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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Automated and Cooperative Vehicle Merging at Highway On-Ramps

Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. Here we present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Cooperative Game Approach to Optimal Merging Sequence and On-Ramp Merging Control of Connected and Automated Vehicles

Vehicle merging is one of the main causes of reduced traffic efficiency, increased risk of collision, and fuel consumption. Connected and automated vehicles (CAVs) can improve traffic efficiency, increase safety, and reduce the negative environmental impacts through effective communication and control. Therefore, to improve the traffic efficiency and reduce the fuel consumption in on-ramp scenarios, this paper addresses the global and optimal coordination of the CAVs in a merging zone. In this work, a cooperative multi-player game-based optimization framework and an algorithm are presented to coordinate vehicles and achieve minimum values for the global pay-off conditions. Fuel consumption, passenger comfort, and travel time within the merging control zone were used as the pay-off conditions. After analyzing the characteristics of the merging control zone and selecting the appropriate control decision duration, multi-player games were decomposed into multiple two-player games. An optimal merging strategy was, thereby, derived from a pay-off matrix, and minimum payoffs were predicted for a number of different potential strategies. The optimal trajectory corresponding to the predicted minimum payoffs was then utilized as the control law to coordinate the vehicles merging. The proposed control scheme derives an optimal merging sequence and an optimal trajectory for each vehicle. The effectiveness of the proposed model is validated through simulation. Finally, the proposed controller is compared with two alternative methods to demonstrate its potential to reduce fuel consumption and travel time and to improve passenger comfort and traffic efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Impact of Partial Penetrations of Connected and Automated Vehicles on Fuel Consumption and Traffic Flow

This paper addresses the problem of analyzing the effects of partial penetrations of optimally coordinated connected and automated vehicles (CAVs) on fuel consumption and travel time under low, medium, and heavy traffic volumes. We develop a microscopic simulation framework to enhance our understanding of the interactions between human-driven vehicles and CAVs in a merging on-ramp scenario. We show that fuel consumption is adversely affected for medium and high traffic while benefits are realized for travel time under the same traffic conditions. As a result, we also show that higher penetrations of CAVs contribute to more stable traffic patterns.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

SU-D-BRD-06: Creating a Safety Net for a Fully Automated, Script Driven Electronic Medical Record

Purpose: Demonstrate the effectiveness of in-house software in ensuring EMR workflow efficiency and safety. Methods: A web-based dashboard system (WBDS) was developed to monitor clinical workflow in real time using web technology (WAMP) through ODBC (Open Database Connectivity). Within Mosaiq (Elekta Inc), operational workflow is driven and indicated by Quality Check Lists (QCLs), which is triggered by automation software IQ Scripts (Elekta Inc); QCLs rely on user completion to propagate. The WBDS retrieves data directly from the Mosaig SQL database and tracks clinical events in real time. For example, the necessity of a physics initial chart check can be determined by screening all patients on treatment who have received their first fraction and who have not yet had their first chart check. Monitoring similar “real” events with our in-house software creates a safety net as its propagation does not rely on individual users input. Results: The WBDS monitors the following: patient care workflow (initial consult to end of treatment), daily treatment consistency (scheduling, technique, charges), physics chart checks (initial, EOT, weekly), new starts, missing treatments (>3 warning/>5 fractions, action required), and machine overrides. The WBDS can be launched from any web browser which allows the end user complete transparency and timely information. Since the creation of the dashboards, workflow interruptions due to accidental deletion or completion of QCLs were eliminated. Additionally, all physics chart checks were completed timely. Prompt notifications of treatment record inconsistency and machine overrides have decreased the amount of time between occurrence and execution of corrective action. Conclusion: Our clinical workflow relies primarily on QCLs and IQ Scripts; however, this functionality is not the panacea of safety and efficiency. The WBDS creates a more thorough system of checks to provide a safer and near error-less working environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-P-36: Automation of Linear Accelerator Star Shot Measurement with Advanced XML Scripting and Electronic Portal Imaging Device

Purpose: To verify a method used to automatically acquire jaw, MLC, collimator and couch star shots for a Varian TrueBeam linear accelerator utilizing Developer Mode and an Electronic Portal Imaging Device (EPID). Methods: An XML script was written to automate motion of the jaws, MLC, collimator and couch in TrueBeam Developer Mode (TBDM) to acquire star shot measurements. The XML script also dictates MV imaging parameters to facilitate automatic acquisition and recording of integrated EPID images. Since couch star shot measurements cannot be acquired using a combination of EPID and jaw/MLC collimation alone due to a fixed imager geometry, a method utilizing a 5mm wide steel ruler placed on the table and centered within a 15×15cm2 open field to produce a surrogate of the narrow field aperture was investigated. Four individual star shot measurements (X jaw, Y jaw, MLC and couch) were obtained using our proposed as well as traditional film-based method. Integrated EPID images and scanned measurement films were analyzed and compared. Results: Star shot (X jaw, Y jaw, MLC and couch) measurements were obtained in a single 5 minute delivery using the TBDM XML script method compared to 60 minutes for equivalent traditional film measurements. Analysis of the images and films demonstrated comparable isocentricity results, agreeing within 0.3mm of each other. Conclusion: The presented automatic approach of acquiring star shot measurements using TBDM and EPID has proven to be more efficient than the traditional film approach with equivalent results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Potential Energy Implications of Connected and Automated Vehicles: Exploring Key Leverage Points through Scenario Screening and Analysis

Connected and automated vehicle (CAV) technologies could transform the transportation system over the coming decades, but face vehicle and systems engineering challenges, as well as technological, economic, demographic, and regulatory issues. The authors have developed a system dynamics model for generating, analyzing, and screening self-consistent CAV adoption scenarios. Results can support selection of scenarios for subsequent computationally intensive study using higher-resolution models. The potential for and barriers to large-scale adoption of CAVs have been analyzed using preliminary quantitative data and qualitative understandings of system relationships among stakeholders across the breadth of these issues. Although they are based on preliminary data, the results map possibilities for achieving different levels of CAV adoption and system-wide fuel use and demonstrate the interplay of behavioral parameters such as how consumers value their time versus financial parameters such as operating cost. By identifying the range of possibilities, estimating the associated energy and transportation service outcomes, and facilitating screening of scenarios for more detailed analysis, this work could inform transportation planners, researchers, and regulators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

An automated workflow to screen alkene reductases using high-throughput thin layer chromatography

Background: Synthetic biology efforts often require high-throughput screening tools for enzyme engineering campaigns. While innovations in chromatographic and mass spectrometry-based techniques provide relevant structural information associated with enzyme activity, these approaches can require cost-intensive instrumentation and technical expertise not broadly available. Moreover, complex workflows and analysis time can significantly impact throughput. To this end, we develop an automated, 96-well screening platform based on thin layer chromatography (TLC) and use it to monitor in vitro activity of a geranylgeranyl reductase isolated from Sulfolobus acidocaldarius (SaGGR). Results: Unreduced SaGGR products are oxidized to their corresponding epoxide and applied to thin layer silica plates by acoustic printing. These derivatives are chromatographically separated based on the extent of epoxidation and are covalently ligated to a chromophore, allowing detection of enzyme variants with unique product distributions or enhanced reductase activity. Herein, we employ this workflow to examine farnesol reduction using a codon-saturation mutagenesis library at the Leu377 site of SaGGR. We show this TLC-based screen can distinguish between fourfold differences in enzyme activity for select mutants and validated those results by GC–MS. Conclusions: With appropriate quantitation methods, this workflow can be used to screen polyprenyl reductase activity and can be readily adapted to analyze broader catalyst libraries whose products are amenable to TLC analysis.

09 BIOMASS FUELS↗
DOE OSTI2021

EXPLAINABLE AND TRUSTWORTHY DIAGNOSTICS ACHIEVABLE THROUGH PROCESS-BASED AUTOMATED REASONING

An approach has been developed that incorporates domain knowledge to obtain a more explainable and trustworthy equipment health monitoring diagnosis than might otherwise be obtained from a purely data-driven method. Physics-based models serve to constrain the realizable solution space and render a more trusted diagnosis. An automated reasoning algorithm performs backward chaining to infer a diagnosis that is consistent with logic statements that have been evaluated as true. This diagnosis is made explainable to an operator by providing the forward chaining path that elucidates for inspection and validity testing those truths implied by the diagnosis.

automated reasoning↗
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define "explicit" input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Using the Beopt Automated Residential Simulation Test Suite to Enable Comparative Analysis Between Energy Simulation Engines (Preprint)

Verification and validation are crucial software quality control procedures to follow when developing and implementing models. This is particularly important because a variety of stakeholders rely on accurate predictions from building simulation programs. This study uses the BEopt Automated Residential Simulation Test Suite (BARTS) to facilitate comparison of two energy simulation engines across various building components and includes building models that isolate the impacts of specific components on annual energy consumption. As a case study, BARTS has been used to identify important discrepancies between the engines for several components of the building models. These discrepancies are caused by differences in the algorithms used by the engines or coding errors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Automated metadata--final project report

This report summarizes the work of the Automated Metadata, Provenance Cataloging, and Navigable Interfaces: Ensuring the Usefulness of Extreme-Scale Data Project (MPO Project) funded by the United States Department of Energy (DOE), Offices of Advanced Scientific Computing Research and Fusion Energy Sciences. Initially funded for three years starting in 2012, it was extended for 6 months with additional funding. The project was a collaboration between scientists at General Atomics, Lawrence Berkley National Laboratory (LBNL), and Massachusetts Institute of Technology (MIT). The group leveraged existing computer science technology where possible, and extended or created new capabilities where required. The MPO project was able to successfully create a suite of software tools that can be used by a scientific community to automatically document their scientific workflows. These tools were integrated into workflows for fusion energy and climate research illustrating the general applicability of the project’s toolkit. Feedback was very positive on the project’s toolkit and the value of such automatic workflow documentation to the scientific endeavor.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Energy Implications of Current Travel and the Adoption of Automated Vehicles

Current travel patterns and energy usage could be dramatically disrupted by new vehicle technologies, specifically in the case of automated vehicle (AV) technology. AV development is rapidly progressing and some experts anticipate it will be widely available to the public within 10 to 30 years (Litman and Litman 2018; Bansal and Kockelman 2017). This transformation in transportation could have a significant impact on the way people travel and potentially on overall energy consumption. AV adoption could have a wide range of potential energy implications, depending on their usage and the efficiency of the AVs. To better understand these implications, a random survey of more than 1,000 adults living in the continental United States was conducted for the National Renewable Energy Laboratory (NREL) using the Opinion Research Corporation (ORC) survey methodology (See A.1). While other survey efforts at the time of the study broadly investigated the barriers to acceptance of AVs, this study uniquely investigated how the technology will affect driving and commuting habits. The results were analyzed to better understand which groups of people will likely adopt AV technology first, how respondents currently travel, and how respondents may change their travel patterns if AVs are widely adopted.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units

Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units

Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

DC-AC Tool: Fully Automating the Acquisition of AC Power Flow Solution

DC2AC is an automated tool for achieving a converged AC power flow solution from any dispatch, e.g., determined using DC power flow model based optimal power flow. The entire process is free of human interference. It is usually encountered in practice that, even with a DC power flow solution, acquiring the solved AC power flow solution, if exists, sometimes could be a challenging task, especially during the planning stage. It is also difficult to distinguish the unsolvable cases from diverging iterations. Manual adjustments to approach the desired power flow condition has been largely relied on in the past using lots of engineering heuristics. This tool provides a systematic way to first achieve a solvable AC power flow case by modifying the power flow condition, and then try to track the AC power flow solution while gradually removing the adopted changes. If all adopted changes can be completely removed, then the original AC power flow solution is obtained. Otherwise, insights for actionable controls are derived to help operation and planning. Currently, this tool has been implemented in Python using SIEMENS PTI PSS/E as power flow solver, where only adjusting generator terminal voltage set point is considered as an available means to try to turn an unsolved power flow to a solved one. In future, more means should be considered, including the operation of tap-changing transformers, switched shunts and redispatch of active power.

24 POWER TRANSMISSION AND DISTRIBUTION↗