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This study introduces a GPU-based parallel computing approach that combines the phase-field model (PF) and the lattice Boltzmann model (LBM). By establishing a coupled multiphase field model incorporating physical external fields such as flow field, temperature field, and solute field, the research simulates the growth of single grains and multiple grains under the influence of natural convection. The variations in dendritic morphology, flow field, and solute field during dendritic solidification processes are observed. Initially, the study analyzes the morphology of equiaxed dendrites and the growth patterns of primary dendrites arms under natural convection conditions. The evolution of equiaxed dendrites in single grains and multiple grains under various conditions is investigated. Furthermore, the study explores the impact of different anisotropy strengths on the growth of single grains and multiple grains under natural convection. Notably, a distinct "necking" phenomenon is observed when the anisotropy strength of a single grain is 0.05. In the case of multiple grains, where competition between dendrites is present in addition to the influence of natural convection, a pronounced "necking" phenomenon is evident at an anisotropy strength of 0.03. Moreover, OpenCL parallel technology is designed on the GPU platform to accelerate the solution of the model. The parallelization of the phase-field model coupled with the LBM model on the GPU demonstrates a clear advantage. The parallel computation based on GPU not only exhibits absolute superiority but also shows more significant acceleration effects as the computational domain increases. [ABSTRACT FROM AUTHOR]

A multi-phase model was established to imitate the growth of algal and dendritic grains during directional solidification. We studied the effects of temperature on the growth of bi-crystals and quantitatively analyzed the influence of anisotropic strength, thermal gradient, and pulling velocity on the evolution of bi-crystals. The results show that both weaker anisotropy strength and smaller pulling velocity can maintain the formation of seaweed tissue. The increase in the pulling velocity can degenerate the seaweed grains into dendrites and improve the growth rate of the dendrites, which make grain B produce more spindles, thereby accelerating the elimination of grain A. The thermal gradient is inversely proportional to the average initial spacing of dendrites. When the thermal gradient is too small, dendritic dendrites produce developed secondary dendrite arms, which, in turn, develop into tertiary dendrite arms to occupy the grain boundary, accelerating the elimination of seaweed grains. In addition, the multi-phase field model is solved by using central processing unit serial computation, single MPI (message passing interface) parallel programming method calculation, and MPI+OpenMP hybrid parallel programming structure, and the relevant factors affecting the efficiency of program operation are analyzed and tested. By comparing the computational efficiency of the three methods, it can be seen that the MPI+OpenMP hybrid parallel programming technology can make full use of computing resources in the case of large computing scale, further optimize the MPI parallel model, and obtain a higher acceleration ratio. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecast plays an important role in the safe and stable operation of large-scale wind power integrated grid system. In this paper, a new hybrid model for short-term wind speed forecasting based on hyperparameter optimization and error correction is proposed, where the forecasting period is 5, 10, and 15 min, respectively, for three sites. The empirical wavelet transform is used to decompose the original wind speed series. Then, the Elman neural network and kernel extreme learning machine, which adopt Bayesian optimization algorithm for hyperparameter optimization, are used as predictors for wind speed prediction and error processing, respectively. In addition, a new error correction model using wind speed as model input is proposed. In order to verify the performance of the proposed model, three datasets collected from different real-world wind farms in Gansu and Xinjiang were considered as a case study to comprehensively evaluate the prediction performance of ten forecasting models. The results reveal that the proposed model has higher prediction accuracy and better prediction performance than the contrast models. [ABSTRACT FROM AUTHOR]

Based on the muti-component eutectic multi-phase field model of S. G. Kim, W. T. Kim, T. Suzuki et al. [J. Cryst. Growth 261, 135–158 (2004)], the high-performance computing method of hardware and software architecture of OpenCL + graphics processing unit (GPU) was studied. Taking CBr4–C2Cl6 as an example, the evolution process of large-scale three-dimensional eutectic structure growth is realized by concurrent execution of multiple processes and multiple threads on two heterogeneous platforms of AMD and NVIDIA, respectively. The effects of different initial lamellar spacing and flow on eutectic lamellar morphology were also studied. The results show that, with the increasing of eutectic lamellar spacing, the morphology of eutectic lamellar changes are as follows: the symmetrically steady-state perpendicular to the interface is based on growth, the slightly oscillating state is unstable, and the large oscillating state is unstable; Under the condition of forced convection, the symmetrically alternating growth pattern of the original eutectic lamellar was broken, the melt flow led to the change of eutectic growth morphology, and the eutectic lamellar grew in the opposite direction of the flow. At the same computing scale, compared with the serial algorithm on the central processing unit platform, the acceleration ratio on the single GPU on the heterogeneous platform reaches 24.3 times and 21.6 times respectively, which improves the computing efficiency. At the same time, with its strong floating-point computing power to obtain more accurate simulation results and achieve the dual needs of computational efficiency and portability, it has also proven to solve the problems of a large amount of calculation, low efficiency and limited to qualitative research existing in the traditional phase-field models. [ABSTRACT FROM AUTHOR]

Based on the AFEPack adaptive software package, the H-adaptive finite element method was used to solve the three-dimensional phase field model on the Linux platform. The growth of the three-dimensional dendrites in the undercooling melt was simulated numerically, and the effects of different undercooling on three-dimensional dendritic growth were studied. The real growth process of three-dimensional dendritic was reproduced. The dendritic growth pattern that we obtained was consistent with the crystallization theory. In order to verify the reliability of the simulation results, the three-dimensional dendrite tip growth rate and the tip radius were compared with the classical theory and the same conditions under the literature value. The results are compared with those obtained by Jeong et al. and obtained by finite difference method under the same conditions. It is found that the results obtained by H-adaptive finite element method are in agreement with the calculated results obtained by these two methods. It is proved that the H-adaptive finite element method is feasible to simulate the growth of three-dimensional dendrites. The method greatly reduces the computational cost of the phase field model solution and improves the efficiency of the solution. [ABSTRACT FROM AUTHOR]

A microstructure numerical model is an intensive computational problem, for which the simulation time is too long and the simulation scale is too small. To solve these two problems, in this article, we use MPI+CUDA hybrid particle heterogeneous parallel computing to implement the dendrite growth simulation of a PF-LBM phase-field 3D model. Message Passing Interface (MPI) can be used to conduct coarse granularity division, to break through the limitation of the simulate scale in a single machine. In each node, fine-grained division is implemented by the Compute Unified Device Architecture (CUDA) parallel way to realize the completely parallelism intra-node, and to improve overall computational efficiency. At the same time, in this article, the "pseudo three-dimensional array" programming method is brought up in CUDA programming, and also to improve the CUDA random number generation method, in order to simplify the CUDA array programming and reduce the CUDA random number generation time purposes. Experiments show that at the same simulation scale, the speed-up ratio with 21 nodes MPI+CUDA was 57, which was increased 54% over the 21 nodes MPI. Under the condition of computing efficiency close, the largest simulation scale with 21 nodes MPI+CUDA was 4203, which is 13 times to single GPU. Therefore, the MPI + CUDA hybrid granularity parallel method proposed in this paper also has the advantages of high computational efficiency of the GPU and MPI to expand the simulation scale. [ABSTRACT FROM AUTHOR]

In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used. [ABSTRACT FROM AUTHOR]