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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Advancing SiC clad fuel performance model: bridging micro- and macro-scale models and experimental validation

This report presents a workflow for advancing fuel performance modeling of SiC composite cladding for light-water reactors by linking microscale, experimental data-informed finite element analysis with rod-scale fuel performance codes such as BISON. The workflow uses X-ray computed tomography (XCT) to capture the actual geometry and processing-induced defects of as-fabricated SiC composite tube specimens, particularly porosity and wall-thickness variations, and converts the segmented XCT volumes into image-based finite element meshes for high fidelity structural analysis.

Koyanagi, Takaaki [Oak Ridge National Laboratory (↗
DOE OSTI2024

Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning

Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Examination of synthetic gas puff imaging diagnostic data from a gyrokinetic turbulence code

A synthetic gas puff imaging (GPI) diagnostic has been developed for the purpose of validating the three-dimensional gyrokinetic turbulence code XGC. The synthetic diagnostic is described and applied to XGC simulations of two Alcator C-Mod discharges. The turbulence characteristics deduced from the resulting simulated GPI frames, using analysis techniques similar to those applied to experimental data, are compared with locally derived characteristics extracted directly from the XGC output. The comparison of the two is shown to be potentially impacted by misalignment between the GPI view and the magnetic field, the dependence of the light emission on the electron density and temperature, and spatial and temporal variations in the neutral gas cloud induced by the turbulent plasma fluctuations. We conclude that quantitative and, in some cases even qualitative, validation of turbulence simulations need to account for these effects. While we cannot directly compare our results with experimental data due to the absence of high quality GPI data from the shots and times simulated by XGC, we do relate the overall characteristics obtained from the synthetic GPI analysis to published Alcator C-Mod GPI data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Unified Description of Cuprate Superconductors by Fractionalized Electrons Emerging from Integrated Analyses of Photoemission Spectra and Quasiparticle Interference

Electronic structure of high-temperature superconducting cuprates is studied by analyzing experimental data independently obtained from two complementary spectroscopies: one, quasiparticle interference (QPI) measured by scanning-tunneling microscopy, and the other, angle-resolved photoemission spectroscopy (ARPES). We combine these two sets of data in a unified theoretical analysis. Through explicit calculations of experimentally measurable quantities, we show that a simple two-component fermion model (TCFM) representing electron fractionalization succeeds in reproducing various detailed features of these experimental data: ARPES and QPI data are concomitantly reproduced by the TCFM in full energy and momentum spaces. The measured QPI pattern reveals a signature characteristic of the TCFM, distinct from the conventional single-component prediction, supporting the validity of the electron fractionalization in the cuprates. The integrated analysis also solves the puzzles of ARPES and QPI data that are seemingly inconsistent with each other. The overall success of the TCFM offers a comprehensive understanding of the electronic structure of the cuprates, in particular, the unoccupied side of the spectra, of which momentum-resolved structure has long been unexplored experimentally. We further predict that a characteristic QPI pattern should appear in the unoccupied high-energy part if the fractionalization is at work. We propose that integrated-spectroscopy analyses offer a promising way to explore challenging issues of strongly correlated electron systems.

Sakai, Shiro [Sophia University; RIKEN Center for ↗
DOE OSTI2024

Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected immortalized human lung epithelial cells (A549) (MOI 5) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Using real-time data analysis to conduct next-generation synchrotron fatigue studies

Next-generation experimental techniques, like high energy X-ray diffraction microscopy (HEDM), usher in new opportunities to collect the grain-scale data necessary for understanding the evolving processes that drive fatigue failure. In this study, we present a framework for monitoring the evolution of a deforming polycrystal, in real-time, by applying principal component analysis (PCA) to raw X-ray diffraction image data. We applied this framework to inform in-situ HEDM measurements of a cyclically loaded Inconel-718 superalloy. Further, we discovered correlations between PCA of the diffraction data and the physical processes in the polycrystal. Lastly, we discuss extending this framework in future HEDM fatigue studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Full-Cell Model for Direct Toluene Electro-Hydrogenation Electrolysis

Liquid organic hydrogen carriers (LOHCs) are organic molecules that undergo a hydrogenation/dehydrogenation cycle to enable storage and transportation of hydrogen fuel under ambient conditions. One promising LOHC candidate is toluene, which can be converted to methylcyclohexane (MCH) electrochemically, enabling a decarbonized process when green electricity is used. In this study, we developed a full-cell model for the direct electro-hydrogenation of toluene to MCH, utilizing a zero-gap membrane electrode assembly architecture. The model incorporates electrochemical kinetics, ionic transport, water transport across the membrane, and mass transport effects. Electrochemical kinetics are characterized using Tafel analysis on Pt/Ru catalyst. The model is validated against experimental data, including polarization curves, Faradaic efficiencies, and water crossover. A voltage breakdown analysis shows that the performance is dominated by kinetic losses, and the model is used to carry out a comparison of different toluene electro-hydrogenation reaction catalysts. Finally, a sensitivity analysis is conducted on key design parameters illustrating which can be modified to maximize electrolyzer performance. The cathode specific surface area and cathode porous transport layer thickness (PTL) have the largest impact on the current density, while the PTL thickness and Pt loading in the PTL have the largest impact on Faradaic efficiency.

Ehlinger, Victoria M. [Lawrence Livermore National↗
DOE OSTI2020

Validation and Parametric Investigations Using a Lumped Thermal Parameter Model of an Internal Permanent Magnet Motor

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet (IPM) synchronous motor within the commercially available Motor-CAD® modeling environment. The IPM motor’s stator is studied at steady state, and winding losses are generated by a constant DC current. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% — for the highest temperature in the slot windings and the end windings — was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot windings and end windings. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance. The latter is often ignored in the literature, whereas its impact on temperature rise was found to be more significant than any other contact resistance within the stator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Validation and Parametric Investigations Using a Lumped Thermal Parameter Model of an Internal Permanent Magnet Motor: Preprint

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet (IPM) synchronous motor within the commercially available Motor-CAD® modeling environment. The IPM motor’s stator is studied at steady state, and winding losses are generated by a constant DC current. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot windings and the end windings - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot windings and end windings. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance. The latter is often ignored in the literature, whereas its impact on temperature rise was found to be more significant than any other contact resistance within the stator.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography

Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hydrogen Permeation Analysis Tool

There is a rising need for the measurement and understanding of hydrogen transport properties in materials for the energy and transportation industries. The Hydrogen Permeation Analysis Tool (HyPAT) provides a user friendly interface for batch analysis of dynamic hydrogen permeation data to calculate the transport properties permeability, solubility, and diffusivity. A built in literature database provides an easy comparison to known values. A common method for measuring these hydrogen transport properties is dynamic pressure rise permeation experiments, for example, those done in the Static Gas Absorption and Permeation (SGAP) experiment at INL. Most laboratories develop individual, in-house codes for data analysis. HyPAT provides a platform that can be adopted by other user groups for such experimental analysis and significantly reduce personnel time spent performing data analysis.

Fuerst, ThomasF↗
DOE OSTI2022

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multiphysics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a lumped parameter thermal network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available MOTOR-CAD modeling environment. The validation is based on temperature comparison with experimental data and with more detailed finite element analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations, and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LPTN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

42 ENGINEERING↗
DOE OSTI2022

Does a Free Electron Laser Exhibit Non-Standard Statistics? [Slides and Report]

The originally proposed work under this award sought to develop a theoretical basis for previous experimental observations in free-electron laser (FEL) physics involving certain non-classical effects in the light produced by these devices. A sound and valid theoretical interpretation of these experiments would represent a transformational understanding of free-electron lasers, and of electron-photon interactions in general. The specific objective of the completed work, as described in the attached progress report by Jeongwan Park, was to perform a detailed analysis of an experiment by Chen and Madey (Physical Review Letters; volume 86 number 26, 2001) concerning the purported sub-Poissonian photon statistics in an FEL. Since that published work contradicted FEL theory as presently understood, the purpose of the work under the present subcontract was to obtain a better understanding of the quantum nature of the FEL, to investigate the possibility of theoretical and experimental evidence for the non-standard photon statistics of the FEL, and to critically examine the experimental evidence through a re-evaluation of the data analysis of Chen and Madey. The results of this study showed that there were numerous experimental conditions under which the observed photon statistics could be explained by combining both the photon clustering property and the dead-time effect, and consequently, that the observed photon statistics in the Chen-Madey experiment did not require sub-Poissonian photon statistics for their explanation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Dissolved gas recovery from water using a sidestream hollow-fiber membrane module: First principles model synthesis and steady-state validation

This paper presents a first-principles model for the recovery of dissolved gases from liquids using a sidestream hollow-fiber membrane module. The model avoids the use of new empirical coefficients, thus providing a parametric understanding of the process behavior for future design and optimization of membrane modules. This type of first-principles model could be particularly useful when gas recovery is beneficial to biological or chemical reactions of interest, such as the acetogenesis reactions in two-stage anaerobic digesters. The steady-state behavior of the model was validated against both new experimental data for the recovery of H 2 , CH 4 and H 2 –CH 4 mixtures from pure water, as well as existing published data. The modeled gas recovery predictions agreed with experimental data to an absolute average error of 13%, and an average R value of 0.98. Parametric analysis of mixed-gas recovery suggests possible key transition points in the composition of the recovered gases. For example, at 40 °C, increasing trans-membrane pressure while keeping hydraulic residence time (HRT) under 0.5 s will result in an increase in the ratio of H 2 to CH 4 recovered. Otherwise, increasing trans-membrane pressure will instead decrease the ratio of H 2 to CH 4 recovered. The model has potential to be extended to transient analysis, but has yet to be validated with transient experimental data. Furthermore, this model was successfully implemented in both Python and MATLAB, and provides valuable insights for future net-energy optimization for anaerobic digestion systems with in-situ gas recovery.

Anaerobic Digestion↗
DOE OSTI2023

Electrochemical Doping of Halide Perovskites by Noble Metal Interstitial Cations

Abstract Metal halide perovskites are an attractive class of semiconductors, but it has proven difficult to control their electronic doping by conventional strategies due to screening and compensation by mobile ions or ionic defects. Noble‐metal interstitials represent an under‐studied class of extrinsic defects that plausibly influence many perovskite‐based devices. In this work, doping of metal halide perovskites is studied by electrochemically formed Au + interstitial ions, combining experimental data on devices with a computational analysis of Au + interstitial defects based on density functional theory (DFT). Analysis suggests that Au + cations can be easily formed and migrate through the perovskite bulk via the same sites as iodine interstitials (I i + ). However, whereas I i + compensates n‐type doping by electron capture, the noble‐metal interstitials act as quasi‐stable n‐dopants. Experimentally, voltage‐dependent, dynamic doping by current density–time ( J–t ), electrochemical impedance, and photoluminescence measurements are characterized. These results provide deeper insight into the potential beneficial and detrimental impacts of metal electrode reactions on long‐term performance of perovskite photovoltaic and light‐emitting diodes, as well as offer an alternative doping explanation for the valence switching mechanism of halide‐perovskite‐based neuromorphic and memristive devices.

14 SOLAR ENERGY↗
DOE OSTI2022

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. Furthermore, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗