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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hemp fiber reinforced dual dynamic network vitrimer biocomposites with direct incorporation of amino silane

Natural fiber composites are inexpensive and renewable alternatives to traditional fiber reinforced polymers (FRPs). However, existing natural fiber composites primarily rely on petrochemical based thermosetting matrices, which are difficult to recycle due to their stable crosslinked network structures. To address this challenge, it is desirable to develop natural fiber composites with inherently recyclable biobased matrices. In this study, we developed a dual dynamic network vitrimer matrix from hempseed oil and limonene derivatives and demonstrated its application in hemp fiber reinforced composites. To improve the weak interface between hemp fiber and the polymer matrix, a common challenge in the realm of natural fiber composites, we directly incorporated amino silane into the vitrimer matrix. The amino silane participated in the polymer network, increasing the crosslink density and toughening the matrix, as evidenced by a significantly improved impact strength from 3.5 kJ/m 2 to 10.3 kJ/m 2 . Moreover, the incorporation of amino silane resulted in a lower water absorption by 11% in 7-day soaking for the composites, demonstrating improved fiber/matrix interfacial interaction. Furthermore, our biobased vitrimer matrix exhibits both imine and hydroxy-ester dynamic bonds, which enable the recycling of the biocomposite through a mild and cost-effective aminolysis process (100°C, 3h, ambient pressure). The decomposed polymer matrix was successfully reused as a polyol for polyurethane adhesives, and the surface morphology of recovered hemp fibers was analyzed and compared with that of the original fibers. Furthermore, these findings will help broaden the use of vitrimers for practical natural fiber composite applications, raise awareness of the problem of recycling natural fiber composite waste, and shed light on the interfacial challenge of natural fiber reinforced vitrimer composites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Poor reliability of public charging stations can impede the growth of the electric vehicle market

How does the reliability of public charging infrastructure affect electric vehicle (EV) adoption? Substantial public and private investments are expanding EV charging networks, but concerns are growing about the poor reliability of existing chargers and its potential impacts on EV adoption. Using data from a nationwide survey, we employ a choice model to quantify the effects of perceived charging reliability on Americans’ intentions to purchase new or used EVs. By randomly assigning participants to receive information characterizing public charging as either very reliable or very unreliable, we show a causal effect of reliability perceptions on EV purchase intentions. In conclusion, we find that differences in perceived reliability are equivalent to changing price by 32 % of purchasing budget or changing range by 366 miles, underscoring the importance of reliable public charging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

Peng, Zhaoqiang↗
DOE OSTI2025

Surface water and groundwater FTICR-MS, NPOC, and TN from nine wetlands and three upland wells at the Tanglewood Biological Station, Alabama

This dataset supports a broader study examining wetland hydrobiogeochemical responses to flood disturbance and the subsequent impacts on watershed nutrient export. The study was designed following ICON (integrated, coordinated, open, and networked) principles. Samples were collected from nine wetlands and three upland wells at the Tanglewood Biological Station, Alabama in August 2024 and February 2025, during the dry and wet season, respectively. The contents include geochemistry (dissolved organic carbon measured as non-purgeable organic carbon; total dissolved nitrogen) and organic matter characterization (FTICR-MS). Related water level data from the same locations can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253. Additional geochemistry will be published in a separate data package. For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions.This dataset is comprised of (1) a folder containing environmental context photos; (2) file-level metadata; (3) data dictionary; (4) field metadata; (5) readme; (6) international generic sample number (IGSN) mapping file; (7) the field protocol; and (8) a subfolder with sample data. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total nitrogen data and averages; (3) methods codes; and (4) a subfolder of 12 Tesla (12T) Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data. All files are .csv, .pdf, .jpeg, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data

Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Precursor Analysis Report: Industroyer2 and Wiper Malware Targeting Ukrainian Energy Provider 2022

The Industroyer2 and Wiper Malware Targeting Ukrainian Energy Provider 2022 Precursor Analysis Report leverages publicly available information about the Industroyer2 cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. An adversary attempted to cause a blackout in Ukraine in April 2022 by using the Industroyer2 malware against a regional Ukrainian energy provider. The adversary targeted eight high-voltage electrical substations and utilized the malware in tandem with disk wipers for Windows, Linux, and Solaris operating systems in an attempt to make response and recovery efforts more difficult. The adversary reused a piece of the original Industroyer malware designed to open circuit breakers and de-energize target substations. The adversary gained initial access to the victim’s enterprise network through unknown means in February 2022 and was able to perform reconnaissance, pivot to the operations network, and reside in the system for at least 51 days. This gave the adversary a detailed understanding of the environment and allowed them to customize the Industroyer2 malware to the victim’s operations network. However, defenders detected and stopped the attack before the adversary could achieve their intended impact. Had the Industroyer2 attack been successful, it could have caused a blackout for more than two million people during the early stages of Russia’s invasion of Ukraine. Researchers and analysts identified 22 unique techniques (used in a sequence of 31 steps) utilized during the attack with a total of 297 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Twenty-three of the identified techniques used during the Industroyer2 cyber attack were precursors to the triggering event. Analysis identified 224 observables associated with these precursor techniques, 122 of which were assessed to have an increased likelihood of being perceived in the 51 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1988

Performance issues in management of the Space Station Information System

The onboard segment of the Space Station Information System (SSIS), called the Data Management System (DMS), will consist of a Fiber Distributed Data Interface (FDDI) token-ring network. The performance of the DMS in scenarios involving two kinds of network management is analyzed. In the first scenario, how the transmission of routine management messages impacts performance of the DMS is examined. In the second scenario, techniques for ensuring low latency of real-time control messages in an emergency are examined.

Johnson, Marjory J.↗
NASA NTRS1991

Satellite B-ISDN traffic analysis

The impact of asynchronous transfer mode (ATM) traffic on the advanced satellite broadband integrated services digital network (B-ISDN) with onboard processing is reported. Simulation models were built to analyze the cell transfer performance through the statistical multiplexer at the earth station and the fast packet switch at the satellite. The effectiveness of ground ATM cell preprocessing was established, as well as the performance of several schemes for improving the down-link beam utilization when the space segment employs a fast packet switch.

Shyy, Dong-Jye↗
NASA NTRS2004

NASA LCLUC Program: An Integrated Forest Monitoring System for Central Africa

Central Africa has the second largest unfragmented block of tropical rain forest in the world; it is also one of the largest carbon and biodiversity reservoirs. With nearly one-third of the forest currently allocated for logging, the region is poised to undergo extensive land-use change. Through the mapping of the forests, our Integrated Forest Monitoring System for Central Africa (INFORMS) project aims to monitor habitat alteration, support biodiversity conservation, and promote better land-use planning and forest management. Designed as an interdisciplinary project, its goal is to integrate data acquired from satellites with field observations from forest inventories, wildlife surveys, and socio-economic studies to map and monitor forest resources. This project also emphasizes on collaboration and coordination with international, regional, national, and local partners-including non-profit, governmental, and commercial sectors. This project has been focused on developing remote sensing products for the needs of forest conservation and management, insuring that research findings are incorporated in forest management plans at the national level. The societal impact of INFORMS can be also appreciated through the development of a regional remote sensing network in central Africa. With a regional office in Kinshasa, (www.OSFAC.org), the contribution to the development of forest management plans for 1.5 million hectares of forests in northern Republic of Congo (www.tt-timber.com), and the monitoring of park encroachments in the Albertine region (Uganda and DRC) (www.albertinerift.org).

Laporte, Nadine↗
NASA NTRS2017

The Scintillation Prediction Observations Research Task (SPORT) Mission

SPORT is a science mission using a 6U CubeSat and integrated ground network that will (1) advance understanding and (2) enable improved predictions of scintillation occurrence that impact GPS signals and radio communications. This is the science of Space Weather. SPORT is an international partnership with NASA, U.S. institutions, the Brazilian National Institute for Space Research (INPE), and the Technical Aeronautics Institute under the Brazilian Air Force Command Department (DCTA/ITA).

SPORT↗
NASA NTRS2022

Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

Kolawole Ogunsina↗
DOE OSTI2022

EVs@Scale Deep Dive - SCM/VGI (Day 1: SCM/VGI Analysis)

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 from year 1 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.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

EVs@Scale Deep Dive - SCM/VGI (Day 2: SCM/VGI Demonstration)

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 from year 1 of the project encompasses the progress and future plans for the demonstration components of the FUSE (Flexible charging to Unify the grid and transportation Sectors for Evs at scale) project.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2023

Electric Vehicles at Scale (EVs@Scale) Laboratory Consortium Deep-Dive Technical Meeting: May 18, 2023

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 from year 2 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.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Smart Charge Management and Vehicle Grid Integration Deep Dive

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.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks

The paper presents a distributed approach for operating a network of inverter-based energy storage resources embedded in a bulk power system. Departing from their traditional role of steady-state reserves, the storage assets in the network are utilized as frequency-responsive resources shaping system dynamics. The power electronics converter systems interfacing the storage resources are equipped with local controllers designed to respond under transient disturbances. To this end, a safety-constrained distributed control strategy is explored. The paper compares the performance of converter-interfaced grid-forming and grid-following storage networks for fast frequency control and disturbance containment/localization. Sensitivity studies are performed to study the impact of storage size, steady-state dispatch, and controller design on dynamic performance. The findings are presented through case studies with results from the IEEE test systems.

Chatterjee, Kaustav (ORCID:0000000153273860)↗