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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. In conclusion, the proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.

42 ENGINEERING↗
DOE OSTI2020

Automated, Unmanned Detection and Quantification of Fugitive Methane Emissions

Compressor stations used to move natural gas are one of the largest sources of fugitive methane emissions in the midstream sector, accounting for approximately 50% of all fugitive emissions (Zimmerle et al., 2015). This problem is most widespread at reciprocating compressors (Subramanian et al., 2015) where faulty seals are a key contributor to methane emissions (Johnson et al., 2015). As such, there is a significant need for a robust technology that could provide an early indication of an unexpected emission. Equally important, the technology needs to be able to account for biogenic versus anthropogenic sources of methane. One means of indirectly making this determination, is to leverage optical technologies that can autonomously pinpoint the source of such leaks. This presentation discusses recent work funded by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), focused on the development of an innovative remote sensing technology that can reliably and autonomously detect fugitive methane emissions in near real-time, using computer vision and deep learning. The technology called the Smart Methane Leak Detection (SLED/M) system was initially developed to monitor facilities such as compressor stations in a stationary, pan-tilt-zoom configuration. The system has recently been adapted to monitor facilities from an unmanned aerial system (UAS). The speed and maneuverability of UAS platforms are attractive to leak detection and repair program operators, but introduce several challenges. Many existing methane detection algorithms rely on mostly static backgrounds becoming unusable with motion. In addition, top-down views of fugitive methane emissions present differently in Optical Gas Imagers (OGI) compared to looking across the plume. Our work has focused on overcoming these challenges, enhancing the operators ability to detect methane emissions, and pinpoint their sources. Another recent adaptation to SLED/M is the ability to quantify methane emissions using passive sensors (OGI, thermal camera), environmental conditions, plume modeling, and deep learning. SLED/M advances the state-of-the-art for methane emission detection and quantification by focusing on three key critical criteria for effective methane emission mitigation: (1) autonomy (no need for a human to be in the loop), (2) high reliability (low false alarm rates), and (3) real-time performance. Results from this work will be presented.

03 NATURAL GAS↗
NASA NTRS2000

Active Control Technology at NASA Langley Research Center

NASA Langley has a long history of attacking important technical opportunities from a broad base of supporting disciplines. The research and development at Langley in this subject area range from the test tube to the test flight. The information covered here will range from the development of innovative new materials, sensors and actuators, to the incorporation of smart sensors and actuators in practical devices, to the optimization of the location of these devices, to, finally, a wide variety of applications of these devices utilizing Langley's facilities and expertise. Advanced materials are being developed for sensors and actuators, as well as polymers for integrating smart devices into composite structures. Contributions reside in three key areas: computational materials; advanced piezoelectric materials; and integrated composite structures. The computational materials effort is focused on developing predictive tools for the efficient design of new materials with the appropriate combination of properties for next generation smart airframe systems. Research in the area of advanced piezoelectrics includes optimizing the efficiency, force output, use temperature, and energy transfer between the structure and device for both ceramic and polymeric materials. For structural health monitoring, advanced non-destructive techniques including fiber optics are being developed for detection of delaminations, cracks and environmental deterioration in aircraft structures. The computational materials effort is focused on developing predictive tools for the efficient design of new materials with the appropriate combination of properties for next generation smart airframe system. Innovative fabrication techniques processing structural composites with sensor and actuator integration are being developed.

Antcliff, Richard R.↗
NASA NTRS2010

Computational Analysis of Dynamic SPK(S8)-JP8 Fueled Combustor-Sector Performance

Civil and military flight tests using blends of synthetic and biomass fueling with jet fuel up to 50:50 are currently considered as "drop-in" fuels. They are fully compatible with aircraft performance, emissions and fueling systems, yet the design and operations of such fueling systems and combustors must be capable of running fuels from a range of feedstock sources. This paper provides Smart Combustor or Fuel Flexible Combustor designers with computational tools, preliminary performance, emissions and particulates combustor sector data. The baseline fuel is kerosene-JP-8+100 (military) or Jet A (civil). Results for synthetic paraffinic kerosene (SPK) fuel blends show little change with respect to baseline performance, yet do show lower emissions. The evolution of a validated combustor design procedure is fundamental to the development of dynamic fueling of combustor systems for gas turbine engines that comply with multiple feedstock sources satisfying both new and legacy systems.

Ryder, R.↗
DOE OSTI2023

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. In conclusion, the potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

42 ENGINEERING↗
DOE OSTI2018

Light-Emitting Metasurfaces: Simultaneous Control of Spontaneous Emission and Far-Field Radiation

Light-emitting sources and devices permeate every aspect of our lives and are used in lighting, communications, transportation, computing, and medicine. Advances in multifunctional and “smart lighting” would require revolutionary concepts in the control of emission spectra and directionality. Such control might be possible with new schemes and regimes of light–matter interaction paired with developments in light-emitting materials. We show that all-dielectric metasurfaces made from III–V semiconductors with embedded emitters have the potential to provide revolutionary lighting concepts and devices, with new functionality that goes far beyond what is available in existing technologies. Specifically, we use Mie-resonant metasurfaces made from semiconductor heterostructures containing epitaxial quantum dots. By controlling the symmetry of the resonant modes, their overlap with the emission spectra, and other structural parameters, we can enhance the brightness by 2 orders of magnitude, as well as reduce its far-field divergence significantly.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1990

New developments in adaptive methods for computational fluid dynamics

New developments in a posteriori error estimates, smart algorithms, and h- and h-p adaptive finite element methods are discussed in the context of two- and three-dimensional compressible and incompressible flow simulations. Applications to rotor-stator interaction, rotorcraft aerodynamics, shock and viscous boundary layer interaction and fluid-structure interaction problems are discussed.

Oden, J. T.↗
NASA NTRS1992

Propellant production and useful materials: Hardware data from components and the systems

Research activities at the University of Arizona/NASA Space Engineering Research Center are described; the primary emphasis is on hardware development and operation. The research activities are all aimed toward introducing significant cost reductions through the utilization of resources locally available at extraterrestrial sites. The four logical aspects include lunar, Martian, support, and common technologies. These are described in turn. The hardware realizations are based upon sound scientific principles which are used to screen a host of interesting and novel concepts. Small scale feasibility studies are used as the screen to allow only the most promising concepts to proceed. Specific examples include: kg/day-class oxygen plant that uses CO2 as the feed stock, spent stream utilization to produce methane and 'higher' compounds (using hydrogen from a water electrolysis plant), separation of CO from the CO2, reduction of any iron bearing silicate (lunar soils), production of structural components, smart sensors and autonomous controls, and quantitative computer simulation of extraterrestrial plants. The most important feature of all this research continues to be the training of high-quality students for our future in space.

Ramohalli, Kumar↗
NASA NTRS2002

Aerodynamic Database Development for Mars Smart Lander Vehicle Configurations

An aerodynamic database has been generated for the Mars Smart Lander Shelf-All configuration using computational fluid dynamics (CFD) simulations. Three different CFD codes, USM3D and FELISA, based on unstructured grid technology and LAURA, an established and validated structured CFD code, were used. As part of this database development, the results for the Mars continuum were validated with experimental data and comparisons made where applicable. The validation of USM3D and LAURA with the Unitary experimental data, the use of intermediate LAURA check analyses, as well as the validation of FELISA with the Mach 6 CF(sub 4) experimental data provided a higher confidence in the ability for CFD to provide aerodynamic data in order to determine the static trim characteristics for longitudinal stability. The analyses of the noncontinuum regime showed the existence of multiple trim angles of attack that can be unstable or stable trim points. This information is needed to design guidance controller throughout the trajectory.

Bobskill, Glenn J.↗
DOE OSTI2024

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2018

A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.

14 SOLAR ENERGY↗
DOE OSTI2019

A Highly Efficient Control Framework for Centralized Residential Charging Coordination of Large Electric Vehicle Populations

The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve global coordination benefits. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
NASA NTRS2011

Development of CCSDS DCT to Support Spacecraft Dynamic Events

This report discusses the development of Consultative Committee for Space Data Systems (CCSDS) Design Control Table (DCT) to support spacecraft dynamic events. The Consultative Committee for Space Data Systems (CCSDS) Design Control Table (DCT) is a versatile link calculation tool to analyze different kinds of radio frequency links. It started out as an Excel-based program, and is now being evolved into a Mathematica-based link analysis tool. The Mathematica platform offers a rich set of advanced analysis capabilities, and can be easily extended to a web-based architecture. Last year the CCSDS DCT's for the uplink, downlink, two-way, and ranging models were developed as well as the corresponding input and output interfaces. Another significant accomplishment is the integration of the NAIF SPICE library into the Mathematica computation platform.

Smart GUI↗