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Results for “COMPUTER MODELING”
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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Computational modeling of corneal and scleral collagen photocrosslinking
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Manifold learning for the emulation of spatial fields from computational models
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A computational model for nanosecond pulse laser-plasma interactions
A multi-physics numerical model for laser-induced optical breakdown and laser-plasma interaction in a non-equilibrium gas is presented, accounting for: production of priming electrons via multi-photon ionization, energy absorption, cascade ionization, induced hydrodynamic response, and shock formation and propagation. The gas is governed by the Navier-Stokes equations, with non-equilibrium effects taken into account by means of a two-temperature model. The space-time dependence of the laser beam is modeled with a flux-tube formulation for the Radiative Transfer Equation. The flow governing equations are discretized in space using a second-order finite volume method. The semi-discrete equations are marched in time using an implicit-explicit (IMEX) dual time-stepping strategy, where diffusion and chemistry are solved implicitly, whereas convection is explicit. Application to a 20 ns long 50 mJ pulse laser-induced breakdown in quiescent O 2 shows the advantages of this temporal discretization during and just after the laser pulse, while a less-expensive symmetric Strang splitting (with implicit chemistry) is sufficient for the post-breakdown gas dynamics after ≃ 0.1 μs. The integrated model is shown to reproduce key features of corresponding experiments.
Computational modeling and experiments of an elastoviscoplastic fluid in a thin mold-filling geometry
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Computational modeling of graphite degradation in molten salt reactors: Role of infiltration
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.
A comparison of computational models for predicting yield sooting index
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.
Computational modelling and experimental tank testing of the multi float WaveSub under regular wave forcing
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.
Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning
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.
Experimental testing and computational modeling of a radial packed bed for thermal energy storage
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Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis
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.
A Family of Layered Phosphates Crystallizing in a Rare Geometrical Isomer of the Phosphuranylite Topology: Synthesis, Characterization, and Computational Modeling of A 4 [(UO 2 ) 3 O 2 (PO 4 ) 2 ] (A = Alkali Metal) Exhibiting Intralayer Ion Exchange
Single crystals of eight new layered uranyl phosphates were grown from alkali chloride fluxes: Cs 1.4 K 2.6 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], Cs 0.7 K 3.3 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], Rb 1.4 K 2.6 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], K 4 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], K 2.9 Na 0.9 Rb 0.2 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], K 2.1 Na 0.7 Rb 1.2 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], Cs 1.7 K 4.3 [(UO 2 ) 5 O 5 (PO 4 ) 2 ], and Rb 1.6 K 4.4 [(UO 2 ) 5 O 5 (PO 4 ) 2 ]. All structures crystallize in the monoclinic space group, P 2 1 / c and contain uranyl phosphate layers with alkali metals located between the layers for charge balance. Ion exchange experiments on Cs 0.7 K 3.3 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], Rb 1.4 K 2.6 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], and K 4 [(UO 2 ) 3 O 2 (PO 4 ) 2 ] demonstrated that Cs and Rb cations cannot be exchanged for K cations; however, K cations can be readily exchanged for Na, Rb, and Cs. Enthalpies of formation were calculated from density functional theory (DFT) and volume-based thermodynamics (VBT) for all six structures. A value for the enthalpy of formation of the phosphuranylite sheet, [(UO 2 ) 3 O 2 (PO 4 ) 2 ] 4– , was derived using single-ion additive methods coupled with VBT. Finally, DFT and VBT calculations were used to justify results of the ion exchange experiments. Cs 0.7 K 3.3 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], Rb 1.4 K 2.6 [(UO 2 ) 3 O 2 (PO 4 ) 2 ], and K 4 [(UO 2 ) 3 O 2 (PO 4 ) 2 ] exhibit typical luminescence of the uranyl group.
Computational Modeling of the Nature and Role of Ga Species for Light Alkane Dehydrogenation Catalyzed by Ga/H-MFI
Ga-exchanged H-MFI zeolites are highly active for the dehydrogenation of light alkanes; however, both the nature of the active gallium species and the associated dehydrogenation mechanism have been difficult to establish. In this study, we examine the activity of Ga species in Ga/H-MFI by calculating the free energy landscapes on which all reactions occur. To this end, we use a hybrid quantum mechanics/molecular mechanics model for all electronic structure calculations. Quantum chemical calculations were carried out with a range-corrected functional and a good representation of dispersive interactions. The molecular mechanics part of our approach captures the long-range effects of Coulombic and dispersive interactions due to atoms in the extended framework. The rate-determining TS (RDTS) is identified by analysis of the free energy landscape for each mechanism, using the energetic span model. Our analysis reveals that, for reduced Ga/H-MFI, univalent and divalent gallium hydrides, [GaH 2 ] + and [GaH] 2+ , respectively, are more active for ethane dehydrogenation in comparison to H + sites and Ga + sites. [GaH] 2+ sites consistently emerge as the most active sites for light alkane dehydrogenation, providing significant enthalpic stabilization to C-H cleavage TSs via alkyl and carbenium dehydrogenation routes. In contrast, carbenium-like C-H cleavage TSs occurring on Brønsted acid sites are enthalpically less favorable due to their limited electronic interactions with framework O atoms and H + sites. Activation enthalpy barriers for dehydrogenation determined using the energetic span analysis are in good agreement with those measured experimentally. Accounting for entropy of activation reveals that constrained TSs become less favorable in free energy with increasing chain length. Here, we also find that an increase in enthalpic favorability of the alkyl mechanism is observed with increasing chain length for the TS responsible for the second C-H cleavage step leading to alkene formation.
Establishing Cost-Effective Computational Models for the Prediction of Lanthanoid Binding in [Ln(NO 3 )] 2+ (with Ln = La to Lu)
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Dynamics of episodic supershear in the 2023 M7.8 Kahramanmaraş/Pazarcik earthquake, revealed by near-field records and computational modeling
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.