以CFD-DEM为基础的养殖槽排污性能及底坡优化
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摘要
由于能提高资源利用率,减少环境污染,低碳高效池塘循环流水养殖(IPA)作为一项新型养殖技术被大力推广。为了提高养殖过程中的集污排污效率,本研究拟采用构造负坡底面的方法对养殖槽结构进行优化。通过建立二维养殖槽简化模型,结合计算流体力学—离散单元法(CFD-DEM)模拟计算与核偏最小二乘(KPLS)建模方法,建立槽内垂向流速分布与底面坡度和粗糙度的关系模型。在此基础上结合泥沙运动理论,获得了槽内颗粒起动流速与单宽输沙率模型,在构建颗粒起动和输运两方面的性能指标后,利用基于偏好的多目标粒子群算法(DP-MOPSO)寻求最优底面坡度。寻优结果显示,随着底面粗糙度的增加,最优坡度略有减小,范围为0.013～0.015;仿真实验结果显示,构造最优底坡可有效提高颗粒的起动概率和槽体的颗粒运输能力,且对于表面较为粗糙槽体,坡型底面在颗粒起动方面的优越性更为显著,说明通过构建底坡来改变水流结构,从而实现养殖槽排污性能的优化是合理的。
Because of improving resource utilization and reducing environmental pollution, IPA(Intensive Pond Aquaculture) technology has been widely promoted as a new breed aquaculture technology. In order to improve the efficiency of pollution collection and discharge in the process of aquaculture, the method of constructing negative slope bottom is used to reconstruct aquaculture tanks. By establishing a two-dimensional simplified model of aquaculture tank and combining the CFD-DEM simulation and KPLS(Kernel Partial Least-Squares Regression) modeling method, a model that can reflect the relationship between the vertical velocity distribution and the bottom slope was established. On this basis, the model of the particle incipient velocity and the single wide suspended load transport rate in the tank was obtained by the theory of sediment motion. After setting up two performance indexes of particle motion and transportation, the DP-MOPSO(Preference-based Multi-objective Particle Swarm Optimization) method was used to get the optimal bottom slope. The optimization results show that with the increase of the bottom roughness, the optimal gradient is slightly reduced and the range is about 0.013-0.015. The simulation results show that the optimal bottom slope can effectively improve the moving probability and the transport capacity of particles, and for the rough surface, the superiority of the slope bottom is more significant. It shows that it is reasonable to optimize the discharge performance of aquaculture tanks by constructing the bottom slope to change the flow structure.
Because of improving resource utilization and reducing environmental pollution, IPA(Intensive Pond Aquaculture) technology has been widely promoted as a new breed aquaculture technology. In order to improve the efficiency of pollution collection and discharge in the process of aquaculture, the method of constructing negative slope bottom is used to reconstruct aquaculture tanks. By establishing a two-dimensional simplified model of aquaculture tank and combining the CFD-DEM simulation and KPLS(Kernel Partial Least-Squares Regression) modeling method, a model that can reflect the relationship between the vertical velocity distribution and the bottom slope was established. On this basis, the model of the particle incipient velocity and the single wide suspended load transport rate in the tank was obtained by the theory of sediment motion. After setting up two performance indexes of particle motion and transportation, the DP-MOPSO(Preference-based Multi-objective Particle Swarm Optimization) method was used to get the optimal bottom slope. The optimization results show that with the increase of the bottom roughness, the optimal gradient is slightly reduced and the range is about 0.013-0.015. The simulation results show that the optimal bottom slope can effectively improve the moving probability and the transport capacity of particles, and for the rough surface, the superiority of the slope bottom is more significant. It shows that it is reasonable to optimize the discharge performance of aquaculture tanks by constructing the bottom slope to change the flow structure.
引文
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[20]Jeong G S.A sediment concentration distribution based on a revised prandtl mixing theory[J].Journal of Korea Water Resources Association,1997,30(1):3-13.
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[22]Zhou X,Shen J,Li Y G.Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control[J].Journal of Southeast University(English Edition),2014,30(4):449-455.
[2]黄滨,刘滨,雷霁霖,等.工业化循环水福利养殖关键技术与智能装备的研究[J].水产学报,2013,37(11):1750-1760.Huang B,Liu B,Lei J L,et al.The research on key technology and intelligent equipment of aquaculture welfare in industrial circulating water mode[J].Journal of Fisheries of China,2013,37(11):1750-1760(in Chinese).
[3]王玮,陆庆刚,顾海涛,等.微孔曝气增氧机的增氧能力试验[J].水产学报,2010,34(1):97-100.Wang W,Lu Q G,Gu H T,et al.The oxygen-enriched capacity experiment of micropore aerator[J].Journal of Fisheries of China,2010,34(1):97-100(in Chinese).
[4]万俊,何建京,王泽.光滑壁面明渠负坡流速分布特性[J].长江科学院院报,2010,27(4):32-35.Wan J,He J J,Wang Z.Velocity distribution characteristics in smooth open channel with adverse slope[J].Journal of Yangtze River Scientific Research Institute,2010,27(4):32-35(in Chinese).
[5]Song T,Graf W H.Velocity and turbulence distribution in unsteady open-channel flows[J].Journal of Hydraulic Engineering,1996,122(3):141-154.
[6]Coleman N L.Effects of suspended sediment on the open-channel velocity distribution[J].Water Resources Research,1986,22(10):1377-1384.
[7]张红武.泥沙起动流速的统一公式[J].水利学报,2012,43(12):1387-1396.Zhang H W.A unified formula for incipient velocity of sediment[J].Journal of Hydraulic Engineering,2012,43(12):1387-1396(in Chinese).
[8]孙志林,邵凯,许丹,等.浑水推移质分组输沙研究[J].水利学报,2012,43(1):99-105.Sun Z L,Shao K,Xu D,et al.Fractional bedload transport in turbid waters[J].Journal of Hydraulic Engineering,2012,43(1):99-105(in Chinese).
[9]曲延鹏,陈颂英,王小鹏,等.不同湍流模型对圆射流数值模拟的讨论[J].工程热物理学报,2008,29(6):957-959.Qu Y P,Chen S Y,Wang X P,et al.Discussion on the numerical simulation of ax-symmetric jet with different turbulent model[J].Journal of Engineering Thermophysics,2008,29(6):957-959(in Chinese).
[10]Wanik A,Schnell U.Some remarks on the PISO and SIMPLE algorithms for steady turbulent flow problems[J].Computers&Fluids,1989,17(4):555-570.
[11]肖柏青,李然,戎贵文.基于欧拉-拉格朗日方法的明渠中悬移质淤积数值模拟[J].水力发电学报,2015,34(9):46-51.Xiao B Q,Li R,Rong G W.Eulerian-Lagrangian simulations of suspended sediment deposition in open channels[J].Journal of Hydroelectric Engineering,2015,34(9):46-51(in Chinese).
[12]Tong Z B,Zheng B,Yang R Y,et al.CFD-DEMinvestigation of the dispersion mechanisms in commercial dry powder inhalers[J].Powder Technology,2013,240:19-24.
[13]Chen X,Li Y,Niu X J,et al.A general two-phase turbulent flow model applied to the study of sediment transport in open channels[J].International Journal of Multiphase Flow,2011,37(9):1099-1108.
[14]喻黎明,邹小艳,谭弘,等.基于CFD-DEM耦合的水力旋流器水沙运动三维数值模拟[J].农业机械学报,2016,47(1):126-132.Yu L M,Zou X Y,Tan H,et al.3D numerical simulation of water and sediment flow in hydrocyclone based on coupled CFD-DEM[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):126-132(in Chinese).
[15]闫银发,孟德兴,宋占华,等.槽轮式补饲机颗粒动力学数值模拟与试验[J].农业机械学报,2016,47(S1):249-253.Yan Y F,Meng D X,Song Z H,et al.Particle kinetic simulation and experiment for flute-wheel feeding machine[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(S1):249-253(in Chinese).
[16]程烨,张根广,吴彰松,等.泥沙不同起动模式的起动概率对比分析[J].泥沙研究,2017,42(5):31-35.Cheng Y,Zhang G G,Wu Z S,et al.Comparison of entrainment probabilities in different incipient motion models[J].Journal of Sediment Research,2017,42(5):31-35(in Chinese).
[17]Wang X K,Wang Z Y,Yu M Z,et al.Velocity profile of sediment suspensions and comparison of log-law and wake-law[J].Journal of Hydraulic Research,2001,39(2):211-217.
[18]Bai Y F,Xiao J,Yu L.Kernel partial least-squares regression[C]//Proceedings of 2006 IEEE International Joint Conference on Neural Network.Vancouver,BC,Canada:IEEE,2006:1231-1238.
[19]孙志林,张超凡,杜利华,等.非均匀悬移质输沙率[J].水利学报,2016,47(4):501-508.Sun Z L,Zhang C F,Du L H,et al.Transport rate of nonuniform suspended load[J].Journal of Hydraulic Engineering,2016,47(4):501-508(in Chinese).
[20]Jeong G S.A sediment concentration distribution based on a revised prandtl mixing theory[J].Journal of Korea Water Resources Association,1997,30(1):3-13.
[21]王丽萍,江波,邱飞岳.基于决策偏好的多目标粒子群算法及其应用[J].计算机集成制造系统,2010,16(1):140-148.Wang L P,Jiang B,Qiu F Y.Multi-objective particle swarm optimization based on decision preferences and its application[J].Computer Integrated Manufacturing Systems,2010,16(1):140-148(in Chinese).
[22]Zhou X,Shen J,Li Y G.Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control[J].Journal of Southeast University(English Edition),2014,30(4):449-455.