians, and the evaluation of alignment tolerances. The shaped pupil coronagraphs demonstrated in the high contrast imaging testbed (HCIT) at JPL has heretofore assumed perfect imaging of the pupil onto the shaped pupil mask. Similarly, the design and performance modeling of the WFIRST CGI instrument makes the same assumption. In this paper we estimate and compare the image of the pupil mask at the shaped pupil plane in the case of the coronagraph testbed, and the image of the telescope pupil at the shaped pupil plane for the WFIRST CGI instrument. The estimated pupil images include field curvature and aberrations resulting from the layout of the parabolic mirror imaging system, which is optimized for the source image, not the image of the pupil. We also show the results of direct measurement of the image of the pupil mask at the shaped pupil plane in the coronagraph testbed.

Crowther, Blake↗
NASA NTRS2022

Precision Thermal Control Technology to Enable Thermally Stable Telescopes

Abstract. The precision thermal control (PTC) project was a multiyear effort initiated in fiscal year 2017 to mature the technology readiness level (TRL) of technologies required to enable ultra-thermally stable ultraviolet/optical/infrared space telescope primary-mirror assemblies for ultra-high-contrast observations of exoplanets. PTC had three objectives: (1) validate thermal optical performance models, (2) derive thermal system stability specifications, and (3) demonstrate multi-zonal active thermal control. PTC successfully achieved its objectives and matured active thermal control technology to at least TRL-5. PTC’s key accomplishments are a demonstration of better than 2-mK root-mean-square stable thermal control of the 1.5-m ultra-low expansion (ULE®) Advanced Mirror Technology Development-2 (AMTD-2) mirror when exposed to thermal disturbances in a relevant thermal/vacuum environment, and the ability to shape the 1.5-m AMTD-2 mirror to picometer precision. Additionally, an analysis approach is demonstrated for quantifying thermally induced mid-spatial frequency error which can cause speckle noise in the coronagraph dark hole.

thermal control↗
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗
NASA NTRS2022

Design of an Open Rotor, Braced-Wing Electric Transport

A notional, 19-passenger, battery-powered, fully electric airplane is investigated in this study. It is designed to serve the regional aviation market of the near future. Three strategies are used to reduce energy demands placed on its batteries: 1) a high aspect ratio, braced wing, 2) two efficient open rotor propellers, and 3) flying at comparatively low cruise speeds. It has an otherwise conventional airframe architecture. Parametric system weight, aerodynamic, propeller, and mission performance models are developed. Single-rotation and contra-rotation propellers are studied. Implementation of a novel selective noise reduction system is also investigated. During sizing and optimization of its design variables, battery cell specific energy is treated as a technology parameter that is varied to determine its influence on mission range. To achieve a minimum range success criterion of 250 nmi with reserves, it is found that battery cell specific energy must be at least 600 W-h/kg, more than twice the capability of today’s lithium-ion cells.

Electric Aircraft↗