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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Materials aging at the mesoscale: Kinetics of thermal, stress, radiation activations

The complexity of materials aging may be seen as a result of the interplay between several activation processes operating on multiple spatial and temporal scales. Though the disciplines involved may seem disparate at first, material aging fundamentally could be linked by the same set of underlying activations and responses of the system. We view how recent studies of shear-induced deformation and rheological flow initiated in the soft-matter community can be leveraged to probe the mechanisms of radiation damage in nuclear materials. Bridging these two traditionally separate areas of research demonstrates the emerging notions of mesoscale science as a research frontier concerned with linking macroscale behavior to microscale processes in driven systems. We believe the combining of microstructure-sensitive measurements with fundamental theories and mechanism-specific simulations is crucial to addressing metastable materials responses of strongly activated states.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Improved methodology to assess modification and completion of landfill gas management in the aftercare period

Highlights: Black-Right-Pointing-Pointer Performance-based evaluation of landfill gas control system. Black-Right-Pointing-Pointer Analytical framework to evaluate transition from active to passive gas control. Black-Right-Pointing-Pointer Focus on cover oxidation as an alternative means of passive gas control. Black-Right-Pointing-Pointer Integrates research on long-term landfill behavior with practical guidance. - Abstract: Municipal solid waste landfills represent the dominant option for waste disposal in many parts of the world. While some countries have greatly reduced their reliance on landfills, there remain thousands of landfills that require aftercare. The development of cost-effective strategies for landfill aftercare is in society's interest to protect human health and the environment and to prevent the emergence of landfills with exhausted aftercare funding. The Evaluation of Post-Closure Care (EPCC) methodology is a performance-based approach in which landfill performance is assessed in four modules including leachate, gas, groundwater, and final cover. In the methodology, the objective is to evaluate landfill performance to determine when aftercare monitoring and maintenance can be reduced or possibly eliminated. This study presents an improved gas module for the methodology. While the original version of the module focused narrowly on regulatory requirements for control of methane migration, the improved gas module also considers best available control technology for landfill gas in terms of greenhouse gas emissions, air quality, and emissions of odoriferous compounds. The improved module emphasizes the reduction or elimination of fugitive methane by considering the methane oxidation capacity of the cover system. The module also allows for the installation of biologically active covers or other features designed to enhance methane oxidation. A methane emissions model, CALMIM, was used to assist with an assessment of the methane oxidation capacity of landfill covers.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Thermo-mechanical characterization of shale using nanoindentation

Abstract Shale can be a potential buffer for high-level radioactive nuclear wastes. To be an effective buffer while subject to waste heat, shale's mechanical response at elevated temperature must be known. Many researchers have experimentally characterized the mechanical behavior of various shales at different length scales in adiabatic conditions. However, its mechanical performance at elevated temperatures at the nano-scale remains unknown. To investigate the temperature dependency of nanomechanical properties of shale, we conducted both experimental and numerical studies. In this study, we measured mechanical and fracture properties of shale, such as hardness, elastic modulus, anisotropy, and fracture toughness from 25 °C up to 300 °C at different bedding planes. Statistical analysis of the results suggests that hardness and fracture toughness significantly increased at temperatures from 100 to 300 °C; while, temperature does not have a significant impact on elastic modulus. Data also shows that the bedding plane orientations have a substantial impact on both mechanical and fracture properties of shale at the nano-scale leading to distinct anisotropic behavior at elevated temperature below 100 °C. Additionally, we numerically investigated the mechanical performance of the shale samples at room temperature to gain an insight into its mechanical response through the thickness. Numerical results were validated against the experimental results, confirming the simulation can be used to predict shale deformation at the nano-scale or potentially be used in multi-scale simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A molecular view of DNA-conjugated nanoparticle association energies

Nanoparticles functionalized with short sequences of DNA represent a promising platform for customizable self assembly. Though much recent research has focused on the phase behavior and assembly of these structures, little has been done to precisely characterize the pairwise interaction between particles. Here we present a detailed calculation of the association between DNA-nanoparticle conjugates using 3SPN. 2, a coarse-grained model of DNA that accounts for molecular structure and base-pairing. We compare our results to those obtained experimentally using mm sized particles and analyze the free energy surfaces that characterize interparticle hybridization. Next, we study the importance of three-body effects and their impact on particle association and melting. Lastly, we explore the observation by Park et al. [Nature, 451, 553 (2008)] that DNA-nanoparticle crystallization can be inhibited by the deletion of a single nucleotide. Using our model, we suggest that the role of this nucleotide is to disrupt frustration.

Lequieu, Joshua P.↗
DOE OSTI2011

On the Electron Paramagnetic Resonance Studies in Mixed Alkali Borate Glasses

Mixed alkali effect in oxide based glasses is one of the current research activity and studies on the behavior of spectroscopic parameters in these systems are quite important to understand the basic nature of this phenomenon. EPR studies of mixed alkali glasses Li{sub 2}O-K{sub 2}O-ZnO-B{sub 2}O{sub 3} doped with Fe{sup 3+} and Mn{sup 2+} were carried out at room temperature. The EPR spectra show typical resonances of d{sup 5} system (Fe{sup 3+} and Mn{sup 2+}) in all the measured glass specimens. Evaluated hyperfine constant, number of paramagnetic centers and paramagnetic susceptibility values show deviation from the linearity with the progressive substitution of the Li ion with K in glass network.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Chaos control of the micro-electro-mechanical resonator by using adaptive dynamic surface technology with extended state observer

This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Putting tracking differentiator into chaos controller solves the ‘explosion of complexity’ of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Boiling peak heat flux for steady inhomogeneous heat transfer in superfluid He 4

Superfluid helium-4 (He II) is a widely adopted coolant in scientific and engineering applications owing to its exceptional heat transfer capabilities. However, boiling can spontaneously occur on a heating surface in He II when the heat flux exceeds a threshold value $q^*$, referred to as the peak heat flux. While the parameter $q^*$ holds paramount importance in the design of He II based cooling systems, extensive research has primarily focused on its behavior in steady homogeneous heat transfer from a flat heating surface. For inhomogeneous heat transfer from curved surfaces, $q^*$ exhibits intricate dependance on parameters such as the He II bath temperature $T_b$, the immersion depth $h$, and the curvature radius $R_0$ of the heating surface. A comprehensive understanding on how $q^*$ depends on these parameters remains elusive. In this paper, we report our systematic study on $q^*$ for steady heat transfer from cylindrical and spherical heaters in He II. We compute $q^*$ for a wide range of parameter combinations $(T_b, h, R_0)$ by solving the He II two-fluid equations of motion. The generated data have allowed us to develop a robust correlation that accurately reproduces $q^*$ for all the parameter combinations we explored. Furthermore, our findings, particularly the establishment of the correlation, carry valuable implications for emergent applications that involve steady inhomogeneous heat transfer in He II systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2011

Data Intensive Architecture for Scalable Cyber Analytics

Cyber analysts are tasked with the identification and mitigation of network exploits and threats. These compromises are difficult to identify due to the characteristics of cyber communication, the volume of traffic, and the duration of possible attack. In this paper, we describe a prototype implementation designed to provide cyber analysts an environment where they can interactively explore a month’s worth of cyber security data. This prototype utilized On-Line Analytical Processing (OLAP) techniques to present a data cube to the analysts. The cube provides a summary of the data, allowing trends to be easily identified as well as the ability to easily pull up the original records comprising an event of interest. The cube was built using SQL Server Analysis Services (SSAS), with the interface to the cube provided by Tableau. This software infrastructure was supported by a novel hardware architecture comprising a Netezza TwinFin® for the underlying data warehouse and a cube server with a FusionIO drive hosting the data cube. We evaluated this environment on a month’s worth of artificial, but realistic, data using multiple queries provided by our cyber analysts. As our results indicate, OLAP technology has progressed to the point where it is in a unique position to provide novel insights to cyber analysts, as long as it is supported by an appropriate data intensive architecture.

Olsen, Bryan K.↗
DOE OSTI2013

In situ studies of nucleation and assembly at soft-hard interfaces

The overall goal of this project was the exploration of new ways to make organic and hybrid (organic-inorganic) materials for energy-related applications. Towards this end, our research focused on the structure and behavior of molecular monolayers at interfaces (including floating monolayers, transferred Langmuir-Blodgett monolayers, and self-assembled monolayers), as well as the biomimetic nucleation of inorganic crystals at soft-hard interfaces. The project resulted in a number of 'firsts' and other notable achievements, which are described in the report.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Detection of Forced Oscillations in Power Systems with Multichannel Methods

The increasing availability of high fidelity, geographically dispersed measurements in power systems improves the ability of researchers and engineers to study dynamic behaviors in the grid. One such behavior that is garnering increased attention is the presence of forced oscillations. Power system engineers are interested in forced oscillations because they are often symptomatic of the malfunction or misoperation of equipment. Though the resulting oscillation is not always large in amplitude, the root cause may be serious. In this report, multi-channel forced oscillation detection methods are developed. These methods leverage previously developed detection approaches based on the periodogram and spectral-coherence. Making use of geographically distributed channels of data is shown to improved detection performance and shorten the delay before an oscillation can be detected in the online environment. Results from simulated and measured power system data are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Advanced Reactors-Intermediate Heat Exchanger (IHX) Coupling: Theoretical Modeling and Experimental Validation

The overall goal of the research project was to model the behavior of the advanced reactorintermediate heat exchange system and to develop advanced control techniques for off-normal conditions. The specific objectives defined for the project were: 1. To develop the steady-state thermal hydraulic design of the intermediate heat exchanger (IHX); 2. To develop mathematical models to describe the advanced nuclear reactor-IHX-chemical process/power generation coupling during normal and off-normal operations, and to simulate models using multiphysics software; 3. To develop control strategies using genetic algorithm or neural network techniques and couple these techniques with the multiphysics software; 4. To validate the models experimentally The project objectives were accomplished by defining and executing four different tasks corresponding to these specific objectives. The first task involved selection of IHX candidates and developing steady state designs for those. The second task involved modeling of the transient and offnormal operation of the reactor-IHX system. The subsequent task dealt with the development of control strategies and involved algorithm development and simulation. The last task involved experimental validation of the thermal hydraulic performances of the two prototype heat exchangers designed and fabricated for the project at steady state and transient conditions to simulate the coupling of the reactor- IHX-process plant system. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed high-temperature molten salt facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Sensitivity analysis of FeCrAl cladding and U3Si2 fuel under accident conditions

The purpose of this milestone report is to highlight the results of sensitivity analyses performed on two accident tol- erant fuel concepts: U3Si2 fuel and FeCrAl cladding. The BISON fuel performance code under development at Idaho National Laboratory was coupled to Sandia National Laboratories’ DAKOTA software to perform the sensitivity analyses. Both Loss of Coolant (LOCA) and Station blackout (SBO) scenarios were analyzed using main effects studies. The results indicate that for FeCrAl cladding the input parameters with greatest influence on the output metrics of interest (fuel centerline temperature and cladding hoop strain) during the LOCA were the isotropic swelling and fuel enrichment. For U3Si2 the important inputs were found to be the intergranular diffusion coefficient, specific heat, and fuel thermal conductivity. For the SBO scenario, Young’s modulus was found to be influential in FeCrAl in addition to the isotropic swelling and fuel enrichment. Contrarily to the LOCA case, the specific heat of U3Si2 was found to have no effect during the SBO. The intergranular diffusion coefficient and fuel thermal conductivity were still found to be of importance. The results of the sensitivity analyses have identified areas where further research is required including fission gas behavior in U3Si2 and irradiation swelling in FeCrAl. Moreover, the results highlight the need to perform the sensitivity analyses on full length fuel rods for SBO scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Modular Growth and Nucleation Formulations in Modular Plasticity Models

Recent investigations like the second and third Sandia Fracture Challenges have characterized and demonstrated the performance of a variety of failure techniques and models. These surveys have considered a wide breadth of models encapsulating both general failure criteria as well as those focusing on pore nucleation and growth. Extensive reviews exist on both topics. The former category generally consists of classic models like the Johnson-Cook or Wilkins criteria. These models were recently added to modular plasticity models in the Library of Advanced Materials for Engineering (LAME) as criteria for use with element death capabilities. The latter category was not treated in that effort. There exists a large class of failure models based on predicting the evolution of pores and failure associated with such microstructures. While the exact mechanisms and corresponding impact on the macroscale behavior remain an active area of research, a large suite of formulations have been proposed combining different features of both pore nucleation and subsequent growth. The most famous of these are based on the popular Gurson model of pore growth derived via micromechanical analysis assuming a plastically incompressible matrix. Numerous other models exist for both growth and nucleation and the Cocks-Ashby growth and Horstemeyer-Gokhale nucleation models have been used successfully in recent Sandia Fracture Challenges. This specific combination is colloquially referred to as the "BCJ-failure model as it has been frequently used with the Bammann-Chisea-Johnson plasticity model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
NASA NTRS1966

Information Flow in an R and D Laboratory

Statistical analysis of hypotheses concerning roles of technological gatekeeper and primary groups in flow of information in small research and development laboratory

EXPECTANCY HYPOTHESIS↗