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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Validation Testing for Automated Solubility Measurement Equipment Final Report

Laboratory tests have been completed to test the validity of automated solubility measurement equipment using sodium nitrate and sodium chloride solutions (see test plan WRPS-1404441, “Validation Testing for Automated Solubility Measurement Equipment”). The sodium nitrate solution results were within 2-3% of the reference values, so the experiment is considered successful using the turbidity meter. The sodium chloride test was done by sight, as the turbidity meter did not work well using sodium chloride. For example, the “clear” turbidity reading was 53 FNU at 80 °C, 107 FNU at 55 °C, and 151 FNU at 20 °C. The sodium chloride did not work because it is granular and large; as the solution was stirred, the granules stayed to the outside of the reactor and just above the stir bar level, having little impact on the turbidity meter readings as the meter was aimed at the center of the solution. Also, the turbidity meter depth has an impact. The salt tends to remain near the stir bar level. If the meter is deeper in the slurry, it will read higher turbidity, and if the meter is raised higher in the slurry, it will read lower turbidity (possibly near zero) because it reads the “clear” part of the slurry. The sodium chloride solution results, as measured by sight rather than by turbidity instrument readings, were within 5-6% of the reference values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Opportunities for Automated Demand Response in California Agricultural Irrigation

Pumping water for agricultural irrigation represents a significant share of California’s annual electricity use and peak demand. It also represents a large source of potential flexibility, as farms possess a form of storage in their wetted soil. By carefully modifying their irrigation schedules, growers can participate in demand response without adverse effects on their crops. This report describes the potential for participation in demand response and automated demand response by agricultural irrigators in California, as well as barriers to widespread participation. The report first describes the magnitude, timing, location, purpose, and manner of energy use in California. Typical on-­farm controls are discussed, as well as common impediments to participation in demand response and automated demand response programs. Case studies of demand response programs in California and across the country are reviewed, and their results along with overall California demand estimates are used to estimate statewide demand response potential. Finally, recommendations are made for future research that can enhance the understanding of demand response potential in this industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Development of an Automated Decision-Making Tool for Supervisory Control System

This technical report was generated as a product of the Supervisory Control for Multi-Modular Small Modular Reactor (SMR) Plants project within the Instrumentation, Control and Human-Machine Interface technology area under the Advanced Small Modular Reactor (AdvSMR) Research and Development Program of the US Department of Energy. The report documents the definition of strategies, functional elements, and the structural architecture of a supervisory control system for multi-modular AdvSMR plants. This research activity advances the state of the art by incorporating real-time, probabilistic-based decision-making into the supervisory control system architectural layers through the introduction of a tiered-plant system approach. The report provides background information on the state of the art of automated decision-making, including the description of existing methodologies. It then presents a description of a generalized decision-making framework, upon which the supervisory control decision-making algorithm is based. The probabilistic portion of automated decision-making is demonstrated through a simple hydraulic loop example.

42 ENGINEERING↗
DOE OSTI2017

Phase II: Automated System for Aneuploidy Detection in Sperm Final Report CRADA No. TC-1554-98

This was a collaborative effort between the University of California, Lawrence Livermore National Laboratory (LLNL) and Cellomics, Inc. (formerly BioDx and Biological Detection, Inc.) to develop an automated system for detecting human sperm aneuploidy. Aneuploidy (an abnormal number of chromosomes) is one of the major categories of chromosomally abnormal sperm, which results in chromosomally defective pregnancies and babies. An automated system would be used for testing the effects of toxic agents and for other research and clinical applications. This collaborated effort was funded by a National Institutes of Environmental Health Services, Phase II, Small Business Innovation Research Program (SBIR) grant to Cellornics (Contract No. N44-ES-82004).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

An Overview of the Automated Dispatch Controller Algorithms in the System Advisor Model (SAM)

Three automatic dispatch modes have been added to the battery model within the System Adviser Model. These controllers have been developed to perform peak shaving in an automated fashion, providing users with a way to see the benefit of reduced demand charges without manually programming a complicated dispatch control. A flexible input option allows more advanced interaction with the automated controller. This document will describe the algorithms in detail and present brief results on its use and limitations.

25 ENERGY STORAGE↗
DOE OSTI2018

Automated Image Analysis Corrosion Working Group Update: February 1, 2018

These are slides for the automated image analysis corrosion working group update. The overall goals were: automate the detection and quantification of features in images (faster, more accurate), how to do this (obtain data, analyze data), focus on Laser Scanning Confocal Microscope (LCM) data (laser intensity, laser height/depth, optical RGB, optical plus laser RGB).

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

Pattern recognition is the identification of structures and regularities in data. In high energy physics, it is a vital stage in the reconstruction of events recorded by fine-granularity detectors. The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. Whereas the human brain excels at identifying features in the recorded events, it is a significant challenge to develop an automated solution. The Pandora Software Development Kit (SDK) provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. In particular, it promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each aim to address a specific task in a particular topology; a series of many tens of algorithms then carefully build-up a picture of the event and, together, provide a robust automated pattern recognition solution. Building on successful use of the Pandora SDK for pattern recognition at collider experiments, a sophisticated chain of algorithms has been created to perform pattern recognition for neutrino experiments utilising LAr TPCs like MicroBooNE. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction. In this document, we present details of the Pandora pattern recognition algorithms used to reconstruct cosmic-ray and neutrino events in LAr TPCs. We also present metrics that assess the current reconstruction performance using simulated data from MicroBooNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Real-Time Automated Health Information Technology Hazard Detection

The objective of this project is to develop an automated surveillance tool that will help the US Department of Veterans Affairs (VA) to detect potential hazards in health information technology (HIT) systems. To achieve this objective, Oak Ridge National Laboratory (ORNL) researchers have developed computational methods and a surveillance tool that will detect safety concerns and identify opportunities to improve HIT and safety within the VA system using Corporate Data Warehouse (CDW) data. This work is limited to the safe operation and safe use of HIT. It does not address the use of HIT to make health care safer. This project leveraged ORNL expertise in computational sciences and its computing resources accordingly.This report documents the FY 2019 accomplishments on this project. There were three major tasks. The first task developed data extraction approaches, which helped provide a better overall understanding of the important data elements in CDW. The outputs of this task served as inputs into the second task, which is the hazard detection and characterization task. This second task involved development of three methods that can be categorized into probabilistic model, event-based hazards detection, and system-based reliability performance evaluation. The third task dealt with the design, development, and demonstration of the automated surveillance tool. The tool currently resides in ORNL’s Private Health Information enclave at Oak Ridge, Tennessee, and operates on the VA’s CDW data. The developed capabilities and tool have been tested on CDW data from different clinical domains including Consult, Radiology, Laboratory, and Medication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Boosting Energy Efficiency of Heterogeneous Connected Automated Vehicle (CAV) Fleets via Anticipative and Cooperative Vehicle Guidance

In 2017, the Department of Energy funded a team at Clemson University and Argonne National Laboratory to develop collaborative perception and anticipative/predictive vehicle guidance schemes for Connected and Automated Vehicles (CAVs) and to quantify the energy saving potential of this technology in large scale traffic microsimulations at different levels of technology penetration and also experimentally. The project goal was demonstrating up to a 10% energy saving potential from different aspects of the implementation with a focus on reducing unnecessary braking events by anticipatory speed and lane selection. This project introduced novel anticipative car following and lane selection schemes for Connected and Automated Vehicles (CAVs). Our control schemes benefited from prediction of human driver behavior, information exchange between CAVs, and sometimes from collaboration to save energy, reduce braking events, and harmonize traffic. The energy savings was first demonstrated by traffic micro-simulations and then via a novel Vehicle-In-the-Loop (VIL) experimental testbed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Developing an Eco-Cooperative Automated Control System (Eco-CAC)

The goal of the project was to develop a novel Eco-Cooperative Automated Control (Eco-CAC) system that integrates vehicle dynamics (VD) control with connected and automated vehicle (CAV) applications. In particular, the team developed a novel integrated control system that (1) routes vehicles in a fuel/energy-efficient manner for internal combustion engine vehicles (ICEVs), battery-only electric vehicles (BEVs), and hybrid electric vehicles (HEVs); (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles within a cooperative platoon to minimize its fuel/energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Overview of the Process Automation and Control Group (E-3) [Slides]

Process Automation and Control (E-3) is a part of the Weapons Associate Laboratory Directorate (DDW) which includes Weapons Engineering Directorate (WE) and the Engineering Technology and Design (E) Division. There are three teams: Fabrication and Assembly, Process Controls, and Automation and Robotics. After an overview of the group, information is provided about an internship and how to apply.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Development of an Assessment Methodology That Enables the Nuclear Industry to Evaluate Adoption of Advanced Automation

Nuclear power has a crucial role in providing safe, reliable, and economical carbon-free electricity for today and the future. For continued operation, many of the existing United States nuclear power plants will begin the subsequent license renewal process for extending their operating license periods. As plants extend their expected operating lifetimes, there is a significant opportunity to modernize. These plants have a much stronger business case with these extended mission periods to modernize and significantly enhance their economic viability in current and future energy markets by implementing digital technologies that support innovation, efficiency gains, and business-model transformation. Ensuring continued safety and reliability is crucial. Transformative digital technologies—including automation—that fundamentally change the concept of operation for the nuclear power plant operating model requires a critical focus on the human and technology integration element. Further, the nuclear industry has historically been reluctant to modernize due to having a risk adverse culture and lack of clarity for a transformative new state vision (Joe & Remer, 2019; Thomas et al., 2020). Common barriers include (1) the perceived value and return on investment (ROI) of digital technology, (2) the perceived risk associated with licensing, regulatory, and cybersecurity, and (3) insufficient guidance for performing digital modifications to power generation systems. This work presents a methodology to address these barriers and support the industry in adopting advanced automation and digital technology through developing a transformative vision and implementation strategy that will address the human and technology integration element. This research leverages previous LWRS Program and industry results. It draws specifically on previous LWRS Program research in the areas of advanced alarm systems, computer-based procedures, model informed decision support, and advanced human-system interface displays (e.g., overviews and task-based). The modernization methodology can be used to guide transformative thinking when integrating a set of vendor-specific capabilities to support a new concept of operations and a utility’s end-state vision. The results of this research are organized into six major sections: - Section 1 introduces the need for supporting large-scale digital modifications that will renew the technology base for extended operating life beyond 60 years - Section 2 describes the challenges that the nuclear industry is enduring with modernizing. - Section 3 summarizes the primary standards and guidance. - Section 4 presents earlier work from the LWRS Program regarding the development of a transformative conceptual design for an advanced control room of a hybrid plants. - Section 5 presents a methodology that is designed at addressing the challenges in the industry today in achieving a transformative new state vision and concept of operations. - Conclusions and next steps of this research are provided in Section 6.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Automated Production of Optimization-Based Control Logics for Dynamic Facade Systems, with Experimental Application to Two-Zone External Venetian Blinds

The primary goal of this research is to devise a system that produces controllers for complex fenestration systems that perform nearly as well as Model Predictive Control but at a level of cost and implementation complexity that rivals simple heuristic controls. To this end, a cloud-based automated controller production system has been set up for a motorized external Venetian blind device, with a simple web interface that can be used by non-experts. The computation cost per controller is in the range of a few dollars, and the control logic is simple enough to be implemented on small and cheap distributed controllers. The web interface allows the user to specify some details of their particular building and window configuration, including orientation, latitude, interior geometries, and lighting and HVAC system parameters. Upon submittal, a cloud-based system configures the necessary files and commands, and then runs thousands of optimizations with them. Once the calculations are finished, the system produces a lookup table and interpolation-based controller scripts that can be used on a simple and cheap distributed controller. This paper describes the underlying models and optimization processes. It also describes the resulting control logics for two cases tested at Lawrence Berkeley National Laboratory’s Advanced Windows Testbed Facility: illuminance maximization subject to glare constraints; and lighting + HVAC energy minimization. The performance of the model-based controllers produced by the automated web-based system are compared to a heuristic ‘block beam’ controller in physical experiments at the Testbed. The experimental results are supplemented by simulation experiments with the same configuration as the Testbed. The results show the illuminance maximizing controller significantly outperforms the heuristic controller in terms of glare avoidance, and also outperforms it in terms of hours of daylight autonomy. The energy minimizing controller also outperforms the heuristic controller. This paper also discusses how the web-based system may be extended to consider other configurations, such as electrochromic windows and thermally massive HVAC systems. Potential roles for this type of system within the building design and construction industry are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Automating neutron resonances classification with Machine Learning [Slides]

Team reported the following accomplishments: the development of a Machine-Learning method to properly assign spins to neutron resonances (automated, general, reproducible); full integration with evaluated resonances in the Atlas (automation of new editions); training and optimization in synthetic data; validation and deployment to real experimental resonances. Future perspectives include exploration of other classifiers and hyper-parameter combinations, further validation with well-known nucleus (e.g. 235 U), and publication pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Using Systems Theoretic Process Analysis and Causal Analysis to Map and Manage Organizational Information to Enable Digitalization and Information Automation

The overarching goal of this Light Water Reactor Sustainability Program–supported research and development project is to provide planning tools and comprehensive guidance to utilities considering or undertaking full nuclear plant modernization. The results of this research will provide the nuclear industry with a comprehensive and usable solution, including guidance, lessons learned, methods, and planning tools. This research is currently working to provide guidance on digitalization and information automation to enable the evolution of data to information, insight, and action—thereby allowing utilities to operate safely and cost-competitively with all other electrical generation sources. Light Water Reactor Sustainability Program researchers have also recently started investigating how human and technology integration principles, information automation, and digitalization enable data evolution. These researchers are currently in the process of validating the use of System-Theoretic Process Analysis to define high-level safety constraints in the United States Nuclear Regulatory Commission’s problem identification and resolution process (i.e., a plant compliance information gathering activity). The next step in this research, which is described in the following sections of this report, is to map out data evolution in a use case to identify inefficiencies in another aspect of plant compliance information gathering and communication activities—event investigations and root cause analyses.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Automation and Quality Control for CMS Outer Tracker Module Assembly at Fermilab’s SiDet

Automation and quality control techniques for 2S and PS sensor modules for the CMS Outer Tracker for the HL-LHC era have been developed at the Silicon Detector facility at Fermilab. Examples include a laser scanning procedure to check manufacturing specifications for the 2S sensor modules, and an automated gluing process to accurately assemble PS sensor modules. Processes were verified and documented for the production stage of the Outer Tracker.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Development of Microreactor Automated Control System (MACS): Surrogate Plant-level Modeling and Control Algorithms Integration

This report discusses progress on the modeling and integration task as part of the development of Microreactor Automated Control System (MACS). What follows is a discussion of the software tools used to develop a surrogate microreactor plant-level model, MACS module development, and a summary of the findings from integration with a hardware-in-the-loop (HIL) setup at Idaho National Laboratory (INL). Oak Ridge National Laboratory worked with INL to understand available hardware at INL (such as existing control platforms). With this information and using a preliminary framework for MACS with software interfaces defined, surrogate models and initial automated control algorithms were created for integration into the hardware to provide a HIL demonstration platform for MACS. Available reduced order reactor models that leverage existing microreactor neutronics and thermal hydraulics models have been integrated in the surrogate plant-level model. The surrogate model has been evaluated for sensitivity of all parameters and the MACS software modules (including the surrogate model) have been demonstrated to interact with the hardware. The integration, however, pointed to the need for further improvements to the framework—and specifically improvements to the interfaces to ensure that the HIL simulator is capable of longer-term stable operation with MACS in the loop. These improvements will be the focus of future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Automated Luminaire Design and Local Manufacturing for Highly Efficient, Customized Lighting Solutions

In response to the growing demand for tailorable and energy-efficient lighting solutions, Eaton and partners Synopsys and Rensselaer Polytechnical Institute were funded to develop an automated luminaire design method that was capable of achieving best in class efficiency while being locally manufactured at an affordable cost. This program was broken into four key technology areas, automated optics design, additively manufactured optical material characterization, printed electronics, and additively manufactured mechanical components.

99 GENERAL AND MISCELLANEOUS↗