American Institute of Mathematical Sciences

Advanced
July  2005, 1(3): 405-414. doi: 10.3934/jimo.2005.1.405

The model of heat transfer of the arctic snow-ice layer in summer and numerical simulation

Yila Bai 1, , Haiqing Zhao 1, , Xu Zhang 1, , Enmin Feng 1, and  Zhijun Li 2,

1. 

Department of Applied Mathematics, Dalian University of Technology, Dalian, Liaoning, 116024, P.R., China, China, China
2. 

State Key Lab. of Coastal and offshore Eng, Dalian University of Technology, Dalian, Liaoning, 116024, P.R., China

Received  September 2004 Revised  March 2005 Published  July 2005

For the Arctic ice layer with high temperature in summer, the concepts of enthalpy degree, specific enthalpy and enthalpy conduction coefficient etc. are introduced in terms of the concept of enthalpy in calorifics. Heat conduction equation of enthalpy is constructed in the process of phase transformation of the Arctic ice. The condition of determinant solution and the identification model of diffusion coefficient of enthalpy are presented. Half implicit difference scheme and Schwartz alternating direction iteration are applied to solve the enthalpy conduction equation and sensitivity equation. Furthermore the Newton-Raphson algorithm is used for identification. The numerical results illustrate that the mathematic model and optimization algorithm are precise and feasible by the data(2003.8)of the Second Chinese Arctic Research Expedition in situ.
Keywords: sea ice, the Arctic, numerical simulation., parameter identification.
Mathematics Subject Classification: 49J20, 65C20, 68U20, 65N0.
Citation: Yila Bai, Haiqing Zhao, Xu Zhang, Enmin Feng, Zhijun Li. The model of heat transfer of the arctic snow-ice layer in summer and numerical simulation. Journal of Industrial and Management Optimization, 2005, 1 (3) : 405-414. doi: 10.3934/jimo.2005.1.405
[1]

Qinxi Bai, Zhijun Li, Lei Wang, Bing Tan, Enmin Feng. Parameter identification and numerical simulation for the exchange coefficient of dissolved oxygen concentration under ice in a boreal lake. Journal of Industrial and Management Optimization, 2018, 14 (4) : 1463-1478. doi: 10.3934/jimo.2018016
[2]

Tram Thi Ngoc Nguyen, Anne Wald. On numerical aspects of parameter identification for the Landau-Lifshitz-Gilbert equation in Magnetic Particle Imaging. Inverse Problems and Imaging, 2022, 16 (1) : 89-117. doi: 10.3934/ipi.2021042
[3]

Pingping Niu, Shuai Lu, Jin Cheng. On periodic parameter identification in stochastic differential equations. Inverse Problems and Imaging, 2019, 13 (3) : 513-543. doi: 10.3934/ipi.2019025
[4]

Yuepeng Wang, Yue Cheng, I. Michael Navon, Yuanhong Guan. Parameter identification techniques applied to an environmental pollution model. Journal of Industrial and Management Optimization, 2018, 14 (2) : 817-831. doi: 10.3934/jimo.2017077
[5]

Xianbo Sun, Zhanbo Chen, Pei Yu. Parameter identification on Abelian integrals to achieve Chebyshev property. Discrete and Continuous Dynamical Systems - B, 2021, 26 (10) : 5661-5679. doi: 10.3934/dcdsb.2020375
[6]

Gong Chen, Peter J. Olver. Numerical simulation of nonlinear dispersive quantization. Discrete and Continuous Dynamical Systems, 2014, 34 (3) : 991-1008. doi: 10.3934/dcds.2014.34.991
[7]

Nicolas Vauchelet. Numerical simulation of a kinetic model for chemotaxis. Kinetic and Related Models, 2010, 3 (3) : 501-528. doi: 10.3934/krm.2010.3.501
[8]

Petr Bauer, Michal Beneš, Radek Fučík, Hung Hoang Dieu, Vladimír Klement, Radek Máca, Jan Mach, Tomáš Oberhuber, Pavel Strachota, Vítězslav Žabka, Vladimír Havlena. Numerical simulation of flow in fluidized beds. Discrete and Continuous Dynamical Systems - S, 2015, 8 (5) : 833-846. doi: 10.3934/dcdss.2015.8.833
[9]

Qinqin Chai, Ryan Loxton, Kok Lay Teo, Chunhua Yang. A unified parameter identification method for nonlinear time-delay systems. Journal of Industrial and Management Optimization, 2013, 9 (2) : 471-486. doi: 10.3934/jimo.2013.9.471
[10]

Barbara Kaltenbacher, William Rundell. On the identification of the nonlinearity parameter in the Westervelt equation from boundary measurements. Inverse Problems and Imaging, 2021, 15 (5) : 865-891. doi: 10.3934/ipi.2021020
[11]

Ana I. Muñoz, José Ignacio Tello. Mathematical analysis and numerical simulation of a model of morphogenesis. Mathematical Biosciences & Engineering, 2011, 8 (4) : 1035-1059. doi: 10.3934/mbe.2011.8.1035
[12]

Andriy Sokolov, Robert Strehl, Stefan Turek. Numerical simulation of chemotaxis models on stationary surfaces. Discrete and Continuous Dynamical Systems - B, 2013, 18 (10) : 2689-2704. doi: 10.3934/dcdsb.2013.18.2689
[13]

Yue Qiu, Sara Grundel, Martin Stoll, Peter Benner. Efficient numerical methods for gas network modeling and simulation. Networks and Heterogeneous Media, 2020, 15 (4) : 653-679. doi: 10.3934/nhm.2020018
[14]

Sergio Amat, Pablo Pedregal. On a variational approach for the analysis and numerical simulation of ODEs. Discrete and Continuous Dynamical Systems, 2013, 33 (4) : 1275-1291. doi: 10.3934/dcds.2013.33.1275
[15]

Michal Beneš, Pavel Eichler, Jakub Klinkovský, Miroslav Kolář, Jakub Solovský, Pavel Strachota, Alexandr Žák. Numerical simulation of fluidization for application in oxyfuel combustion. Discrete and Continuous Dynamical Systems - S, 2021, 14 (3) : 769-783. doi: 10.3934/dcdss.2020232
[16]

Kari Eloranta. Archimedean ice. Discrete and Continuous Dynamical Systems, 2013, 33 (9) : 4291-4303. doi: 10.3934/dcds.2013.33.4291
[17]

Masahiro Yamaguchi, Yasuhiro Takeuchi, Wanbiao Ma. Population dynamics of sea bass and young sea bass. Discrete and Continuous Dynamical Systems - B, 2004, 4 (3) : 833-840. doi: 10.3934/dcdsb.2004.4.833
[18]

Pavel Krejčí. The Preisach hysteresis model: Error bounds for numerical identification and inversion. Discrete and Continuous Dynamical Systems - S, 2013, 6 (1) : 101-119. doi: 10.3934/dcdss.2013.6.101
[19]

Zhongyi Huang, Peter A. Markowich, Christof Sparber. Numerical simulation of trapped dipolar quantum gases: Collapse studies and vortex dynamics. Kinetic and Related Models, 2010, 3 (1) : 181-194. doi: 10.3934/krm.2010.3.181
[20]

Eberhard Bänsch, Steffen Basting, Rolf Krahl. Numerical simulation of two-phase flows with heat and mass transfer. Discrete and Continuous Dynamical Systems, 2015, 35 (6) : 2325-2347. doi: 10.3934/dcds.2015.35.2325

2021 Impact Factor: 1.411

Tools

Metrics

  • PDF downloads (91)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy