Needle Network Model Research Articles

Dendritic needle network modeling of the Columnar-to-Equiaxed Transition. Part II: three dimensional formulation, implementation and comparison with experiments

The dendritic needle network (DNN) model tracks the diffusion-controlled growth of branches in the hierarchically structured dendritic network, thereby bridging the well-separated scales traditionally simulated by phase-field and coarse-grained models. In particular, the DNN model relaxes the assumptions on the dendrite growth kinetics and grain structures commonly used in the coarse-grained models. In part I of this paper, we proposed a two-dimensional (2D) version of the DNN model and applied it to investigate the Columnar-to-Equiaxed Transition (CET) to clarify the influence of these assumptions on the prediction of the CET. In order to overcome the limitations inherent in a 2D model, part II presents here a fully three-dimensional (3D) version of the DNN model and its application to the CET. After validation of the 3D model, we perform simulations to study the solidification of Al-7 wt.% Si alloy and compare the results to the experimental measurements conducted on board of the International Space Station in the framework of the CET in SOLidification processing (CETSOL) project. The comparison shows that the present 3D DNN model is able to provide quantitative prediction of the position and the type of CET at experimental time and length-scales, without any adjustable parameters.

Dendritic needle network modeling of the Columnar-to-Equiaxed transition. Part I: two dimensional formulation and comparison with theory

The columnar-to-equiaxed transition is a technologically important phenomenon that controls the grain structure in the casting of alloys. Analytical and coarse-grained models, used to investigate this transition, rely on several assumptions concerning dendrite growth kinetics and grain structures. In the first part of this two part paper, we test these assumptions using a two-dimensional (2D) dendritic needle network model that describes both the transient growth dynamics of primary, secondary and higher order branches of the dendritic network within each grain and the solutal interactions between grains that can grow with arbitrary shapes. Our results provide novel insights into the columnar-to-equiaxed transition, distinguishing between abrupt and progressive transitions with different grain structures. Furthermore, they highlight the limitations of commonly used assumptions in analytical and coarse-grained numerical models of this transition. These results are extended to 3D in part II of the paper.

A comparative in situ X-radiography and DNN model study of solidification characteristics of an equiaxed dendritic Al-Ge alloy sample

In situ X-radiography imaging during the isothermal solidification of a 200 µm thin Al-24 at.% Ge sample was performed to determine dendritic growth rates, liquid concentrations and projected solid fractions. The experimental data allowed to follow dendrite tip growth velocities with high accuracy all the way from the initial transient growth related decrease of velocity via the expected increase of velocity during free dendrite growth to its final decrease during neighbor interacted growth. Concentration profiles in front of individual dendrite tips were measured to analyze the increase in far-field solute concentration due to solute accumulation. By time-resolved imaging of almost the entire 12 mm diameter sample, the global melt concentration could be derived. This enabled to estimate the initial nucleation undercooling of this un-refined alloy, which was so far unresolved. The experimental results are compared to a three-dimensional dendrite needle network model that simulates the experimental conditions, such as hexagonal dendrite structures and confined sample geometry. Although the simulation results are very sensitive to variations of the parameters undercooling and dendrite selection constant, one model parameter set is found that reproduces all experimentally measured solidification characteristics. The thorough experiment-model comparison indicates that the dendritic growth rates in this experimental configuration can be lower by a factor of 7 depending on undercooling than in the non-confined case.

Three-dimensional needle network model for dendritic growth with fluid flow

We present a first implementation of the Dendritic Needle Network (DNN) model for dendritic crystal growth in three dimensions including convective transport in the melt. The numerical solving of the Navier-Stokes equations is performed with finite differences and is validated by comparison with a classical benchmark in fluid mechanics for unsteady flow. We compute the growth behavior of a single equiaxed crystal under a forced convective flow. As expected, the resulting dendrite morphology differs strongly from the case of the purely diffusive regime and from similar two-dimensional simulations. The resulting computationally efficient simulations open the way to studying mechanisms of microstructure selection in presence of fluid flow, using realistic alloy and process parameters.

Dendritic Needle Network Modeling of the Columnar-to-Equiaxed Transition. Part II: Three Dimensional Formulation, Implementation and Comparison with Experiments

The dendritic needle network (DNN) model tracks the diffusion-controlled growth of branches in the hierarchically structured dendritic network, thereby bridging the well-separated scales traditionally simulated by phase-field and coarse-grained models. In particular, the DNN model relaxes the assumptions on the dendrite growth kinetics and grain structures commonly used in the coarse-grained models. In part I of this paper, we proposed a two-dimensional (2D) version of the DNN model and applied it to investigate the Columnar-to-Equiaxed Transition (CET) to clarify the influence of these assumptions on the prediction of the CET. In order to overcome the limitations inherent in a 2D model, part II presents here a fully three-dimensional (3D) version of the DNN model and its application to the CET. After validation of the 3D model, we perform simulations to study the solidification of Al-7wt.%Si alloy and compare the results to the experimental measurements conducted on board of the International Space Station (ISS)in the framework of the CET in SOLidification processing project. The comparison shows that the present 3D DNNmodel is able to provide quantitative prediction of the position and the type of CET at experimental time and length-scales, without any adjustable parameters.

Multiscale dendritic needle network model of alloy solidification with fluid flow

We present a mathematical formulation of a multiscale model for solidification with convective flow in the liquid phase. The model is an extension of the dendritic needle network approach for crystal growth in a binary alloy. We propose a simple numerical implementation based on finite differences and step-wise approximations of parabolic dendritic branches of arbitrary orientation. Results of the two-dimensional model are verified against reference benchmark solutions for steady, unsteady, and buoyant flow, as well as steady-state dendritic growth in the diffusive regime. Simulations of equiaxed growth under forced flow yield dendrite tip velocities within 10% of quantitative phase-field results from the literature. Finally, we perform illustrative simulations of polycrystalline solidification using physical parameters for an aluminum-10wt%copper alloy. Resulting microstructures show notable differences when taking into account natural buoyancy in comparison to a purely diffusive transport regime. The resulting model opens new avenues for computationally and quantitatively investigating the influence of fluid flow and gravity-induced buoyancy upon the selection of dendritic microstructures. Further ongoing developments include an equivalent formulation for directional solidification conditions and the implementation of the model in three dimensions, which is critical for quantitative comparison to experimental measurements.

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure, supported by quantitative simulations of microstructure formation and its mechanical behavior.

Three-dimensional dendritic needle network model for alloy solidification

We present a three-dimensional (3D) formulation of the multiscale Dendritic Needle Network (DNN) model for dendritic microstructure growth. This approach is aimed at simulating quantitatively the solidification dynamics of complex hierarchical networks in spatially extended dendritic arrays, hence bridging the scale gap between phase-field simulations at the scale of a few dendrites and coarse-grained simulations on the larger scale of entire polycrystalline structures. In the DNN model, the dendritic network is represented by a network of branches that interact through the solutal diffusion field. The tip velocity V(t) and tip radius ρ(t) of each needle is determined by combining a standard solvability condition that fixes the product ρ2V and a solute flux conservation condition that fixes the product ρV2 in 2D and ρV in 3D as a function of a solutal flux intensity factor F(t). The latter measures the intensity of the solute flux in the dendrite tip region and can be calculated by contour (2D) or surface (3D) integration around the tip of each needle. We first present an extended formulation of the 2D DNN model where needles have a finite thickness and parabolic tips. This formulation remains valid for a larger range of tip Péclet number than the original thin needle formulation and is readily extended to 3D needles with paraboloidal tips. The 3D DNN model based on this thick-needle formulation is developed for both isothermal and directional solidification. Model predictions are validated by comparisons with known analytical solutions that describe the early transient and steady-state growth regimes. We exploit the power of the DNN model to characterize the competitive growth of well-developed secondary branches in 3D on the scale of the diffusion length. The results show that the length of active secondary branches increases as a power law of distance behind the tip with an exponent in good quantitative agreement with experimental measurements. Finally, we apply the model to simulate the three-dimensional directional solidification of an Al-7wt% Si alloy, which we directly compare to observed microstructures from microgravity experiments onboard the International Space Station. The predictions of selected microstructural features, such as dendrite arm spacings, show a good agreement with experiments. The computationally-efficient DNN model opens new avenues for investigating the dynamics of large dendritic arrays at length and time scales relevant to solidification experiments and processes.

Three-dimensional Dendritic Needle Network model with application to Al-Cu directional solidification experiments

We present a three-dimensional (3D) extension of a previously proposed multi-scale Dendritic Needle Network (DNN) approach for the growth of complex dendritic microstructures. Using a new formulation of the DNN dynamics equations for dendritic paraboloid-branches of a given thickness, one can directly extend the DNN approach to 3D modeling. We validate this new formulation against known scaling laws and analytical solutions that describe the early transient and steady-state growth regimes, respectively. Finally, we compare the predictions of the model to in situ X-ray imaging of Al-Cu alloy solidification experiments. The comparison shows a very good quantitative agreement between 3D simulations and thin sample experiments. It also highlights the importance of full 3D modeling to accurately predict the primary dendrite arm spacing that is significantly over-estimated by 2D simulations.