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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

Accelerating cosmological inference with Gaussian processes and neural networks – an application to LSST Y1 weak lensing and galaxy clustering

ABSTRACT Studying the impact of systematic effects, optimizing survey strategies, assessing tensions between different probes and exploring synergies of different data sets require a large number of simulated likelihood analyses, each of which cost thousands of CPU hours. In this paper, we present a method to accelerate cosmological inference using emulators based on Gaussian process regression and neural networks. We iteratively acquire training samples in regions of high posterior probability which enables accurate emulation of data vectors even in high dimensional parameter spaces. We showcase the performance of our emulator with a simulated 3×2 point analysis of LSST-Y1 with realistic theoretical and systematics modelling. We show that our emulator leads to high-fidelity posterior contours, with an order of magnitude speed-up. Most importantly, the trained emulator can be re-used for extremely fast impact and optimization studies. We demonstrate this feature by studying baryonic physics effects in LSST-Y1 3×2 point analyses where each one of our MCMC runs takes approximately 5 min. This technique enables future cosmological analyses to map out the science return as a function of analysis choices and survey strategy.

Astronomy & Astrophysics↗
DOE OSTI2025

Mechanistic Insights Into Fatigue Life Enhancement of High‐Strength Steel via Ultrasonic Impact Treatment

In this work we explore the impact of Ultrasonic Impact Treatment (UIT) on the fatigue performance of high‐strength microalloyed steel commonly used in crankshaft applications. Building on prior observations of fatigue life improvement with UIT, this work focuses on unraveling the process–structure–performance relationships underpinning these enhancements. Microstructural analysis revealed significant grain refinement in the near‐surface layers, with deformation depth increasing at higher impact energies. This led to increased surface hardness and the development of deeper compressive residual stresses, particularly in samples treated with higher impact energy. Rotating bending fatigue testing showed a substantial improvement in fatigue life for UIT‐treated samples, with the endurance limit nearly doubling compared with untreated specimens. Fractographic analysis revealed a transition in crack initiation from surface defects in untreated samples to interior regions in UIT‐treated samples, characterized by the formation of noninclusion‐induced granular bright facets (GBFs). The observed fatigue enhancement is attributed to the synergistic effects of strain hardening and compressive residual stresses, which increase the surface crack threshold and promote interior crack initiation. This study provides new mechanistic insight into UIT‐induced fatigue resistance and interior failure behavior in high‐strength steels.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Characterization of Covetic Materials for Process Control. Final CRADA report

GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterization of Radiation Induced Defects Across Scales

The Whole Powder Pattern Modelling (WPPM) approach of diffraction line profile analysis (LPA) was applied for the first time on PuO 2 in order to understand the evolution of the microstructural properties as a function of process conditions. Crystallite size distributions and defect concentrations were determined by WPPM on three different PuO 2 samples - including one with a bimodal distribution and compared to the result of more common LPA analyses used in PuO 2 literature studies (e.g. Scherrer equation, double Voigt method). The conclusion was that a lognormal distribution of spherical crystallites with dislocations most closely represent the microstructure of PuO 2 produced by heating Pu 2 (C 2 O 4 ) 3 (H 2 O) 6 •3H 2 O between 650°C and 850°C. The volume-weighted mean column length (Lvol) parameter obtained from the double Voigt method for the monomodal samples were the same as the Lvol obtained from the WPPM within uncertainty. The two LPA methods did not agree on the Lvol parameters of the bimodal distribution sample with only the WPPM getting values that were consistent with the individual components. The WPPM can potentially provide more insight into future studies of the influences on the microstructure properties of PuO 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Arsenic uptake in bacterial calcite

Bio-mediated processes for arsenic (As) uptake in calcite were investigated by means of X-ray Diffraction (XRD) and Xray Absorption Spectroscopy (XAS) coupled with X-ray Fluorescence measurements. The environmental bacterial strain Bacillus licheniformis BD5, sampled at the Bullicame Hot Springs (Viterbo, Central Italy), was used to synthesize calcite from As-enriched growth media. Both liquid and solid cultures were applied to simulate planktonic and biofilm community environments, respectively. Bacterial calcite samples cultured in liquid media had an As enrichment factor (Kd) 50 times higher than that from solid media. The XRD analysis revealed an elongation of the crystal lattice along the c axis (by 0.03Å) for biogenic calcite, which likely resulted from the substitution of larger arsenate for carbonate in the crystal. The XAS data also showed a clear difference in the oxidation state of sorbed As between bacterial and abiotic calcite. Abiotic chemical processes yielded predominantly As(V) uptake whereas bacterial precipitation processes led to the uptake of both As(III) and As(V). The presence of As(III) in bacterial calcite is proposed to result from subsequent reduction of arsenate to arsenite by bacterial activities. To the best of our knowledge, this is the first experimental observation of the incorporation of As(III) in the calcite crystal lattice, revealing a critical role of biochemical processes for the As cycling in nature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

Abstract Background A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. Results We present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. Conclusions Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Analysis of RF losses and material characterization of samples removed from a Nb 3 Sn-coated superconducting RF cavity

Nb 3 Sn (T c ≈18 K and H sh ≈400 mT) is a prospective material to replace Nb (T c ≈9 K and H sh ≈200 mT) in SRF accelerator cavities for significant cost reduction and performance enhancement. Because of its material properties, Nb3Sn is best employed as a thin film (coating) inside an already built RF cavity structure. A particular test cavity noted as C3C4 was a 1.5 GHz single-cell Nb cavity, coated with Nb3Sn using Sn vapor diffusion process at Jefferson Lab. Cold measurements of the coated cavity indicated the superconducting transition temperature of about 18 K. Subsequent RF measurements indicated field-dependent surface resistance both at 4.3 K and 2.0 K. After initial cold measurements, the cavity RF loss distribution was studied with a thermometry mapping system. Loss regions were identified with thermometry and were cut out for material analysis. The presence of significantly thin patchy regions and other carbon-rich defects is associated with strong local field-dependent surface resistance. This paper summarizes RF and thermometry results correlated with material science findings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Large-Grained Cylindrical Block Copolymer Morphologies by One-Step Room-Temperature Casting

We report a facile method of ordering block copolymer (BCP) morphologies in which the conventional two-step casting and annealing steps are replaced by a single-step process where microphase separation and grain coarsening are seamlessly integrated within the casting protocol. This is achieved by slowing down solvent evaporation during casting by introducing a non-volatile solvent into the BCP casting solution what effectively prolongs the duration of the grain-growth phase. We demonstrate the utility of this solvent evaporation annealing (SEA) method by producing well-ordered large molecular-weight BCP thin films in a total processing time shorter than 3 minutes without resorting to any extra laboratory equipment other than a basic casting-device i.e., spin- or blade-coater. By analyzing the morphologies of the quenched samples, we identify a relatively narrow range of polymer concentration in the wet film, just above the order-disorder concentration, to be critical for obtaining large-grained morphologies. This finding is corroborated by the analysis of the grain-growth kinetics of horizontally-oriented cylindrical domains where relatively large growth exponents (1/2) are observed, indicative of a more rapid defect-annihilation mechanism in the concentrated BCP solution than in thermally-annealed BCP melts. Furthermore, the analysis of temperature-resolved kinetics data allows us to calculate the Arrhenius activation energy of the grain-coarsening in this one-step BCP ordering process.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Organic contamination detection for isotopic analysis of water by laser spectroscopy

Hydrogen and oxygen stable isotope ratios (δ 2 H, δ 17 O, and δ 18 O values) are commonly used tracers of water. These ratios can be measured by isotope ratio infrared spectroscopy (IRIS). However, IRIS approaches are prone to errors induced by organic compounds present in plant, soil, and natural water samples. A novel approach using 17 O-excess values has shown promise for flagging spectrally contaminated plant samples during IRIS analysis. A systematic assessment of this flagging system is needed to prove it useful. Errors induced by methanol and ethanol water mixtures on measured IRIS and isotope ratio mass spectrometry (IRMS) results were evaluated. For IRIS analyses both liquid- and vapour-mode (via direct vapour equilibration) methods are used. The δ 2 H, δ 17 O, and δ 18 O values were measured and compared with known reference values to determine the errors induced by methanol and ethanol contamination. In addition, the 17 O-excess contamination detection approach was tested. This is a post-processing detection tool for both liquid and vapour IRIS triple-isotope analyses, utilizing calculated 17 O-excess values to flag contaminated samples. Organic contamination induced significant errors in IRIS results, not seen in IRMS results. Methanol caused larger errors than ethanol. Results from vapour-IRIS analyses had larger errors than those from liquid-IRIS analyses. The 17 O-excess approach identified methanol driven error in liquid- and vapour-mode IRIS samples at levels where isotope results became unacceptably erroneous. For ethanol contaminated samples, a mix of erroneous and correct flagging occurred with the 17 O-excess method. Here our results indicate that methanol is the more problematic contaminant for data corruption. The 17 O-excess method was therefore useful for data quality control. Organic contamination caused significant errors in IRIS stable isotope results. These errors were larger during vapour analyses than during liquid IRIS analyses, and larger for methanol than ethanol contamination. The 17 O-excess method is highly sensitive for detecting narrowband (methanol) contamination error in vapour and liquid analysis modes in IRIS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

DOE JGI Metagenome Workflow

The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Dual stable isotopes of CH 4 from Yellowstone hot-springs suggest hydrothermal processes involving magmatic CO 2

Volcanism and post-magmatism contribute both significant annual CH 4 fluxes to the atmosphere (on par with other natural sources such as forest fire and wild animal emissions) and have been implicated in past climate-change events. The Yellowstone hot spot is one of the largest volcanic systems on Earth and is known to emit methane in addition to other greenhouse gases (e.g. carbon dioxide) but the ultimate source of this methane flux has not been elucidated. Here we use dual stable isotope analysis (δ 2 H and δ 13 C) of CH 4 (g) sampled from ten high-temperature geothermal pools in Yellowstone National Park to show that the predominant flux of CH4(g) is abiotic. The average δ 13 C and δ 2 H values of CH 4 (g) emitted from hot springs (-26.7 (±2.4) and -236.9 (±12.0) ‰, respectively) are not consistent with biotic (microbial or thermogenic) methane sources, but are within previously reported ranges for abiotic methane production. Correlation between δ 13 C CH4 and δ 13 C-dissolved inorganic C (DIC) also suggests that CO 2 is a parent C source for the observed CH 4 (g). Moreover, CH 4 -CO 2 isotopic geothermometry was used to estimate CH 4 (g) formation temperatures ranging from ~ 250 - 350°C, which is just below the temperature estimated for the hydrothermal reservoir and consistent with the hypothesis that subsurface, rock-water interactions are responsible for large methane fluxes from this volcanic system. An understanding of conditions leading to the abiotic production of methane and associated isotopic signatures are central to understanding the evolutionary history of deep carbon sources on Earth.

58 GEOSCIENCES↗
DOE OSTI2012

Fe-24 wt.%Cr-4.1 wt.%C hardfacing alloy: Microstructure and carbide refinement mechanisms with ceria additive

The microstructure and carbide refinement mechanisms of Fe-24 wt.%Cr-4.1 wt.%C hardfacing alloys with 0 wt.%, 0.5 wt.%, 1.0 wt.%, 2 wt.%, and 4 wt.% ceria additives have been systematically investigated in this work. Optical microscopy, field emission scanning electron microscopy with energy dispersive spectrometer, and X-ray diffraction were collectively used to study the microstructure, the phase components, and the chemical formation of inclusion formed in the welding process. Wear-resistance of the alloys was comparatively studied using an abrasive wear testing machine. The structure analysis results show that the Fe-Cr-C hardfacing alloy mainly consists of martensite, retained austenite, MC carbide and M{sub 7}C{sub 3} carbide. With increasing ceria additive contents, the average size of the primary M{sub 7}C{sub 3} carbide decreases and reaches a most refined state in the alloy with 2 wt.% ceria additives. Comparative wear tests data shows that the wear resistance of the hardfacing alloys with ceria additives is better than that without ceria additive. In a good agreement with the carbide refinement results, the wear resistance of the alloy reaches an optimum level in the sample with 2 wt.% ceria additive. The main RE inclusion type identified with in-situ XRD analysis is RE inclusion Ce{sub 2}O{sub 2}S. Thermodynamics calculation confirms that this type of RE inclusion could precipitate prior to M{sub 7}C{sub 3} carbides, and act as a heterogeneous nucleus for M{sub 7}C{sub 3} in the welding process, which effectively provides a mechanism for significant refinement of the M{sub 7}C{sub 3} carbide and improves its wear resistance. - Graphical Abstract: Rare Earth inclusion (Ce{sub 2}O{sub 2}S) distributes in the primary M{sub 7}C{sub 3} carbide. Moreover, Ce{sub 2}O{sub 2}S, which acts as heterogeneous nuclei of the primary M{sub 7}C{sub 3} carbide, is medium effective. Therefore, the primary M{sub 7}C{sub 3} carbide has been refined. Highlights: Black-Right-Pointing-Pointer Micro-hardness of primary M{sub 7}C{sub 3} carbide in Fe-Cr-C hardfacing alloy is 1594 HV. Black-Right-Pointing-Pointer RE inclusion Ce{sub 2}O{sub 2}S can be observed in the primary M{sub 7}C{sub 3} carbide. Black-Right-Pointing-Pointer Ce{sub 2}O{sub 2}S as heterogeneous nuclei of the Cr{sub 7}C{sub 3} is medium effective. Black-Right-Pointing-Pointer Primary carbide is most refined with 2 wt.% ceria additive.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Measuring Hydroxylammonium, Nitrate, and Nitrite Concentration with Raman Spectroscopy for 238 Pu Supply Program

Optical spectroscopy has great potential to improve the timeliness of important analytical measurements for the 238 Pu Supply Program by enabling real-time analysis of process solutions within radiochemical hot cells. Hydroxylammonium (H 3 NOH + ]), nitrate (NO 3 - ), and nitrite (NO 2 - ) are three important molecular species present in multiple aqueous streams throughout the program’s flow sheet. The concentration of each species normally goes uncharacterized during processing. However, rapid quantification may benefit the program because uncertainties in kinetic redox expressions describing hydroxylammonium (HA) and nitrite reactions with Np and Pu leads to uncertainty in process model predictions for solvent extraction runs. HA concentration is normally quantified using ion chromatography, a technique that requires diluted grab samples taken out of the hot cell and analyzed off-line. Here we evaluate Raman spectroscopy for determining the concentration of H 3 NOH + , NO 3 - , and NO 2 - in benchtop tests to determine limits of detection. We also consider the possibility of using Raman spectroscopy in both glove box and hot cell environments for rapid analysis and pinpoint additional development opportunities needed to make this application successful.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fracturing process of micro-concrete under uniaxial and triaxial compression: Insights from in-situ X-ray mechanical tests

This paper presents an experimental study of concrete at meso-scale (aggregates, macro-pores and mortar matrix) in order to get a better understanding of the local failure mechanisms known to drive the macroscopic mechanical behaviour of the material. The main originality comes from conducting in-situ X-ray mechanical tests on micro-concrete samples of realistic composition (including cement, sand, aggregates and water), under uniaxial compression and, for the first time, under triaxial compression at 5 MPa, 10 MPa and 15 MPa confining pressures. A timeseries analysis of the set of 3D images coming from each test allows for the measurement of the 3D kinematic fields (displacement and strain fields) throughout the experiments. The different failure patterns observed for each loading path are discussed, along with a quantification of the 3D fracturing processes at the scale of the largest heterogeneities (aggregates and macro-pores). With an increasing level of confinement, the transition from brittle to ductile response is observed, as well as an increase of the strength of the material. The pronounced impact of the meso-scale heterogeneities of concrete on their local failure mechanisms is highlighted. It is shown that strain localisation mainly originates between aggregates and mortar matrix, with the shape and location of the largest aggregates and macro-pores essentially driving the propagation of the cracking network.

36 MATERIALS SCIENCE↗