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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Basic Research Needs for Transformative Manufacturing (Brochure)

Manufacturing is central to the nation’s prosperity and security. Manufacturing currently represents about 12% of the gross domestic product, provides nearly 13 million jobs, and accounts for about 25% of energy use. The nation’s economy relies heavily on wide-ranging manufacturing sectors - all of which share common challenges including data issues, lack of physics and chemistry-based models across scales, and resource constraints in a global environment. Furthermore, there are many hurdles that must be overcome to move basic science innovations to market. Addressing broad-ranging challenges demands a basic-science strategy that underpins applied research activities. This strategy would accelerate innovation and transform manufacturing. A Basic Research Needs workshop for Transformative Manufacturing was held in March 2020. The focus of the workshop was to identify the basic science research priorities that could accelerate innovation to transform manufacturing in the future. This was the first workshop of its kind to examine how basic energy science can drive manufacturing forward and innovate new ways to manufacture goods. Five Priority Research Directions were identified that address these science challenges: (1) innovative synthetic approaches to enable scalable assembly of matter, (2) computational methods and theoretical models to transform how manufacturing processes are controlled, (3) new characterization tools that can handle the necessary complexity, scales, and processing speeds to meet manufacturing needs, (4) new science to address opportunities relevant to sustainable and energy-efficient manufacturing, and (5) foundational approaches to co-design of materials, process, and products.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Basic Research Needs for Transformative Manufacturing

This report is based on a Basic Research Needs workshop for Transformative Manufacturing, which was held March 9 - 11, 2020. The focus of the workshop was to identify the basic science research priorities that could accelerate innovation to transform manufacturing in the future. This was the first workshop of its kind to examine how basic energy science can drive manufacturing forward and innovate new ways to manufacture goods. Five Priority Research Directions were identified that address these science challenges: (1) innovative synthetic approaches to enable scalable assembly of matter, (2) computational methods and theoretical models to transform how manufacturing processes are controlled, (3) new characterization tools that can handle the necessary complexity, scales, and processing speeds to meet manufacturing needs, (4) new science to address opportunities relevant to sustainable and energy efficient manufacturing, and (5) foundational approaches to co-design of materials, process, and products.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Malware Analysis Report for SolarWinds.Orion.Core.BusinessLayer.dll

SolarWinds Orion is a software suite designed to provide “single pane of glass” monitoring of network devices and applications. On December 13 2020, FireEye revealed that SolarWinds Orion was the victim of a supply chain attack and that the SolarWinds signed file SolarWinds.Orion.Core.BusinessLayer.dll contained a backdoor. SolarWinds deployed the back-doored patch to customers beginning in March 2020. FireEye first identified this backdoor and labeled it SUNBURST. The backdoor is composed of roughly 4,000 lines1 of lightly obfuscated .NET within the SolarWinds.Orion.Core.BusinessLayer.OrionImprovementBusinessLayer class of the malicious dll. The backdoor’s capabilities include: the ability to detect and potentially disable antivirus and forensics tools, basic command and control via DNS, and more advanced command and control via HTTP.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

High-throughput chemical imaging for optimizing biofuel synthesis using synthetic biology (Final Technical Report)

Fatty acids can be produced biosynthetically in microbes and these compounds can serve as precursors to biodiesels and other high value oleochemicals. However, progress on engineering fatty acid biosynthesis, and biofuel synthesis more generally, has been hindered by current quantification methods that are either indirect or not amenable to high-throughput or single-cell resolution screening. In this project, we assembled an interdisciplinary team with complimentary expertise in synthetic biology and microscopy, metabolic engineering, and chemical imaging to address these challenges. We used chemical imaging to directly measure lipid biosynthesis in Escherichia coli engineered to produce fatty acids, obtaining detailed single-cell resolution measurements. We deployed stimulated Raman scattering (SRS) microscopy in concert with multiplexed genome engineering and gene circuit design strategies from synthetic biology to optimize production of fatty acids. These results provided novel insight into cell-to-cell heterogeneity present in biofuel production strains. In addition, we introduced new chemical imaging methods which are label-free and do not require fluorescent reporters. These efforts were complemented by other studied developing foundational tools for regulation and control, which offer excellent potential for advancing researchers’ ability to rapidly design, build, and test strains for enhanced biofuel synthesis.

09 BIOMASS FUELS↗
DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems

This project advances a unified, open-source framework for the optimal co-design of thermal, electrical, and control systems in grid-interactive efficient districts (GEDs). As communities integrate growing levels of distributed energy resources, traditional approaches that model thermal and electrical networks independently lead to reduced efficiency, limited flexibility, and missed opportunities for coordinated operation. To address these challenges, the research team developed a comprehensive suite of physics-based models, control algorithms, and software tools that enable holistic simulation, optimization, and demonstration of district-scale energy systems.

14 SOLAR ENERGY↗
DOE OSTI2024

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Energy & Fuels↗
DOE OSTI2020

Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance

Acoustic metamaterials have proven to be a versatile tool for the precise control and manipulation of sound waves. One of the promising designs of acoustic metamaterials employ the arrays of bubbles and find applications for soundproofing, blast mitigation, and many others. An obvious advantage of bubble-based metamaterials is their ability to be relatively thin while absorbing low-frequency sound waves. The vast majority of theories developed to predict resonant behavior of bubble-based metamaterials capitalize on Minnaert frequency. Here, we propose a novel theoretical approach to characterize bubble-based metamaterials that are based on our previous findings for a single bubble trapped in circular cavity modeled as a thin clamped plate. We obtain analytical expressions for resonant frequencies of bubble metascreens using self-consistent approximation. Two geometry factors, distance between bubble centers and distance between bubble center and interface of acoustic impedance change, are taken into account. We demonstrate the existence of multiple bandgaps and possibility of switching between them via adjustment of geometry parameters and reflector properties.

Gritsenko, Dmitry (ORCID:0000000248181512)↗
DOE OSTI2017

Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint

A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

25 ENERGY STORAGE↗
DOE OSTI2020

Hardware-in-the-loop Testing of Signal Temporal Logic Frequency Control in a Microgrid

This paper presents a hardware-in-the-loop (HITL) testing and lessons learned for a signal temporal logic (STL) frequency control in a microgrid setting. In contrast to most, if not all, traditional control mechanisms, the STL control allows for including temporal constraints in the control formulation to handle sophisticated specifications from grid codes and protection relays. This feature provides richer descriptions of control specifications to address both magnitude and time simultaneously, hence a valuable tool for power system operation and control. An ultra-low voltage (24V) HITL microgrid platform consisting of two type-3 wind turbine generators and one diesel generator is used to test the proposed STL frequency control. In experimental case studies, we demonstrate that the HITL frequency responses closely match the simulated ones and strictly satisfy the designed STL control specifications.

Ferrari Maglia, Max↗
DOE OSTI2020

Power system model parameter conditioning tool

A power system model parameter conditioning tool including a server control processor in communication with phasor measurement unit monitored data records of multiple disturbance events, a model calibration unit providing event screening, power system model simulation, and simultaneous tuning of model parameters. The model calibration performing a simulation using default model parameters, the processor comparing the simulation results to the monitored data. If the prediction is within threshold, then terminating conditioning; else performing parameter identifiability analysis to determine differing effects of various model parameters on power system model accuracy, selecting a parameter set causing a degradation in power system model prediction, and updating the default model parameters corresponding to members of the parameter set with values selected to reduce the degradation. A method and a non-transitory computer readable medium are also disclosed.

Baone, Chaitanya Ashok↗
DOE OSTI2016

Development of Inspection Tools for the AY-102 Double-Shell Tank at the Hanford DOE Site - 16383

Florida International University (FIU) has been developing inspection tools to assist in identifying the location of the leak in the primary tank of AY-102. This effort has led to the development of a magnetic miniature rover and a pneumatic pipe crawler. The rover will travel through the refractory cooling channels and the crawler will travel through the air supply lines to the central plenum underneath the primary tank. Both tools will provide visual feedback and potentially identify the location of the leak. The magnetic miniature tool is a remote controlled rover with four wheels driven by independent micro DC motors. The rover will have to navigate through over 40 feet of channel that vary in width from 1.5 to 3.0 inches with four 90 deg. turns to reach the center of the tank. To avoid debris in the channel, the device will travel upside down magnetically attached to the bottom of the primary tank. The pneumatic pipe crawler is a worm type robot with a modular design, composed of interchangeable cylindrical modules connected with flexible links. The design is an evolution of previous peristaltic crawlers developed at FIU, and uses pneumatic actuators to emulate the contractions of the peristaltic movement. The crawler must travel approximately 100 feet through three and four inch piping and traverse through vertical risers, elbows and reducers to reach the central plenum. Prototypes for both the rover and the crawler have been designed and manufactured. Initial bench scale testing has validated the design concepts, demonstrating their ability to navigate through complex geometries. Future work will include full scale testing and evaluation of the systems' reliability. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

The Telbot master-arm

It is the weight lifter with maximum flexibility: The TELBOT provides high capacity and infinite rotary motion across all axes. Special jobs call for a special robot: The TELBOT from Waelischmiller Engineering. The TELBOT particularly shows its strength in places where human operators cannot work and heavy work has to be done. For example in the nuclear industry or chemical plants. The TELBOT is a multifunctional, modular and automated robotic system with unique characteristics: no cable in or on the TELBOT-arms for unlimited, fast and precise arm motions as well as maximum efficiency and highest comfort; smooth, sealed arm and joint bodies ensure maximum radiation resistance, making the TELBOT robot the perfect helper for the nuclear sector; the TELBOT can not only be utilised for an extremely long time but is also maintenance-free and easy to decontaminate. They therefore represent an investment that will still pay out in 30 years time; sophisticated engineering skill is apparent not least in the slim design with no loss of high capacity; tools can be remotely controlled with extreme precision and the high repeat accuracy and simple implementation of complex processes ensure trouble-free and intuitive operation; programmed motion sequences bring with them noticeably comfortable and speedy operation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Controlled Environment Agriculture SiteScout (CEA SiteScout) v1.0

This is a geospatial online decision support tool designed to help identify locations where controlled environment agriculture (CEA) can be cited to take advantage of energy and water resources. It also quantifies the benefits of selected energy and water efficiency measures.

Stokes-Draut, Jennifer [Lawrence Berkeley National↗
DOE OSTI2022

An Analysis of Grid Operator Survey Responses: Inexperience, Workload and Fatigue in the Control Room

Although a wide array of tools and technologies have been developed over the last decade to support power grid operators, deployment of these tools has been less successful. One reason for unsuccessful deployment may be an inadequate understanding of the factors that contribute to operator error in the control room. An analysis of operators’ current vulnerabilities may provide the baseline understanding needed to inform new technology integration. In an attempt to learn more about these vulnerabilities and their perceived impact on human error we collected and analyzed survey data from 20 electric grid control room operators. We asked survey respondents to consider the various operator, technology and interaction vulnerabilities that may arise during work in the control room and record their attitudes and experiences toward each. Results suggest operator inexperience, high mental workload and fatigue are the most common vulnerabilities experienced during a shift. Survey results were analyzed to explore these vulnerabilities in greater depth.

Inexperience, Workload, Fatigue↗
DOE OSTI2022

Constraining Physical Understanding of Aerosol Loading, Biogeochemistry, and Snowmelt Hydrology from Hillslope to Watershed Scale in the East River Scientific Focus Area

The mountain snowpack is a critical component of regional hydrology, ecology, biogeochemistry, and climate in the Western US. This project leveraged the East River Scientific Focus Area (SFA), as an outdoor laboratory to address a significant gap in our understanding of the mountain snowpack; namely, how atmospheric constituent deposition on snowpack influences snow energy balance and nutrient/chemical cycling, and how snowmelt timing and intensity exerts controls on emergent biogeochemical and ecohydrologic behavior. This aligned with East River SFA goals to integrate landscape scale measurements and physical based modeling tools to improve understanding of controls on runoff production, ecohydrology, biogeochemical cycling, and land surface energy partitioning in high mountain watersheds. The project included observations of snowpack at multiple scales, measurements of snow and deposited aerosols properties, in situ time series of surface energy balance, and spatially distributed, process-based snowmelt modeling.

54 ENVIRONMENTAL SCIENCES↗