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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluation of Hands-On Cybersecurity Skills Development

Cybersecurity is a relatively new and quickly advancing industry, though there is a deficit in industry professionals throughout the world. This deficit is a result of the inability of education to keep up with the continuously changing cyber-threat landscape. A lack of effective, accessible, hands-on training platforms is a barrier for students and professionals who seek to gain the skills and abilities necessary to enter the workforce. Additionally, the lack of adequate training makes it difficult for industry experts to upskill or stay up to date on new advancements within the field of cybersecurity. The goal of this cohort is to discover what makes an effective or ineffective training platform, what platforms are currently available, and to determine the next steps for hands-on learning. Hands-on experience is necessary to create workforce ready professionals, yet it is underutilized by students and professionals of cybersecurity due to costs, availability, and ease of use. This cohort has uncovered hands-on training solutions, identified many effective training solutions, and explored potential improvement to cybersecurity training. These findings resulted in the cohort moving beyond research to test the resources that currently exist or are in development, and to apply that knowledge to discovering and developing engaging hands-on training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗
DOE OSTI2016

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Multiple Benefits of Industrial Energy Efficiency – Lessons Learned and New Initiatives

Past research has shown that energy efficiency implementation in manufacturing can yield additional, quantifiable benefits in areas such as maintenance, production and environmental performance. However, these types of benefits don’t often get identified or estimated during energy efficiency assessments on industrial plants or systems. In addition, these types of benefits are often omitted from conventional performance metrics, leading to overly modest payback calculations and an imperfect understanding of the impact of energy efficiency in manufacturing. If the non-energy related or multiple benefits of energy efficiency measures were to be estimated with good certainty in industrial energy assessments, the true magnitude of energy efficiency measures could be understood, leading to better and more accurate return on investment estimates for energy efficiency projects. According to the IEA report “Capturing the Multiple Benefits of Energy Efficiency (2014),” the monetary value of non-energy benefits stemming from industrial energy efficiency implementation could be in the range of 40% to 50% of the value of energy savings per measure, which could lower energy-efficiency project paybacks by more than half. In order to integrate the potential multiple benefits of energy-efficiency investments in energy efficiency assessments, these benefits need to be identified and quantified during energy efficiency assessments. To help achieve this an effort is underway to develop a training platform to enable companies and individuals who perform energy assessments to include multiple benefits of energy efficiency in project evaluations. This platform will include tools to analyze energy-saving projects, training workshops, and communications techniques and materials to provide the knowledge base to effectively integrate multiple benefits in the investment evaluations of energy efficiency measures. By integrating multiple benefit analyses in the energy assessments and assessment reports it is expected that the business case for energy efficiency will be bolstered leading to greater implementation of energy efficiency projects in manufacturing. This paper will discuss previous research showing the multiple benefits of energy efficiency in manufacturing and the analytical and communications framework that will enable the integration of multiple benefits in energy efficiency assessments.

Lung, Bruce↗
DOE OSTI2019

INL CyberStrike

Explore the source record for details and available documents.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Virtual Environment Platform for OT/IT Training Enhancement

TRADITIONAL TECHNIQUES OT/IT Concepts Operational Technology (OT) and Information Technology (IT) concepts can often be difficult to visualize Teaching Methods Traditional teaching methods lack the intuitive & immersive aspects of hands-on activities Caveat: Unless taught by Team B! Physical Limitations Digital Twins require existing systems/hardware to mirror UPDATED TECHNIQUES Virtual Environment By making use of a virtual environment, we can represent abstract concepts in a more approachable and digestible way Increased Engagement Students are more engaged with the activities and are more likely to retain the information they are given. New/Emerging Technologies As the system is currently growing and developing, the technologies in use, as well as those represented by the system, stay up-to-date.

Deroller, Nicholas F.↗
DOE OSTI2024

Accessible Training and Shared Capitalization Platforms for Low-Income Solar Finance

From March 2020 through November 2023, the University of New Hampshire Carsey Center for Impact Finance and its partners worked to create accessible training programs and shared capitalization platforms to enable community finance institutions – such as credit unions, community banks, and Community Development Financial Institutions (“CDFI”s) – to expand their engagement in solar finance in low-income communities.

14 SOLAR ENERGY↗
DOE OSTI2018

Integration of Solar Training into Allied Professional Development Platforms - Final Technical Report

The Interstate Renewable Energy Council (IREC) designed the “Integration of Solar Training into Allied Professional Development Platforms” project to surmount barriers to the expansion of solar technology caused by gaps in knowledge impacting on-the-job performance of allied professionals. At project start, questionnaires deployed nationally indicated that only 9% of firefighters felt certain they could shut down a PV system to the extent possible, and only a third of code officials thought they could even discern the difference between a PV and solar thermal system. Therefore, the project objective was to measurably and significantly increase the number of code officials and fire service professionals with solar knowledge directly applicable to their job function. IREC identified specific gaps in current knowledge, and integrated research-proven adult learning principles and technology solutions to enable a cost-effect but impactful reach. The project design ensured that solar training is a continuing component of the targeted sector professional development toolkit. To this end, the key partners recruited and cultivated were existing, trusted training partners in the targeted sectors: International Code Council Solar Rating and Certification Corporation (ICC-SRCC), International Association of Electrical Inspectors (IAEI), International Association of Fire Fighters (IAFF) and National Association of State Fire Marshals (NASFM). During the project, IREC held 79 courses in 25 states that trained 4,156 people, offered a PV in the 2017 National Electric Code webinar attended by 787 people, developed on-line training for code officials with 2,298 on-line learners, and developed an on-line training for firefighters with 4,930 on-line learners. In addition, the project participated in 28 conferences and reached over 537,000 professionals with solar information.

14 SOLAR ENERGY↗
DOE OSTI2024

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

DOE Hybrid and Electric Vehicle Test Platform

Based on the contract NT-42790 to the Department of Energy, “Plug-in Hybrid Ethanol Research Platform”, Advanced Vehicle Research Center (AVRC) Virginia has successfully developed the phase I electric drive train research platform which has been named as Laboratory Rapid Application Testbed (LabRAT). In phase II, LabRAT is to be upgraded into plug-in hybrid research platform, which will be capable of testing power systems for electric vehicles, and plug-in hybrid electric vehicles running on conventional as well as alternative fuels. LabRAT is configured as a rolling testbed with plentiful space for installing various component configurations. Component connections are modularized for flexibility and are easily replaced for testing various mechanisms. LabRAT is designed and built as a full functional vehicle chassis with a steering system, brake system and four wheel suspension. The rear drive axle offers maximum flexibility with a quickly changeable gear ratio final drive to accommodate different motor speed requirements. The electric drive system includes an electric motor which is mechanically connected to the rear axle through an integrated speed/torque sensor. Initially, a 100 kW UQM motor and corresponding UQM motor controller is used which can be easily replaced with another motor/controller combination. A lithium iron phosphate (LiFePO4) battery pack is installed, which consists of 108 cells of 100 AH capacity, giving the total energy capacity of 32.5 kWh. Correspondingly, a fully functional battery management system (BMS) is installed to perform battery cell operation monitoring, cell voltage balancing, and reporting battery real time operating parameters to vehicle controller. An advanced vehicle controller ECU is installed for controlling the drive train. The vehicle controller ECU receives traction or braking torque command from driver through accelerator and brake pedal position sensors and battery operating signals from the BMS through CAN BUS, and then generates motor torque command (traction or braking) to the motor controller based on the control algorithm software embedded in the vehicle controller ECU. The vehicle controller ECU is a re-programmable electronic control unit. Any control algorithm software developed can be easily downloaded to vehicle controller ECU to test any newly developed control strategy. The flexibility of the control system significantly enhances the practical applicability of the LabRAT. A new test methodology has been developed for the LabRAT simulating any vehicles running on road with different weights from compact passenger car to light duty truck on an AC or eddy current dynamometers without much effort for modification of the system. LabRAT is equipped with a fully functional data acquisition system supplied by CyberMetrix. The measurement points along the drive train are DC electric power between battery pack and motor controller input, AC electric power between motor controller and electric motor, mechanical power between motor and rear axle. The data acquisition system is designed with more capability than current requirements in order to meet the requirements for phase II.

33 ADVANCED PROPULSION SYSTEMS↗