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At least 1,009 records · Page 56

Seismic Monitoring at the Farnsworth CO2-EOR Field Using Time-Lapse Elastic-Waveform Inversion of 3D-3C VSP Data

During the Development Phase of the U.S. Southwest Regional Partnership on Carbon Sequestration, supercritical CO2 was continuously injected into the deep oil-bearing Morrow B formation of the Farnsworth Unit in Texas for Enhanced Oil Recovery (EOR). The project injected approximately 94 kilotons of CO2 to study geologic carbon storage during CO2-EOR. A three-dimensional (3D) surface seismic dataset was acquired in 2013 to characterize the subsurface structures of the Farnsworth site. Following this data acquisition, the baseline and three time-lapse three-dimensional three-component (3D-3C) vertical seismic profiling (VSP) data were acquired at a narrower surface area surrounding the CO2 injection and oil/gas production wells between 2014 and 2017 for monitoring CO2 injection and migration. With these VSP datasets, we inverted for subsurface velocity models to quantitatively monitor the CO2 plume within the Morrow B formation. We first built 1D initial P-wave (Vp) and S-wave (Vs) velocity models by upscaling the sonic logs. We improved the deep region of the Vp and Vs models by incorporating the deep part of a migration velocity model derived from the 3D surface seismic data. We improved the shallow region of 3D Vp and Vs models using 3D traveltime tomography of first arrivals of VSP downgoing waves. We further improved the 3D baseline velocity models using elastic-waveform inversion (EWI) of the 3D baseline VSP upgoing data. Our advanced EWI method employs alternative tomographic and conventional gradients and total-variation-based regularization to ensure the high-fidelity updates of the 3D baseline Vp and Vs models. We then sequentially applied our 3D EWI method to the three time-lapse datasets to invert for spatiotemporal changes of Vp and Vs in the reservoir. Our inversion results reveal the volumetric changes of the time-lapse Vp and Vs models and show the evolution of the CO2 plume from the CO2 injection well to the oil/gas production wells.

42 ENGINEERING↗

Influence of solvent structure and hydrogen bonding on catalysis at solid–liquid interfaces

Solvent molecules interact with reactive species and alter the rates and selectivities of catalytic reactions by orders of magnitude. Specifically, solvent molecules can modify the free energies of liquid phase and surface species via solvation, participating directly as a reactant or co-catalyst, or competitively binding to active sites. These effects carry consequences for reactions relevant for the conversion of renewable or recyclable feedstocks, the development of distributed chemical manufacturing, and the utilization of renewable energy to drive chemical reactions. First, we describe the quantitative impact of these effects on steady-state catalytic turnover rates through a rate expression derived for a generic catalytic reaction (A → B), which illustrates the functional dependence of rates on each category of solvent interaction. Second, we connect these concepts to recent investigations of the effects of solvents on catalysis to show how interactions between solvent and reactant molecules at solid–liquid interfaces influence catalytic reactions. Furthermore, this discussion demonstrates that the design of effective liquid phase catalytic processes benefits from a clear understanding of these intermolecular interactions and their implications for rates and selectivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Investigation of Chemo-Mechanical Phenomena and Li Metal Penetration in All-Solid-State Lithium Metal Batteries Using In Situ Optical Curvature Measurements

Solid-electrolytes (SEs) can provide a pathway to increase energy-density in lithium metal batteries. However, lithium metal penetration through garnet based LLZO solid electrolytes has been identified as a critical failure process. This phenomenon is related to chemo-mechanical processes which are difficult to probe. In particular, characterizing the dynamic mechanical deformations that occur in electrode-SE structures is very challenging. Here study reports in situ curvature measurements that are thus designed to probe chemo-mechanical phenomena that occur during lithium plating. The novel experimental cell configuration created for this work shows that pressure builds up in the Li metal during plating, up until the point where short circuits occur. The resulting data are analyzed with a detailed finite element model (FEM) to quantitatively evaluate stress evolution. The results show that Li metal plating within a surface flaw can produce stress build-up prior to short-circuiting. The combined results from both the experiments and the FEM suggest that it is critical to minimize surface defects and flaws during the manufacturing processes.

25 ENERGY STORAGE↗

Advances in imaging, scattering, spectroscopy, and machine learning-aided approaches for multiscale characterization of cementitious systems

Recent progress in methods used in the multiscale characterization of cementitious systems is reviewed, focusing on advances in imaging, scattering, and spectroscopy. The review includes relevant applications and developments in machine learning and other data analytics approaches to enhance characterization. Developments in imaging using light and electron microscopy as well as x-ray (i.e., from synchrotron) methods are summarized. Updates include scanning electron microscopy (SEM), transmission electron microscopy (TEM), tomography, and holography. A critical overview of spectroscopy (e.g., MAS NMR, Raman) and scattering (e.g., neutron, x-ray, synchrotron x-ray) methods is provided, and the intersection of these with imaging is developed (e.g., Raman imaging). Additionally, the paper summarizes recent developments in and implementations of state-of-the-art machine-learning algorithms and data analytics methods for automated, systematic, and/or quantitative analyses of image data sets. The review considers but is not limited to the application of these methods for investigating the hydration and microstructure development of cement phases, low-carbon-footprint cements (e.g., limestone calcined clay cements, LC3), environmental interactions (e.g., ASR) and model systems. Finally, the present work provides a critical presentation of advances in characterization methods that link together the composition and multiscale structure of cementitious materials.

42 ENGINEERING↗

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗

Active populations and growth of soil microorganisms are framed by mean annual precipitation in three California annual grasslands

Climate influences soil microbial composition and function, but the relative importance of a site's historic climate versus its more immediate environmental conditions is unclear. Using quantitative stable isotope probing (qSIP), we characterized actively growing soil microbial communities and soil properties in three California annual grasslands that span a rainfall gradient and have developed on similar parent material. The soils were assayed in the wet winter season, when environmental conditions are most similar across sites. Since growing populations might be expected to be most responsive to contemporary environmental conditions, we hypothesized that the structure of growing microbial communities would be more similar across the gradient than that of total communities (i.e., including non-growing populations). In addition, we hypothesized that population growth rates would be slowest in the driest site, reflecting a legacy effect of low soil moisture on microbial growth. Soils along the rainfall gradient differed in pH, texture, and cation exchange capacity, but not in total C, C:N or dominant minerals. The radiocarbon (14C) age of soil C (reflecting turnover time) increased with mean annual precipitation but soil respiration was uniformly modern, reflecting microbial reliance on recent C inputs across the sites. The structure of both total and growing microbial communities differed across sites. Across major microbial phyla, including the Actinobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes and Proteobacteria, bacterial growth rates were consistently lower in the site with the lowest mean annual precipitation. Taxa that were growing at the dry site alone grew more slowly than taxa that grew at multiple sites. These results reflect the influence of climate history and point to the role of environmental filtering at the driest site in shaping its slower growing microbial community, possibly reflecting adaptation to repeated exposure to water stress. Lastly, across taxa, the growth rate of a taxon at one site was correlated with its growth rate in the other sites. Furthermore, this growth rate coherence is likely a consequence of genetically determined physiological traits and is consistent with the idea that evolutionary history constrains growth rate.

Environmental filtering↗

Synthesis and Evaluation of Cu@ZnO Core@Shell Nanowires for Use in the Carbon Dioxide Thermal Reduction Reaction

Copper-based core@shell nanomaterials are of interest for the catalytic hydrogenation of carbon dioxide toward value-added products. In this context, we have developed a facile, microwave-based procedure for the reliable and reproducible synthesis of Cu@ZnO core@shell nanowires. A systematic assessment of the effect of rationally varying various reaction conditions on this protocol was completed in order to better evaluate the growth process of these core–shell motifs. We determined that among different reaction parameters, it was the critical role of reaction time which enabled the quantitatively reliable growth of external shells with tunable thicknesses of up to 20 nm. As a demonstration of the material’s practical viability, catalytic testing was subsequently performed for the reverse water–gas shift reaction (CO 2 + H 2 → CO + H 2 O), with the evolution of the process followed with in situ X-ray diffraction and X-ray absorption spectroscopy in order to probe structural changes and gauge stability. These tests found the catalysts to be effective at converting CO 2 to CO, with notable stability detected in the shell layer and no observed alloying between copper and zinc. Furthermore, our studies support the idea that the Cu–ZnO and CuO x –ZnO interfaces are essential for the effective activation of CO 2 and H 2 .

36 MATERIALS SCIENCE↗

Reconstruction of effective potential from statistical analysis of dynamic trajectories

The broad incorporation of microscopic methods is yielding a wealth of information on the atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here, we develop a method for the stochastic reconstruction of effective local potentials solely from observed structural data collected from molecular dynamics simulations (i.e., data analogous to those obtained via atomically resolved microscopies). Using the silicon vacancy defect in graphene as a model, we apply the statistical framework presented herein to reconstruct the free energy landscape from the calculated atomic displacements. Evidence of consistency between the reconstructed local potential and the trajectory data from which it was produced is presented, along with a quantitative assessment of the uncertainty in the inferred parameters.

74 ATOMIC AND MOLECULAR PHYSICS↗

Carbon-Based Quantum Information Science with Symmetry Protected Topological States (Final Report, DOE-BES award DE-SC0023105)

This research program established the scientific foundation for the rational, bottom-up design, synthesis, isolation, and investigation of symmetry-protected topological (SPT) electron spin qubits embedded in graphene nanoribbons (GNRs). The work focused on integrating atomically precise low-dimensional carbon nanostructures with emerging quantum logic architectures, providing a pathway toward scalable quantum materials for next-generation computing and sensing technologies. A central component of the program was the elucidation of fundamental relationships between real-space molecular architecture, local spin density distributions, electronic band dispersion, and energy level alignment in atomically precise GNR systems. These correlations define key operational parameters of SPT qubits and were systematically investigated to establish quantitative benchmarks against established molecular and solid-state spin qubit platforms. Attention was given to properties critical for quantum device performance, e.g. decoherence times, spectral sharpness of energy transitions, and tunable exchange interactions between spin states. The research demonstrated that these parameters can be engineered with atomic precision through scalable bottom-up synthetic strategies. Theory-guided design played a central role in identifying candidate structures hosting topologically protected spin states. Experimental validation was performed using both ensemble measurements and single-molecule characterization. In addition to advances in quantum materials synthesis, the program developed and applied spin-sensitive scanning probe microscopy techniques capable of directly probing quantum states and dynamic processes with atomic-scale spatial resolution. These capabilities enabled direct observation and characterization of quantum structures at the single-atom level. While the research activities were primarily hypothesis-driven fundamental investigations, the program adopted a comprehensive materials-by-design framework aimed at translating scientific discoveries into technological concepts compatible with scalable and intelligent manufacturing approaches.

36 MATERIALS SCIENCE↗

Quantitative SANS and multi-model analysis of spacer-dependent micellization of urea-based gemini surfactants

The micellization behavior of urea-based cationic gemini surfactants was investigated using small-angle neutron scattering (SANS) with multi-model form factor analysis. A homologous series of surfactants with urea group included in the hydrophobic tail and polymethylene spacers consisting of two to ten methylene units was analyzed using three form factor models: a core–shell ellipsoid and two variants of homogeneous ellipsoids. The results from all models show a consistent trend of the micelle structures, confirming that the spacer length critically influences micellar geometry, aggregation number, and hydration. The surfactant with four CH 2 groups in the spacer formed the largest micelles with the highest aggregation number, while longer spacers led to progressively smaller, more compact aggregates. The shell hydration—quantified as the volume fraction of heavy water within the hydrophilic region—decreased systematically with increasing spacer length due to enhanced hydrophobicity of the headgroup-spacer region. Intermicellar interactions, modeled as screened Coulomb interaction using the rescaled mean spherical approximation (RMSA), revealed the strongest electrostatic repulsion for the case of four methylene groups in the spacer, corresponding to the highest micellar charge and largest interparticle spacing. The observed spacer-dependent trends were robust across all modeling approaches, demonstrating that the spacer length serves as a key structural determinant of self-assembly in this type of urea-based gemini systems. These findings provide insight into the design of gemini surfactants with tailored aggregation behavior for applications in drug delivery, nanostructure templating, and solubilization technologies.

Core–shell ellipsoid model↗

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗

13 C NMR study of amino acid salts in facilitated transport membranes for post-combustion carbon capture

Some of amino acid salts (AASs) have been demonstrated to be effective mobile carriers in facilitated transport membranes (FTMs) to achieve superior CO 2 permeance and CO 2 /N 2 selectivity for CO 2 capture from flue gas. Understandings of how the structures of different AASs affect the chemistry of the amine–CO 2 reaction are essential for the future development of more efficient AAS mobile carriers. In this study, the reaction chemistry of selected AASs with CO 2 was investigated by 13 C nuclear magnetic resonance (NMR) spectroscopy. The CO 2 loading and the distribution of major reaction products, including carbamate and bicarbonate products of each studied AAS were quantitatively analyzed. The positive correlation between the CO 2 loading of AAS (mol CO 2 /g AAS) and the CO 2 permeance of FTMs suggested that AAS with a higher CO 2 loading may improve the performance of FTMs. Our results also showed that increasing the steric hindrance of AAS could be a practical way to promote the bicarbonate reaction pathway and thus potentially increase the CO 2 loading (mol CO 2 /mol AAS). Moreover, 2-(1-piperazinyl)ethylamine (PZEA) was found to be a more effective multi-amine than piperazine (PZ) for synthesizing AASs due to the presence of more effective nitrogen sites per molecule. The superior CO 2 permeance of the PZEA-Sar membrane is also attributed to its more uniform membrane formation. As a result, the knowledge gained from this study will inform the rational design of more effective AAS carriers for CO 2 capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Single-component multilayered self-assembling nanoparticles presenting rationally designed glycoprotein trimers as Ebola virus vaccines

Ebola virus (EBOV) glycoprotein (GP) can be recognized by neutralizing antibodies (NAbs) and is the main target for vaccine design. Here, we first investigate the contribution of the stalk and heptad repeat 1-C (HR1 C ) regions to GP metastability. Specific stalk and HR1 C modifications in a mucin-deleted form (GPΔmuc) increase trimer yield, whereas alterations of HR1 C exert a more complex effect on thermostability. Crystal structures are determined to validate two rationally designed GPΔmuc trimers in their unliganded state. We then display a modified GPΔmuc trimer on reengineered protein nanoparticles that encapsulate a layer of locking domains (LD) and a cluster of helper T-cell epitopes. In mice and rabbits, GP trimers and nanoparticles elicit cross-ebolavirus NAbs, as well as non-NAbs that enhance pseudovirus infection. Repertoire sequencing reveals quantitative profiles of vaccine-induced B-cell responses. This study demonstrates a promising vaccine strategy for filoviruses, such as EBOV, based on GP stabilization and nanoparticle display.

60 APPLIED LIFE SCIENCES↗

First principles reactive simulation for equation of state prediction

The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman–Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. Furthermore we demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Investigation of Nanoparticle Degradation in Hydrogen Fuel Cell Systems through Automated Electron Microscopy

Proton exchange membrane fuel cells (PEMFC) are promising devices for the deployment of hydrogen-powered heavy-duty vehicles, providing a higher efficiency for similar driving range and fueling time than the existing ones. However, PEMFCs still encounter durability challenges mainly due to catalyst degradation in the cathode. Mitigating these performance losses requires a better understanding of the degradation mechanisms under heavy-duty accelerated stress tests (ASTs) [1]. Scanning transmission electron microscopy (STEM) combined with energy dispersive X-ray spectroscopy (EDS) are key tools for the analysis of Pt and PtCo nanoparticle size, spatial distribution and composition [2]. Electron tomography is also used to determine the rate and type of degradation of catalyst nanoparticles as a function of their position on the carbon support. In this work, automated data acquisition software, paired with a custom Python code, have been used to study the effect of different accelerated stress tests (ASTs) on nanoparticle coarsening [2]. Figure 1 shows high-angle annular dark-field (HAADF)-STEM images and EDS maps comparing the cathodes of membrane electrode assemblies (MEAs) following an electrocatalyst AST performed under H2/N2 with that of the heavy-duty AST performed under H2/air. We will discuss how AST conditions affect considerably the spatial distribution of the nanoparticles across the electrode between the membrane and microporous layer. Although the median particle size increased more in the MEA aged under the heavy-duty AST, as determined using a high-throughput image analysis, the quantitative EDS measurements demonstrate that the electrocatalyst AST resulted in more Pt and Co dissolution from the cathode, which is another important indicator of electrocatalyst degradation. We will further present the impact of the relative humidity (% RH) on the degradation mechanisms demonstrated using the same approach. Electron tomography has been used to distinguish the Pt nanoparticles residing on the carbon support surface (exterior) from those within the pore structure (interior) in order to determine the relative stability of interior and exterior nanoparticles. As shown in Figure 2, we will compare the Pt catalyst particle size at the beginning of test (BOT) and end of test (EOT), and discuss the importance of automating the electron tomography workflow, i.e. acquisition, reconstruction, and visualization, to increase sampling and determine the standard deviation of these measurement. The outlook for utilizing low-dose cryo-tomography for limiting damage to the catalyst, support, and especially proton-conducting ionomer will also be discussed [3].

Amichi, Lynda↗

Electro-physical properties of surface-barrier diodes on low-resistance n-Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 crystals

The electro-physical properties of Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 n-type conductivity single crystals with relatively low resistivity were studied. The determined resistivity of semiconductor single crystals Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 n-type conductivity is equal to ρ≈2 Ω-cm at 293K. The activation energy of dark conductivity, measured from the temperature dependence of the resistivity of the In/Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 /In structure, is equal to ΔE≈0.11eV. From optical measurements of the absorption coefficient from the energy of photons in the region of large α, the band-gap width of the single crystal was found, which is equal to E g =1.53eV. Diode structures of Ni/Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 /In obtained by vacuum sputtering of Ni on the surface of a single crystal n-Cd 0.96 Mn 0.04 Te 0.96 Se 0.04 were manufactured and studied. Here, the rectification coefficient at a voltage of U=1V is equal to 10 8 . Within the framework of the Sah-Noys-Shockley carrier generation-recombination model, a quantitative match of the experimental measurements of the volt-ampere characteristics with the calculation was achieved.

(Cd,Mn)(Te,Se)↗

High Performance PEFC Electrode Structures

Raytheon Technologies Research Center (RTRC) in collaboration with Ion Power Inc. and the University of Arkansas at Little Rock and the FC-PAD consortium executed a project that: (1) developed comprehensive models of oxygen and proton transport in cathode catalyst layers that provided new insights about losses associated with reducing platinum loading to 0.1 mg/cm 2 , (2) designed, fabricated, characterized, and tested cells featuring carbon-supported catalyst layers with low platinum loadings that met efficiency and power density targets set forth by the Hydrogen and Fuel Cells Technology Office, and (3) designed, fabricated, characterized, and tested novel catalyst structures. The modeling efforts produced eight scientific publications that shared new descriptions of oxygen and proton transport in catalyst layers with the broader technical community. The key physical insights from the models are expressed in straightforward algebraic expressions, facilitating easy adoption by other researchers. The models developed during the project explain why resistance to oxygen transport is inversely proportional to interfacial area of platinum – an observation that eluded quantitative explanation for a decade. The models quantitatively predict polarization when measured morphological parameters and transport properties are input. Membrane-electrode assemblies made during the project met stated goals for current at high voltage and power density at rated voltage for transportation fuel cells at the end of budget period 2. High performance was achieved by developing a diffusion layer with low resistance to oxygen transport and a PtCo/C cathode catalyst layer capable of supporting rapid proton and oxygen transport. The project was subsequently directed to curtail experimental work and focus on modeling during budget period 3. Ion Power Inc. developed new expertise in fabricating catalyst layers containing platinum-cobalt catalysts, and processing very thin membranes. The University of Arkansas at Little Rock developed novel nanocolumnar self-supported thin film electrocatalysts that can be grown on carbon supports using a simple high pressure sputter deposition method. The nanocolumnar microstructure provides adequate surface-to-volume ratio for efficient platinum utilization, and the conformal platinum shell with larger crystal grain sizes covering the carbon support surface may eliminate durability issues associated with catalyst dissolution, agglomeration, and carbon corrosion.

08 HYDROGEN↗

An Engineering Approach for Weld Creep Lifetime Assessment Based on Local Property Measurement

The localized creep failure in the heat-affected zone (HAZ) of Grade 91 steel weldments has been identified as one of the most important factors causing significantly shortened service lifetime and structural integrity issues of welded components in advanced fossil and nuclear power plants. To conduct a reliable creep lifetime assessment, a new engineering assessment approach has been developed by incorporating the experimentally determined local properties of the heterogeneous HAZ. By creep testing a purposely simulated HAZ specimen with in situ digital image correlation (DIC) technique, the highly gradient creep properties across the HAZ of Grade 91 steel was quantitatively measured. A physical creep cavitation constitutive model was proposed to investigate the local creep deformation and damage accumulation within the heterogeneous HAZ, which takes into account the nucleation of creep cavities and their growth by both grain boundary diffusion and creep deformation. The relationship among the local material property, creep strain accumulation, and evolution characteristic of creep cavities was established. The approach was then utilized to investigate the creep response and subsequent life for an ex-service 9% Cr steel weldment by incorporating the effects of pre-existing damages which developed and accumulated during long-term services. The predicted results exhibited quantitative agreement with the DIC measurement in terms of both nominal/local creep deformation as well as the subsequent life under the test conditions at 650 and 80 MPa.

Zhang, Wei↗