Computational modeling and experiments of an elastoviscoplastic fluid in a thin mold-filling geometry
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Molten salt reactors (MSRs) often employ graphite as a moderator and reflector. An important challenge for deploying graphite in these reactors is that, due to limited experimental data, our understanding of graphite’s structural integrity in molten salt environments remains incomplete. Here, this study addresses heat generation from fuel-bearing salt that has infiltrated open pores in the graphite, driven primarily by pressure differentials. This is one of multiple identified physical and chemical mechanisms through which molten salt could potentially degrade graphite. Thermally driven stresses are quantified using the Molten-Salt Reactor Experiment (MSRE) graphite moderator elements as a case study. Finite element simulations predict stress distributions at varying infiltration levels, indicating that thermal stresses increase with higher infiltration. Rare-event simulations using the parallel subset simulation framework identify the combinations and corresponding ranges of input parameters that lead to stresses above a specified threshold. In particular, combinations involving high infiltration amounts, high power density, and low thermal conductivity tend to induce the highest stresses. Under the inputs and assumptions considered in this work, the magnitudes of the thermally driven stresses are quite low, with a very low likelihood of causing failure due to exceeding the graphite’s tensile strength. Additionally, rare-event simulations were performed for two more scenarios: a scaled-up moderator geometry and a localized hotspot in the original geometry. Both cases resulted in increased susceptibility to failure, though not to a detrimental extent. Furthermore, the combined effects of irradiation and infiltration-induced thermal stresses were evaluated. The results showed that thermal stresses from infiltration were negligible compared to those caused by irradiation. The findings of such a study are inherently component-specific, but the methodology presented here could be used for similar assessments of salt-infiltration effects in other graphite components.
Sooting propensity, a measurement of how much particulate matter is produced when a fuel is burned, is a property of significant interest among researchers who are striving to discover the next generation of cleaner, more efficient fuels and fuel additives. Many compounds are not viable as fuels and/or fuel additives, and as a result, designing cleaner-burning biofuels using only experimental techniques is inefficient. Predictive models have been instrumental in reducing this inherent difficulty, providing researchers with a tool to preemptively screen compounds before production and testing. The present work compares the accuracies and interpretabilities of existing models used to predict a particular measure of sooting propensity, Yield Sooting Index (YSI). These models include artificial neural networks, graph neural networks, and multivariate equations. A novel equation for predicting YSI based on atom path count and bond order is proposed, which can highlight key structural components that contribute to YSI. It was found that artificial neural networks slightly outperform graph neural networks and greatly outperform multivariate equations in blind (test set) prediction accuracy; however, graph neural networks and multivariate equations provide significantly more interpretability as to how compound structure relates to YSI. Predictions of YSI are compared to experimental measurements for previously un-tested compounds with cetane numbers comparable to diesel fuel (50-60) (butyl decanoate, ethyl decanoate, 1,4-bis(ethenoxymethyl)cyclohexane, and 5-heptyloxolan-2-one), and it was found that these compounds produce significantly less soot compared to diesel fuel.
A submerged wave device generates energy from the relative motion of floating bodies. In WaveSub, three floats are joined to a reactor; each connected to a spring and generator. Electricity generated damps the orbital movements of the floats. The forces are non-linear and each float interacts with the others. Tuning to the wave climate is achieved by changing the line lengths, so there is a need to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design. to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design.
The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.
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Microkinetic modeling based on density functional theory (DFT) derived energetics is important for addressing fundamental questions in catalysis. The quantitative fidelity of microkinetic models (MKMs), however, is often insufficient to conclusively infer the mechanistic details of a specific catalytic system. Here, this can be attributed to a number of factors such as an incorrect model of the active site for which DFT calculations are performed, deficiencies in the hypothesized reaction mechanism, inadequate consideration of the surface environment under reaction conditions, and intrinsic errors in the DFT exchange-correlation functional. Despite these limitations, we aim at developing a rigorous understanding of the reaction mechanism and of the nature of the active site for heterogeneous catalytic chemistries under reaction conditions. By achieving parity between experimental and modeling outcomes through robust parameter estimation and by ensuring coverage-consistency between DFT calculations and MKM predictions, it is possible to systematically refine the mechanistic model and, thereby, our understanding of the catalytic active site in situ.
Abstract The 2023 M7.8 Kahramanmaraş/Pazarcik earthquake was larger and more destructive than what had been expected. Here we analyzed nearfield seismic records and developed a dynamic rupture model that reconciles different currently conflicting inversion results and reveals spatially non-uniform propagation speeds in this earthquake, with predominantly supershear speeds observed along the Narli fault and at the southwest (SW) end of the East Anatolian Fault (EAF). The model highlights the critical role of geometric complexity and heterogeneous frictional conditions in facilitating continued propagation and influencing rupture speed. We also constrained the conditions that allowed for the rupture to jump from the Narli fault to EAF and to generate the delayed backpropagating rupture towards the SW. Our findings have important implications for understanding earthquake hazards and guiding future response efforts and demonstrate the value of physics based dynamic modeling fused with near-field data in enhancing our understanding of earthquake mechanisms and improving risk assessment.
Passive NO x adsorbers (PNA) using Pd/zeolites have emerged as a promising solution for the reduction of cold-start emissions from vehicle exhaust. However, the nature of the active sites and the mechanisms underlying NO x adsorption in Pd/zeolites remain a subject of ongoing investigation. In this study, we employ quantum chemical simulations to investigate the structure of Pd species in cation-exchange sites at isolated Al and Al pairs in the 6-ring and 8-ring of the CHA framework before the introduction of NO x . Our calculations show that the speciation of Pd in these exchange sites strongly depends on the precise Al arrangement within the framework, as well as the operating conditions. Ionically dispersed Pd is found to be the most favorable species over a wide range of oxidizing and reducing conditions. Small oligomers of PdO and metallic Pd do not appear to be competitive at either isolated Al or Al pairs. Notably, our calculations show that ion exchange sites other than next–next–nearest neighbor Al pairs in the 6-ring will be preferentially occupied by Pd + instead of Pd 2+ . The stability of Pd + in the zeolite environment is an interesting contrast with its rareness in molecular Pd compounds. Nonetheless, a detailed analysis of the electronic structure shows that predicted Pd oxidation states are consistent with chemical intuition for all complexes investigated in this study. We also discuss the potential ambiguity in Pd characterization provided by typical experimental techniques such as XANES, EXAFS and UV-Vis, and highlight the need for additional EPR spectroscopy studies to further elucidate the initial Pd speciation in zeolites for PNA applications.
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Abstract Quasibrittle materials are featured by a strain-softening constitutive behavior under many loading scenarios, which could eventually lead to localization instability. It has long been known that strain localization would result in spurious mesh sensitivity in finite element (FE) simulations. Previous studies have shown that, for the case of fully localized damage, the mesh sensitivity can be mitigated through energy regularization of the material constitutive law. However, depending on the loading configuration and structural geometry, quasibrittle structures could exhibit a complex damage process, which involves both localized and diffused damage patterns at different stages of loading. This study presents a generalized energy regularization method that considers the spatial and temporal evolution of damage pattern. The method introduces a localization parameter, which describes the local damage pattern. The localization parameter governs the energy regularization of the constitutive model, which captures the transition from diffused to localized damage during the failure process. The method is cast into an isotropic damage model, and is further extended to rate-dependent behavior. The energy regularization scheme is directly incorporated into the kinetics of damage growth. The model is applied to simulate static and dynamic failures of ceramic specimens. It is shown that the present model is able to effectively mitigate the spurious mesh sensitivity in FE simulations of both types of failure. The present analysis demonstrates the essential role of mechanism-based energy regularization of constitutive relation in FE simulations of quasibrittle fracture.
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A focus of the U.S. Department of Energy is to improve production yield and reduce the cost of Low Enriched Uranium (LEU)-molybdenum alloy (U-10Mo) monolithic fuel plates that will be replacing High Enriched Uranium (HEU) oxide dispersion fuels used currently in the United States High Performance Research Reactors (USHPRR). One area of improvement is lowering the high transverse waviness and longitudinal waviness currently present within rolled foils prior to cladding to produce fuel plates. Traditional rolling manufacturing techniques for other metal foils use winders to pull and straighten the foil as it is rolled back and forth to the final thickness. This approach cannot be used to roll thin U-10Mo foils (0.008-0.025” thick) because only small castings can be rolled due to nuclear criticality safety concerns. As a result, the fuel foils are too short (1 m in length) to use traditional winders. Therefore, it is crucial to identify other rolling parameters (i.e., roller friction, axial tension load, roller diameter, and roll pass reduction percent) that might reduce transverse waviness and longitudinal waviness in the rolled fuel foil and develop a high-yield, low-cost multi-pass rolling manufacturing process. This report documents a systematic finite element modeling study to investigate the effects of numerous rolling parameters to reduce resulting transverse waviness and longitudinal waviness in the fuel foil during multi-pass rolling of U-10Mo foils. The rolling of a U-10Mo plate with initial dimensions of 1”x1”x 0.048” is modeled using Abaqus CAE. This rolling is modeled to undergo eight 20% reduction roll passes to a final fuel foil thickness of 0.01”. The elastic-plastic constitutive model of the U-10Mo alloy was input to the fuel foil rolling model. The rollers were modeled as rigid bodies. A comparison of rolling friction coefficients of 0.3 and 0.7 over a wide range of applied axial tension loads were investigated in order to evaluate the effect of using a lubricant during rolling. The effect of roller diameter on the resulting transverse waviness and longitudinal waviness of the fuel foil over a wide range of axial tension loads were also investigated by modeling rollers 7/8” and 3.75” in diameter. The results of this systematic finite element method study will aid manufacturers in producing low transverse waviness and reduced longitudinal waviness in U-10Mo fuel foils.
Charge heterogeneity is a prevalent feature in many electrochemical systems. In a commercial cathode of Li-ion batteries, the composite is hierarchically structured across multiple length scales including the sub-micron single-crystal primary-particle domains up to the macroscopic particle ensembles. The redox kinetics of charge transfer and mass transport strongly couples with mechanical stresses. This interplay catalyzes substantial heterogeneity in the charge (re)distribution, stresses, and mechanical damage in the composite electrode during charging and discharging. We assess the heterogeneous electrochemistry and mechanics in a LiNi x Mn y Co z O 2 (NMC) cathode using a fully coupled electro-chemo-mechanics model at the cell level. A microstructure-resolved model is constructed based on the synchrotron X-ray tomography data. We calculate the stress field in the composite and then quantitatively evaluate the kinetics of surface charge transfer and Li transport biased by mechanical stresses. We further model the cyclic behavior of the cell. The repetitive deformation of the active particles and the weakening of the interfacial strength cause gradual increase of the interfacial debonding. The mechanical damage impedes electron transfer, incurs more charge heterogeneity, and results in the capacity degradation in batteries over cycles.
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