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VOF(Volume Of Fluid)方法能够通过在欧拉网格上使用离散的体积分数域表示光滑界面，在不可混合流体的数值模拟中得到广泛应用。针对多相流仿真中的液滴曲率计算问题，开发了一种计算界面曲率的算法。首先提出了一种新的数据生成方法，在液滴界面上进行随机采样，增强网格内体积分数的信息量，并调整取值范围以覆盖正负曲率。然后改进了传统的深度神经网络（DNN）模型，使其在计算曲率时保持对称性。基于VOF方法与该模型，利用目标单元及邻近单元体积分数计算曲率。最后选取最优模型并应用于Basilisk软件中，以提高计算曲率的准确性和稳定性。测试结果表明，其计算曲率时准确稳定。在计算小半径液滴曲率时，误差减小了25%至50%，并能用于液滴融合仿真，证明了其应用价值。
Abstract：The VOF(Volume Of Fluid) method can represent smooth interfaces through discrete volume fraction fields on an Eulerian grid, which is widely used in the numerical simulation of immiscible fluids. An algorithm for calculating droplet curvature in multiphase flow simulations has been developed. Firstly, a new data generation method is proposed, involving random sampling on droplet interfaces to enhance the information content of volume fractions within the grid and adjust the value range to cover both positive and negative curvatures. Secondly, the traditional deep neural network(DNN) model is improved to maintain symmetry when calculating curvature. Based on the VOF method and the improved DNN model, curvature is calculated using the volume fractions of the target cell and its neighboring cells. Finally, the optimal model is selected and applied in the Basilisk software to improve the accuracy and stability of curvature calculations. Test results show that the curvature calculation is accurate and stable. When calculating the curvature of small-radius droplets, the error is reduced by 25% to 50%, and the model can be used for droplet merging simulations, proving its application value.
[1]刘静,殷义,时明伟.界面追踪和拉格朗日粒子追踪耦合计算方法[J].工业炉,2021,43(1):17-23.
[2]朱光普.孔隙尺度两相流动及界面追踪数值模拟研究及应用[D].青岛:中国石油大学(华东),2020.
[3] FREDERIC G,DAVID F,RON F.Sharp interface approaches and deep learning techniques for multiphase flows[J].Journal of Computational Physics,2019,380:442-463.
[4] TRYGGVASON G,BUNNER B,ESMAEELI A,et al.A front-tracking method for the computations of multiphase flow[J].Journal of Computational Physics,2001,169(2):708-759.
[5] SHIN S,YOON I,JURIC D.The local front reconstruction method for direct simulation of two-and three-dimensional multiphase flows[J].Journal of Computational Physics,2011,230(17):6605-6646.
[6] HIRT C W,NICHOLS B D.Volume of fluid(VOF)method for the dynamics of free boundaries[J].Journal of Computational Physics,1981,39(1):201-225.
[7] SUSSMAN M,SMEREKA P,OSHER S.A level set approach for computing solutions to incompressible two-phase flow[J].Journal of Computational Physics,1994,114(1):146-159.
[8] YOUNGS D.Time-dependent multi-material flow with large fluid distortion[M].New York:Academic Press,1982.
[9] BRACKBILL J U,KOTHE D B,ZEMACH C.A continuum method for modeling surface tension[J].Journal of Computational Physics,1992,100(2):335-354.
[10] FRANCOIS M M,CUMMINS S J,DENDY E D,et al.A balanced-force algorithm for continuous and sharp interfacial surface tension models within a volume tracking framework[J].Journal of Computational Physics,2006,213:141-173.
[11] POPINET S.An accurate adaptive solver for surface-tension-driven interfacial flows[J].Journal of Computational Physics,2009,228(16):5838-5866.
[12]刘强,吴健.不同截面计算方法对液滴在电场作用下变形数值模拟精度的影响[J].计算力学学报,2023,40(6):979-984.
[13]戎毅,廖凯龙,孙恩慧,等.基于流线曲率法的超临界二氧化碳离心压缩机通流计算方法[J].热力发电,2024,53(2):124-132.
[14] CAZE R D,MARCEL S.Neurons with dendrites can perform linearly separable computations with low resolution synaptic weights[J].F1000Research,2021,9:1174.
[15] NASRABADI N M.Pattern recognition and machine learning[J].Journal of Electronic Imaging,2007,16(4):049901.
[16]张冬灿,张国良,李俊学,等.基于HOG特征与卷积神经网络的闭环检测算法[J].四川轻化工大学学报(自然科学版),2022,35(4):49-55.
[17]战庆亮,葛耀君,白春锦.基于尾流时程目标识别的流场参数选择研究[J].力学学报,2021,53(10):2692-2702.
[18]胡震宇,王子路,陈坚强,等.基于深度神经网络的横流转捩预测[J].力学学报,2023,55(1):38-51.
[19]赵西增,徐天宇,谢玉林,等.基于卷积神经网络的涵洞式直立堤波浪透射预测[J].力学学报,2021,53(2):330-338.
[20]岳杰顺,权晓波,叶舒然,等.水下发射水动力的多尺度预测网络研究[J].力学学报,2021,53(2):339-351.
[21]陈皓,郭明明,田野,等.卷积神经网络在流场重构研究中的进展[J].力学学报,2022,54(9):2343-2360.
[22]吴磊,肖左利.基于人工神经网络的亚格子应力建模[J].力学学报,2021,53(10):2667-2681.
[23] PATEL H V,PANDA A,KUIPERS J A M,et al.Computing interface curvature from volume fractions:a machine learning approach[J].Computers&Fluids,2019,193:104263.
[24] HE K M,ZHANG X Y,REN S Q,et al.Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//2015IEEE International Conference on Computer Vision(ICCV),Santiago,Chile,December 7-13,2015:1026-1034.
基本信息:
DOI：
中图分类号:O359
引用信息:
[1]曾西平,刘牧远.基于VOF法和改进DNN模型的三维液滴曲率算法[J].四川轻化工大学学报(自然科学版),2025,38(02):37-46.
基金信息:
国家自然科学基金青年科学基金项目(11902275)