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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graphene Nanopattern for Single-Crystal Film Growth, Defect Reduction and Layer Transfer

Thin film heterostructures are key building blocks for advanced electronic and optoelectronic devices. For this, direct heteroepitaxy has been pursued for decades, although it has been challenging to reduce crystal defects stemming from lattice mismatches and thermal mismatches between materials. The layer transfer method has been proposed as an alternative approach, wherein dissimilar materials are separately grown and then hetero-integrated. However, the applicability of layer transfer techniques is limited by several technical challenges, such as controllability, throughput, and damage to the substrate. Remote epitaxy, which is a recently developed method to produce single-crystalline membranes, is a promising approach but cannot be applied to elemental materials such as Si and Ge. In this work, we report graphene nanopattern as a universal template for the growth of single-crystal thin films that can be exfoliated as a freestanding form. This is realized by the chemical inertness of graphene, which allows selective nucleation at the exposed region, followed by lateral overgrowth onto graphene to form a planarized thin film. By employing graphene nanopattern, both group IV and III-V materials are utilized as the substrate as well as the epilayer. The epilayer can be exfoliated at the graphene interface because partially covered graphene effectively weakens the interface, which is corroborated by theoretical analyses of spalling theory. We reveal that graphene nanopattern not only works as a weakened interface for exfoliation, but also allows for dislocation reduction in heteroepitaxial films. This is because of the flexibility and the dangling-bond-free nature of graphene, which provides an additional path for strain relaxation. Therefore, these results represent a meaningful step toward production of high-quality single-crystal membranes that can be hetero-integrated.

Kim, Hyunseok↗
DOE OSTI2020

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Finally, our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Methods for Estimating Hydrogen Fuel Tank Characteristics

The pressure vessels needed to store hydrogen for next-generation hydrogen fuel cell vehicles are expected to be a substantial portion of the total system mass, volume, and cost. Gravimetric capacity, volumetric capacity, and cost per kilogram of usable hydrogen are key performance metrics that the U.S. Department of Energy (DOE) uses to determine the viability of hydrogen fuel cell systems. Research and development related to hydrogen storage systems covers a wide range of potential operating conditions, from cryogenic temperatures to high temperatures (above ambient) and low pressure to high pressure. Researchers at PNNL have developed methods for estimating these key pressure vessel characteristics to support on-board hydrogen storage system design and performance evaluation and to support decision-making about DOE hydrogen storage system research investments. This article describes the pressure tank estimation methodology that has been used as a stand-alone calculation and has been incorporated into larger system evaluation tools. The methodology estimates the geometry, mass, and material cost of type I, type III, and type IV pressure vessels based on operating pressure and material strength at the system's operating temperature, using classical thin-wall and thick-wall pressure vessel stress calculations. The geometry, mass, and material cost requirements of the pressure vessel have significant impacts on the total system performance. For example, hydrogen storage materials that can separately achieve a very high hydrogen density can be deemed impractical for use in fuel cell vehicle hydrogen storage systems because the pressure tank containing them is too large, heavy, or expensive. This article describes the design philosophy and analytical process of the tank characteristic estimation methodology, which has been implemented in spreadsheet calculation tools and system-level analysis tools used by DOE researchers. Each of the three tank types (type I, type III, and type IV) uses a different analysis methodology with some common elements. This article also provides examples of implementing the methodology to perform parametric studies of all three pressure vessel types. The goal of this article is to present the methodology in sufficient detail so it can be implemented in other hydrogen fuel cell vehicle design and analysis tools.

42 ENGINEERING↗
DOE OSTI2024

Lattice QED in an external magnetic field: Evidence for dynamical chiral symmetry breaking

We simulate quantum electrodynamics (QED) in a strong constant homogeneous external magnetic field on a Euclidean space-time lattice using the rational hybrid Monte Carlo method, developed for simulating lattice quantum chromodynamics (QCD). Our primary goal is to measure the chiral condensate in the limit when the input electron mass m is zero. We observe a nonzero value, indicating that the external magnetic field catalyzes chiral symmetry breaking as predicted by approximate truncated Schwinger-Dyson methods. Such behavior is associated with dominance by the lowest Landau level which causes the effective dimensional reduction from 3 + 1 dimensions to 1 + 1 dimensions for charged particles (electrons and positrons) where the attractive forces of QED can produce chiral symmetry breaking with a dynamical electron mass and associated chiral condensate. Since our lattice simulations use bare (lattice) parameters, while the Schwinger-Dyson analyses work with renormalized quantities, direct numerical comparison will require renormalization of our lattice results. Published by the American Physical Society 2024

Kogut, J. B. (ORCID:000000030151548X)↗
DOE OSTI2025

Closing the Accuracy Gap in Tandem Photovoltaic Testing: An Accessible and Efficient Spectral Tuning Method Using LED-Based Simulators for Research Laboratories and Industry

Accurate performance calibration of multijunction (MJ) solar cells is critical for advancing this technology toward large-scale terrestrial application, yet existing testing methods developed by photovoltaics calibration laboratories remain prohibitively complex and resource intensive for most research laboratories. Current approaches rely on expensive multisource simulators and/or intricate spectral tuning algorithms, limiting accessibility and hindering standardized comparisons of emerging MJ technologies such as perovskite-based tandems. This paper introduces a streamlined spectral irradiance adjustment method for light-emitting diode (LED)-based solar simulators, which are increasingly adopted in the photovoltaics community due to their versatility and cost-effectiveness. The method we present bins LED channels into groups based on the number of junctions of the MJ photovoltaic device under test (DUT) and their corresponding band gaps and incorporates an automated tuning algorithm that eliminates the need to adjust each channel’s irradiance individually. This tuning algorithm requires the relative spectral responsivities of both the DUT and a broadband reference cell, as well as a calibrated spectroradiometer. Measurement validation across various MJ devices, including III-V and perovskite/silicon tandems, demonstrates excellent agreement within 1% with well-established xenon-tungsten multisource simulators and associated tuning algorithms. By enabling precise spectral tuning with readily available equipment and a simple tuning algorithm, our approach bridges the measurement accuracy gap between research laboratories and accredited testing facilities, fostering more reliable device comparisons and accelerating the translation of MJ technologies into real-world energy systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Single Channel Infrasound Detection Using Machine Learning

Infrasound, low frequency sound less than 20 Hz, is generated by both natural and anthropogenic sources. Infrasound sensors measure pressure fluctuations only in the vertical plane and are single channel. However, the most robust infrasound signal detection methods rely on stations with multiple sensors (arrays), despite the fact that these are sparse. Automated methods developed for seismic data, such as short-term average to long-term average ratio (STA/LTA), often have a high false alarm rate when applied to infrasound data. Leveraging single channel infrasound stations has the potential to decrease signal detection limits, though this cannot be done without a reliable detection method. Therefore, this report presents initial results using (1) a convolutional neural network (CNN) to detect infrasound signals and (2) unsupervised learning to gain insight into source type.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Determining Magnetic Field Directions and Omni-directional Fluxes of Particles from STPSat-6 ZPS Plasma Measurements

This report summarizes our recent data derivation efforts on STPSat-6 ZPS plasma measurements, presenting final outcomes. We begin by outlining the methodology developed to determine local magnetic field directions from measured directional intensities of ~20 keV ions, leveraging symmetry in particles’ pitch-angle distributions, and the determined field directions over a storm period are shown with errors analyzed through comparison with NOAA GOES measurements. Next, we further assess the validity of the method and quantify its overall high performance while acknowledging certain caveats. Finally, given local magnetic field directions and ZPS data, we explain how omni-directional fluxes of particles can be derived from ZPS measurements with limited directional coverage, and estimate the related errors under several theoretical scenarios. The methods developed in this study can be applied for processing and augmenting ZPS data for the next, and the insights gained here can also inform instrument design in the future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Degradation of solid oxide electrolysis cells: Phenomena, mechanisms, and emerging mitigation strategies—A review

Solid oxide electrolysis cell (SOEC) is a promising electrochemical device with high efficiency for energy storage and conversion. However, the degradation of SOEC is a significant barrier to commercial viability. In this review article, the typical degradation phenomena of SOEC are summarized, with great attention into the anodes/oxygen electrodes, including the commonly used and newly developed anode materials. Meanwhile, mechanistic investigations on the electrode/electrolyte interfaces are provided to unveil how the intrinsic factor, oxygen partial pressure , and the electrochemical operation conditions, affect the interfacial stability of SOEC. At last, this paper also presents some emerging mitigation strategies to circumvent long-term degradation, which include novel infiltration method, development of new anode materials and engineering of the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reexamining the variational two-particle reduced density matrix for nuclear systems

We report that in most nuclear many-body methods, observables are calculated using many-body wave functions explicitly. The variational two-particle reduced density matrix method is one of the few exceptions to the rule. Ground-state energies of both closed-shell and open-shell nuclear systems can indeed be evaluated by minimizing a constrained linear functional of the two-particle reduced density matrix. However, it has virtually never been used in nuclear theory, because nuclear ground states were found to be well overbound, contrary to those of atoms and molecules. Consequently, we introduced new constraints in the nuclear variational two-particle reduced density matrix method, developed recently for atomic and molecular systems. Our calculations then show that this approach can provide a proper description of nuclear systems where only valence neutrons are included. For the nuclear systems where both neutrons and protons are active, however, the energies obtained with the variational two-particle reduced density matrix method are still overbound. The possible reasons for the noticed discrepancies and solutions to this problem will be discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Investigating the Impacts of Outdoor Lighting (Final Report)

This report documents the research associated with the Depart of Energy supported project “Investigating the Health Impact of Outdoor Lighting.” This project was developed to consider how alternative solid state lighting options, particularly the spectral power distribution, effected the hormone melatonin which ultimately could lead to potential health issues. The project also provides an opportunity to gather other secondary data with respect to visual performance in a controlled environment. These tasks were developed to consider how alternative solid state lighting applications impacted performance. This report is a complete documentation of the tasks undertaken as part of this project. The report includes a literature review and consideration of the background data which are part of this field of study as well as a documentation of the methods developed as part of the project. The melatonin results, the primary dependent variable are included in the attached peer-review paper that has been recent published (Gibbons et al., 2022). Our discussion of these results are included in that publication. Other results from this project will be documented in one or more future publications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Simultaneous Celestial Navigation

GPS technology is used in a multitude of applications around the world for navigation. However, GPS can be contested or denied and thus alternatives to GPS are needed for positioning in high-consequence systems. This project investigates a non-RF based method to produce an estimated geolocation using stellar/celestial measurements in combination with measurements from inclinometers and a real time clock. The initial iteration of this method, developed in FY20/21, achieved an estimated location accuracy within approximately 3 kilometers of the true location with a 3-sigma bound of about 8 kilometers. The next iteration aims to enhance this technology further, targeting an estimation accuracy within 100 meters of the true location, representing a 30-fold improvement over the initial method.

42 ENGINEERING↗
DOE OSTI2020

Small-Scale Drying: FY2020 Interim Report

This report documents the interim experimental status of small-scale drying tests currently being performed at Pacific Northwest National Laboratory. These small-scale drying tests are to develop the technical bases for sensors, techniques, and approaches that will be used to address the challenges faced when determining moisture content from gas samples taken at the North Anna Nuclear Generating Station for the High Burnup Demonstration Project. Specifically, the purpose of these tests is to support Sandia National Laboratories gas sample methods and analyses through a series of small-scale experiments. Key focus areas include performing tests to: • Correlate sample bottle measurements to in-cask conditions • Estimate the amount of trapped and absorbed water on cask features and surfaces that can contribute to water vapor • Assess the accuracy of the hygrometer used in the measurements under similar conditions • Identify additional methods for measuring humidity in casks easily and accurately using lessons-learned from Sandia National Laboratories, including direct gravimetric and isotopic tracer techniques. To address these topics this report outlines a series of five tests and any data collected to date that were planned for FY 2020 that include 1) humidity measurement method development, 2) gas bottle sampling tests, 3) surface drying tests on cladding, 4) small-scale drying of cladding, and 5) small-scale drying of guide-tube/dashpots. This interim report documents the experimental background, design and progress of these small-scale drying tests. Further data gathered will be summarized in the FY 2021 report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network

This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.

Li, Chengcheng↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

BlueCRAB Domain Overlapping Coupling: Theory & Verification

Coupling low- and high-fidelity codes is a useful way to model complex engineering systems. Thanks to the high-fidelity code, complex phenomena can be resolved in areas of the system where this is required, yet the efficiency of the lower-fidelity code is still retained in modeling the rest of the system. This document details the theory and implementation of coupling two different thermal-hydraulic codes: the system thermal-hydraulics (STH) code System Analysis Module (SAM) and the coarse-mesh computational fluid dynamics (CFD) code Pronghorn. Both applications are included in the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite and are based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. The domain overlapping (DO) coupling approach was adopted, as it offers proven advantages over more conventional domain decomposition methods. In the DO coupling, SAM provides Pronghorn with boundary conditions that depend on the system-level simulation of the entire plant. In return, the overlapping coupled SAM components–termed “surrogate components”–receive friction factors and source terms computed online based on the Pronghorn simulation. The framework is designed to be generic and enable coupling regardless of geometry and the number of inlet/outlet boundaries in the DO coupled domain. The developed method leads to consistent pressure drops, enthalpies, and scalar concentrations when comparing the coupled SAM and Pronghorn simulations. This document presents the DO coupling approach, along with two verification and two demonstration cases. The proposed problems explore different physical aspects relevant to nuclear reactor analysis, including buoyancy-driven flows, complex flow patterns, and multiple inlets and outlets. Periodically, new versions of this “BlueCRAB Domain Overlapping Coupling: Theory & Verification” report will be issued to reflect future developments and verification tests.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Intelligent System Partitioning for Agent-Based Security Constrained Optimal Power Flow

This project developed scalable, computationally efficient algorithms to solve realistic large-scale power system optimization problems as part of a larger series of competitions run by ARPA-E. These problems are important because the secure and reliable operation of the power grid, especially under increased uncertainty and variability, is growing increasingly challenging. The economic feasibility of the proposed methods developed by our team is quite low, considering it’s a purely software-based solution to operate power grids more efficiently. The technical effectiveness, as evidenced by our performance in the competition, balances heuristics and approximations to provide a tradeoff between speed and accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗