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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Establishing and Monitoring an Aseptic Workspace

When are aseptic operations necessary? In order to meet certain bioburden requirements, some components must undergo dry heat microbial reduction (DHMR) or other sterilizing procedures. If sensitive surfaces must be re-exposed after DHMR, this could compromise the bioburden levels. Recontaminating sterilized surfaces could be costly both in time by requiring repeated DHMR and risk to the hardware, which may not be compatible with repeated high temperature bakes. In order to prevent recontamination of the sensitive surfaces, an aseptic environment and sterile technique must be employed. Aseptic environments mean working in a space with almost no detectable bioburden in the air or on surfaces. Ideally, DHMR happens as late as possible to avoid requiring aseptic operations, as it can be considered a high-risk operations. Preparing the cleanroom for aseptic operations Establishing an ISO (International Organization for Standardization) class 5 space to minimize airborne particles. Maintain low bioburden in the cleanroom by using biocidal cleaners. Using multiple biocidal techniques decreases the likelihood of selecting for resistant microorganisms. 70% Isopropyl Alcohol (IPA) denatures the proteins in a microorganism (note: 70% IPA is better at killing microorganisms than 100% IPA) 7% hydrogen peroxide: damages DNA and proteins through oxygen radical damage. Ultraviolet-C (UV-C) lamps: causes crosslinking in DNA which prevents replication. Monitor cleanroom regularly for bioburden trending: Standard bioassay: Swab or wipe samples of cleanroom surfaces processed for colony forming unity (viable or spore selected); Rapid bioassay: Adenosine triphosphate (ATP) or Limulus amebocyte lysate (LAL) for a bioburden snapshot. High levels can signal an immediate re-cleaning before standard bioassay samples are taken. Airborne monitoring: Active (pulling air through a filter) or passive (particle fallout) for bioburden. Verify bioburden levels just before aseptic operation. Test hardware and cleanroom surfaces and air 3 days before the planned aseptic operation. Rapid bioburden just before aseptic operation to ensure room was not re-contaminated. Preparing personnel and tools: Personnel training. Everyone in the cleanroom: Standard cleanroom certification Everyone on the team: 1 day Planetary Protection overview. Aseptic operators only: Half-day aseptic operations training. Covers sterile garmenting/gloves, Sterile handling with a focus on contact transfer risk, tool/GSE preparation, and two-operator system for opening sterilized tools/components. Tool sterilization: All tools to be used during an aseptic operation need to be identified. Compatible tools are sterilized by DHMR or Autoclave. Double wrapped so that the exterior bag can be handled by a non-sterile operator, and the sterile. Tools that are not compatible with high heat do not come in contact with sensitive surfaces: either substitutes are found, or tools are isolated by wrapping in sterile foil. During an aseptic operation. Pre-task to make sure everyone understands the operations, who is handling what, and when the most critical surfaces will be exposed. Monitoring during the operation. Bioburden: active and passive airborne bioburden sampling, glove-tip dabs onto a plate after completion of operation (3 days for results). Particles: real time particle counter constantly running, with alarm for exceeding ISO 5 conditions.

aseptic processing↗
DOE OSTI2018

Ergonomic glovebox workspace layout tool and associated method of use

The present invention provides an elongate tool that aides in the placement of objects and machinery within a glovebox, such that the objects and machinery can be safely handled by a user. The tool includes a plurality of visual markings (in English units, metric units, other units, grooves, ridges, varying widths, etc.) that indicate distance from the user within the glovebox, optionally broken into placement preference zones that are color coded, grayscale coded, or the like.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Mixed Reality Training and Workspace Integration

Mixed reality technology is being used across Idaho National Laboratory (INL) to enhance training and workforce development. Projects involving mixed reality such as lockout/tagout , the Industrial Control System Laboratory , and a Department of Defense microreactor demonstration have benefited from these innovative approaches. Several key concerns exist with integrating this new technology, including ease of use, ease of setup, and maintenance through the lifecycle. These aspects need to be properly addressed to ensure that this technology remains attractive and useful to operations teams as well as engineers and researchers at INL. Primarily, this requires buy-in from information technology management, designing software/hardware-agnostic tools, and reducing friction with technology integration. To be successful, regular operational use of this new and unfamiliar technology will require straightforward user interfaces, simple setup processes, and feature-rich experiences that reduce the time needed to benefit from the technology. INL is improving its toolset to allow for a streamlined deployment and use of mixed reality technologies that are directly available to researchers and engineers.

99 GENERAL AND MISCELLANEOUS↗
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 OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2011

SAPHIRE 8 Volume 1 - Overview and Summary

The Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) is a software application developed for performing a complete probabilistic risk assessment (PRA) using a personal computer (PC) running the Microsoft Windows operating system. SAPHIRE Version 8 is funded by the U.S. Nuclear Regulatory Commission (NRC) and developed by the Idaho National Laboratory (INL). INL's primary role in this project is that of software developer and tester. However, INL also plays an important role in technology transfer by interfacing and supporting SAPHIRE users, who constitute a wide range of PRA practitioners from the NRC, national laboratories, the private sector, and foreign countries. SAPHIRE can be used to model a complex system’s response to initiating events and quantify associated consequential outcome frequencies. Specifically, for nuclear power plant applications, SAPHIRE 8 can identify important contributors to core damage (Level 1 PRA) and containment failure during a severe accident which leads to releases (Level 2 PRA). It can be used for a PRA where the reactor is at full power, low power, or at shutdown conditions. Furthermore, it can be used to analyze both internal and external initiating events and has special features for managing models such as flooding and fire. It can also be used in a limited manner to quantify risk in terms of release consequences to the public and environment (Level 3 PRA). In SAPHIRE 8, the act of creating a model has been separated from the analysis of that model in order to improve the quality of both the model (e.g., by avoiding inadvertent changes) and the analysis. Consequently, in SAPHIRE 8, the analysis of models is performed by using what are called Workspaces. Currently, there are Workspaces for three types of analyses: (1) the NRC’s Accident Sequence Precursor program, where the workspace is called “Events and Condition Assessment (ECA);” (2) the NRC’s Significance Determination Process (SDP); and (3) the General Analysis (GA) workspace. Workspaces are independent of each other and modifications or calculations made within one workspace will not affect another. In addition, each workspace has a user interface and reports tailored for their intended uses. This report provides an overview of the functions and features available in SAPHIRE 8 and presents general instructions for using the software. Since SAPHIRE 8 expands upon Version 7, new and improved features will be discussed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Vision-Guided Automation and Assistance

Robotic automation and assistance is an excellent way to increase efficiency and safety when working in a potentially hazardous workspace. However, many traditional automation approaches rely on the precise placement of items to be manipulated. The scope of such automated tasks is also limited to the manipulation of one type of object since there is no way to distinguish between different objects. The addition of a vision pipeline, where robot vision is used to localize and recognize objects, can allow the robot to perform more complex tasks in a variable workspace. In my vision pipeline, a depth camera is used to generate a 3D point cloud of the workspace. Objects to be manipulated are isolated in the point cloud by filtering out shared surfaces and using Euclidean cluster extraction to group clusters of points. The locations of the objects are then estimated from the point cloud of the isolated objects. The localization of objects with robot vision allows for variable workspaces, reducing the overhead associated with requiring all objects to be precisely placed in the workspace. For object recognition, deep learning is used to train a neural network that classifies objects from the raw point clouds. More complex tasks that require multiple objects to be manipulated differently can be achieved through the vision pipeline. Through its ability to visualize the workspace, robot vision greatly improves efficiency and safety of automated and assistive tasks.

42 ENGINEERING↗
DOE OSTI2016

Spatial and rotational quality assurance of 6DOF patient tracking systems

Purpose: External tracking systems used for patient positioning and motion monitoring during radiotherapy are now capable of detecting both translations and rotations. In this work, the authors develop a novel technique to evaluate the 6 degree of freedom 6(DOF) (translations and rotations) performance of external motion tracking systems. The authors apply this methodology to an infrared marker tracking system and two 3D optical surface mapping systems in a common tumor 6DOF workspace. Methods: An in-house designed and built 6DOF parallel kinematics robotic motion phantom was used to perform motions with sub-millimeter and subdegree accuracy in a 6DOF workspace. An infrared marker tracking system was first used to validate a calibration algorithm which associates the motion phantom coordinate frame to the camera frame. The 6DOF positions of the mobile robotic system in this space were then tracked and recorded independently by an optical surface tracking system after a cranial phantom was rigidly fixed to the moveable platform of the robotic stage. The calibration methodology was first employed, followed by a comprehensive 6DOF trajectory evaluation, which spanned a full range of positions and orientations in a 20 × 20 × 16 mm and 5° × 5° × 5° workspace. The intended input motions were compared to the calibrated 6DOF measured points. Results: The technique found the accuracy of the infrared (IR) marker tracking system to have maximal root-mean square error (RMSE) values of 0.18, 0.25, 0.07 mm, 0.05°, 0.05°, and 0.09° in left–right (LR), superior–inferior (SI), anterior–posterior (AP), pitch, roll, and yaw, respectively, comparing the intended 6DOF position and the measured position by the IR camera. Similarly, the 6DOF RSME discrepancy for the HD optical surface tracker yielded maximal values of 0.46, 0.60, 0.54 mm, 0.06°, 0.11°, and 0.08° in LR, SI, AP, pitch, roll, and yaw, respectively, over the same 6DOF evaluative workspace. An earlier generation 3D optical surface tracking unit was observed to have worse tracking capabilities than both the IR camera unit and the newer 3D surface tracking system with maximal RMSE of 0.69, 0.74, 0.47 mm, 0.28°, 0.19°, and 0.18°, in LR, SI, AP, pitch, roll, and yaw, respectively, in the same 6DOF evaluation space. Conclusions: The proposed technique was found to be effective at evaluating the performance of 6DOF patient tracking systems. All observed optical tracking systems were found to exhibit tracking capabilities at the sub-millimeter and subdegree level within a 6DOF workspace.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

A Framework for a Supervisory Expert System for Robotic Manipulators with Joint-Position Limits and Joint-Rate Limits

This report addresses the problem of path planning and control of robotic manipulators which have joint-position limits and joint-rate limits. The manipulators move autonomously and carry out variable tasks in a dynamic, unstructured and cluttered environment. The issue considered is whether the robotic manipulator can achieve all its tasks, and if it cannot, the objective is to identify the closest achievable goal. This problem is formalized and systematically solved for generic manipulators by using inverse kinematics and forward kinematics. Inverse kinematics are employed to define the subspace, workspace and constrained workspace, which are then used to identify when a task is not achievable. The closest achievable goal is obtained by determining weights for an optimal control redistribution scheme. These weights are quantified by using forward kinematics. Conditions leading to joint rate limits are identified, in particular it is established that all generic manipulators have singularities at the boundary of their workspace, while some have loci of singularities inside their workspace. Once the manipulator singularity is identified the command redistribution scheme is used to compute the closest achievable Cartesian velocities. Two examples are used to illustrate the use of the algorithm: A three link planar manipulator and the Unimation Puma 560. Implementation of the derived algorithm is effected by using a supervisory expert system to check whether the desired goal lies in the constrained workspace and if not, to evoke the redistribution scheme which determines the constraint relaxation between end effector position and orientation, and then computes optimal gains.

Mutambara, Arthur G. O.↗
NASA NTRSDate not supplied

A New Concurrent Engineering Tool for the New Mission Design Center at NASA Ames Research Center

The NASA Ames Mission Design Center (MDC) has undergone a significant transformation process in recent years, culminating in becoming a true Concurrent Engineering Center. The underlying goal of this transformation is to better serve the Ames PI community in a cost-effective and rapidly-responsive way, providing quality products pivotal to the decision-making proposal strategy of our center. The four pillars of the change focus on four areas: personnel, physical places, tools, and training. Personnel: the MDC switched from using a dedicated pool of engineers—mostly entry-level career—for a matrixed approach with more experienced Subject Matter Experts, usually involved in spaceflight projects. These SMEs get detailed to the MDC for the duration of the study on a part-time or full-time basis. Physical places: The physical aspect pertains mostly to the layout of the new Engineering building and the dedicated concurrent engineering layout to foster collaboration. The current plan includes a dedicated space for concurrent engineering sessions as well as dedicated project rooms for concept studies. Tool: the MDC team is developing a new in-house concurrent engineering tool to facilitate concurrent engineering sessions. The team leveraged the many lessons learned and insight gained from using the current concurrent engineering tool, in use since 2007. SMEs have been involved throughout its design and development, ensuring accuracy and providing validity to the implementation approach. This new tool is database-driven and allows a team to work concurrently on the same model of a mission concept. “Workspaces” are provided for each of the traditional spacecraft mission design disciplines, including cost accounting and systems engineering. There are multiple tiers of fidelity available within each workspace, which can be completed parametrically or independently from the other disciplines as needed or synchronously with the other disciplines. A “commit” step synchronizes a workspace with the mission database and identifies any potential conflicts, along with the user who entered the conflicting data. A Mission Summary workspace enables a Study Lead to run an effective CE session, displaying key graphics, workspace status information, and technical and monetary budget information. This new CE tool will be undergoing Beta Testing with its primary user base and a limited Parts database in the summer of 2020. The team plans to release the first fully operational version in December 2020. Training: working in early concept development and maturation for a space mission requires a unique mindset and being comfortable with uncertainties in an environment where requirements are not yet fully developed, and where changes and trade-offs happen at a high tempo. The MDC started a weekly training program to ensure a common knowledge base on how do develop and mature early concept studies, as well as how to operate in a concurrent engineering environment. In conclusion, the MDC transformation will provide the overall PI community focused on cost-effective small satellite scientific missions with a powerful tool to address and mature early concepts more effectively and efficiently.

David Mauro↗
DOE OSTI2014

Systems and methods for sensing occupancy

A computer implemented method for sensing occupancy of a workspace includes creating a difference image that represents luminance differences of pixels in past and current images of the workspace resulting from motion in the workspace, determining motion occurring in regions of the workspace based on the difference image, and altering a workspace environment based at least in part on the determined motion. The method also includes determining which pixels in the difference image represent persistent motion that can be ignored and determining which pixels representing motion in the difference image are invalid because the pixels are isolated from other pixels representing motion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HERO WEC V1 Upgrade - 2023 Laboratory Testing (processed data)

The following submission includes processed laboratory data from NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of MATLAB workspaces. This dataset was created using NREL's Large Amplitude Motion Platform (LAMP) and collected between August and September 2023. Included with this submission is a test log of all the processed data "HERO WEC LAMP test run log.xlsx" so that the user can easily find the data of interest. Additionally, more detailed descriptions of the type of data and how it was processed, or calculated, can be found in the document titled "Lamp Data Description.docx". The MATLAB workspaces can be visualized using the file "LAMP_Data_Viewer_Ver2.m/mlx". The user simply needs to upload the workspace of interest and run the file "LAMP_Data_Viewer_Ver2.m/mlx". Both the .m and .mlx file format has been provided depending on the user's preference. The MATLAB workspaces have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water sine wave profile (not run in drivetrain configuration) - Heave - Heave only sine wave profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) For those interested in the raw, unprocessed, data the authors have created a separate submission, linked below. This submission includes the raw TDMS files and associated files necessary to translate the data into either python or MATLAB formats. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
NASA NTRS1996

Optical Potential Field Mapping System

The present invention relates to an optical system for creating a potential field map of a bounded two dimensional region containing a goal location and an arbitrary number of obstacles. The potential field mapping system has an imaging device and a processor. Two image writing modes are used by the imaging device, electron deposition and electron depletion. Patterns written in electron deposition mode appear black and expand. Patterns written in electron depletion mode are sharp and appear white. The generated image represents a robot's workspace. The imaging device under processor control then writes a goal location in the work-space using the electron deposition mode. The black image of the goal expands in the workspace. The processor stores the generated images, and uses them to generate a feedback pattern. The feedback pattern is written in the workspace by the imaging device in the electron deposition mode to enhance the expansion of the original goal pattern. After the feedback pattern is written, an obstacle pattern is written by the imaging device in the electron depletion mode to represent the obstacles in the robot's workspace. The processor compares a stored image to a previously stored image to determine a change therebetween. When no change occurs, the processor averages the stored images to produce the potential field map.

Reid, Max B.↗
DOE OSTI2015

Development of Personalized Radiant Cooling System for an Office Room

The building industry nowadays is facing two major challenges increased concern for energy reduction and growing need for thermal comfort. These challenges have led many researchers to develop Radiant Cooling Systems that show a large potential for energy savings. This study aims to develop a personalized cooling system using the principle of radiant cooling integrated with conventional all-air system to achieve better thermal environment at the workspace. Personalized conditioning aims to create a microclimatic zone around a single workspace. In this way, the energy is deployed only where it is actually needed, and the individual s needs for thermal comfort are fulfilled. To study the effect of air temperature along with air temperature distribution for workspace, air temperature near the vicinity of the occupant has been obtained as a result of Computational Fluid Dynamics (CFD) simulation using FLUENT. The analysis showed that personalized radiant system improves thermal environment near the workspace and allows all-air systems to work at higher thermostat temperature without compromising the thermal comfort, which in turn reduces its energy consumption.

CFD↗
DOE OSTI2015

Mixed Initiative Visual Analytics Using Task-Driven Recommendations

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support tasks involved in discovery and sensemaking, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems, at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with such analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Researchers studying the sensemaking process have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present a candidate set of design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences on user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.

active data↗