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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Simulation Test of a Head-Worn Display with Ambient Vision Display for Unusual Attitude Recovery

Head-Worn Displays (HWDs) are envisioned as a possible equivalent to a Head-Up Display (HUD) in commercial and general aviation. A simulation experiment was conducted to evaluate whether the HWD can provide an equivalent or better level of performance to a HUD in terms of unusual attitude recognition and recovery. A prototype HWD was tested with ambient vision capability which were varied (on/off) as an independent variable in the experiment testing for attitude awareness. The simulation experiment was conducted in two parts: 1) short unusual attitude recovery scenarios where the aircraft is placed in an unusual attitude and a single-pilot crew recovered the aircraft; and, 2) a two-pilot crew operating in a realistic flight environment with "off-nominal" events to induce unusual attitudes. The data showed few differences in unusual attitude recognition and recovery performance between the tested head-down, head-up, and head-worn display concepts. The presence and absence of ambient vision stimulation was inconclusive. The ergonomic influences of the head-worn display, necessary to implement the ambient vision experimentation, may have influenced the pilot ratings and acceptance of the concepts.

Arthur, Jarvis (Trey) J., III↗

NASA NTRS2018

2040 Vision: A Roadmap for Integrated, Multiscale Materials and System Modeling and Simulation

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will describe the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations). This roadmap is a community consensus document and is a result of over 450 professionals input obtain via: i) four society workshops (AIAA, NAFEMS, and two TMS), ii) community-wide survey, and iii) the establishment of 9 expert panels (one per key element) consisting on average 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. It is NASA's hope and desire that this roadmap provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.

Multiscale Modeling↗

NASA NTRS2019

Demonstration of Stereo Vision for Deorbit Descent and Landing

Planetary landers need to reduce velocity at low altitude for soft landing. Traditionally, estimating velocity and altitude has been performed with radar sensors whose performance meets the specific mission needs. There are not very many options for these sensors and they are difficult to include in a flight system either due to obsolescence, prohibitive cost or difficulty in accommodation. Recently, alternative sensing modalities are being pursued including Doppler LiDAR and vision. This paper describes results from a recent helicopter field test of a binocular stereo vision system for deorbit descent and landing applications. The system consisted of two 18.6 ̊ field of view cameras mounted 1.7m apart. Post processing of the images showed ranging accuracy better than 1% up to 500mand 17 cm/s velocimetry accuracy at 37m. For a flight system these images could be input into an FPGA-based processor which processes dense stereo and visual odometry in less than 1 second to achieve the stereo ranging frame rates required for soft landing. When coupled with vision based Terrain Relative Navigation this stereo system enables landing accuracies on the order of 10m.

Sternberg, David C.↗

NASA NTRS2017

A Minimal State Augmentation Algorithm for Vision-Based Navigation without Using Mapped Landmarks

This paper describes MAVeN (Minimal State Augmentation Algorithm for Vision-Based Navigation), which is a new algorithm for vision-based navigation that has only 21 states, yet is able to track features in successive camera images and use them to propagate estimates of the spacecraft position and velocity. The filter dimension drops to 12 if attitude information is already available. The low filter dimension makes MAVeN a very reliable and practical algorithm for real-time flight implementation. The main idea is to project observed features onto a rough shape model of the ground surface, which are then used by the filter as pseudo-landmarks. The shape model is assumed to be known beforehand, as would be obtained from prior surveillance of the landing site from orbit. MAVeN does not require pre-mapped landmarks, so it is able to navigate terrain that has not been previously observed up close. This property is especially important for close proximity operations in small body missions where ground surface features are being seen for the first time at close range. MAVeN is also able to hover motionless above the ground without position error growth, which is unusual for this class of vision-based navigation algorithms.

San Martin, A. Miguel↗

NASA NTRS2018

An “EPIIC” Vision to Evolve Project Integration, Innovation, and Collaboration with Broad Impact for How NASA Executes Complex Projects

Evolving Project Integration, Innovation, and Collaboration (EPIIC) is a vision defined to transform the way projects manage information to support real-time decisions, capture best practices and lessons learned, perform assessments, and manage risk across a portfolio of projects. The foundational project management needs for data and information will be revolutionized through innovations on how we manage and access that data, implement configuration control, and certify compliance. The embedded intelligence of new interactive data interfaces integrate technical and programmatic data such that near real time analytics can be accomplished to more efficiently and accurately complete systems engineering and project management tasks. The system-wide data analytics that are integrated into customized data interfaces allows the growing team of engineers and managers required to develop and implement major NASA missions the ability to access authoritative source(s) of system information while greatly reducing the labor required to complete system assessments. This would allow, for example, much of what is accomplished in a scheduled design review to take place as needed, between any team members, at any time. An intelligent data interface that rigorously integrates systems engineering and project management information in near real time can provide substantially greater insight for systems engineers, project managers, and the large diverse teams required to complete a complex project. System engineers, programmatic personnel (those who focus on cost, schedule, and risk), the technical engineering disciplines, and project management can realize immediate benefit from the shared vision described herein. Implementation of the vision also enables significant improvements in the performance of the engineered system being developed.

Moreland, Robert J.↗

NASA NTRS2019

Evaluation of a Head-Worn Display with Ambient Vision Cues for Unusual Attitude Recovery

A Commercial Aviation Safety Team (CAST) study of 18 loss-of-control events determined that a lack of external visual references was a contributing factor in 17 of these events. CAST recommended that manufacturers should develop and implement virtual day-VMC display systems, such as synthetic vision (SV) or equivalent systems (CAST Safety Enhancement, SE-200). In support of this recommended action, CAST has requested studies to define minimum requirements for virtual day-visual meteorological conditions (VMC) displays to improve flight crew awareness of airplane attitude. NASA’s research in Virtual day-VMC displays, known as synthetic vision systems, are intended to support intuitive flight crew attitude awareness similar to a day-VMC-like environment, especially if they could be designed to create visual dominance. A study was conducted to evaluate the utility of ambient vision (AV) cues paired with virtual Head-Up Display (HUD) symbology on a prototype head-worn display (HWD) during recovery from unusual attitudes in a simulated environment. The virtual-HUD component meets the requirement that the HWD may be used as an equivalent display to the HUD. The presence of AV cueing leverages the potential that a HWD has over the HUD for spatial disorientation prevention. The simulation study was conducted as a single-pilot operation, under realistic flight scenarios, with off-nominal events occurring that were capable of inducing unusual attitudes. Independent variables of the experiment included: 1) AV capability (on vs off) 2) AV display opaqueness (transparent vs opaque) and display location (HWD vs traditional head- down displays); AV cues were only present when the HWD was being worn by the subject pilot.

Nicholas, Stephanie N.↗

NASA NTRSDate not supplied

Vision 2030 Aircraft Propulsion Grand Challenge Problem: Full-engine CFD Simulations with High Geometric Fidelity and Physics Accuracy

In 2014 NASA published the outcome of the 2030 CFD Vision study: “CFD Vision 2030: A path to Revolutionary Computational Aerosciences” (Slotnick et al., 2014). The study provided a comprehensive review of the state of the art of CFD in 2014 for aerospace applications including, but not limited to, numerical algorithms, physics models, MDAO and HPC hardware. The study also proposed four conceptual ideas of Grand Challenge problems that would benefit from advances outlined in the roadmap including “off-design turbofan engine transient simulation”. The proposed challenges served as a starting point for more detailed problem descriptions that would benefit from advances in simulation. The objective of this paper is to build upon the NASA 2030 CFD Vision study and provide a detailed overview of what needs to take place to enable accurate and efficient simulation of flow through an aircraft engine at off-design condition for transient operation . Execution of the proposed roadmap would significantly advance aircraft engine development by reducing program cost, reducing program development timelines and enabling design objectives associated with Specific Fuel Consumption (SFC), noise, weight and durability.

compressor↗

NASA NTRS2021

Future Model-Based Systems Engineering Vision and Strategy Bridge for NASA

A vision for the future of model-based systems engineering (MBSE) at NASA in 2029 and a strategy bridge towards that future are presented. Strategic thinking and leading change concepts were used to analyze reports and presentations on global trends and visionary thinking about the future of systems and digital engineering. The context, strategic time horizon, stakeholders, strategic challenges, strategic advantages, driving forces, and opportunities were considered. The analysis resulted in a future vision of MBSE that shows what NASA systems engineers and digital machines will do to perform rapid, extraordinary, and unprecedented missions. The NASA systems engineer, in this future vision, works with a global project team in a virtual and collaborative environment, engineers the system, and uses digital approaches as the routine and default way of working. The digital machines provide data-driven and automated mission designs; have a backbone of program and project management, systems engineering, and product life-cycle management; and are a knowledge-sharing infrastructure. The NASA systems engineer and the systems engineering team are envisioned to use digital machines to plan and perform rapid exploration missions, develop a digital twin that lasts across the life cycle, and develop enduring and adaptable systems. NASA has an engineering enterprise and a life-cycle management framework that endure, adapt, and respond. A strategy bridge based on the Baldrige Criteria for Performance Excellence Framework and lessons learned from a recent MBSE initiative illuminates a way forward from today to this desired future. The bridge lays out a strategy for leaders and recommends investments of today for immediate benefits and for benefits in 2029.

model-based systems engineering, digital engineeri↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface. In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha-Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

Suman Sinha Ray↗

DOE OSTI2014

PERFORMING DIAGNOSTICS ON THE SPALLATION NEUTRON SOURCE VISION BEAM LINE TO ELIMINATE HIGH VIBRATION LEVELS AND PROVIDE A SUSTAINABLE OPERATION

The Spallation Neutron Source (SNS) at the Oak Ridge National Laboratory (ORNL) provides variable energy neutrons for a variety of experiments. The neutrons proceed down beam lines to the experiment hall, which houses a variety of experiments and test articles. Each beam line has one or more neutron choppers which filter the neutron beam based on the neutron energy by using a rotating neutron absorbing material passing through the neutron beam. Excessive vibration of the Vision beam line, believed to be caused by the T0 chopper, prevented the Vision beam line from operating at full capacity. This problem had been addressed several times by rebalancing/reworking the T0 beam chopper but the problem stubbornly persisted. To determine the cause of the high vibration, dynamic testing was performed. Twenty-seven accelerometer and motor current channels of data were collected during drive up, drive down, coast down, and steady-state conditions; resonance testing and motor current signature analysis were also performed. The data was analyzed for traditional mechanical/machinery issues such as misalignment and imbalance using time series analysis, frequency domain analysis, and operating deflection shape analysis. The analysis showed that the chopper base plate was experiencing an amplified response to the excitation provided by the T0 beam chopper. The amplified response was diagnosed to be caused by higher than expected base plate flexibility, possibly due to improper grouting or loose floor anchors. Based on this diagnosis, a decision was made to dismantle the beam line chopper and remount the base plate. Neutron activation of the beam line components make modifications to the beam line especially expensive and time consuming due to the radiation handling requirements, so this decision had significant financial and schedule implications. It was found that the base plate was indeed loose because of improper grouting during its initial installation. The base plate was modified by splitting it into multiple sections, isolating the T0 chopper from the rest of the beam line, and each section was then reinstalled and re-grouted. After these modifications, the vibration levels were reduced by a factor of 30. The reduction in vibration level was sufficient to allow the Vision beam line to operate at full capacity for the first time since its completed construction date.

: Diagnostics↗

DOE OSTI2018

Translator Plan: A Coordinated Vision for Fiscal Years 2018-2020

In June of 2017, the Translator Group met to develop this coordinated three-year vision plan, incorporating key feedback and aligning to ARM’s mission priorities. This plan responds to a shift in how we determine our priorities, given the new needs of the ARM Facility. In the past, individual Translators have determined priorities in conversation with individual DOE Atmospheric System Research (ASR) working groups. To better support ARM’s Decadal Vision (https://www.arm.gov/publications/programdocs/doe-sc-arm-14-029.pdf), however, the Translator Group is instead developing a coordinated response to needs from our user community to better balance resources and skills among participants. This approach agrees with direction from ARM leadership and the ARM-ASR Coordination Team (AACT). To develop this plan the Translator Group reviewed feedback received from the User Executive Committee (UEC) and the Triennial Review, as well as priorities from ASR working groups and Principal Investigators (PIs), the LES ARM Symbiotic Simulation and Observation (LASSO) project, and new instrumentation and activities as described by the ARM Technical Director. In particular, we are responding to the advice that we were trying to do too much, and should focus on providing additional support to data quality, uncertainty assessment, a timeline for producing core VAPs from ARM Mobile Facility (AMF) campaigns, and supporting key aspects of the Decadal Vision.

value-added products, User Executive Committee, AR↗

DOE OSTI2020

Material sorting using a vision system

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification. The material sorting system may include an x-ray fluorescence system to perform a classification of the materials in combination with the vision system, whereby the classification efforts of the vision system and x-ray fluorescence system are combined in order to classify and sort the materials.

Kumar, Nalin↗

DOE OSTI2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

Aarrestad, Thea Klaeboe [ETH, Zurich (main)]↗

DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Mitigation of spatial nonstationarity with vision transformers

Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.

58 GEOSCIENCES↗

DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗