Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5623-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-5623-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Dec 2021
Research article |
| 10 Dec 2021
| 10 Dec 2021
A generalized stress correction scheme for the Maxwell elasto-brittle rheology: impact on the fracture angles and deformations
Mathieu Plante
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Québec, Canada
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Québec, Canada
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Cited articles
Amitrano, D. and Helmstetter, A.:
Brittle creep, damage and time to failure in rocks,
J. Geophys. Res.-Sol. Ea.,
111, B11201, https://doi.org/10.1029/2005JB004252, 2006. a, b
Amitrano, D., Grasso, J.-R., and Hantz, D.:
From diffuse to localised damage through elastic interaction,
Geophys. Res. Lett.,
26, 2109–2112, 1999. a
Arthur, J. R. F., Dunstan, T., Al-Ani, Q. A. J. L., and Assadi, A.:
Plastic deformation and failure in granular media,
Géotechnique,
27, 53–74, https://doi.org/10.1680/geot.1977.27.1.53, 1977. a, b
Balendran, B. and Nemat-Nasser, S.:
Double sliding model for cyclic deformation of granular materials, including dilatancy effects,
J. Mech. Phys. Solids,
41, 573–612, https://doi.org/10.1016/0022-5096(93)90049-L, 1993. a, b, c
Bardet, J.:
Orientation of shear bands in frictional soils,
J. Eng. Mech.-ASCE,
117, 1466–1484, https://doi.org/10.1061/(ASCE)0733-9399(1991)117:7(1466), 1991. a, b
Bolton, M. D.:
The strength and dilatancy of sands,
Geotechnique,
36, 65–78, https://doi.org/10.1680/geot.1986.36.1.65, 1986. a
Bouchat, A. and Tremblay, B.:
Using sea-ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice,
J. Geophys. Res.-Oceans,
122, 5802–5825, https://doi.org/10.1002/2017JC013020, 2017. a
Bouchat, A., Hutter, N. C., Chanut, J., Dupont, F., Dukhovskoy, D. S., Garric, G., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Òlason, E. Ö., Rampal, P., Rasmussen, T. A. S., Talandier, C., Tremblay, B., and Wang, Q.:
Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields,
Earth and Space Science Open Archive,
p. 36, https://doi.org/10.1002/essoar.10507397.1, 2021. a, b, c
Bouillon, S. and Rampal, P.:
Presentation of the dynamical core of neXtSIM, a new sea ice model,
Ocean Model.,
91, 23–37, https://doi.org/10.1016/j.ocemod.2015.04.005, 2015. a
Carrier, A., Got, J.-L., Peltier, A., Ferrazzini, V., Staudacher, T., Kowalski, P., and Boissier, P.:
A damage model for volcanic edifices: Implications for edifice strength, magma pressure, and eruptive processes,
J. Geophys. Res.-Sol. Ea.,
120, 567–583, https://doi.org/10.1002/2014JB011485, 2015. a
Coon, M. D., Maykut, G. A., Pritchard, R. S., Rothrock, D. A., and Thorndike, A. S.:
Modeling the pack ice as an elastic-plastic material,
AIDJEX bulletin,
24, 1–105, https://doi.org/10.1017/CBO9781107415324.004, 1974. a, b
Coulomb, C.:
Test on the applications of the rules of maxima and minima to some problems of statics related to architecture,
Mem. Math. Phys.,
7, 343–382, 1773. a
Damsgaard, A., Adcroft, A., and Sergienko, O.:
Application of Discrete Element Methods to Approximate Sea Ice Dynamics,
J. Adv. Model. Earth Sy.,
10, 2228–2244, https://doi.org/10.1029/2018MS001299, 2018. a
Dansereau, V., Weiss, J., Saramito, P., Lattes, P., and Coche, E.: Ice bridges and ridges in the Maxwell-EB sea ice rheology, The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, 2017. a, b
Erlingsson, B.:
Two-Dimensional Deformation Patterns in Sea Ice,
J. Glaciol.,
34, 301–308, https://doi.org/10.3189/S0022143000007061, 1988. a
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.:
A new modeling framework for sea-ice mechanics based on elasto-brittle rheology,
Ann. Glaciol.,
52, 123–132, https://doi.org/10.3189/172756411795931499, 2011. a
Hata, Y. and Tremblay, L. B.:
Anisotropic internal thermal stress in sea ice from the Canadian Arctic Archipelago,
J. Geophys. Res.-Oceans,
120, 5457–5472, https://doi.org/10.1002/2015JC010819, 2015. a
Herman, A.: Discrete-Element bonded-particle Sea Ice model DESIgn, version 1.3a – model description and implementation, Geosci. Model Dev., 9, 1219–1241, https://doi.org/10.5194/gmd-9-1219-2016, 2016. a
Hibler III, W. D. and Schulson, E. M.:
On modeling the anisotropic failure and flow of flawed sea ice,
J. Geophys. Res.-Oceans,
105, 17105–17120, https://doi.org/10.1029/2000JC900045, 2000. a
Hunke, E. C. and Dukowicz, J. K.:
An Elastic–Viscous–Plastic Model for Sea Ice Dynamics,
J. Phys. Oceanogr.,
27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a, b
Hutter, N., Losch, M., and Menemenlis, D.:
Scaling Properties of Arctic Sea Ice Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in Satellite Observations,
J. Geophys. Res.-Oceans,
123, 672–687, https://doi.org/10.1002/2017JC013119, 2018. a
Hutter, N. C., Bouchat, A., Dupont, F., Dukhovskoy, D. S., Koldunov, N. V., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Òlason, E. Ö., Rampal, P., Rasmussen, T. A. S., Talandier, C., Tremblay, B., and Wang, Q.:
Sea Ice Rheology Experiment (SIREx), Part II: Evaluating simulated linear kinematic features in high-resolution sea-ice simulations,
Earth and Space Science Open Archive,
p. 35, https://doi.org/10.1002/essoar.10507396.1, 2021. a, b, c, d
Itkin, P., Spreen, G., Hvidegaard, S. M., Skourup, H., Wilkinson, J., Gerland, S., and Granskog, M. A.:
Contribution of Deformation to Sea Ice Mass Balance: A Case Study From an N-ICE2015 Storm,
Geophys. Res. Lett.,
45, 789–796, https://doi.org/10.1002/2017GL076056, 2018. a
Karimi, K. and Barrat, J.-L.:
Correlation and shear bands in a plastically deformed granular medium,
Sci. Rep.,
8, 4021, https://doi.org/10.1038/s41598-018-22310-z, 2018. a
Kimmritz, M., Danilov, S., and Losch, M.:
The adaptive EVP method for solving the sea ice momentum equation,
Ocean Model.,
101, 59–67, https://doi.org/10.1016/j.ocemod.2016.03.004, 2016. a
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling, H., Rakowsky, N., Scholz, P., Sein, D., Wang, Q., and Jung, T.:
Fast EVP Solutions in a High-Resolution Sea Ice Model,
J. Adv. Model. Earth Sy.,
11, 1269–1284, https://doi.org/10.1029/2018MS001485, 2019. a
Kozo, T. L.:
Initial model results for Arctic mixed layer circulation under a refreezing lead,
J. Geophys. Res.-Oceans,
88, 2926–2934, https://doi.org/10.1029/JC088iC05p02926, 1983. a
Ledley, T. S.:
A coupled energy balance climate-sea ice model: Impact of sea ice and leads on climate,
J. Geophys. Res.-Atmos.,
93, 15919–15932, https://doi.org/10.1029/JD093iD12p15919, 1988. a
Lemieux, J.-F., Tremblay, B., Thomas, S., Sedláček, J., and Mysak, L. A.:
Using the preconditioned Generalized Minimum RESidual (GMRES) method to solve the sea-ice momentum equation,
J. Geophys. Res.-Oceans,
113, C10004, https://doi.org/10.1029/2007JC004680, 2008. a, b, c
Lemieux, J.-F., Knoll, D. A., Losch, M., and Girard, C.:
A second-order accurate in time IMplicit–EXplicit (IMEX) integration scheme for sea ice dynamics,
J. Comput. Phys.,
263, 375–392, https://doi.org/10.1016/j.jcp.2014.01.010, 2014. a, b, c, d
Li, X., Krueger, S. K., Strong, C., Mace, G. G., and Benson, S.:
Midwinter Arctic leads form and dissipate low clouds,
Nat. Commun.,
11, 206, https://doi.org/10.1038/s41467-019-14074-5, 2020. a
Lüpkes, C., Vihma, T., Birnbaum, G., and Wacker, U.:
Influence of leads in sea ice on the temperature of the atmospheric boundary layer during polar night,
Geophys. Res. Lett.,
35, L03805, https://doi.org/10.1029/2007GL032461, 2008. a
Marko, J. R. and Thomson, R. E.:
Rectilinear leads and internal motions in the ice pack of the western Arctic Ocean,
J. Geophys. Res.,
82, 979–987, https://doi.org/10.1029/JC082i006p00979, 1977. a, b
Matsumura, Y. and Hasumi, H.:
Brine-Driven Eddies under Sea Ice Leads and Their Impact on the Arctic Ocean Mixed Layer,
J. Phys. Oceanogr.,
38, 146–163, https://doi.org/10.1175/2007JPO3620.1, 2008. a
Maykut, G. A.:
Large-scale heat exchange and ice production in the central Arctic,
J. Geophys. Res.-Oceans,
87, 7971–7984, https://doi.org/10.1029/JC087iC10p07971, 1982. a
Mohr, O.:
Welche Umstände bedingen die Elastizitätsgrenze und den Bruch eines Materials,
Z. Ver. Dtsch. Ing.,
46, 1572–1577, 1900. a
Murakami, S.:
Continuum damage mechanics: a continuum mechanics approach to the analysis of damage and fracture,
https://doi.org/10.1007/978-94-007-2666-6, 2012. a, b
Overland, J. E., McNutt, S. L., Salo, S., Groves, J., and Li, S.:
Arctic sea ice as a granular plastic,
J. Geophys. Res.-Oceans,
103, 21845–21867, https://doi.org/10.1029/98JC01263, 1998. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b, c
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a, b
Rice, J. R.:
Solid Mechanics,
Harvard University, Cambridge, MA, 2010. a
Ringeisen, D., Tremblay, L. B., and Losch, M.: Non-normal flow rules affect fracture angles in sea ice viscous–plastic rheologies, The Cryosphere, 15, 2873–2888, https://doi.org/10.5194/tc-15-2873-2021, 2021. a, b
Roscoe, K. H.:
The Influence of Strains in Soil Mechanics,
Géotechnique,
20, 129–170, https://doi.org/10.1680/geot.1970.20.2.129, 1970. a
Schulson, E. M.:
Compressive shear faults within arctic sea ice: Fracture on scales large and small,
J. Geophys. Res.-Oceans,
109, C07016, https://doi.org/10.1029/2003JC002108, 2004. a, b, c
Sodhi, D. S.: Ice arching and the drift of pack ice through restricted channels, Crrel report 77-18, Cold Regions Research And Engineering Laboratory, Hanover, New Hampshire, 1977. a
Stern, H. L., Rothrock, D. A., and Kwok, R.:
Open water production in Arctic sea ice: Satellite measurements and model parameterizations,
J. Geophys. Res.-Oceans,
100, 20601–20612, https://doi.org/10.1029/95JC02306, 1995. a
Sulsky, D. and Peterson, K.:
Toward a new elastic–decohesive model of Arctic sea ice,
Physica D, special Issue: Fluid Dynamics: From Theory to Experiment,
240, 1674–1683, https://doi.org/10.1016/j.physd.2011.07.005, 2011. a, b, c
Tabata, T.:
A Measurement of Visco-Elastic Constants of Sea Ice,
Journal of the Oceanographical Society of Japan,
11, 185–189, 1955. a
Timco, G. and Weeks, W.:
A review of the engineering properties of sea ice,
Cold Reg. Sci. Technol.,
60, 107–129, https://doi.org/10.1016/j.coldregions.2009.10.003, 2010. a
Tremblay, L.-B. and Hakakian, M.:
Estimating the Sea Ice Compressive Strength from Satellite-Derived Sea Ice Drift and NCEP Reanalysis Data,
J. Phys. Oceanogr.,
36, 2165–2172, https://doi.org/10.1175/JPO2954.1, 2006. a
Tuhkuri, J. and Lensu, M.:
Laboratory tests on ridging and rafting of ice sheets,
J. Geophys. Res.-Oceans,
107, 8-1–8-14, https://doi.org/10.1029/2001JC000848, 2002. a, b
Turnbull, I. D., Torbati, R. Z., and Taylor, R. S.:
Relative influences of the metocean forcings on the drifting ice pack and estimation of internal ice stress gradients in the Labrador Sea,
J. Geophys. Res.-Oceans,
122, 5970–5997, https://doi.org/10.1002/2017JC012805, 2017.
a
Vardoulakis, I.:
Shear band inclination and shear modulus of sand in biaxial tests,
Int. J. Numer. Anal. Met.,
4, 103–119, https://doi.org/10.1002/nag.1610040202, 1980. a
Wachter, L., Renshaw, C., and Schulson, E.:
Transition in brittle failure mode in ice under low confinement,
Acta Mater.,
57, 345–355, https://doi.org/10.1016/j.actamat.2008.09.021, 2009. a
Wang, K.:
Observing the yield curve of compacted pack ice,
J. Geophys. Res.-Oceans,
112, C05015, https://doi.org/10.1029/2006JC003610, 2007. a
Wilchinsky, A. V. and Feltham, D. L.:
A continuum anisotropic model of sea-ice dynamics,
P. Roy. Soc. Lond. A Math.,
460, 2105–2140, https://doi.org/10.1098/rspa.2004.1282, 2004. a, b
Wilchinsky, A. V., Heorton, H. D. B. S., Feltham, D. L., and Holland, P. R.:
Study of the Impact of Ice Formation in Leads upon the Sea Ice Pack Mass Balance Using a New Frazil and Grease Ice Parameterization,
J. Phys. Oceanogr.,
45, 2025–2047, https://doi.org/10.1175/JPO-D-14-0184.1, 2015. a
Short summary
We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
We propose a generalized form for the damage parameterization such that super-critical stresses...
