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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 271 records · Page 15

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NASA NTRS1995

Follow-the-leader algorithm for the payload inspection and processing system

This report summarizes the author's summer 1995 work at NASA Kennedy Space Center in the Advanced System Division. The assignment was path planning for the Payload Inspection and Processing System (PIPS). PIPS is an automated system, programmed off-line for inspection of Space Shuttle payloads after integration and prior to launch. PIPS features a hyper-redundant 18-dof serpentine truss manipulator capable of snake-like motions to avoid obstacles. The path planning problem was divided into two segments: (1) determining an obstacle-free trajectory for the inspection camera at the manipulator tip to follow; and (2) development of a follow-the-leader (FTL) algorithm which ensures whole-arm collision avoidance by forcing ensuing links to follow the same tip trajectory. The summer 1995 work focused on the FTL algorithm. This report summarizes development, implementation, testing, and graphical demonstration of the FTL algorithm for prototype PIPS hardware. The method and code was developed in a modular manner so the final PIPS hardware may use them with minimal changes. The FTL algorithm was implemented using MATLAB software and demonstrated with a high-fidelity IGRIP model. The author also supported implementation of the algorithm in C++ for hardware control. The FTL algorithm proved to be successful and robust in graphical simulation. The author intends to return to the project in summer 1996 to implement path planning for PIPS.

Williams, Robert L., II↗

DOE OSTI2019

SolarAPP+ ® (Solar Automated Permit Processing Plus) [SWR-19-51]

SolarAPP+ ®: Safe and affordable home solar through permitting automation. NREL has launched a collaborative effort with key code officials, authorities having jurisdiction (AHJs), and the solar industry to develop an instant online solar permitting platform for code compliant residential systems. SolarAPP (Solar Automated Permit Processing) will: -Provide a flexible, web-based PV-permitting tool for residential systems at no-cost to AHJs. -Enable standardization of instant permitting processes. -Evaluate applications for safety and code compliance. -Offer opportunities to incorporate energy storage and expand to other market segments.

Cook, Jeffrey↗

NASA NTRS1989

Structures for remotely deployable precision antennas

There is a need for completely deployable large antenna reflectors capable of efficiently handling millimeter-wave electromagnetic radiation. The structural concepts and technologies that are appropriate to fully automated deployment of dish-type antennas with solid reflector surfaces were studied. First, the structural requirements are discussed. Then, existing concepts for fully deployable antennas are described and assessed relative to the requirements. Finally, several analyses are presented that evaluate the effects of beam steering and segmented reflector design on the accuracy of the antenna.

Hedgepeth, John M.↗

NASA NTRS1992

Feature detection in satellite images using neural network technology

A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

Augusteijn, Marijke F.↗

NASA NTRS2015

Technology Development of Automated Rendezvous and Docking/Capture Sensors and Docking Mechanism for the Asteroid Redirect Crewed Mission

This paper will describe the technology development efforts NASA has underway for Automated Rendezvous and Docking/Capture (AR&D/C) sensors and a docking mechanism and the challenges involved. The paper will additionally address how these technologies will be extended to other missions requiring AR&D/C whether robotic or manned. NASA needs AR&D/C sensors for both the robotic and crewed segments of the Asteroid Redirect Mission (ARM). NASA recently conducted a commonality assessment of the concept of operations for the robotic Asteroid Redirect Vehicle (ARV) and the crewed mission segment using the Orion crew vehicle. The commonality assessment also considered several future exploration and science missions requiring an AR&D/C capability. Missions considered were asteroid sample return, satellite servicing, and planetary entry, descent, and landing. This assessment determined that a common sensor suite consisting of one or more visible wavelength cameras, a threedimensional LIDAR along with long-wavelength infrared cameras for robustness and situational awareness could be used on each mission to eliminate the cost of multiple sensor developments and qualifications. By choosing sensor parameters at build time instead of at design time and, without having to requalify flight hardware, a specific mission can design overlapping bearing, range, relative attitude, and position measurement availability to suit their mission requirements with minimal nonrecurring engineering costs. The resulting common sensor specification provides the union of all performance requirements for each mission and represents an improvement over the current systems used for AR&D/C today. These sensor specifications are tightly coupled to the docking system capabilities and requirements for final docking conditions. The paper will describe NASA's efforts to develop a standard docking system for use across NASA human spaceflight missions to multiple destinations. It will describe the current design status and the considerations and technologies involved in developing this docking mechanism.

Hinkel, Heather↗

NASA NTRS2016

Technology Development of Automated Rendezvous and Docking/Capture Sensors and Docking Mechanism for the Asteroid Redirect Crewed Mission

This paper will describe the technology development efforts NASA has underway for Automated Rendezvous and Docking/Capture (AR&D/C) sensors and a docking mechanism and the challenges involved. The paper will additionally address how these technologies will be extended to other missions requiring AR&D/C whether robotic or manned. NASA needs AR&D/C sensors for both the robotic and crewed segments of the Asteroid Redirect Mission (ARM). NASA recently conducted a commonality assessment of the concept of operations for the robotic Asteroid Redirect Vehicle (ARV) and the crewed mission segment using the Orion spacecraft. The commonality assessment also considered several future exploration and science missions requiring an AR&D/C capability. Missions considered were asteroid sample return, satellite servicing, and planetary entry, descent, and landing. This assessment determined that a common sensor suite consisting of one or more visible wavelength cameras, a three-dimensional LIDAR along with long-wavelength infrared cameras for robustness and situational awareness could be used on each mission to eliminate the cost of multiple sensor developments and qualifications. By choosing sensor parameters at build-time instead of at design-time and, without having to requalify flight hardware, a specific mission can design overlapping bearing, range, relative attitude, and position measurement availability to suit their mission requirements with minimal non-recurring engineering costs. The resulting common sensor specification provides the union of all performance requirements for each mission and represents an improvement over the current systems used for AR&D/C today. These sensor specifications are tightly coupled to the docking system capabilities and requirements for final docking conditions. The paper will describe NASA's efforts to develop a standard docking system for use across NASA human spaceflight missions to multiple destinations. It will describe the current design status and the considerations and technologies involved in developing this docking mechanism.

Hinkel, Heather↗

DOE OSTI2025

Are there differences in public interest toward automated vehicles’ ownership in burdened and non-burdened communities?

This study examines differences in public interest toward automated vehicle (AV) adoption between burdened communities (BCs) and non-burdened communities (non-BCs). The study uniquely captures travel behavior by integrating the census tract-level community indicators with the 2017–2019 Puget Sound Household Travel Survey. Public interest in AV ownership is modeled as a five-level ordered outcome, ranging from ‘Not interested at all’ to ‘Very interested.’ Segmented ordered logit models and Random Forest are used to identify key drivers and barriers to AV adoption across BCs and non-BCs. Results show that longer commute times, lack of personal vehicles, and concerns about AV performance in adverse weather conditions are significantly associated with higher interest levels toward owning AVs in BCs. In conclusion, the findings offer insights for developing targeted policies to promote AV adoption and address differences between BCs and non-BCs in accessing emerging transportation technologies.

Automated vehicles↗

NASA NTRS2012

Face-Sheet Quality Analysis and Thermo-Physical Property Characterization of OOA and Autoclave Panels

Increased application of polymer matrix composite (PMC) materials in large vehicle structures requires consideration of non-autoclave manufacturing technology. The NASA Composites for Exploration project, and its predecessor, Lightweight Spacecraft Structures and Materials project, were tasked with the development of materials and manufacturing processes for structures that will perform in a heavy-lift-launch vehicle environment. Both autoclave and out of autoclave processable materials were considered. Large PMC structures envisioned for such a vehicle included the payload shroud and the interstage connector. In this study, composite sandwich panels representing 1/16th segments of the barrel section of the Ares V rocket fairing were prepared as 1.8 m x 2.4 m sections of the 10 m diameter arc segment. IM7/977-3 was used as the face-sheet prepreg of the autoclave processed panels and T40-800B/5320-1 for the out of autoclave panels. The core was 49.7 kilograms per square meters (3.1 pounds per cubic feet (pcf)) aluminum honeycomb. Face-sheets were fabricated by automated tape laying 153 mm wide unidirectional tape. This work details analysis of the manufactured panels where face-sheet quality was characterized by optical microscopy, cured ply thickness measurements, acid digestion, and thermal analysis.

Miller, Sandi G.↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

changepoint detection↗

NASA NTRS2015

Technology Development of Automated Rendezvous and Docking/Capture Sensors and Docking Mechanism for the Asteroid Redirect Crewed Mission

This paper will describe the technology development efforts NASA has underway for Automated Rendezvous and Docking/Capture (AR and D/C) sensors and a docking mechanism and the challenges involved. The paper will additionally address how these technologies will be extended to other missions requiring AR and D/C whether robotic or manned. NASA needs AR&D/C sensors for both the robotic and crewed segments of the Asteroid Redirect Mission (ARM). NASA recently conducted a commonality assessment of the concept of operations for the robotic Asteroid Redirect Vehicle (ARV) and the crewed mission segment using the Orion crew vehicle. The commonality assessment also considered several future exploration and science missions requiring an AR and D/C capability. Missions considered were asteroid sample return, satellite servicing, and planetary entry, descent, and landing. This assessment determined that a common sensor suite consisting of one or more visible wavelength cameras, a threedimensional LIDAR along with long-wavelength infrared cameras for robustness and situational awareness could be used on each mission to eliminate the cost of multiple sensor developments and qualifications. By choosing sensor parameters at build time instead of at design time and, without having to requalify flight hardware, a specific mission can design overlapping bearing, range, relative attitude, and position measurement availability to suit their mission requirements with minimal nonrecurring engineering costs. The resulting common sensor specification provides the union of all performance requirements for each mission and represents an improvement over the current systems used for AR and D/C today. These sensor specifications are tightly coupled to the docking system capabilities and requirements for final docking conditions. The paper will describe NASA's efforts to develop a standard docking system for use across NASA human spaceflight missions to multiple destinations. It will describe the current design status and the considerations and technologies involved in developing this docking mechanism.

Hinkel, Heather↗

DOE OSTI2024

Thermo-Fluid Modeling Framework for Supercomputing Digital Twins: Part 2, Automated Cooling Models

The development of digital twins for the purpose of improving the energy efficiency of supercomputing facilities is a non-trivial endeavor that is complicated by the difficulty of creating physics-based thermo-fluid cooling system models (CSMs). Within ExaDigit---an open-source framework for liquid-cooled supercomputing digital twins---a thermo-fluid modeling framework is being developed. This effort has been segmented into two with two companion papers describing each portion of the overall effort. Part 1 focuses on the development of a cooling system library in Dymola for the Frontier supercomputer at Oak Ridge National Laboratory {\cite{Kumar2024}. Part 2, this paper, describes an effort to create a template-based auto-generation methodology for CSMs, called \textit{AutoCSM}. In this paper, an overview of the initial AutoCSM architecture and workflow is provided, along with a practical example using the Oak Ridge Leadership Computing Facility's (OLCF) Frontier supercomputer CSM. AutoCSM will (1) improve ExaDigiT's user accessibility by providing a flexible workflow for modularizing the creation of the CSM system and control logic, (2) decrease the development time of CSMs, and (3) standardize the method for incorporating CSMs into the ExaDigiT framework.

Greenwood, Scott↗

DOE OSTI2024

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗

NASA NTRS1988

STS-26/Discovery Preparations for Launch

This NASA Kennedy Space Center two-part video release is comprised of footage covering STS-26 launch preparations from the arrival of the Tracking and Data Relay Satellite (TDRS) at the Orbiter Processing Facility (OPF) to the lift and mate of the external tanks. The STS-26 flight crew include: Frederick H. (Rick) Hauck, mission commander; Richard O. Covey, pilot; John M. (Mike) Lounge, mission specialist; David C. Hilmers, mission specialist; and George D. (Pinky) Nelson, mission specialist. The primary payload of STS-26 is the TDRS while the secondary payloads include the Physical Vapor Transport of Organic Solids (PVTOS); Protein Crystal Growth (PCG); Infrared Communications Flight Experiment (IRCFE); Aggregation of Red Blood Cells (ARC); Isoelectric Focusing Experiment (IFE); Mesoscale Lightning Experiment (MLE); Phase Partitioning Experiment (PPE); Earth-Limb Radiance Experiment (ELRAD); Automated Directional Solidification Furnace (ADSF) and two Shuttle Student Involvement Program (SSIP) experiments. Launch preparation footage includes flight crew arrival at KSC, rollout of Discovery to Pad B, OV-103 Discovery power-up, main engine unpacking and installation, solid rocket boosters' arrival prep and stacking, and aft skirt to aft segment mating.

Source record↗

DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗

NASA NTRS1992

Automated detection of jet contrails using the AVHRR split window

This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.

Engelstad, M.↗

NASA NTRS1996

Meshing of a Spiral Bevel Gearset with 3D Finite Element Analysis

Recent advances in spiral bevel gear geometry and finite element technology make it practical to conduct a structural analysis and analytically roll the gearset through mesh. With the advent of user specific programming linked to 3D solid modelers and mesh generators, model generation has become greatly automated. Contact algorithms available in general purpose finite element codes eliminate the need for the use and alignment of gap elements. Once the gearset is placed in mesh, user subroutines attached to the FE code easily roll the gearset through mesh. The method is described in detail. Preliminary results for a gearset segment showing the progression of the contact lineload is given as the gears roll through mesh.

Bibel, George D.↗

DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗