“…In many cases, only the specimen weight change could not only provide the wrong kinetical constant value but also mislead the alloy comparison. The image analysis is also used for evaluation of oxidation kinetics together or separately from the TGA [1]. Simulation illustrates that the image results interpretation will depend on spallation intensity, and it is recommended that additional data may be needed to get a reliable oxidation constant.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, there can be changes related to the interface processes, such as grain boundary corrosion, dissolution or precipitation of strengthening phases, depletion of alloying elements, which could result in a local solid-state phase transformation. These and the other high temperature degradation processes, which could occur in different alloys, are described in the recent review [1].…”
Cr–Ni austenitic steels offer significant high temperature corrosion protection by forming a surface oxide layer. However, above critical service conditions (temperature, atmosphere, thermal cycling), oxidized surface can experience intensive degradation because of scale spallation, which could be detrimental to the in-service life. To predict the effect of scale spallation on oxidation kinetics, a simulation was implemented using a stochastic model. The model considers topological parameters and intensity of spallation which can occur, while delivering a true oxidation constant. The experimental procedure identified the amount of formed spalled scale and topology of spallation based on the use of element mapping of the surface. This information was used to determine a true kinetic constant for a corresponding spallation intensity in oxidized Cr–Ni austenitic steel. To illustrate the capability of the stochastic model, a parametric analysis was performed. The model verified how the spallation parameters could change the oxidation processes from parabolic growth of an adhered oxide layer without spallation to a mixed linear-parabolic, or with a constant thickness of residual scale at high spallation intensity. The spallation model will be used in a separate article to characterize high temperature surface degradation of several Cr–Ni austenitic steels during harsh oxidation environments.
“…In many cases, only the specimen weight change could not only provide the wrong kinetical constant value but also mislead the alloy comparison. The image analysis is also used for evaluation of oxidation kinetics together or separately from the TGA [1]. Simulation illustrates that the image results interpretation will depend on spallation intensity, and it is recommended that additional data may be needed to get a reliable oxidation constant.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, there can be changes related to the interface processes, such as grain boundary corrosion, dissolution or precipitation of strengthening phases, depletion of alloying elements, which could result in a local solid-state phase transformation. These and the other high temperature degradation processes, which could occur in different alloys, are described in the recent review [1].…”
Cr–Ni austenitic steels offer significant high temperature corrosion protection by forming a surface oxide layer. However, above critical service conditions (temperature, atmosphere, thermal cycling), oxidized surface can experience intensive degradation because of scale spallation, which could be detrimental to the in-service life. To predict the effect of scale spallation on oxidation kinetics, a simulation was implemented using a stochastic model. The model considers topological parameters and intensity of spallation which can occur, while delivering a true oxidation constant. The experimental procedure identified the amount of formed spalled scale and topology of spallation based on the use of element mapping of the surface. This information was used to determine a true kinetic constant for a corresponding spallation intensity in oxidized Cr–Ni austenitic steel. To illustrate the capability of the stochastic model, a parametric analysis was performed. The model verified how the spallation parameters could change the oxidation processes from parabolic growth of an adhered oxide layer without spallation to a mixed linear-parabolic, or with a constant thickness of residual scale at high spallation intensity. The spallation model will be used in a separate article to characterize high temperature surface degradation of several Cr–Ni austenitic steels during harsh oxidation environments.
“…In addition to these, stress corrosion and fatigue problems are also related to these residual stresses. Residual stresses and corrosion, which affect the lifetime of structural components working at high temperatures, must be considered [2]. The compressive residual stresses in a 316L stainless steel increase in the resistance to pit initiation [3].…”
A study of the migration of the grain boundary misorientation and its relationship with the residual stresses through time immediately after the completion of a thermomechanical simulation has been carried out. After physically simulating an intercritically overheated welding heat affected zone, the variation of the misorientation of grain contours was observed with the electron backscatter diffraction (EBSD) technique and likewise the variation of the residual stresses of welding with RAYSTRESS equipment. It was observed that the misorientation of the grain contours in an ASTM DH36 steel was modified after the thermomechanical simulation, which corresponds to the measured residual stress variation along the first week of monitoring, with compressive residual stresses ranging from 195 MPa to 160 MPa. The changes in misorientation indicate that the stress relaxation phenomenon is associated with the evolution of the misorientation in the microstructure caused by the welding procedure. On the first day, there was a fraction of 4% of the kernel average misorientation (KAM) values at 1° misorientation and on the fourth day, there was a fraction of 7% of the KAM values at 1° misorientation.
“…Diffusion simulations have become widely used to predict composition evolutions in high temperature materials, e.g. in alloy-coating systems or in alloys subject to selective oxidation [1][2][3][4][5][6]. In a substitutional alloy subject to vacancy-mediated diffusion, a composition gradient will generate diffusion, which in turn may have consequences associated with the Kirkendall effect [7][8][9][10].…”
mentioning
confidence: 99%
“…Several numerical tools used today to simulate interdiffusion [1,2,4,5] rely on Ågren's formalism [18,19], which also considers an ideal lattice and therefore cannot, by construction, generate Kirkendall porosity. Methods were introduced [20][21][22] to estimate pore fractions as a post-processing step of simulations run in this configuration.…”
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