Results for “AUGMENTATION” — Page 16

NASA NTRS2019

Improving Accuracy of Structural Dynamic Modification with Augmented Residual Vectors

It is often important to perform sensitivity analysis to determine how a structural model will be impacted by design changes. Often, the structural analysts will manually make changes to the finite element model (FEM) to determine the effects. But when dealing with a large FEM with millions of degrees of freedom these manual changes can be cumbersome and calculation of the effects can computationally expensive. Therefore, it is desirable to determine the effects of model changes through approximation methods. One common technique is to determine the analytical sensitivity of the FEM model with respect to the given change. These analytical sensitivities are valid when small changes are made to the structural model, but invalid if large changes need to be assessed. Another approach is to use Structural Dynamic Modification (SDM) to create a surrogate model to analyze model changes. SDM is a widely-used sensitivity method and is used in applications of model updating, uncertainty quantification, and model design studies. SMD is valid for moderate (10-20 percent) changes in the structural model, but model approximations are often needed for large parameter changes (greater than 20 percent). Structural Dynamic Modification can be improved by using residual vectors to augment the surrogate model formulation from SDM. Adding the residual modes increases the fidelity of the surrogate model while keeping the computational cost low. This paper discusses the application and limitations of the augmented residual modes method to two structures: the Integrated Spacecraft and Payload Element (ISPE) of the Space Launch System (SLS) and the full SLS as it is configured during its Integrated Modal Test (IMT).

Stewart, Eric C.↗

NASA NTRS2019

Numerical Predictions of Dust-Induced Heat Flux Augmentation in Hypersonic Blunt-Body Flows Using a Discontinuous Galerkin Multiphase Flow Solver

Recent interest in human-scale missions to Mars has motivated the need for high-fidelity simulations of reentry flows. During a dust storm, there can be high levels of suspended dust in the Martian atmosphere, which cannot only enhance erosion of thermal protection systems but also transfer energy and momentum to the shock layer, thereby significantly augmenting the surface heat flux. Second-order finite-volume schemes are typically employed for hypersonic flow simulations, but such schemes suffer from a number of disadvantages. An attractive alternative is discontinuous Galerkin methods, which benefit from arbitrarily high spatial order of accuracy, geometric flexibility, and other properties. To enable accurate computations of high-speed particle-laden flows, an Euler-Lagrange methodology was developed in which the Eulerian field of the carrier gas is calculated using a discontinuous Galerkin scheme while the disperse phase is treated with Lagrangian particle tracking. We discuss challenges associated with coupling these two formulations and how to handle them. Momentum and energy transfer between the carrier gas and the particle phase is considered, and the importance of accounting for interparticle collisions is assessed. In addition, we describe the physical model of the particle phase and examine effects of its uncertainties on the numerical solution. We demonstrate the performance of the Euler-Lagrange method in representative testcases, with focus on the accurate prediction of particle trajectories and heating augmentation. Quantitative comparisons with experiments are provided.

Ching, Eric J.↗

NASA NTRSDate not supplied

Augmented fidelity as an alternative to the average fidelity

We provide description of different techniques to assess performance of quantum gates. In particular we focus on newly introduced metric called augmented fidelity, which provides extension to standard average fidelity by taking the average over von Mises-Fisher and polar cap distributions. We show the differences between standard average and augmented fidelities.

quantum computing↗

NASA NTRS2021

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems↗

NASA NTRS2022

Test data analysis of the thermodynamic vent system-augmented top spray injector liquid nitrogen transfer experiments

Traditionally, a cryogenic tank must be pre-chilled to some “target” temperature before the main vent valve can be closed to attempt a non-vented fill (NVF) of cryogenic liquid propellant. This methodology is particularly attractive for performing in-space transfer of cryogens due to the unknown location of the liquid/vapor interface in microgravity and the high likelihood of venting liquid if the vent valve is opened during transfer. This paper presents in-depth test data analysis of a Thermodynamic Vent System (TVS) augmented injector used for cryogenic tank chilldown and fill experiments of a thin-walled Titanium tank. Eight tests were conducted using liquid nitrogen across a range of inlet conditions and boundary conditions, and three different chilldown/fill methods. For four of the tests, the injector sprays liquid into the tank as normal, but also uses a TVS heat exchanger to cool the metallic injector itself as well as the main incoming liquid stream. Results show that using the TVS augmented injector simplifies transfer operation via enhanced condensation at the injector surface at the cost of sacrificing only a small amount of propellant.

No-vent Fill↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Research and development is being conducted to support data-driven design decisions for manual control and human involvement in the lunar landing task under the Human Landing System (HLS) program within the Artemis campaign. A human-in-the-loop simulator evaluation of the manual control of a lunar landing vehicle in the final approach and landing phase was conducted at NASA Langley Research Center in the Lunar Flight Deck simulator using the Altair Design and Analysis Cycle (DAC)-2 government reference vehicle. The objective was to perform a direct comparison of control law types with display aiding for various rotational control powers being considered under HLS. Ten subjects (four NASA test pilots and six current pilot astronauts) provided Cooper-Harper ratings, NASA Task Load Index workload ratings, and qualitative comments. The piloting task was to assume manual control of the vehicle (including vertical descent rate) at 150 m above the landing zone, fly to a redesignated landing target (which was up to 75 m radially from the center of the landing zone) and to touch down within a position accuracy of 5m. The data showed that the display augmentation in the form of a “hover cue” significantly improved the pilot’s ability to control translation and create satisfactory handling qualities for otherwise sluggish configurations; however, the investigation also showed that display augmentation is not a panacea. Handling qualities problems, including pilot-induced oscillations, and higher workload for the lowest control powers can still be evident.

Lynda J. Kramer↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Research and development is being conducted to support data-driven design decisions for manual control and human involvement in the lunar landing task under the Human Landing System (HLS) program within the Artemis campaign. A human-in-the-loop simulator evaluation of the manual control of a lunar landing vehicle in the final approach and landing phase was conducted at NASA Langley Research Center in the Lunar Flight Deck simulator using the Altair Design and Analysis Cycle (DAC)-2 government reference vehicle. The objective was to perform a direct comparison of control law types with display aiding for various rotational control powers being considered under HLS. Ten subjects (four NASA test pilots and six current pilot astronauts) provided Cooper-Harper ratings, NASA Task Load Index workload ratings, and qualitative comments. The piloting task was to assume manual control of the vehicle (including vertical descent rate) at 150 m above the landing zone, fly to a redesignated landing target (which was up to 75 m radially from the center of the landing zone) and to touch down within a position accuracy of 5m. The data showed that the display augmentation in the form of a “hover cue” significantly improved the pilot’s ability to control translation and create satisfactory handling qualities for otherwise sluggish configurations; however, the investigation also showed that display augmentation is not a panacea. Handling qualities problems, including pilot-induced oscillations, and higher workload for the lowest control powers can still be evident.

Lynda Kramer↗

NASA NTRS2023

Using Virtual Reality to Envision Deployment of Spacesuit-Compatible Augmented Reality Displays for Lunar Surface Operations

The National Aeronautics and Space Administration (NASA) aims to land crew on the lunar surface to establish a sustainable presence and develop operational concepts for future long-duration missions. New technologies will be necessary to extend planning and execution capabilities for lunar surface activities. NASA’s Joint Augmented Reality Visual Informatics System (Joint AR) is one such technology. Joint AR is a suit-mounted augmented reality (AR) display and computes system which facilitates unprecedented information exchange and data visualization capabilities between mission support operators and suited crew. This paper describes challenges associated with developing AR technology for an envisioned work domain by applying a sociotechnical lens to the iterative testing and development of novel AR technology through virtual reality (VR). A VR testbed was established to simulate a representative lunar surface environment, enabling a series of three human-in-the-loop (HITL) tests evaluating AR navigation interfaces for exploration extravehicular activity (xEVA). Our findings identify several considerations for future Joint AR design and testing efforts, including challenges with data overload, attentional demands, and environment-related perceptual challenges. Trade-offs and potential approaches are discussed to mitigate these challenges and improve future Joint AR testing fidelity.

Jacob Keller↗

NASA NTRS2023

Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics System (Joint AR) Product

Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.

Paromita Mitra↗

NASA NTRSDate not supplied

Using Virtual Reality to Envision Deployment of Spacesuit-Compatible Augmented Reality Displays for Lunar Surface Operations

The National Aeronautics and Space Administration (NASA) aims to land crew on the lunar surface to establish a sustainable presence and develop operational concepts for future long-duration missions. New technologies will be necessary to extend planning and execution capabilities for lunar surface activities. NASA’s Joint Augmented Reality Visual Informatics System (Joint AR) is one such technology. Joint AR is a suit-mounted augmented reality (AR) display and computes system which facilitates unprecedented information exchange and data visualization capabilities between mission support operators and suited crew. This paper describes challenges associated with developing AR technology for an envisioned work domain by applying a sociotechnical lens to the iterative testing and development of novel AR technology through virtual reality (VR). A VR testbed was established to simulate a representative lunar surface environment, enabling a series of three human-in-the-loop (HITL) tests evaluating AR navigation interfaces for exploration extravehicular activity (xEVA). Our findings identify several considerations for future Joint AR design and testing efforts, including challenges with data overload, attentional demands, and environment-related perceptual challenges. Trade-offs and potential approaches are discussed to mitigate these challenges and improve future Joint AR testing fidelity.

Matthew Miller↗

NASA NTRS2023

Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics System (Joint AR) Product

Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.

Paromita Mitra↗

NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗

NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗

NASA NTRSDate not supplied

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗

DOE OSTI2014

Methods and systems relating to an augmented virtuality environment

Systems and methods relating to an augmented virtuality system are disclosed. A method of operating an augmented virtuality system may comprise displaying imagery of a real-world environment in an operating picture. The method may further include displaying a plurality of virtual icons in the operating picture representing at least some assets of a plurality of assets positioned in the real-world environment. Additionally, the method may include displaying at least one virtual item in the operating picture representing data sensed by one or more of the assets of the plurality of assets and remotely controlling at least one asset of the plurality of assets by interacting with a virtual icon associated with the at least one asset.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Augmented reality building operations tool

A method (700) for providing an augmented reality operations tool to a mobile client (642) positioned in a building (604). The method (700) includes, with a server (660), receiving (720) from the client (642) an augmented reality request for building system equipment (612) managed by an energy management system (EMS) (620). The method (700) includes transmitting (740) a data request for the equipment (612) to the EMS (620) and receiving (750) building management data (634) for the equipment (612). The method (700) includes generating (760) an overlay (656) with an object created based on the building management data (634), which may be sensor data, diagnostic procedures, or the like. The overlay (656) is configured for concurrent display on a display screen (652) of the client (642) with a real-time image of the building equipment (612). The method (700) includes transmitting (770) the overlay (656) to the client (642).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module that creates plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.

Fan, Yangxin↗

DOE OSTI2023

Systems, devices, and methods for performing augmented reality responsive to monitoring user behavior

Systems, devices, and methods are described for performing augmented reality (AR) to assist user performing a task in an environment. An AR device may be configured to capture real-time data. An AR engine may be configured to monitor user behavior from the real-time data responsive to feature extraction from the real-time data, compare the user behavior to pre-defined work procedures, and generate augmented reality objects to be output by the AR device.

Yoon, SuJong↗