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DOE OSTI2020

Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

Bringing "Scientific Expeditions" Into the Schools

Two new technologies, the FASTexpedition and Remote FAST, have been developed that provide remote, 3D, high resolution, dynamic, interactive viewing of scientific data (such as simulations or measurements of fluid dynamics). The FASTexpedition permits one to access scientific data from the World Wide Web, take guided expeditions through the data, and continue with self controlled expeditions through the data. Remote FAST permits collaborators at remote sites to simultaneously view an analysis of scientific data being controlled by one of the collaborators. Control can be transferred between sites. These technologies are now being used for remote collaboration in joint university, industry, and NASA projects in computational fluid dynamics (CFD) and wind tunnel testing. Also, NASA Ames Research Center has initiated a project to make scientific data and guided expeditions through the data available as FASTexpeditions on the World Wide Web for educational purposes. Previously, remote visualiZation of dynamic data was done using video format (transmitting pixel information) such as video conferencing or MPEG movies on the Internet. The concept for this new technology is to send the raw data (e.g., grids, vectors, and scalars) along with viewing scripts over the Internet and have the pixels generated by a visualization tool running on the viewer's local workstation. The visualization tool that is currently used is FAST (Flow Analysis Software Toolkit). The advantages of this new technology over using video format are: 1. The visual is much higher in resolution (1280xl024 pixels with 24 bits of color) than typical video format transmitted over the network. 2. The form of the visualization can be controlled interactively (because the viewer is interactively controlling the visualization tool running on his workstation). 3. A rich variety of guided expeditions through the data can be included easily. 4. A capability is provided for other sites to see a visual analysis of one site as the analysis is interactively performed. Control of the analysis can be passed from site to site. 5. The scenes can be viewed in 3D using stereo vision. 6. The network bandwidth used for the visualization using this new technology is much smaller than when using video format. (The measured peak bandwidth used was 1 Kbit/sec whereas the measured bandwidth for a small video picture was 500 Kbits/sec.)

Watson, Val↗

DOE OSTI2022

In situ laser profilometry for material segmentation and digital reconstruction of a multicomponent additively manufactured part

In addition to its ability to produce geometrically complex parts, additive manufacturing offers a unique opportunity to collect data about a component while it is being fabricated. However, there has only been limited effort to characterize parts morphologically and compositionally in situ. In this article, we present a layer-by-layer, laser profilometry-based in situ characterization technique as a method to digitally reconstruct a multi-material part. Data collected by the laser profilometer yields height maps and grayscale images which are voxelized using purpose-built software to volumetrically reconstruct the part. Additionally, the same part was also analyzed using X-ray computed tomography (CT) which was not able to resolve the different compositional regions within the part, but captured the filament morphology. The part was then bisected to compare the digital reconstruction to the actual part morphology and composition. Overall, the digital reconstruction was in good agreement with both the CT and bisected images. Deviations between the digital reconstruction and the CT/bisected images are likely the result of image segmentation settings or material shifts after data was collected. The in situ characterization method demonstrated here sets the stage for real time process monitoring and paves the way for additively manufactured parts that are “born qualified.”

36 MATERIALS SCIENCE↗

DOE OSTI2020

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗

DOE OSTI2017

Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems

Validating the correctness of human detection vision systems is crucial for safety applications such as pedestrian collision avoidance in autonomous vehicles. The enormous space of possible inputs to such an intelligent system makes it difficult to design test cases for such systems. In this report, we present our tool MAYA that uses an error model derived from a convolutional neural network (CNN) to explore the space of images similar to a given input image, and then tests the correctness of a given human or object detection system on such perturbed images. We demonstrate the capability of our tool on the pre-trained Histogram-of-Oriented-Gradients (HOG) human detection algorithm implemented in the popular OpenCV toolset and the Caffe object detection system pre-trained on the ImageNet benchmark. Our tool may serve as a testing resource for the designers of intelligent human and object detection systems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2007

Analysis and Down Select of Flow Passages for Thermal Hydraulic Testing of a SNAP Derived Reactor

As past of the Vision for Space Exploration, man will return to the moon. To enable safe and productive time on the lunar surface will require adequate power resources. To provide the needed power and to give mission planners all landing site possibilities, including a permanently dark crater, a nuclear reactor provides the most options. Designed to be l00kWt providing approx. 25kWe this power plants would be very effective in delivering dependable, site non-specific power to crews or robotic missions on the lunar surface. An affordable reference reactor based upon the successful SNAP program of the 1960's and early 1970's has been designed by Los Alamos National Laboratory that will meet such a requirement. Considering current funding, environmental, and schedule limitations this lunar surface power reactor will be tested using non-nuclear simulators to simulate the heat from fission reactions. Currently a 25kWe surface power SNAP derivative reactor is in the early process of design and testing with collaboration between Los Alamos National Laboratory, Idaho National Laboratory, Glenn Research Center, Marshall Space Flight Center, and Sandia National Laboratory to ensure that this new design is affordable and can be tested using non-nuclear methods as have proven so effective in the past. This paper will discuss the study and down selection of a flow passage concept for a approx. 25kWe lunar surface power reactor. Several different flow passages designs were evaluated using computational fluid dynamics to determine pressure drop and a structural assessment to consider thermal and stress of the passage walls. The reactor design basis conditions are discussed followed by passage problem setup and results for each concept. A recommendation for passage design is made with rationale for selection.

Godfroy, T. J.↗

NASA NTRSDate not supplied

Implementation of Two Local Correlation-Based Transition Models in OVERFLOW 2.3e

Accurate prediction of the laminar-to-turbulent boundary layer transition is highlighted as a vital component of NASA's CFD Vision 2030 Study. With an increased emphasis on greener air transports and sustainable aviation, transition modeling is anticipated to have an added significance, particularly in the applications related to laminar flow technology. However, unmanned aerial vehicles, crewed reentry vehicles, and ground-to-flight extrapolation all benefit from transition models. It is useful to incorporate a variety of models in CFD solvers because there isn't a single transition model that is ideal for the complete spectrum of applications. While the Langtry-Menter γ–Reθt model has been widely used for CFD predictions of flows with laminar, transitional, and turbulent boundary layers, it does not meet the criteria for Galilean invariance, a desirable attribute for rotorcraft applications. To help overcome that limitation, we have implemented Menter's SST-based γ transition model within NASA’s OVERFLOW Overset CFD code (version 2.3e). In addition, an SA-based implementation of the Langtry-Menter γ–Reθt transition model has also been implemented in view of the increased robustness of the SA-turbulence model for external aerodynamic applications. An initial assessment of the implemented models has been carried out using benchmark 2D test cases involving flat plates and the NLF-0416 airfoil, focusing on bypass, Tollmien-Schlichting, and laminar separation bubble-induced transition scenarios. In comparison to the Langtry-Menter γ–Reθt model, both models were found to produce increased computation efficiency, principally as a result of the decrease from four to three auxiliary transport equations. Results from the initial analysis will be presented, along with additional details pertaining to the deployment of these models.

CFD modeling↗

DOE OSTI2024

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2024

Starling Formation-Flying Optical Experiment: Initial Operations and Flight Results

This paper presents initial flight results for angles-only navigation of a swarm of small spacecraft, conducted during the Starling Formation-Flying Optical Experiment (StarFOX). StarFOX is one of four experiments aboard the NASA Starling mission, which consists of four CubeSats launched in July 2023. Angles-only methods apply inter-satellite bearing angles obtained by on-board cameras for navigation, increasing satellite autonomy and enabling new mission concepts. Nevertheless, prior flight demonstrations have only featured one observer and target and have relied upon a-priori target orbit knowledge for initialization, translational maneuvers to resolve target range, and external absolute orbit updates to maintain convergence. StarFOX overcomes these limitations by applying the angles-only Absolute and Relative Trajectory Measurement System (ARTMS), which integrates three novel algorithms. Image Processing detects and tracks multiple targets in images, using multi-hypothesis methods and domain-specific kinematic modeling, and computes target bearing angles. Batch Orbit Determination computes initial swarm orbit estimates from bearing angle batches, via iterative batch least squares and sampling of the weakly observable target range. Sequential Orbit Determination leverages an adaptive, efficient unscented Kalman filter with nonlinear models to refine swarm state estimates over time. Multi-observer measurements shared over an intersatellite link are seamlessly fused to enable robust absolute and relative orbit determination. Initial StarFOX experiments are the first demonstrations of autonomous angles-only navigation for a satellite swarm, including multi-target and multi-observer relative navigation; autonomous initialization of navigation for unknown targets; and simultaneous absolute and relative orbit determination. Relative navigation accuracy of 1% (single observer) and 0.2% (multi-observer) of the inter-satellite range is achieved under challenging measurement conditions. Results demonstrate promising performance with regards to ongoing StarFOX campaigns and the application of angles-only navigation to future distributed missions.

Relative Navigation↗

DOE OSTI2021

SIS-AOP Cueing/Segmenting Algorithm (FOA_SIS-AOP) Using the Sandia FOA 4.0 Framework

For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats

Despite numerous calls for improvement, the U.S. biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for “situational awareness,” but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure, national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leverages advances in ‘big data’ analytics and learning inference engines—a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and, (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the U.S. national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration within the U.S. government.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2004

An Introduction to Flight Software Development: FSW Today, FSW 2010

Experience and knowledge gained from ongoing maintenance of Space Shuttle Flight Software and new development projects including Cockpit Avionics Upgrade are applied to projected needs of the National Space Exploration Vision through Spiral 2. Lessons learned from these current activities are applied to create a sustainable, reliable model for development of critical software to support Project Constellation. This presentation introduces the technologies, methodologies, and infrastructure needed to produce and sustain high quality software. It will propose what is needed to support a Vision for Space Exploration that places demands on the innovation and productivity needed to support future space exploration. The technologies in use today within FSW development include tools that provide requirements tracking, integrated change management, modeling and simulation software. Specific challenges that have been met include the introduction and integration of Commercial Off the Shelf (COTS) Real Time Operating System for critical functions. Though technology prediction has proved to be imprecise, Project Constellation requirements will need continued integration of new technology with evolving methodologies and changing project infrastructure. Targets for continued technology investment are integrated health monitoring and management, self healing software, standard payload interfaces, autonomous operation, and improvements in training. Emulation of the target hardware will also allow significant streamlining of development and testing. The methodologies in use today for FSW development are object oriented UML design, iterative development using independent components, as well as rapid prototyping . In addition, Lean Six Sigma and CMMI play a critical role in the quality and efficiency of the workforce processes. Over the next six years, we expect these methodologies to merge with other improvements into a consolidated office culture with all processes being guided by automated office assistants. The infrastructure in use today includes strict software development and configuration management procedures, including strong control of resource management and critical skills coverage. This will evolve to a fully integrated staff organization with efficient and effective communication throughout all levels guided by a Mission-Systems Architecture framework with focus on risk management and attention toward inevitable product obsolescence. This infrastructure of computing equipment, software and processes will itself be subject to technological change and need for management of change and improvement,

Gouvela, John↗

NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗

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↗

NASA NTRS2014

Clearance Analysis of CTC2 (on ELC4) to S-TRRJ HRS Radiator Rotation Envelope

In response to the planned retirement of the Space Shuttle Program, International Space Station (ISS) management began stockpiling spare parts on the ISS. Many of the larger orbital replacement units were stored on the Expedite the Processing of Experiments to Space Station (EXPRESS) Logistics Carriers (ELCs) mounted on the end of the S3 and P3 truss segments, immediately outboard of the Thermal Radiator Rotary Joints (TRRJs) and their attached radiators. In an August 2009 computer-aided design (CAD) assessment, it was determined that mounting the Cargo Transport Container (CTC) 2 on the inboard face of ELC4 as planned would create insufficient clearance between the CTC2 and the rotational envelope of the radiators when the TRRJs were rotated to a gamma angle of 35.0 degrees. The true clearance would depend on how the Unpressurized Cargo Carrier Attachment System (UCCAS) was mounted to the S3 truss and how the ELC4 was attached to it. If the plane of the UCCAS attachment points were tilted even slightly inboard, it would significantly change the clearance between CTC2 and the Starboard TRRJ (S-TRRJ) radiators. Additionally, since CTC2 would be covered in multilayer insulation (MLI), the true outer profile of CTC2 was not captured in the CAD models used for the clearance assessment. It was possible that, even if the S-TRRJ radiators cleared CTC2, they could snag the MLI covering. In the fall of 2010, the Image Science and Analysis Group (ISAG) was asked to perform an on-orbit clearance analysis to determine the location of CTC2 on ELC4 and the S-TRRJ radiators at the angle of closest approach so that a positive clearance could be assured. To provide the measurements as quickly as possible to aid in the assessment, it was decided that the clearance analysis would be broken into two phases. Phase I: The location and orientation of the UCCAS fittings, which support and hold the ELC4 in place, would be measured relative to the ISS Analytical Coordinate System (ISSACS) as defined by nine preexisting Space Vision System (SVS) targets affixed to the forward/zenith side of the S1 and S3 truss segments. The location of the outboard edge of the S-TRRJ radiator would also be measured when positioned at the angle of closest approach to CTC2 (gamma = 35.0 degrees). This data would allow the Digital Pre-Assembly Group to predict how the ELC4 would sit on the UCCAS and how that would translate into the clearance between CTC2 and the S-TRRJ radiators. Phase II: After the ELC4 was delivered and installed into the UCCAS, the position of the CTC2 mounting plate on the inboard face of ELC4, would be measured in the ISSACS coordinate system relative to the SVS control points used in Phase I. Although CTC2 would not yet be mounted on ELC4, the working envelope of CTC2 could be mathematically added to the measured position of ELC4 to produce a best estimate for CTC2's mounted location. Comparing CTC2's best estimated location to the S-TRRJ radiator (measured in Phase I); relative to the ISSACS coordinate system, would provide a direct measurement of the expected clearance. Due to the impending delivery of ELC4 (scheduled for January 2011), planning for the Phase I clearance analysis began immediately. Using the Dynamic Onboard Ubiquitous Graphics (DOUG) program, ISAG designed a way to acquire images of the SVS control points on truss segments S1 and S3, the aft facing edge of the S-TRRJ Heat Rejection Subsystem (HRS) radiator, and the three UCCAS latch mechanisms mounted on the zenith face of the S3 truss using the Space Station Remote Manipulator System (SSRMS). To minimize the number of SSRMS movements, the Special Purpose Dexterous Manipulator (SPDM) would be attached to the SSRMS. This would make it possible to park the SPDM in one position and acquire multiple images by changing the viewing orientation of the SPDM body cameras using the pan/tilt units on which they are mounted. Using this implementation concept, ISAG identified four SSRMS/SPDM positions from which the majority of the needed imagery could be acquired. Five additional images would be acquired using the CP-3 external ISS camera mounted on the S1 truss immediately inboard of ELC4. Based on a photogrammetric simulation, it was estimated that the measured location of the HRS radiator and UCCAS latch points would be accurate to about 0.3 in. in each of the three axes relative to ISSACS. Working with ROBO, ISAG collected 78 images of the ISS December 29, 2010. From this imagery, the best 40 were selected for use in the analysis process. The images were radiometrically enhanced to improve color and contrast and loaded into the FotoG analysis software along with the camera parameters and control data, which consisted of the coordinates for the nine SVS targets on the S1 and S3 trusses in the ISSACS coordinate system.

Liddle, Donn↗

DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗

NASA NTRS2004

Bubble Combustion

A method of energy production that is capable of low pollutant emissions is fundamental to one of the four pillars of NASA s Aeronautics Blueprint: Revolutionary Vehicles. Bubble combustion, a new engine technology currently being developed at Glenn Research Center promises to provide low emissions combustion in support of NASA s vision under the Emissions Element because it generates power, while minimizing the production of carbon dioxide (CO2) and nitrous oxides (NOx), both known to be Greenhouse gases. and allows the use of alternative fuels such as corn oil, low-grade fuels, and even used motor oil. Bubble combustion is analogous to the inverse of spray combustion: the difference between bubble and spray combustion is that spray combustion is spraying a liquid in to a gas to form droplets, whereas bubble combustion involves injecting a gas into a liquid to form gaseous bubbles. In bubble combustion, the process for the ignition of the bubbles takes place on a time scale of less than a nanosecond and begins with acoustic waves perturbing each bubble. This perturbation causes the local pressure to drop below the vapor pressure of the liquid thus producing cavitation in which the bubble diameter grows, and upon reversal of the oscillating pressure field, the bubble then collapses rapidly with the aid of the high surface tension forces acting on the wall of the bubble. The rapid and violent collapse causes the temperatures inside the bubbles to soar as a result of adiabatic heating. As the temperatures rise, the gaseous contents of the bubble ignite with the bubble itself serving as its own combustion chamber. After ignition, this is the time in the bubble s life cycle where power is generated, and CO2, and NOx among other species, are produced. However, the pollutants CO2 and NOx are absorbed into the surrounding liquid. The importance of bubble combustion is that it generates power using a simple and compact device. We conducted a parametric study using CAVCHEM, a computational model developed at Glenn, that simulates the cavitational collapse of a single bubble in a liquid (water) and the subsequent combustion of the gaseous contents inside the bubble. The model solves the time-dependent, compressible Navier-Stokes equations in one-dimension with finite-rate chemical kinetics using the CHEMKIN package. Specifically, parameters such as frequency, pressure, bubble radius, and the equivalence ratio were varied while examining their effect on the maximum temperature, radius, and chemical species. These studies indicate that the radius of the bubble is perhaps the most critical parameter governing bubble combustion dynamics and its efficiency. Based on the results of the parametric studies, we plan on conducting experiments to study the effect of ultrasonic perturbations on the bubble generation process with respect to the bubble radius and size distribution.

Corrigan, Jackie↗