Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “AUGMENTATION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Use of Assistive Technology to Augment API Capabilities

Application Programming Interfaces (APIs) allow for access to data and capabilities of computer applications by developers or users with experience in computer programming. Recent development with both Thermal Desktop and ESATAN-TMS have provided APIs to allow users to develop their own capabilities that interface with the Graphical User Interfaces (GUI) or manipulate the thermal model data. However, these APIs are only as good as the breadth of features in the native code accessible through the API; if a particular code’s feature is not accessible through the API, then users have very limited options besides waiting for updates to the API that expose the necessary functionality, particularly if model data access or user action, such as a button click, is required. However, Assistive Technology features that allow for differently-abled users to more fully experience a software’s capabilities may be creatively utilized to gain further access to data and capabilities not yet exposed by the API. This paper describes the process to augment the features of the OpenTD API via assistive technology and describes how to identify the application instance, navigate GUI elements, updates values on forms, and execute actions such as selecting a listbox item or clicking a button. It concludes with identifying some of the pitfalls to avoid and describes methods to best implement this approach.

Application Programming Interface↗
DOE OSTI2016

Bayesian Optimization Under Mixed Constraints with A Slack-Variable Augmented Lagrangian

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof. We show our new slack “ALBO” compares favorably to the original. Its superiority over conventional alternatives is reinforced on several mixed constraint examples.

Picheny, Victor↗
DOE OSTI2018

Heat pipe augmented passive solar heating system

A heat pipe augmented passive solar space heating system modulates the temperature inside a building structure, particular during heating seasons when the ambient temperature is below levels customarily associated with adequate room comfort, and in some embodiments comprises a solar absorber, a heat pipe surrounding wholly or partially by insulation, the heat pipe having three main sections which are an evaporator at one end proximal to the solar absorber, a condenser at the other end proximal to an interior room being heated, and a rubber adiabatic section between those. Additional aspects such as a mechanical valve in the adiabatic section are disclosed to limit unwanted thermal gains when cooling is needed because the ambient temperatures already exceed room comfort.

14 SOLAR ENERGY↗
DOE OSTI2019

Augmenting C. elegans Microscopic Dataset for Accelerated Pattern Recognition

The detection of cell shape changes in 3D time-lapse images of complex tissues is an important task. However, it is a challenging and tedious task to establish a comprehensive dataset to improve the performance of deep learning models. In the paper, we present a deep learning approach to augment 3D live images of the Caenorhabditis elegans embryo, so that we can further speed up the specific structure pattern recognition. We use an unsupervised training over unlabeled images to generate supplementary datasets for further pattern recognition. Technically, we used Alex-style neural networks in a generative adversarial network framework to generate new datasets that have common features of the C. elegans membrane structure. We also made the dataset available for a broad scientific community.

Lu, Zheng↗
DOE OSTI2021

Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Chen, Yang↗
DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗
DOE OSTI2022

System and method for error detection and correction in virtual reality and augmented reality environments

Embodiments of the present disclosure are related to training one or more of machine learning algorithms in a virtual reality environment for error detection and correction and/or for employing one or more trained machine learning models in an augmented reality environment to detect and/or correct user errors associated the performance of one or more tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Augmented Reality Rendering of Computed Tomography 3-D Density Data

Overall Objectives: Purchase equipment and Software required to develop the technology; Import CT Data for viewing on an AR device; Develop the tools necessary to manipulate the data in AR. With Augmented and Virtual Reality technology becoming more common and affordable everyday, new visualization techniques need to be developed that can leverage this technology to visualize data in new and useful ways. Computed Tomography (CT) scans, are the result of a powerful X-ray machine taking multiple images of the same object. These images represent slices of the original object and have extremely high resolution. Because of the high resolution, CT scanning is often used in the medical field but is becoming increasingly predominate in diverse industrial fields as well. This is an example of a CT scan that shows an object with multiple items inside. These items are represented by different colors in gray scale to indicate different densities. This is also from the data set used for the duration of the project. The data sets usually contain hundreds of slides that looks like just like this. Visualizing these large data sets can be difficult. The general approach for this seedling was to identify the devices and software that are currently in use for data visualization and evaluate them for use in the project. After that a plan was made to convert the data into a format viewable in AR. Tools were then developed to manipulate that data. The project was a success, but there is plenty of room to advance the technology and create better visualizations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Augmented Monitoring and Condition Assessment Program for SNF Wet Storage Life Extension - 20489

Approximately 27 MTHM of spent nuclear fuel (SNF) owned and managed by the U.S. Department of Energy, Office of Environmental Management is stored in the L Basin at the Savannah River Site (SRS). This 'DOE SNF' is comprised of approximately 12,000 aluminum-clad, aluminum-based fuel assemblies (∼7 MTHM), and approximately 2000 non-aluminum fuel assemblies (∼20 MTHM). A program is in progress to perform Non-Destructive Examination (NDE) of the fuel and their storage containers to characterize the materials' condition, and to evaluate the effects of service to enable continued safe wet storage of the SNF. The Augmented Monitoring and Condition Assessment Program (AMCAP), a two-part program to develop and implement remote underwater Non-Destructive Examination of the aluminum SNF, and of the containers of the non-aluminum SNF, respectively, is aimed at the characterization and evaluation of corrosion degradation of aluminum fuel and container materials. The predominant design of the aluminum SNF (ASNF) stored in L Basin is the Materials Test Reactor (MTR) equivalent design, a plate fuel design. These SNF are stored in 5'' diameter tubes or 5'' x 5'' squares (called bundles) that are nominally 12' or up to 14' long. The SNF is de-bundled and inspected using a custom-designed MTR Fuel Inspection Table. The inspection table provides for indexed fuel positioning for a video camera examination with controlled lighting. A total of 10 of the SNF originating from foreign research and test reactors were selected for examination based on burnup, enrichment, and prior damage caused by service/storage history. A special inspection campaign of these 10 assemblies is in progress. The observed corrosion damage included minor to moderate attack from general corrosion, pitting, crevice, end grain and galvanic corrosion. Example results from the completed inspections are shown and discussed. The focus is a comparison of the as-received condition versus the as-found current storage condition that will serve to validate Water Chemistry and Corrosion Monitoring Programs. The non-aluminum SNF (NASNF) stored in L Basin are of diverse design that includes various geometries with claddings of stainless steel, Zircaloy, and Hastelloy. The fuel core materials include uranium alloys, oxides/mixed oxides, and carbides. These SNF materials, originating from early experimental and test reactors, are in various physical forms including single fuel elements and cut pieces. The fuel is stored in L Basin in various configurations including in direct bundled storage in aluminum tubes and in isolation cans that are in the bundles or in a larger over-size storage container (OSC). Concern with inside-out corrosion and the potential loss of configuration control and ability to handle the storage containers prompted the development of remote NDE methods that include visual and UT technologies to assess the condition of the containers. Candidate stored materials were selected for a special inspection campaign; the development of the NDE methods for the inspection for galvanic, crevice and sediment-induced corrosion (inside-out) are discussed. A summary of the SNF storage in L Basin at the SRS, and an overview of the AMCAP to enable continued safe storage of DOE SNF are described. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Virtual and Augmented Realities to Support Development, Training, Deployment, and Operations of Remote Systems - 20516

Throughout the U.S. Department of Energy complex, there is a need to deploy new, unique equipment and systems for cleanup activities, waste processing, infrastructure repair, and decommissioning and demolition in radioactive and hazardous environments. The new equipment often consists of custom systems created by integrating and modifying commercial technologies. In addition, the systems are deployed in unique environments due to limited access, waste properties, and the presence of previously installed equipment or infrastructure that has been left in place. The deployment of custom systems that are 'turnkey' ready for first-time installation and application has its risks. Use of test platforms and mockups for cold (i.e., non-radioactive) testing is one method to mitigate that risk: however, full-scale test mockups for operations such as waste retrieval and tank inspection can be expensive and must be modified to incorporate the uniqueness of each application. An alternative to full-scale mockups is to use three-dimensional (3D) virtual environments and virtual reality (VR) technology to develop interactive virtual test beds. These can be used throughout the down-selection, development, system integration, operational planning, operator training, and mission execution phases of a task. Virtual test beds are readily reconfigured, can be used for multiple tasks, support multiple users in parallel, and can accommodate enhancements and changes not originally planned or conceived in the initial design. Virtual test beds also provide a platform for developing and testing augmented reality (AR) tools for improving operator efficiency. A VR experience has been generated for waste retrieval from a Hanford waste tank to present the potential applications of a virtual test bed. Features of the VR experience are presented, potential applications of VR and AR for remote applications are discussed, and development areas for improving future application are considered. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

System and method for transparent augmented reality

A system and method for displaying augmented reality information for a real object. A transparent display displays the information for the real object within a field of view, such that the information and the real object are simultaneously viewable by a user. Data-gathering peripheral devices gather data of the field of view, including a camera gathering visual data for tracking the real object. An articulating arm moveably supports and facilitates repositioning the display and redirecting the data-gathering peripheral devices in a particular direction. A security interlock disables the data-gathering peripherals when the articulating arm is moved beyond a pre-established threshold, thereby preventing the data-gathering peripherals from gathering data of a real environment outside of a limit of the field of view as determined by the pre-established threshold. The security interlock may include an adjustable contact switch which adjustably defines the pre-established threshold in at least one direction.

Runyan, Paul Ryan↗
DOE OSTI2026

Third-Party Supplier Risk Re-Classification Using Multi-Model Semantic Voting and External Web Augmentation

Risk decisions in many third-party risk management (TPRM) workflows rely on static inherent risk questionnaires (IRQ). These static forms provide a snapshot of the vendor from the business users’ perspective, as these requests are processed without cross-referencing for evidence. Consequently, responses can be misinformed or embellished with inaccuracies, thereby masking the vendor’s true risk to the enterprise. This paper presents a multi-stage verification framework to augment IRQs with web evidence and a deterministic ensemble of large language model assessors to reclassify risk. In a case study of 100 submissions previously misclassified as low risk, the proposed framework correctly identified 76% of the cases as high risk, while the existing workflow identified none. McNemar’s continuity corrected statistics of 74 were obtained with a two sided p-value of 2.65 × 10-23, indicating a significantly more effective workflow compared to the legacy model.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipelines ability to ingest several modalities of information. We present (1) a curated dataset of 93 questions designed to evaluate a pipeline's ability to ingest textual data, tables, images, multimodal data, and cross-document multimodal data; (2) a phrase-level recall metric for correctness; (3) a nearest-neighbor embedding classifier in an attempt to classify pipeline hallucinations; (4) a comparative evaluation of 2 pipelines built with open-source retrieval mechanisms and 4 closed-source foundational models; and (5) a third-party human evaluation of the alignment of our correctness and hallucination metrics. We find that closed-source pipelines significantly outperform open-source pipelines in both the correctness and halucination metrics, with a wider performance gap in questions relying on multimodal and cross-document information. We also find after a human evaluation of our correctness and hallucination metric compared with our questions and pipeline responses, average agreement was 4.62 for correctness 4.53 for hallucination detection on a 1-5 Likert scale with 5 being strongly agree with our determination.

Hildebrand, Samuel [ORNL] (ORCID:0009000465963104)↗
DOE OSTI2024

Variational data augmentation for a learning-based granular predictive model of power outages

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. Here, in this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

54 ENVIRONMENTAL SCIENCES↗