SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The CRADA objective is to 1) Generalize web tools for data analytics and machine learning, and 2) Develop and test a smart-alarm system at Jefferson Lab.
EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.
EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.
As new loads such as rooftop photovoltaics, electric vehicles and other distributed energy resources become commonplace on the distribution grid, the stress on already aging assets begins to escalate. This increased loading and changing dynamics can exacerbate failure rates. While traditional monitoring efforts focus on transmission and generation assets, utilities are now beginning to pay close attention to distribution assets in order to increase reliability indices and reduce cost from unexpected outages. This research develops low-cost and scalable methods to monitoring the health of a critical distribution grid asset: the service transformer. Existing methods in literature are either invasive and thus difficult to implement or require the device to be tested offline in an expensive lab setting. Data from the ubiquitous smart meter as well as a novel Bluetooth based transformer monitor are leveraged to automatically notify the utility of deteriorating or damaged transformers. Voltage, temperature, and vibration are some of the signals measured and analyzed by the proposed algorithms to predict transformer failures. Furthermore, these algorithms are designed to keep the implementation and processing costs low by taking advantage of edge computing where possible.
The "Solar SEED Smart Controller is a charge control device to condition solar power to load voltage with or without Battery (also called a "DC Power Platform") invented by David Gibbs and T4D Labs Inc. T4D Inc. and the SEED Smart Controller invention were selected for American Made Challenges Solar Prize, which includes voucher for NREL to provide technical assistance.
This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]
A lack of highly reliable, full state-space awareness of roadway situations is the current bottleneck for the incremental introduction of smart infrastructure control. NREL's Infrastructure Perception and Control (IPC) lab applies advanced sensing and computation controls to the coordinated movement of vehicles on the road as well as people in large facilities and has produced field test results from a Colorado Springs intersection. In this presentation, NREL discusses the state of smart infrastructure control and opportunities for partnership.
The Smart Highway project is a six mile, limited access roadway being built between Interstate 81 and Blacksburg, Virginia. The initial construction segment will be two miles long and is designed to serve as a test bed and test track for Intelligent Transportation Systems (ITS) research. The Center for Transportation Research (CTR) at Virginia Tech is developing three evaluation tools for its ITS research including DYNAVIMTS (a software framework), and the FLASH Lab (a 1/15th scale model highway and vehicle system). The Smart Highway rounds out the Center's evaluation methodology by allowing full scale operational tests, evaluations, and research under both experimental and conventional traffic conditions. Currently under development is a concept for a fully automated highway using a 'Cooperative Infrastructure Managed System' which involves ultra wide band communication beacons installed in the infrastructure with appropriate sensors, receivers and processors on board the vehicles. The project is part of the research program funded by the National Automated Highway System Consortium. The CTR hopes to develop the automated concept to prototype status by 1997. Other smart transportation and smart engineering concepts are proposed. This presentation will address the goals and objectives of the Smart Highway project, overview its status and importance to the region, and identify some of the transportation technology now under development and planned in the future.
The goal of the project was to develop data to be used for modeling the dynamic characteristics of smart inverters. The inverters were tested in both a lab setting, and a subset was deployed in residential homes for longer term data collection during real-world use.
The U.S. Department of Energy (DOE) Electric Vehicles at Scale Laboratory Consortium (EVs@Scale Lab Consortium) is accelerating research to support the establishment of a secure and scalable national network of charging infrastructure. Critical to this effort is an understanding of the potential grid impacts of EV charging and possible smart charge management (SCM) or vehicle-grid integration (VGI) capabilities that could mitigate these impacts. The EVs@Scale SCM/VGI Pillar is analyzing the impacts of EV charging and developing and demonstrating the capabilities of both SCM and VGI with many different vehicle use cases and grid scenarios. This deep dive discussion of the project encompasses the progress and future plans for the analysis components of the FUSE (Flexible charging to Unify the grid and transportation Sectors for Evs at scale) project.
Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.
Human exploration off planet is severely limited by the cost of launching materials into space and re-supply. Thus materials brought from earth must be light, stable and reliable at destination. Using traditional approaches a lunar or Mars base would require either transporting a hefty store of metals or heavy manufacturing equipment and construction materials for in situ extraction; both would severely limit any other mission objectives. Long-term human space presence requires periodic replenishment, adding a massive cost overhead. Even robotic missions often sacrifice science goals for heavy radiation and thermal protection. Biology has the potential to solve these problems because it can replicate and repair itself, and do a wide variety of chemical reactions including making food, fuel and materials. Synthetic biology can greatly enhance and expand life's evolved repertoire. Using natural and synthetically altered organisms as the feedstock for additive manufacturing could one day make possible the dream of producing bespoke tools, food, smart fabrics and even replacement organs on demand. To this end our lab has produced a proof-of-concept bioprinter with nearly one-cell resolution. Genetically engineering yeast cells to secrete bioproducts subsequent to printing allows the potential to make biomaterials with a fine microstructure. Imagine a production system that, at a few micron scale resolution, can add mollusk shell for compressive strength per unit mass, spider silk or collagen for tensile strength per unit mass, and potentially biologically-deposited wires. Now imagine what new products can be enabled by such a technology, on earth or beyond
Human exploration off planet is severely limited by the cost of launching materials into space and re-supply. Thus materials brought from earth must be light, stable and reliable at destination. Using traditional approaches a lunar or Mars base would require either transporting a hefty store of metals or heavy manufacturing equipment and construction materials for in situ extraction; both would severely limit any other mission objectives. Long-term human space presence requires periodic replenishment, adding a massive cost overhead. Even robotic missions often sacrifice science goals for heavy radiation and thermal protection. Biology has the potential to solve these problems because it can replicate and repair itself, and do a wide variety of chemical reactions including making food, fuel and materials. Synthetic biology can greatly enhance and expand life's evolved repertoire. Using natural and synthetically altered organisms as the feedstock for additive manufacturing could one day make possible the dream of producing bespoke tools, food, smart fabrics and even replacement organs on demand. To this end our lab has produced a proof-of-concept bioprinter with nearly one-cell resolution. Genetically engineering yeast cells to secrete bioproducts subsequent to printing allows the potential to make biomaterials with a fine microstructure. Imagine a production system that, at a few micron scale resolution, can add mollusk shell for compressive strength per unit mass, spider silk or collagen for tensile strength per unit mass, and potentially biologically-deposited wires. Now imagine what new products can be enabled by such a technology, on earth or beyond.
We present the initial results of a proof-of-concept 'smart alarm' for the Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify and diagnose unusual machine states is needed. Our approach leverages a trained neural network capable of alerting operators (a) when an anomalous condition exists in the beamline and (b) identifying the element setting that is the root cause. The tool is based on an inverse model that maps beamline readings (diagnostic readbacks) to settings (beamline attributes operators can modify). The model takes as input readings from the machine and computes machine settings which are compared to control setpoints. Instances where predictions differ from setpoints by a user-defined threshold are flagged as anomalous. Given data corresponding to 354 anomalous injector configurations, the model can narrow the root cause of an anomalous condition to three potential candidates with 94.6% accuracy. Furthermore, compared to the current method of identifying anomalous conditions which raises an alarm when machine parameters drift outside their normal tolerances, the data-driven model can identify 83% more anomalous conditions.
This work concerns the development of environmentally friendly encapsulation technology, specifically designed to incorporate corrosion indicators, inhibitors, and self-healing agents into a coating, in such a way that the delivery of the indicators and inhibitors is triggered by the corrosion process, and the delivery of self-healing agents is triggered by mechanical damage to the coating. Encapsulation of the active corrosion control ingredients allows the incorporation of desired autonomous corrosion control functions such as: early corrosion detection, hidden corrosion detection, corrosion inhibition, and self-healing of mechanical damage into a coating. The technology offers the versatility needed to include one or several corrosion control functions into the same coating.The development of the encapsulation technology has progressed from the initial proof-of-concept work, in which a corrosion indicator was encapsulated into an oil-core (hydrophobic) microcapsule and shown to be delivered autonomously, under simulated corrosion conditions, to a sophisticated portfolio of micro carriers (organic, inorganic, and hybrid) that can be used to deliver a wide range of active corrosion ingredients at a rate that can be adjusted to offer immediate as well as long-term corrosion control. The micro carriers have been incorporated into different coating formulas to test and optimize the autonomous corrosion detection, inhibition, and self-healing functions of the coatings. This paper provides an overview of progress made to date and highlights recent technical developments, such as improved corrosion detection sensitivity, inhibitor test results in various types of coatings, and highly effective self-healing coatings based on green chemistry. The NASA Kennedy Space Centers Corrosion Technology Lab at the Kennedy Space Center in Florida, U.S.A. has been developing multifunctional smart coatings based on the microencapsulation of environmentally friendly corrosion indicators, inhibitors and self-healing agents. This allows the incorporation of autonomous corrosion control functionalities, such as corrosion detection and inhibition as well as the self-healing of mechanical damage, into coatings. This paper presents technical details on the characterization of inhibitor-containing particles and their corrosion inhibitive effects using electrochemical and mass loss methods.Three organic environmentally friendly corrosion inhibitors were encapsulated in organic microparticles that are compatible with desired coatings. The release of the inhibitors from the microparticles in basic solution was studied. Fast release, for immediate corrosion protection, as well as long-term release for continued protection, was observed.The inhibition efficacy of the inhibitors, incorporated directly and in microparticles, on carbon steel was evaluated. Polarization curves and mass loss measurements showed that, in the case of 2MBT, its corrosion inhibition effectiveness was greater when it was delivered from microparticles.
The purpose of the Sandia CIO Cloud Strategy is to establish the strategic direction for the adoption of cloud services and technologies as the prevailing IT solution for Sandia National Laboratories. Sandia’s Chief Information Officer (CIO) will champion unified, site-wide adoption of cloud and will amplify business and mission impacts across the Labs. Sandia’s CIO Cloud Strategy aligns to the Federal Cloud Computing Strategy1 (Cloud Smart) and the Sandia Management and Operating Contract (Prime Contract).
Fossil power generation systems are increasingly vulnerable to attack from both cybercriminals as well as internal threats. These vulnerabilities demand that emerging technologies such as blockchains be utilized to secure the data involved in the information flows within the Supervisory Control and Data Acquisition (SCADA) systems of the fossil power generation plants. The publicly accessible blockchain protocols, although secure, are visible to everyone. Even private blockchains currently are unable to support different levels of access to different participants, which is a critical requirement for the existing SCADA systems running the power plants. In light of the above, novel blockchain protocols that are specifically adapted to fossil power generation environments need to be developed in order to achieve the goal of cybersecure sensor networks. In this work, we address this question by creating a novel blockchain technology, namely smart private ledger, for cybersecure communication within the fossil power generation systems. A lab-scale sensor network consisting of strain and temperature sensors is constructed to develop the ledger. The technology has hierarchical access control which is compatible with the existing SCADA systems in fossil power plants. The sensor data is used with cryptographic digital signatures and secret sharing protocols within the nodes of the blockchain technology. The research results will lead to cybersecurity for machine-to-machine interactions, infrastructure for secure data logging for sensors, decentralized data storage, and second-layer technologies for high volume machine-to-machine interactions in the power plants. The work aims to largely address the concerns for the security of distributed sensor networks in such systems that can be compromised by insider threats and by cybercriminals. The research has led to the training of the next generation of engineers and scientists in the important areas of sensor engineering and blockchain technology.
As more stringent building energy codes and better insulation products combine to yield better performing walls, window efficiency is coming into sharper focus. The U.S. Department of Energy has supported several projects through its national laboratories to improve the thermal performance of windows. At Pacific Northwest National Laboratory (PNNL) in Richland, Washington, the PNNL Lab Homes, two fully monitored identical side-by-side manufactured homes, have been used to test the performance of several window improvements including triple-pane windows, storm windows with low-emissivity (low-e) coatings, smart automated interior insulated shades, and exterior shading products. Findings will be presented on these studies, along with impacts. For example, PNNL’s Lab Home research on low-e storm windows has helped to support a new ENERGY STAR certification, industry standards, and utility incentives. Preliminary findings will also be presented on current Lab Homes experiments focusing on exterior shades. The session will also present findings from related field studies and a market assessment of emerging high-efficiency windows technologies and discuss some of PNNL’s planned field studies that will focus on validating the benefits and costs of the emerging thin triple-pane high-R window.