China
Africa
Explore chapters and articles related to this topic
*
Published in Adedeji B. Badiru, Vhance V. Valencia, David Liu, Additive Manufacturing Handbook, 2017
Microstructure will be predicted using phase field models, and properties will be predicted using crystal plasticity and dislocation dynamics. The incorporation of process optimization, data mining, and UQ will guide the AM process to yield optimized properties and performance. Results of simulations will be validated against measured material properties and data acquired from real-time in situ process monitors using design of experiments. Integrated in-process sensing, monitoring, and control technologies will be developed to ensure the end-processed material properties and component performance. The material being studied is 316L steel.
Kinetics
Published in H. K. D. H. Bhadeshia, Bainite in Steels, 2015
In an ideal scenario, a phase-field model is able to compute quantitative aspects of the evolution of microstructure without explicit intervention, and gives a visual impression of the development of microstructure. The key advantage is that there is no tracking of individual interfaces and that many physical phenomena can be simultaneously modelled within the framework of irreversible thermodynamics. The subject has been widely reviewed11 but a presentation which is intended to be simple without compromising derivations is available for detailed study (Qin and Bhadeshia, 2011). The intention here is to focus on bainite, with only a cursory introduction to the method.
A phase-field study on a eutectic high-entropy alloy during solidification
Published in Philosophical Magazine Letters, 2021
Boina Sagar, Krishanu Biswas, Rajdip Mukherjee
Solidification is defined as the transformation of a liquid phase to a crystalline solid phase when the liquid is cooled below the equilibrium melting temperature. It has technological importance in fields such as casting and welding. The idea to develop phase-field models for the solidification process started with the development of a three-dimensional dendrite for an isothermal transformation of a single-component system [23]. Subsequently, a number of models have been developed for various phase transformations in binary alloy systems such as eutectic reactions [6]. A phase-field model describes a microstructure by detailing compositional domains and interfaces with phase-field variables. The free-energy density values required for the model were calculated using the commercially available thermodynamic modelling software, Thermo-Calc version 2017a. In general, there are two types of phase-field variables: conserved variables and non-conserved variables. A three-phase KKS model [24] has been implemented in MOOSE which uses kernels for the phase-field equations as well as the KKS constraint equations [25]. The module in MOOSE which is based on the KKS model, incorporates tilting functions that are used to prevent the formation of a spurious third phase at a two-phase interface [26].
A parallel finite volume procedure for phase-field simulation of solidification
Published in Numerical Heat Transfer, Part A: Applications, 2019
Peng Ding, Zhe Liu, RuiJie Zhang
Kim et al. [15] presented a double-grid method based on the large difference between the diffusivity of phase field and thermal field in pure melts. The phase field was calculated only in the interface region and the thermal field was calculated only in the thermal boundary layer region. The governing equations were solved with the explicit finite difference method. Ferreira et al. [16] developed an adaptive computation domain technique based on the finite difference method which enables us to expanding the computational domain around the dendrite as the solidification proceeded. Provatas et al. [17] studied the evolution of solidification microstructure using a phase-field model computed on an adaptive finite element grid which dynamically coarsens the grid spacing away from the front. In the work of Rosam et al. [18], they discretized the governing equations using a finite difference method based upon a non-conforming, refined quadrilateral mesh, the multi-grid, and adaptive time-step techniques were employed to expedite the solution process further. Li et al. [19] proposed a fast, robust, and accurate operator splitting method for phase-field simulations of dendritic growth based on finite difference method. In [20], a simple first-order Euler scheme using a TESLA K40 graphics processing unit (GPU) was developed to test the accuracy of different phase-field modes. Takaki [21] reviewed the phase-field modeling and simulations of dendrite growth from the fundamental model to cutting-edge very-large-scale GPU simulations. In [22–24], GPU based finite difference explicit method was used to investigate the dendrite competitive growth in 2 D and 3 D spaces.
Phase-field simulation of magnetic double-hole nanoring and its application in random storage
Published in International Journal of Smart and Nano Materials, 2021
Zengyao Lv, Xiaoyu Zhang, Honglong Zhang, Zhitao Zhou, Duo Xu, Yongmao Pei
Phase-field simulation methods were developed in the 1970s, initially to bypass the difficulty of tracking solid–liquid interfaces when simulating solidified tissue, and were later widely used in various tissue evolution problems. The phase field model describes different phases by introducing continuously changing field variables – order parameters in the entire calculation area. The order parameters change sharply at the interface, and there is no need to track the geometry of the interface. Therefore, the phase field is a diffuse interface model. The phase field theory is based on statistical physics. According to the Ginzburg-Laudau phase transition theory, it reflects the combined effects of diffusion, ordering potential and thermodynamic driving force through differential equations. As a powerful computational simulation method, phase field method is widely used in the simulation and prediction of material-based morphology and microstructive evolution [17]. In recent years, this method has been used to study the micro-evolution and macro-performance of ferromagnetic, ferroelectric and magnetic composite materials. In 2003, Koyama [18] used the phase field method to study the microstructure evolution of ferromagnetic shape memory alloys (MSMAs) Ni2MnGa under applied stress and magnetic field. Its free energy includes chemical-free energy, gradient energy, elastic energy, and magnetic energy. Among them, the magnetic energy only considers anisotropic properties. In 2012, Miehe [15] used node displacement, magnetic potential, and magnetization vector as field variables, established corresponding finite element equations based on elastic mechanics, magnetostatic theory and TDGL evolution equations, compiled 2D magnetic elements, and analyzed the regulation of the magnetic field and stress on the micro/nano structure of permalloy. When solving the demagnetization problem, Miehe takes a large enough external free space, and assume that the magnetic potential at the boundary decays to zero. Without considering the air element, Wang [19] in 2013 established a 3D model and analyzed the regulation of the nano-magnetic structure by strain, magnetic field and their coupling effects. Using this method can not only calculate the boundary of complex shapes, but also calculate the force-magnetic coupling response of materials under complex stress. However, this method is not yet mature enough and needs to be further developed and improved.