2020
DOI: 10.1016/j.jnucmat.2020.152069
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Development of a grain growth model for U3Si2 using experimental data, phase field simulation and molecular dynamics

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Cited by 20 publications
(11 citation statements)
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“…The geometry of the rodlet is represented using a 2D-RZ axisymmetric assumption with a smeared fuel pellet column (i.e., dishes and chamfers are not explicitly modeled). Given that the thermal conductivity of U 3 Si 2 is so high [20] and the athermal term in equations 3.4-3.6 is so small, virtually no creep is observed at nominal operating linear powers (e.g., [20][21][22][23][24][25] kW/m). Thus, in this study, the linear power supplied to the fuel is ramped up over 10,000 seconds from 0 to 35 kW/m and held for ∼2.54 years to observe a still, minute effect due to creep.…”
Section: Fission Gas Behavior Updatesmentioning
confidence: 99%
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“…The geometry of the rodlet is represented using a 2D-RZ axisymmetric assumption with a smeared fuel pellet column (i.e., dishes and chamfers are not explicitly modeled). Given that the thermal conductivity of U 3 Si 2 is so high [20] and the athermal term in equations 3.4-3.6 is so small, virtually no creep is observed at nominal operating linear powers (e.g., [20][21][22][23][24][25] kW/m). Thus, in this study, the linear power supplied to the fuel is ramped up over 10,000 seconds from 0 to 35 kW/m and held for ∼2.54 years to observe a still, minute effect due to creep.…”
Section: Fission Gas Behavior Updatesmentioning
confidence: 99%
“…Further details on the form of the equations and the determination of their uncertainty can be found in [2]. It should be noted that, based on [24] that grain growth of U 3 Si 2 can be ignored. In addition, since the mechanism of densification will be different in U 3 Si 2 compared to UO 2 it was neglected in this study, contrary to previous investigations [2,25].…”
Section: Validationmentioning
confidence: 99%
“…According to experimental data and molecular dynamics (M.D.) simulation results (Beeler et al, 2019), Cheniour et al (2020) set up a phasefield model and investigated how grain size changes with time under ideal circumstances by giving a quantitative relationship. Nevertheless, how the microstructure evolves and whether an underlying mechanism dominates this process remain unknown.…”
Section: Introductionmentioning
confidence: 99%
“…PFM uses a series of free energy functionals and kinetic governing equations to continuously describe the evolution of order parameters, including phase fraction, grain orientation, chemical component and so on, without tracking the location of interfaces. For nuclear fuels and cladding materials, the phase field method has been applied not only to the simulation of fabrication processes (Guo et al, 2018), but also to the modeling of damage and defect evolution after irradiation Liang et al, 2018;Tonks et al, 2018;Cheniour et al, 2020), including bubble evolution, void formation and evolution, pore migration, interstitial loop growth and sink strength, segregation and precipitation. Although, at present, many of phase field simulations should be called qualitative or semi-quantitative, there has been an irreversible trend of gradually transitioning from qualitative PFMs to quantitative models aiming for systemspecific predictive power (Tonks and Aagesen, 2019;Konings and Stoller, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…To this end, several major difficulties need to be overcome, including the inclusion of multiple physical mechanisms, accurate acquisition of model parameters, phase field programming and other computational efficiency issues (Tonks and Aagesen, 2019). At present, there are not many quantitative phase field simulations in the field of nuclear materials, which mainly focus on the microstructural evolution that do not involve complex phase transitions, such as grain growth in nuclear fuels (Ahmed et al, 2014;Mei et al, 2016;Cheniour et al, 2020) and radiation-induced segregation in Fe-Cr binary alloy (Piochaud et al, 2016), etc. The common feature of these simulations is that the input parameters of the phase fields are mainly low-scale parameters, such as lattice constants, elastic constants, interface energy and atomic mobility, which can be directly calculated by DFT or MD.…”
Section: Introductionmentioning
confidence: 99%