Investigating the Precipitation Kinetics and Hardening Effects of \u03b3\u201d in Inconel 625 Using a Combination of Meso-Scale Phase-Field Simulations and Macro-Scale Precipitate Strengthening Calculations

2021 ◽  
Author(s):  
Caleb Yenusah ◽  
Yanzhou Ji ◽  
Yucheng Liu ◽  
Tonya Stone ◽  
Mark Horstemeyer ◽  
...  
Author(s):  
Caleb O. Yenusah ◽  
Yanzhou Ji ◽  
Yucheng Liu ◽  
Tonya W. Stone ◽  
Mark F. Horstemeyer ◽  
...  

Abstract Precipitation strengthening of alloys by the formation of secondary particles (precipitates) in the matrix is one of the techniques used for increasing the mechanical strength of metals. Understanding the precipitation kinetics such as nucleation, growth, and coarsening of these precipitates is critical for evaluating their hardening effects and improving the yield strength of the alloy during heat treatment. To optimize the heat treatment strategy and accelerate alloy design, predicting precipitate hardening effects via numerical methods is a promising complement to trial-and-error-based experiments and the physics-based phase-field method stands out with the significant potential to accurately predict the precipitate morphology and kinetics. In this study, we present a phase-field model that captures the nucleation, growth, and coarsening kinetics of precipitates during isothermal heat treatment conditions. Thermodynamic data, diffusion coefficients, and misfit strain data from experimental or lower length-scale calculations are used as input parameters for the phase-field model. Classical nucleation theory is implemented to capture the nucleation kinetics. As a case study, we apply the model to investigate γ″ precipitation kinetics in Inconel 625. The simulated mean particle length, aspect ratio, and volume fraction evolution are in agreement with experimental data for simulations at 600 °C and 650 °C during isothermal heat treatment. Utilizing the meso-scale results from the phase-field simulations as input parameters to a macro-scale coherency strengthening model, the evolution of the yield strength during heat treatment was predicted. In a broader context, we believe the current study can provide practical guidance for applying the phase-field approach as a link in the multiscale modeling of material properties.


2021 ◽  
Vol 187 ◽  
pp. 110123
Author(s):  
Caleb O. Yenusah ◽  
Yanzhou Ji ◽  
Yucheng Liu ◽  
Tonya W. Stone ◽  
Mark F. Horstemeyer ◽  
...  

2015 ◽  
Author(s):  
Naresh Thadhani ◽  
Arun Gokhale ◽  
Jason Quenneville ◽  
Jennifer Breidenich ◽  
Manny Gonzales ◽  
...  

2021 ◽  
Vol 11 (6) ◽  
pp. 2464
Author(s):  
Sha Yang ◽  
Neven Ukrainczyk ◽  
Antonio Caggiano ◽  
Eddie Koenders

Modelling of a mineral dissolution front propagation is of interest in a wide range of scientific and engineering fields. The dissolution of minerals often involves complex physico-chemical processes at the solid–liquid interface (at nano-scale), which at the micro-to-meso-scale can be simplified to the problem of continuously moving boundaries. In this work, we studied the diffusion-controlled congruent dissolution of minerals from a meso-scale phase transition perspective. The dynamic evolution of the solid–liquid interface, during the dissolution process, is numerically simulated by employing the Finite Element Method (FEM) and using the phase–field (PF) approach, the latter implemented in the open-source Multiphysics Object Oriented Simulation Environment (MOOSE). The parameterization of the PF numerical approach is discussed in detail and validated against the experimental results for a congruent dissolution case of NaCl (taken from literature) as well as on analytical models for simple geometries. In addition, the effect of the shape of a dissolving mineral particle was analysed, thus demonstrating that the PF approach is suitable for simulating the mesoscopic morphological evolution of arbitrary geometries. Finally, the comparison of the PF method with experimental results demonstrated the importance of the dissolution rate mechanisms, which can be controlled by the interface reaction rate or by the diffusive transport mechanism.


2010 ◽  
Vol 160-162 ◽  
pp. 1211-1216
Author(s):  
Zhuang Liu ◽  
Xiao Qing Wu

The impregnation stage of the Resin Transfer Moulding process can be simulated by solving the Darcy equations on a mould model, with a ‘macro-scale’ finite element method. For every element, a local ‘meso-scale’ permeability must be determined, taking into account the local deformation of the textile reinforcement. This paper demonstrates that the meso-scale permeability can be computed efficiently and accurately by using meso-scale simulation tools. We discuss the speed and accuracy requirements dictated by the macro-scale simulations. We show that these requirements can be achieved for two meso-scale simulators, coupled with a geometrical textile reinforcement modeller. The first solver is based on a finite difference discretisation of the Stokes equations, the second uses an approximate model, based on a 2D simulation of the flow.


2019 ◽  
Author(s):  
Milou Straathof ◽  
Michel R.T. Sinke ◽  
Theresia J.M. Roelofs ◽  
Erwin L.A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.


2019 ◽  
Vol 864 ◽  
pp. 1-4 ◽  
Author(s):  
S. Luding

Fluid mechanics and rheology involve many unsolved challenges related to the transport mechanisms of mass, momentum and energy – especially when it comes to realistic, industrially relevant materials. Very interesting are suspensions or granular fluids with solid, particulate ingredients that feature contact mechanics on the micro-scale, which affect the transport properties on the continuum- or macro-scale. Their unique ability to behave as either fluid, or solid or both, can be quantified by non-Newtonian rheological rules, and results in interesting mechanisms such as super-diffusion, shear thickening, fluid–solid transitions (jamming) or relaxation/creep. Focusing on the steady state flow of a granular fluid, one can attempt to answer a long-standing question: how do realistic material properties such as dissipation, stiffness, friction or cohesion influence the rheology of a granular fluid? In a recent paper Macaulay & Rognon (J. Fluid Mech., vol. 858, 2019, R2) shed new light on the effect cohesion can have on mass transport in sheared, sticky granular fluids. On top of the usual diffusive, stochastic modes of transport, cohesion can create and stabilise clusters of particles into bigger agglomerates that carry particles over large distances – either ballistically in the dilute regime, or by their rotation in the dense regime. Importantly, these clusters must not only be larger than the particles (defining the intermediate, meso-scale), but they must also have a finite lifetime, in order to be able to exchange mass with each other, which can seriously enhance transport in sticky granular fluids by rotection, i.e. a combination of rotation and convection.


Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos

The precise control of mass and energy deposition associated with additive manufacturing (AM) processes enables the topological specification and realization of how space can be filled by material in multiple scales. Consequently, AM can be pursued in a manner that is optimized such that fabricated objects can best realize performance specifications. In the present work, we propose a computational multiscale method that utilizes the unique meso-scale structuring capabilities of implicit slicers for AM, in conjunction with existing topology optimization (TO) tools for the macro-scale, in order to generate structurally optimized components. The use of this method is demonstrated on two example objects including a load bearing bracket and a hand tool. This paper also includes discussion concerning the applications of this methodology, its current limitations, a recasting of the AM digital thread, and the future work required to enable its widespread use.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Milou Straathof ◽  
Michel R. T. Sinke ◽  
Theresia J. M. Roelofs ◽  
Erwin L. A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.


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