Modeling of Contaminant Advection-Diffusion Process based on Hammerstein model
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- 英文论文题名：Modeling of Contaminant Advection-Diffusion Process based on Hammerstein model
- 论文作者：Deng-Yin Jiang ; Li-Sheng Hu
- 英文论文作者：Deng-Yin Jiang ; Li-Sheng Hu ; School of Electronic Information and Electrical Engineering ; Shanghai Jiao Tong University
- 年：2017
- 作者机构：School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University;
- 英文论文关键词：Contaminant diffusion ; Model construction ; Parameter estimation ; Hammerstein model
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：X50
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：200k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, the modeling of liquid contaminant fluid dynamics for the associated emission-diffusion process induced by environmental changes is investigated. Due to the contaminants and large fluid liquids interaction, the contaminant diffusion causes the concentration structure to be strong uncertainty. Parameters related to the contaminants concentration distribution,including the emission, concentration, and free flow speed are varied to investigate the characteristics of the contaminant diffusion flow. Motivated by the slowly time-varying properties of the contaminant emission diffusion process, recursive identification of Hammerstein structures is investigated to construct a model of liquid contaminant concentration diffusion process in the rivers flows. The unknown parameters in the model are estimated by using the given data and an algorithm is given for the choice of the appropriate number and type of autoregressive terms, changing trend terms, etc., in the model, which the models of the river flows is constructed by using daily and yearly data. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Finally, the constructed model is tested from the environmental fluid dynamics data based on finite element method.
In this paper, the modeling of liquid contaminant fluid dynamics for the associated emission-diffusion process induced by environmental changes is investigated. Due to the contaminants and large fluid liquids interaction, the contaminant diffusion causes the concentration structure to be strong uncertainty. Parameters related to the contaminants concentration distribution,including the emission, concentration, and free flow speed are varied to investigate the characteristics of the contaminant diffusion flow. Motivated by the slowly time-varying properties of the contaminant emission diffusion process, recursive identification of Hammerstein structures is investigated to construct a model of liquid contaminant concentration diffusion process in the rivers flows. The unknown parameters in the model are estimated by using the given data and an algorithm is given for the choice of the appropriate number and type of autoregressive terms, changing trend terms, etc., in the model, which the models of the river flows is constructed by using daily and yearly data. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Finally, the constructed model is tested from the environmental fluid dynamics data based on finite element method.
In this paper, the modeling of liquid contaminant fluid dynamics for the associated emission-diffusion process induced by environmental changes is investigated. Due to the contaminants and large fluid liquids interaction, the contaminant diffusion causes the concentration structure to be strong uncertainty. Parameters related to the contaminants concentration distribution,including the emission, concentration, and free flow speed are varied to investigate the characteristics of the contaminant diffusion flow. Motivated by the slowly time-varying properties of the contaminant emission diffusion process, recursive identification of Hammerstein structures is investigated to construct a model of liquid contaminant concentration diffusion process in the rivers flows. The unknown parameters in the model are estimated by using the given data and an algorithm is given for the choice of the appropriate number and type of autoregressive terms, changing trend terms, etc., in the model, which the models of the river flows is constructed by using daily and yearly data. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Finally, the constructed model is tested from the environmental fluid dynamics data based on finite element method.
引文
[1]G.I.Taylor,Diffusion by continuous movements,Proc.London Math.Soc,20(1):196-212,1922.
[2]J.W.Elder,The dispersion of marked fluid in turbulent shear flow,Journal of fluid mechanics,5(04):544-560,1959.
[3]G.T.Csanady,Turbulent diffusion in lake huron[J].Journal of Fluid Mechanics,17(03):360-384,1963.
[4]A.Magid,H.A.Fischer,E.Schmatolla,Axonal transportsimple diffusion?,Science,182(4108):180-180,1973.
[5]D.Fischer,A study on zone refining:solidphase impurity diffusion and the influence of separating the impure end,Journal of Applied Physics,44(5):1977-1982,1973.
[6]R.Smith,A delay-diffusion description for contaminant dispersion,Journal of Fluid Mechanics,105:469-486,1981.
[7]J.W.Elder,Turbulent free convection in a vertical slot,Journal of Fluid Mechanics,23(01):99-111,1965.
[8]P.C.Chatwin,On the interpretation of some longitudinal dispersion experiments,Journal of Fluid Mechanics,48(04):689-702,1971.
[9]J.J.Douglas,T.F.Russell,Numerical methods for convectiondominated diffusion problems based on combining the method of characteristics with finite element or finite difference procedures,SIAM Journal on Numerical Analysis,19(5):871-885,1982.
[10]J.T.Oden,I.Babuka,C.E.Baumann,A discontinuous hp finite element method for diffusion problems,Journal of computational physics,146(2):491-519,1998.
[11]C.E.Baumann,J.T.Oden,A discontinuous hp finite element method for convectiondiffusion problems,Computer Methods in Applied Mechanics and Engineering,175(3-4):311-341,1999.
[12]O.Aslan,Numerical modeling of hydrogen diffusion in metals accounting for large deformations,International Journal of Hydrogen Energy,40(44):15227-15235,2015.
[13]Q.Du,M.Gunzburger,R.B.Lehoucq,et al.Analysis and approximation of nonlocal diffusion problems with volume constraints,SIAM review,54(4):667-696,2012.
[14]S.D.Ryan,A.G.Gerber,A.G.L.Holloway,A timedependent Eulerian model of droplet diffusion in turbulent flow,Computers&Fluids,131:1-15,2016.
[15]N.S.Abo-Ghander,F.Logist,J.R.Grace,et al.Heterogeneous modeling of an autothermal membrane reactor coupling dehydrogenation of ethylbenzene to styrene with hydrogenation of nitrobenzene to aniline:Fickian diffusion model,Chemical Engineering and Processing:Process Intensification,77:50-65,2014.
[16]K.Narendra,P.Gallman,An iterative method for the identification of nonlinear systems using a Hammerstein model,IEEE Transactions on Automatic control,11(3):546-550,1966.
[17]F.Chang,R.Luus,A noniterative method for identification using Hammerstein model,IEEE Transactions on Automatic control,16(5):464-468,1971.
[18]F.Ding,Y.Shi,T.Chen,Auxiliary model-based least-squares identification methods for Hammerstein output-error systems,Systems&Control Letters,56(5):373-380,2007.
[19]F.Ding,X.P.Liu,G.Liu,Identification methods for Hammerstein nonlinear systems,Digital Signal Processing,21(2):215-238,2011.
[20]N.Haist,F.Chang,R.Luus,Nonlinear identification in the presence of correlated noise using a Hammerstein model,IEEE Transactions on Automatic Control,18(5):552-555,1973.
[21]E.W.Bai,An optimal two-stage identification algorithm for Hammerstein Wiener nonlinear systems[J].Automatica,34(3):333-338,1998.
[22]E.W.Bai,Decoupling the linear and nonlinear parts in Hammerstein model identification,Automatica,40(4):671-676,2004.
[23]H.F.Chen,Pathwise convergence of recursive identification algorithms for Hammerstein systems,IEEE Transactions on Automatic Control,49(10):1641-1649,2004.
[24]W.Greblicki,Stochastic approximation in nonparametric identification of Hammerstein systems,IEEE Transactions on Automatic Control,47(11):1800-1810,2002.
[25]F.Ding,T.Chen,Hierarchical least squares identification methods for multivariable systems,IEEE transactions on Automatic Control,50(3):397-402,2005.
[26]R.Haber,H.Unbehauen,Structure identification of nonlinear dynamic systemsa survey on input/output approaches,Automatica,26(4):651-677,1990.
[27]J.Sjberg,Q.Zhang,L.Ljung,et al.Nonlinear black-box modeling in system identification:a unified overview,Automatica,31(12):1691-1724,1995.
[2]J.W.Elder,The dispersion of marked fluid in turbulent shear flow,Journal of fluid mechanics,5(04):544-560,1959.
[3]G.T.Csanady,Turbulent diffusion in lake huron[J].Journal of Fluid Mechanics,17(03):360-384,1963.
[4]A.Magid,H.A.Fischer,E.Schmatolla,Axonal transportsimple diffusion?,Science,182(4108):180-180,1973.
[5]D.Fischer,A study on zone refining:solidphase impurity diffusion and the influence of separating the impure end,Journal of Applied Physics,44(5):1977-1982,1973.
[6]R.Smith,A delay-diffusion description for contaminant dispersion,Journal of Fluid Mechanics,105:469-486,1981.
[7]J.W.Elder,Turbulent free convection in a vertical slot,Journal of Fluid Mechanics,23(01):99-111,1965.
[8]P.C.Chatwin,On the interpretation of some longitudinal dispersion experiments,Journal of Fluid Mechanics,48(04):689-702,1971.
[9]J.J.Douglas,T.F.Russell,Numerical methods for convectiondominated diffusion problems based on combining the method of characteristics with finite element or finite difference procedures,SIAM Journal on Numerical Analysis,19(5):871-885,1982.
[10]J.T.Oden,I.Babuka,C.E.Baumann,A discontinuous hp finite element method for diffusion problems,Journal of computational physics,146(2):491-519,1998.
[11]C.E.Baumann,J.T.Oden,A discontinuous hp finite element method for convectiondiffusion problems,Computer Methods in Applied Mechanics and Engineering,175(3-4):311-341,1999.
[12]O.Aslan,Numerical modeling of hydrogen diffusion in metals accounting for large deformations,International Journal of Hydrogen Energy,40(44):15227-15235,2015.
[13]Q.Du,M.Gunzburger,R.B.Lehoucq,et al.Analysis and approximation of nonlocal diffusion problems with volume constraints,SIAM review,54(4):667-696,2012.
[14]S.D.Ryan,A.G.Gerber,A.G.L.Holloway,A timedependent Eulerian model of droplet diffusion in turbulent flow,Computers&Fluids,131:1-15,2016.
[15]N.S.Abo-Ghander,F.Logist,J.R.Grace,et al.Heterogeneous modeling of an autothermal membrane reactor coupling dehydrogenation of ethylbenzene to styrene with hydrogenation of nitrobenzene to aniline:Fickian diffusion model,Chemical Engineering and Processing:Process Intensification,77:50-65,2014.
[16]K.Narendra,P.Gallman,An iterative method for the identification of nonlinear systems using a Hammerstein model,IEEE Transactions on Automatic control,11(3):546-550,1966.
[17]F.Chang,R.Luus,A noniterative method for identification using Hammerstein model,IEEE Transactions on Automatic control,16(5):464-468,1971.
[18]F.Ding,Y.Shi,T.Chen,Auxiliary model-based least-squares identification methods for Hammerstein output-error systems,Systems&Control Letters,56(5):373-380,2007.
[19]F.Ding,X.P.Liu,G.Liu,Identification methods for Hammerstein nonlinear systems,Digital Signal Processing,21(2):215-238,2011.
[20]N.Haist,F.Chang,R.Luus,Nonlinear identification in the presence of correlated noise using a Hammerstein model,IEEE Transactions on Automatic Control,18(5):552-555,1973.
[21]E.W.Bai,An optimal two-stage identification algorithm for Hammerstein Wiener nonlinear systems[J].Automatica,34(3):333-338,1998.
[22]E.W.Bai,Decoupling the linear and nonlinear parts in Hammerstein model identification,Automatica,40(4):671-676,2004.
[23]H.F.Chen,Pathwise convergence of recursive identification algorithms for Hammerstein systems,IEEE Transactions on Automatic Control,49(10):1641-1649,2004.
[24]W.Greblicki,Stochastic approximation in nonparametric identification of Hammerstein systems,IEEE Transactions on Automatic Control,47(11):1800-1810,2002.
[25]F.Ding,T.Chen,Hierarchical least squares identification methods for multivariable systems,IEEE transactions on Automatic Control,50(3):397-402,2005.
[26]R.Haber,H.Unbehauen,Structure identification of nonlinear dynamic systemsa survey on input/output approaches,Automatica,26(4):651-677,1990.
[27]J.Sjberg,Q.Zhang,L.Ljung,et al.Nonlinear black-box modeling in system identification:a unified overview,Automatica,31(12):1691-1724,1995.