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NASA NTRS1998

The Role of Marangoni Convection for the FZ-Growth of Silicon

Fluctuations of the electrical resistivity due to inhomogeneous dopant distribution are still a serious problem for the industrial processing yield of doped silicon crystals. In the case of silicon floating-zone growth, the main sources of these inhomogeneities are time- dependent flows in the liquid phase during the growth process. Excluding radio frequency (RF) induced convection, buoyancy and thermocapillary (Marangoni) convection are the two natural reasons for fluid flow. Both originate from temperature/concentration gradients in the melt, buoyancy convection through thermal/concentrational volume expansion, and thermocapillary convection through the temperature/concentration dependence of the surface tension. To improve the properties of grown crystals, knowledge of the strength, the characteristic, and the relation of these two flow mechanisms is essential. By the use of microgravity, the effect and the strength of buoyancy (gravity dependent) and thermocapillary (gravity independent) convection can be separated and clarified. Applying magnetic fields, both convective modes can be influenced: fluid flow can either be damped (static magnetic fields) or overlaid by a regular flow regime (rotating magnetic fields). Two complementary approaches have been pursued: Silicon full zones (experiments on the German sounding rockets TEXUS 7, 12, 22, 29, and 36) with the maximum temperature at half of the zone height and silicon half zones (experiments on the Japanese sounding rockets TR-IA4 and 6) with the maximum temperature at the top of the melt. With the full zone arrangement, the intensity and the frequency of the dopant striations could be determined and the critical Marangoni number could be identified. The half zone configuration is suited to classify the flow pattern and to measure the amplitude and the frequency of temperature fluctuations in the melt by inserting thermocouples or temperature sensors into the melt. All experiments have been carried out in monoellipsoid mirror furnaces. Typical zone geometries are approx. 8 to 14 mm in diameter and height. The crystals grown under microgravity are compared to crystals grown in static axial magnetic fields (B<5 tesla) and in transversal rotating magnetic fields (B<7.5 mT / f=50 Hz). Experimental results are completed by 3D numerical simulations: the obtained temperature and concentration distribution in the melt confirm the damping effect of rotating magnetic fields and explain the change in the radial segregation under static magnetic fields.

Dold, P.↗

NASA NTRS2014

Truss Assembly and Welding by Intelligent Precision Jigging Robots

This paper describes an Intelligent Precision Jigging Robot (IPJR) prototype that enables the precise alignment and welding of titanium space telescope optical benches. The IPJR, equipped with micron accuracy sensors and actuators, worked in tandem with a lower precision remote controlled manipulator. The combined system assembled and welded a 2 m truss from stock titanium components. The calibration of the IPJR, and the difference between the predicted and the truss dimensions as-built, identified additional sources of error that should be addressed in the next generation of IPJRs in 2D and 3D.

Komendera, Erik↗

NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Atmospheric 3D winds in the horizontal and vertical directions are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we use a state-of-the-art convolutional neural network for optical flow (FlowNetS) in a physics-guided deep learning framework for predicting AMVs in the horizontal direction. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations (DYAMOND) for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

geostationary↗

NASA NTRS2002

Laser-Camera Vision Sensing for Spacecraft Mobile Robot Navigation

The advent of spacecraft mobile robots-free-flyng sensor platforms and communications devices intended to accompany astronauts or remotely operate on space missions both inside and outside of a spacecraft-has demanded the development of a simple and effective navigation schema. One such system under exploration involves the use of a laser-camera arrangement to predict relative positioning of the mobile robot. By projecting laser beams from the robot, a 3D reference frame can be introduced. Thus, as the robot shifts in position, the position reference frame produced by the laser images is correspondingly altered. Using normalization and camera registration techniques presented in this paper, the relative translation and rotation of the robot in 3D are determined from these reference frame transformations.

Maluf, David A.↗

NASA NTRSDate not supplied

Latest Progress and Results in LEO-GEO and GEO-GEO Stereo AMVs

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Atmospheric motion vectors↗

DOE OSTI2020

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_location_comparison API file is an interactive visualization of the updated microseismic event locations and the original seismic catalog. The visualization allows users to view and compare the event locations by dragging, rotating, and zooming in. An updated version of 3D_seismic_velocity_model and associated animations were included, which were calculated with a more strict assumption for quality indicators.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Ultrathin Porous Hydrocarbon Membranes Templated by Nanoparticle Assemblies

Porous polymer membranes are widely desired as catalyst supports, sensors, and active layers for separation membranes. We demonstrate that electron beam irradiation of freely suspended gold or Fe 3 O 4 nanoparticle (NP) monolayer sheets followed by wet chemical etching is a high-fidelity strategy to template two-dimensional (2D) porous crosslinked hydrocarbon membranes. This approach, which relies on secondary electrons generated by the NP cores, can further be used to transform three-dimensional (3D) terraced gold NP supercrystals into 3D porous hydrocarbon membranes. We utilize electron tomography to show how the number of NP layers (monolayer to pentalayer) controls attenuation and scattering of the primary e-beam, which in turn determines ligand crosslink density and 3D pore structure. Furthermore, electron tomography also reveals that many nanopores are vertically continuous due to preferential sintering of NPs. This work demonstrates new routes for the construction of functional nanoporous media.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Salathe, Marco↗

NASA NTRS2006

3D X-Ray Luggage-Screening System

A three-dimensional (3D) x-ray luggage- screening system has been proposed to reduce the fatigue experienced by human inspectors and increase their ability to detect weapons and other contraband. The system and variants thereof could supplant thousands of xray scanners now in use at hundreds of airports in the United States and other countries. The device would be applicable to any security checkpoint application where current two-dimensional scanners are in use. A conventional x-ray luggage scanner generates a single two-dimensional (2D) image that conveys no depth information. Therefore, a human inspector must scrutinize the image in an effort to understand ambiguous-appearing objects as they pass by at high speed on a conveyor belt. Such a high level of concentration can induce fatigue, causing the inspector to reduce concentration and vigilance. In addition, because of the lack of depth information, contraband objects could be made more difficult to detect by positioning them near other objects so as to create x-ray images that confuse inspectors. The proposed system would make it unnecessary for a human inspector to interpret 2D images, which show objects at different depths as superimposed. Instead, the system would take advantage of the natural human ability to infer 3D information from stereographic or stereoscopic images. The inspector would be able to perceive two objects at different depths, in a more nearly natural manner, as distinct 3D objects lying at different depths. Hence, the inspector could recognize objects with greater accuracy and less effort. The major components of the proposed system would be similar to those of x-ray luggage scanners now in use. As in a conventional x-ray scanner, there would be an x-ray source. Unlike in a conventional scanner, there would be two x-ray image sensors, denoted the left and right sensors, located at positions along the conveyor that are upstream and downstream, respectively (see figure). X-ray illumination may be provided by a single source or by two sources. The position of the conveyor would be detected to provide a means of matching the appropriate left- and right-eye images of an item under inspection. The appropriate right- and left-eye images of an item would be displayed simultaneously to the right and left eyes, respectively, of the human inspector, using commercially available stereo display screens. The human operator could adjust viewing parameters for maximum viewing comfort. The stereographic images thus generated would differ from true stereoscopic images by small distortions that are characteristic of radiographic images in general, but these distortions would not diminish the value of the images for identifying distinct objects at different depths.

Fernandez, Kenneth↗

NASA NTRS2013

NASA Activity Update for the 2013 Unmanned Vehicle Systems International (UVSI) Yearbook

This year s report offers a high level perspective on some of the UAS related activities in which NASA is involved, both internal and external to the agency. Internally, NASA issued UAS operational policy on certification of NASA UAS and aircrew. A team of NASA UAS experts and operators analyzed all current procedures and best practices to design the policy. An update to the agencies Aircraft Operations Management Manual incorporated a new chapter to address UAS planning, preflight operations, flight operations, flight crew requirements, airworthiness and flight safety reviews. NASA UAS are classified into three categories based on weight and airspeed. Aircrews, including observers, are classified by how they interface with the UAS, and the policy defines qualifications, training, and currency. The NASA flight readiness approval process identifies risks and mitigations in order to reduce the likelihood and/or consequence of the risk to an acceptable level. The UAS operations process incorporates all aspects of airworthiness, flight standards and range safety exactly the same processes used for NASA manned aircraft operations. NASA has two internal organizations that routinely operate UAS. The Science Mission Directorate utilizes UAS as part of its Airborne Science Program and is the most frequent operator of NASA UAS in both national and international airspace. The Aeronautics Research Mission Directorate conducts UAS flight operations in addition to conducting research important to the UAS community. This past year the Science Mission Directorate supported the Hurricane and Severe Storm Sentimental (HS3) Mission with two NASA Global Hawk platforms. HS3 is a five-year mission specifically targeted to investigate the processes that underlie hurricane formation. During the 2012 portion of this mission the Global Hawk overflew hurricanes Leslie and Nadine in the Atlantic Ocean completing 6 flights and accumulating more than 148 flight hours. Another multi-year mission was initiated last year when the Sensor Integrated Environmental Remote Research Aircraft (SIERRA) UAS began surveying faults in California s Surprise Valley. A team of scientists and engineers from the United States Geological Survey (USGS), NASA Ames Research Center, Central Washington University, and Carnegie Mellon University will measure magnetic fields using ground surveys and the SIERRA to map the geophysics below the surface of Surprise Valley. The data collected will be used to generate 3D maps of the geophysical data of the area. The Aeronautics Mission Directorate continues its collaboration with Boeing to conduct UAS flight operations of the X-48C, a modified version of the X-48B originally built by Cranfield Aerospace, United Kingdom. The Aeronautics Mission Directorate utilizes vehicles of this size for a wide variety of research studies. Most of these operations are conducted within restricted airspace. The Aeronautics Research Mission Directorate also sponsors the UAS in the National Airspace System (NAS) Project, which is working in close cooperation with the Federal Aviation Administration (FAA) to address critical challenges associated with routine UAS operations in civil airspace. The project is focused on separation assurance and collision avoidance systems and algorithms, command and control for non-military operations including spectrum allocation requirements, human system interaction issues, and safety and certification topics.

Bauer, Jeffrey E.↗

DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

Prins, Nicholas↗

NASA NTRS1992

High spatial resolution measurements of ram accelerator gas dynamic phenomena

High spatial resolution experimental tube wall pressure measurements of ram accelerator gas dynamic phenomena are presented. The projectile resembles the centerbody of a ramjet and travels supersonically through a tube filled with a combustible gaseous mixture, with the tube acting as the outer cowling. Pressure data are recorded as the projectile passes by sensors mounted in the tube wall at various locations along the tube. Data obtained by using a special highly instrumented section of tube has allowed the recording of gas dynamic phenomena with a spatial resolution on the order of one tenth the projectile length. High spatial resolution tube wall pressure data from the three regimes of propulsion studied to date (subdetonative, transdetonative, and superdetonative) are presented and reveal the 3D character of the flowfield induced by projectile fins and the canting of the projectile body relative to the tube wall. Also presented for comparison to the experimental data are calculations made with an inviscid, 3D CFD code.

Hinkey, J. B.↗

DOE OSTI2020

Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

A simple, validated approach for design of two-dimensional periodic particle patterns via acoustophoresis

Two-dimensional patterning of microparticles enables a wide range of functional materials, including patterned energy storage electrodes, flexible electronics, and sensor arrays. Particle patterning via acoustics offers an attractive path to generate a wide variety of 2D periodic patterns that introduce tailorable hierarchical porosity, useful for controlling surface area, transport distances, and other properties. This method is most effective with micron scale particles and patterns of tens to hundreds of microns. To enable systematic exploration of the broad design space for such patterns, this work develops a model of 2D and 3D assembly of particles at high loadings and validates the obtained patterns against both experiments and more computationally intensive modeling techniques. Using this simple model, connections are mapped between input parameters (like actuation conditions, particle volume fraction, material properties) and output geometrical features (like void size and shape, pattern connectivity, and surface area) so that they can be tailored to given applications. The utility of this simple model is illustrated by predicting and then experimentally demonstrating new hierarchical patterns resulting from multiple waves of different frequencies interacting. These multiscale patterns offer the potential to lift the limits on surface area, diffusion distances, and other features.

42 ENGINEERING↗

NASA NTRS2016

Stereo Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Image Space Represntation

Micro air vehicles which operate autonomously at low altitude in cluttered environments require a method for on-board obstacle avoidance for safe operation. Prior approaches can be divided between purely reactive approaches, mapping low-level visual features directly to headings to maneuver the vehicle around the obstacle, and deliberative methods that use on-board 3-D sensors to create a 3-D, voxel-based world model, which is then used to generate collision free 3-D trajectories. In this paper, we use forward-looking stereo vision with a large horizontal and vertical field of view and project range from stereo into a novel robot-centered, cylindrical, inverse range map we call an egocylinder. With this implementation we reduce the complexity of our world representation from a 3D map to a 2.5D image space representation, which supports very efficient motion planning and collision-checking. Configuration space expansion is done very efficiently on the egocylinder as an image processing function. Deploying a fast reactive motion planner directly on the configuration space expanded egocylinder image, we demonstrate the effectiveness of this new approach experimentally in an indoor environment.

Micro air vehicles↗

DOE OSTI2026

A Real2Sim Digital Twin Pipeline for Photorealistic Robot Simulation: Evaluating VLA Policy Deployment on a Bimanual Mobile Robot

Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High precision control and deep learning-based corn stand counting algorithms for agricultural robot

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with C robot =1.02×C human -0.86 and a correlation coefficient R=0.96. The mean relative error given by the algorithm is -3.78%, and the standard deviation is 6.76%. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.

97 MATHEMATICS AND COMPUTING↗