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

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Bangladesh Agro-Climatic Environmental Monitoring Project

The Agro-Climatic Environmental Monitoring Project (ACEMP) is based on a Participating Agency Service Agreement (PASA) between the Agency for International Development (AID) and the National Oceanic and Atmospheric Administration (NOAA). In FY80, the Asia Bureau and Office of Federal Disaster Assistance (OFDA), worked closely to develop a funding mechanism which would meet Bangladesh's needs both for flood and cyclone warning capability and for application of remote sensing data to development problems. In FY90, OFDA provided for a High Resolution Picture Transmission (HRPT) receiving capability to improve their forecasting accuracy for cyclones, flooding and storm surges. That equipment is primarily intended as a disaster prediction and preparedness measure. The ACEM Project was designed to focus on the development applications of remote sensing technology. Through this Project, AID provided to the Bangladesh Government (BDG) the equipment, technical assistance, and training necessary to collect and employ remote sensing data made available by satellites as well as hydrological data obtained from data collection platforms placed in major rivers. The data collected will enable the BDG to improve the management of its natural resources.

Vermillion, C.↗
NASA NTRS2000

A High Spatial Resolution Study of Far IR Emission of Galaxies

This grant funded observations, data reduction, professional publications and travel for scientific efforts on the Kuiper Airborne Observatory. The research project was successfully completed. New insights into the distribution of far infrared emission across star forming regions was obtained, and student training was achieved. The efforts contributed towards new observing strategies, such as calibration and intercomparison of data from different infrared astronomical observing platforms, that will impact future NASA missions, such as SOFIA. The results of the effort have been presented in several papers in the refereed literature, including: "The Structure of IR Luminous Galaxies at 100 Microns". " Far Infrared Thermal Emission from the Inner Cooling Flow Region of NGC1275". "Distribution of Light in the "Dusty Hand" Galaxy NGC2146".

Caldwell, Barrie A.↗
DOE OSTI2021

Cyber-Physical Events Emulation Based Transmission and Distribution Co-Simulation for Situation Awareness and Grid Anomaly (SAGA) Detection: Preprint

Energy management of transmission and distribution networks is becoming more challenged with the accelerated increasing of distributed energy resources (DERs) such as distributed photovoltaic (PV) generation and distributed energy storage. To better analyze the impacts of DERs on both transmission and distribution systems, a comprehensive transmission and distribution co-simulation platform should be developed. Furthermore, with DERs more actively participated in system operation such as providing real time grid services, their cyber vulnerability should be better understood to maintain system reliability. This paper discussed a cyber-physical events emulation based transmission & distribution co-simulation platform to perform different cyber events emulation and analyze the impacts of cyber physical events happened in distribution system on the T&D system operation. The case studies with both a transmission network and a synthetic distribution network data validate that the proposed T&D co-simulation platform can perform comprehensive cyber physical events emulation. Therefore, with extensive simulation using the proposed model, the system operator can accumulate adequate training data for the system situation awareness and grid anomaly detection purpose.

31 CESER - Office of Cybersecurity, Energy Securit↗
DOE OSTI2022

Detecting damaged buildings using real-time crowdsourced images and transfer learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

58 GEOSCIENCES↗
DOE OSTI2025

Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation

Protein sequence similarity search is fundamental to biology research, but current methods are typically not able to consider crucial genomic context information indicative of protein function, especially in microbial systems. Here, we present Gaia (Genomic AI Annotator), a sequence annotation platform that enables rapid, context-aware protein sequence search across genomic datasets. Gaia leverages gLM2, a mixed-modality genomic language model trained on both amino acid sequences and their genomic neighborhoods to generate embeddings that integrate sequence-structure-context information. This approach allows for the identification of functionally and/or evolutionarily related genes that are found in conserved genomic contexts, which may be missed by traditional sequence- or structure-based search alone. Gaia enables real-time search of a curated database comprising more than 85 million protein clusters from 131,744 microbial genomes. We compare the homolog retrieval performance of Gaia search against other embedding and alignment-based approaches. We provide Gaia as a web-based, freely available tool.

Jha, Nishant↗
DOE OSTI2023

Sandia Contributions to WaterTAP

SAND2023-06591O Through National Alliance for Water Innovation funding, Sandia will be contributing to the Water treatment Technoeconomic Assessment Platform (WaterTAP) project. WaterTAP is an open-source, Python-based software package that supports the technoeconomic assessment of full water treatment trains. WaterTAP includes a modular water treatment model library spanning a broad set of water treatment processes composed of unit, property, and costing models.

Rawlings, EdnaSoraya↗
DOE OSTI2016

Staghorn: An Automated Large-Scale Distributed System Analysis Platform

Conducting experiments on large-scale distributed computing systems is becoming significantly easier with the assistance of emulation. Researchers can now create a model of a distributed computing environment and then generate a virtual, laboratory copy of the entire system composed of potentially thousands of virtual machines, switches, and software. The use of real software, running at clock rate in full virtual machines, allows experiments to produce meaningful results without necessitating a full understanding of all model components. However, the ability to inspect and modify elements within these models is bound by the limitation that such modifications must compete with the model, either running in or alongside it. This inhibits entire classes of analyses from being conducted upon these models. We developed a mechanism to snapshot an entire emulation-based model as it is running. This allows us to \freeze time" and subsequently fork execution, replay execution, modify arbitrary parts of the model, or deeply explore the model. This snapshot includes capturing packets in transit and other input/output state along with the running virtual machines. We were able to build this system in Linux using Open vSwitch and Kernel Virtual Machines on top of Sandia's emulation platform Firewheel. This primitive opens the door to numerous subsequent analyses on models, including state space exploration, debugging distributed systems, performance optimizations, improved training environments, and improved experiment repeatability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform

n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Take Off! Aeronautics and Aviation Science: Careers and Opportunities

Funded by National Aeronautic and Space Administration's High Performance Computing and Communications/ Learning Technologies Project (HPCC/LTP) Cooperative Agreement, Aeronautics and aviation Science: Careers and Opportunities was operative from July 1995 through July 1998. This project operated as a collaboration with Massachusetts Corporation for Educational Telecommunications, the Federal Aviation Administration, Bridgewater State College and four targeted "core sites" in the greater Boston area: Dorchester, Malden, East Boston and Randolph. In its first and second years, a video series with a participatory website on aeronautics and aviation science was developed and broadcast via "live, interactive" satellite feed. Accompanying teacher and student supplementary instructional materials for grades 6-12 were produced and disseminated by the Massachusetts Corporation for Educational Telecommunications (MCET). In year three, the project team redesigned the website, edited 14 videos to a five part thematic unit, and developed a teacher's guide to the video and web materials supplement for MAC and PC platforms, aligned with national standards. In the MCET grant application it states that project Take Off! in its initial phase would recruit and train teachers at "core" sites in the greater Boston area, as well as opening participation to other on-line users of MCET's satellite feeds. "Core site" classrooms would become equipped so that teachers and students might become engaged in an interactive format which aimed at not only involving the students during the "live" broadcast of the instructional video series, but which would encourage participation in electronic information gathering and sharing among participants. As a Take Off! project goal, four schools with a higher than average proportion of minority and underrepresented youth were invited to become involved with the project to give these students the opportunity to consider career exploration and development in the field of science aviation and aeronautics. The four sites chosen to participate in this project were East Boston High School, Dorchester High School, Randolph Junior-Senior High School and Malden High School. In year 3 Dorchester was unable to continue to fully participate and exited out. Danvers was added to the "core site" list in year 3.

Source record↗
DOE OSTI2018

A Novel Mobile Examination and Remediation Facility for On-Site Remediation of Dry Storage Systems - 18389

Most of the spent nuclear fuel (SNF) in dry storage in the United States is safely and securely stored in dual-purpose (i.e., storage and transportation) canisters [1]. However, some of the SNF is stored in storage-only (i.e., non-transportable) packages, or packages that may become nonconforming for continued storage or transportation for a variety of reasons. Currently, the capability to remediate dry storage systems (DSSs) for SNF that are not transportable or that need to be repaired to maintain confinement boundary integrity during storage service does not exist without on-site access to a spent fuel pool. Hence, there is a potential future need for a mobile facility with remediation capabilities that can be used at multiple storage installations. These capabilities would permit needed repairs to an existing DSS to support continued storage or for the dry transfer of fuel to a new conveyance suitable for transport, if necessary. This is a technical paper that does not take into account the contractual limitations under the Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (Standard Contract) (10 CFR Part 961). Under the provisions of the Standard Contract, DOE does not consider spent nuclear fuel in canisters to be an acceptable waste form, absent a mutually agreed to contract amendment. To the extent discussions or recommendations in this paper conflict with the provisions of the Standard Contract, the Standard Contract provisions prevail. A novel design concept for a Mobile Examination and Remediation Facility (MERF) is proposed with functions and capabilities to remediate a DSS. The proposed MERF would be a multi-purpose facility, remotely operated and computer-controlled with semi-automated equipment to allow trained operators to safely perform remediation activities while keeping worker radiation dose as low as reasonably achievable. It would include a shielded rotating platform with support towers and would use commercial off- the-shelf examination and repair equipment. A slip ring arrangement underneath the rotating platform would allow for the power supply without impeding the continuous rotation and would simplify cable management. The support towers would be mounted on the rotating platform, and each tower would have vertical and horizontal positioning capability for axial and radial tool location. The rotating platform would be attached to the main MERF structure. Capabilities would include visual, surface, and volumetric examinations to fully characterize physical conditions on canister surface before and after a repair; repair techniques, e.g., laser beam welding, friction stir welding, or cold spray, to perform needed remediation activities; and associated equipment for surface preparation such as cutting and grinding equipment. The proposed design would be simple, modular, robust, economically viable, and easily transportable and would have a small footprint. The modular design would present several advantages including compatibility with a variety of different DSSs, on-site assembly, and transportability; it also would be built in interchangeable sections to facilitate decontamination. Further, shielding would be supplemented through interface with the existing transfer cask and storage overpack. Robustness and canister stability would be achieved using pneumatic holders and steel support structures designed with vibration absorbers to reduce impacts and/or a 'pendulum' effect during a seismic event. Thus, the proposed MERF would be designed to: (i) minimize handling of an affected DSS to the extent practicable, (ii) provide radiation shielding and minimize dose to operators and the public during remediation activities, and (iii) enable examination, repair, and repackaging. This paper describes the proposed MERF design concept and capabilities, main components, equipment, different modes of operation, and operating procedures. Structural and safety assessments would need to be performed before final design, licensing, and deployment. The MERF is evaluated with respect to regulatory impact (first of a kind, prior experience), operational impact (safety, security, staffing, ongoing operations, constraints), worker dose (low, medium, high), cost (low, medium, high), schedule (duration, time sequence), and technology readiness level. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. Here, by tuning nanoarray dimensions we have achieved macrospin–vortex bistability and demonstrated a four-state metamaterial spin system, the ‘artificial spin-vortex ice’ (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2003

Shuttle Systems 3-D Applications: Application of 3-D Graphics in Engineering Training for Shuttle Ground Processing

This project illustrates an animation of the orbiter mate to the external tank, an animation of the OMS POD installation to the orbiter, and a simulation of the landing gear mechanism at the Kennedy Space Center. A detailed storyboard was created to reflect each animation or simulation. Solid models were collected and translated into Pro/Engineer's prt and asm formats. These solid models included computer files of the: orbiter, external tank, solid rocket booster, mobile launch platform, transporter, vehicle assembly building, OMS POD fixture, and landing gear. A depository of the above solid models was established. These solid models were translated into several formats. This depository contained the following files: stl for sterolithography, stp for neutral file work, shrinkwrap for compression, tiff for photoshop work, jpeg for Internet use, and prt and asm for Pro/Engineer use. Solid models were created of the material handling sling, bay 3 platforms, and orbiter contact points. Animations were developed using mechanisms to reflect each storyboard. Every effort was made to build all models technically correct for engineering use. The result was an animated routine that could be used by NASA for training material handlers and uncovering engineering safety issues.

Godfrey, Gary S.↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

WaterTAP 0.3 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.4 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.5 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗