Abstract
The paper addresses modeling of avascular and vascular tumor growth within the framework of continuum mechanics and the adopted numerical solution strategies. The models involve tumor cells, both viable and necrotic, healthy cells, extracellular matrix (ECM), interstitial fluid, neovasculature and co-opted blood vessels, nutrients, waste products, and their interaction and evolution. Attention is focused on the more recent models which are much richer than earlier ones, i.e. they address more aspects of this complicated problem. An important element is how the governing equations are obtained and how the many interfaces between the above listed components are dealt with. These considerations suggest the definition of different classes of models comprised of diffusion, single phase flow and multiphase flow models with or without a solid phase. A multiphase flow model in a deforming porous medium (ECM) is chosen as reference model since it appears to invoke the least number of simplifying assumptions and has the largest potential for further development. The strategies adopted in the choice of the many model dependent constitutive relationships are discussed in detail. Two applications referring to two different model classes conclude the paper.
Similar content being viewed by others
Abbreviations
- Eq.:
-
equation
- Eqs.:
-
equations
- REV:
-
Representative Elementary Volume
- TCAT:
-
Thermodynamically Constrained Averaging Theory
- A α :
-
fourth order tensor that accounts for the stress-rate of strain relationship
- a α :
-
adhesion of the phase α
- b :
-
exponent in the pressure-saturations relationship
- C ij :
-
non linear coefficient of the discretized capacity matrix
- \(\mathbf{d}^{\overline{\overline{\alpha}}}\) :
-
rate of strain tensor
- \(D_{0}^{\overline{il}}\) :
-
diffusion coefficient for the species i dissolved in the phase l
- \(D_{eff}^{\overline{il}}\) :
-
effective diffusion coefficient in the multiphase system for the species i dissolved in the phase l
- D s :
-
tangent matrix of the solid skeleton
- e s :
-
total strain tensor
- \(\mathbf{e}_{el}^{s}\) :
-
elastic strain tensor
- \(\mathbf{e}_{vp}^{s}\) :
-
visco-plastic strain tensor
- \(\mathbf{e}_{sw}^{s}\) :
-
swelling strain tensor
- f v :
-
discretized source term associated to the primary variable v
- H :
-
Heaviside step function
- K ij :
-
non linear coefficient of the discretized conduction matrix
- k αs :
-
absolute permeability tensor of the phase α
- \(k_{rel}^{\alpha}\) :
-
relative permeability of the phase α
- N v :
-
vector of shape functions related to the primary variable v
- p α :
-
pressure in the phase α
- \(p_{crit}^{t}\) :
-
tumor pressure above which growth is inhibited
- \(p_{necr}^{t}\) :
-
tumor pressure above which stress causes an increase of the death rate
- R α :
-
resistance tensor
- S α :
-
saturation degree of the phase α
- \(\mathbf{t}_{eff}^{\overline{\overline{s}}}\) :
-
effective stress tensor of the solid phase s
- \(\mathbf{t}_{tot}^{\overline{\overline{s}}}\) :
-
total stress tensor of the solid phase s
- u s :
-
displacement vector of the solid phase s
- x :
-
solution vector
- \(\bar{\alpha}\) :
-
Biot’s coefficient
- \(\gamma_{growth}^{t}\) :
-
growth coefficient
- \(\gamma_{necrosis}^{t}\) :
-
necrosis coefficient
- \(\gamma_{growth}^{\overline{nl}}\) :
-
nutrient consumption coefficient related to growth
- \(\gamma_{0}^{\overline{nl}}\) :
-
nutrient consumption coefficient not related to growth
- \(\theta^{\overline{\overline{\alpha}}}\) :
-
macroscale temperature of the phase α
- δ :
-
exponent in the effective diffusion function for the oxygen
- \(\delta_{a}^{t}\) :
-
additional necrosis induced by pressure excess
- ε :
-
porosity
- ε α :
-
volume fraction of the phase α
- μ α :
-
dynamic viscosity of the phase α
- ρ α :
-
density of the phase α
- σ cl :
-
coefficient in the pressure-saturations relationship
- \(\varsigma^{\overline{\alpha}}\) :
-
chemical potential
- \(\psi^{\overline{\alpha}}\) :
-
gravitational potential
- χ α :
-
solid surface fraction in contact with the phase α
- \(\omega^{N\overline{t}}\) :
-
mass fraction of necrotic cells in the tumor cells phase
- \(\omega^{\overline{nl}}\) :
-
nutrient mass fraction in liquid
- \(\omega_{crit}^{\overline{nl}}\) :
-
critical nutrient mass fraction in liquid for growth
- \(\omega_{env}^{\overline{nl}}\) :
-
reference nutrient mass fraction in the environment
- \(\mathop{M}\limits^{\kappa \to \alpha}\) :
-
inter-phase mass transfer from k to α phase
- ε α r iα :
-
reaction term i.e. intra-phase mass transformation
- \(\mathop{\mathbf{T}}\limits^{\kappa \to \alpha}\) :
-
inter-phase momentum transfer from k to α phase
- b :
-
blood phase
- crit:
-
critical value for growth
- e :
-
endothelial cells
- h :
-
host cell phase
- l :
-
interstitial fluid
- n :
-
nutrient
- necr:
-
critical value for the effect of pressure on the cell death rate
- s :
-
solid
- t :
-
tumor cell phase
- α :
-
phase indicator with α=b,e,h,l,s, or t
References
Addison-Smith B, McElwain DLS, Maini PK (2008) A simple mechanistic model of sprout spacing in tumour-associated angiogenesis. J Theor Biol 250:1–15
Ambrosi D, Preziosi L (2002) On the closure of mass balance models for tumor growth. Math Models Methods Appl Sci 12:737–754
Ambrosi D, Preziosi L (2009) Cell adhesion mechanisms and stress relaxation in the mechanics of tumors. Biomech Model Mechanobiol 8:397–413
Ambrosi D, Preziosi L, Vitale G (2012) The interplay between stress and growth in solid tumors. Mech Res Commun 42:87–91
Anderson A (2005) A hybrid mathematical model of solid tumor invasion: the importance of cell adhesion. Math Med Biol 22:163–186
Anderson DM, McFadden GB, Wheeler AA (1998) Diffuse-interface methods in fluid mechanics. Annu Rev Fluid Mech 30:139–165
Araujo R, McElwain D (2005) A mixture theory for the genesis of residual stresses in growing tissues I: a general formulation. SIAM J Appl Math 65:1261–1284
Armstrong NJ, Painter KJ, Sherratt JA (2006) A continuum approach for modelling cell-cell adhesion. J Theor Biol 243:98–113
Astanin S, Preziosi L (2009) Mathematical modelling of the Warburg effect in tumour cords. J Theor Biol 258(4):578–590
Balding D, McElwain DLS (1985) A mathematical model of tumor-induced capillary growth. J Theor Biol 114:53–73
Baumgartner W, Hinterdorfer P, Ness W, Raab A, Vestweber D, Schindler H, Drenckhahn D (2000) Cadherin interaction probed by atomic force microscopy. Proc Natl Acad Sci USA 97:4005–4010
Bearer EL, Lowengrub JS, Frieboes HB, Chuang YL, Jin F, Wise SM, Ferrari M, Agus DB, Cristini V (2010) Multiparameter computational modeling of tumor invasion. Cancer Res 69:4494–4501
Bidan CM, Kommareddy KP, Rumpler M, Kollmannsberger P, Bréchet YJM, Fratzl P, Dunlop JWC (2012) How linear tension converts to curvature: geometric control of bone tissue growth. PLoS ONE 7(5):e36336. doi:10.1371/journal.pone.0036336
Black PC, Shetty A, Brown GA, Esparza-Coss E, Metwalli AR, Agarwal PK, McConkey DJ, Hazle JD, Dinney CP (2010) Validating bladder cancer xenograft bioluminescence with magnetic resonance imaging: the significance of hypoxia and necrosis. BJU Int 106(11):1799–1804
Breward C, Byrne H, Lewis C (2002) The role of cell-cell interactions in a two-phase model for avascular tumour growth. J Math Biol 45(2):125–152
Breward C, Byrne H, Lewis C (2003) A multiphase model describing vascular tumor growth. Bull Math Biol 65:609–640
Brooks RH, Corey AT (1964) Hydraulic properties of porous media. Hydrol Pap 3, Colorado State University, Fort Collins
Brooks RH, Corey AT (1966) Properties of porous media affecting fluid flow. J Irrig Drain Div Am Soc Civ Eng 92(IR2):61–88
Bussolino F, Arese M, Audero E, Giraudo E, Marchiò S, Mitola S, Primo L, Serini G (2003) Biological aspects of tumor angiogenesis. In: Preziosi L (ed) Cancer modelling and simulation, 1st edn. Chapman and Hall/CRC, London, pp 1–22
Byrne H, Chaplain M (1996) Modelling the role of cell-cell adhesion in the growth and development of carcinomas. Math Comput Model 24:1–17
Byrne H, King J (2003) A two-phase model of solid tumor growth. Appl Math Lett 16:567–573
Byrne H, Preziosi L (2003) Modeling solid tumor growth using the theory of mixtures. Math Meth Biol 20:341–366
Cahn JW, Hilliard JE (1958) Free energy of a nonuniform system: I. Interfacial free energy. J Chem Phys 28:256–267
Chaplain MA (2000) Mathematical modelling of angiogenesis. J Neurooncol 50:37–51
Chapman SJ, Shipley R, Jawad R (2008) Multiscale modeling of fluid transport in tumors. Bull Math Biol 70:2334–2357
Chen X, Friedman A (2003) A free boundary problem for an elliptic-hyperbolic system: an application to tumor growth. SIAM J Math Anal 35:974–986
Colli P, Gilardi G, Podio-Guidugli P (2011) Well-posedness and long-time behaviour for a nonstandard viscous Cahn-Hilliard system. SIAM J Appl Math 71:1849–1870
Colli P, Gilardi G, Podio-Guidugli P, Sprekels J (2012) Distributed optimal control of a non-standard system of phase field equations. Contin Mech Thermodyn 24:437–459
Corey AT, Rathjens CH, Henderson JH, Wyllie MRJ (1956) Three-phase relative permeability. Trans AIME 207:349–351
Cristini V, Lowengrub J, Nie Q (2003) Nonlinear simulation of tumor growth. J Math Biol 46:191–224
Cristini V, Frieboes H, Gatenby R, Caserta S, Ferrari M, Sinek J (2005) Morphologic instability and cancer invasion. Clin Cancer Res 11:6772–6779
Cristini V, Li X, Lowengrub JS, Wise SM (2009) Nonlinear simulation of solid tumor growth using a mixture model: invasion and branching. J Math Biol 58:723–763
de Angelis E, Preziosi L (2000) Advection-diffusion models for solid tumor evolution in vivo and related free boundary problem. Math Models Methods Appl Sci 10:379–407
Deisboeck TS, Wang Z, Macklin P, Cristini V (2011) Multiscale cancer modeling. Annu Rev Biomed Eng 13:127–155
Dunlop JW, Gamsjäger E, Bidan C, Kommareddy KP, Kollmansberger P, Rumpler M, Fischer FD, Fratzl P (2011) The modeling of tissue growth in confined geometries, effect of surface tension. In: Proc CMM-2011 (Warsaw) computer methods in mechanics
Ehlers W, Markert B, Roehrle O (2009) Computational continuum biomechanics and applications to swelling media and growth phenomena. GAMM-Mitt 32(2):135–156
Erbertseder KM (2008) Modeling the spatial and temporal distribution of therapeutic agents in tumor tissues (a continuum approach). Master Thesis, University of Stuttgart
Frieboes HB, Edgerton ME, Fruehauf JP, Rose FRAJ, Worrall LK, Gatenby RA, Ferrari M, Cristini V (2009) Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Res 69:4484–4493
Frieboes HB, Lowengrub JS, Wise S, Zheng X, Macklin P, Bearer E, Cristini V (2007) Computer simulation of glioma growth and morphology. NeuroImage 37(1):S59–S70
Friedman A, Reitich F (1999) Analysis of a mathematical model for the growth of tumors. J Math Biol 38:262–284
Gawin D, Pesavento F, Schrefler BA (2006) Hygro-thermo-chemo-mechanical modelling of concrete at early ages and beyond. Part I: Hydration and hygro-thermal phenomena. Int J Numer Methods Eng 67:299–331
Gerisch A, Chaplain MA (2008) Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion. J Theor Biol 250:684–704
Gomez H, Calo VM, Bazilevs Y, Hughes TRJ (2008) Isogeometric analysis of the Cahn-Hilliard phase field model. Comput Methods Appl Mech Eng 197:4333–4352
Gomez H, Hughes TRJ (2011) Provably unconditionally stable. Second order time-accurate, mixed variational methods for phase field models. J Comput Phys 230:5310–5327
Gray WG, Leijnse A, Kolar RL, Blain CA (1993) Mathematical tools for changing scales in the analysis of physical systems. CRC Press, Boca Raton
Gray WG, Miller CT (2005) Thermodynamically constrained averaging theory approach for modeling flow and transport phenomena in porous medium systems: 1. Motivation and overview. Adv Water Resour 28:161–180
Gray WG, Miller CT (2009) Thermodynamically constrained averaging theory approach for modeling flow and transport phenomena in porous medium systems: 5. Single-fluid-phase transport. Adv Water Resour 32:681–711
Gray WG, Schrefler BA (2007) Analysis of the solid stress tensor in multiphase porous media. Int J Numer Anal Methods Geomech 31:541–581
Gray WG, Schrefler BA, Pesavento F (2010) Work input for unsaturated elastic porous media. J Mech Phys Solids 58:752–765
Greenspan HP (1976) On the growth and stability of cell cultures and solid tumors. J Theor Biol 56:229–242
Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674
Hawkins-Daarud A, van der Zee KG, Oden JT (2012) Numerical simulation of a thermodynamically consistent four-species tumor growth model. Int J Numer Methods Biomed Eng 28:3–24
Hogea CS, Murray BT, Sethian JA (2006) Simulating complex tumor dynamics from avascular to vascular growth using a general level-set method. J Math Biol 53:86–134
Jackson AS, Miller CT, Gray WG (2009) Thermodynamically constrained averaging theory approach for modelling flow and transport phenomena in porous medium systems: 6. Two-fluid-phase flow. Adv Water Resour 32:779–795
Jain RK (1999) Transport of molecules, particles, and cells in solid tumors. Annu Rev Biomed Eng 1:241–263
Jain RK, Stylianopoulos T (2010) Delivering nanomedicine to solid tumors. Nat Rev Clin Oncol 7:653–664
Jou D, Casas-Vazquez J, Lebon G (2001) Extended irreversible thermodynamics. Springer, Berlin
Khain E, Sander LM (2008) Generalized Cahn-Hilliard equation for biological applications. Phys Rev E 77:051129
Kim JS, Lowengrub JS (2005) Phase field modelling and simulation of three phase flows. Interfaces Free Bound 7:435–466
Lanza V, Ambrosi D, Preziosi L (2006) Exogenous control of vascular formulation in vitro: a mathematical model. Netw Heterog Media 1:621–637
Lewis RW, Schrefler BA (1998) The finite element method in the static and dynamic deformation and consolidation of porous media. Wiley, Chichester
Li X, Cristini V, Nie Q, Lowengrub J (2007) Nonlinear three dimensional simulation of solid tumor growth. Discrete Contin Dyn Syst, Ser B 7:581–604
Lowengrub JS, Frieboes HB, Jin F, Chuang Y-L, Li X, Macklin P, Wise SM, Cristini V (2010) Nonlinear modeling of cancer: bridging the gap between cells and tumors. Nonlinearity 23(1):R1–R9. doi:10.1088/0951-7715/23/1/R01
Machklin P, Lowengrub J (2006) An improved geometry-aware curvature discretization for level set methods: application to tumor growth. J Comput Phys 215:392–401
Macklin P, McDougall S, Anderson AR, Chaplain MA, Cristini V, Lowengrub J (2009) Multiscale modelling and nonlinear simulation of vascular tumour growth. J Math Biol 58:765–798
Markert B, Haeberle K (2011) A phase field model for the description of angiogenesis. Comput Aided Medical Eng 3:6–11
Maugin GA (1999) The thermomechanics of nonlinear irreversible behaviors: an introduction. World Scientific, Singapore
McDougall SR, Anderson ARA, Chaplain MAJ (2006) Mathematical modeling of dynamic adaptive tumor-induced angiogenesis: clinical applications and therapeutic targeting strategies. J Theor Biol 241:564–589
McDougall SR, Anderson ARA, Chaplain MAJ, Sherratt J (2002) Mathematical modeling of flow through vascular networks: implications for tumour-induced angiogenesis and chemotherapy strategies. Bull Math Biol 64:673–702
Milde F, Bergdorf M, Koumoutsakos P (2008) A hybrid model for three-dimensional simulations of sprouting angiogenesis. Biophys J 95:3146–3160
Molina JR, Hayashi Y, Stephens C, Georgescu M-M (2010) Invasive glioblastoma cells acquire stemness and increased akt activation. Neoplasia 12(6):453–463
Murthy V, Valliappan S, Khalili-Naghadeh N (1989) Time step constraints in finite element analysis of the Poisson type equation. Comput Struct 31:269–273
Narayanan H, Verner SN, Mills KL, Kemkemer R, Garikipati K (2010) In silico estimates of free energy rates in growing tumor spheroids. J Phys Condens Matter 22:194122. doi:10.1088/0953-8984/22/19/194122
Oden JT, Hawkins A, Prudhomme S (2010) General diffusive-interface theories and an approach to predictive tumor growth modeling. Math Models Methods Appl Sci 20:477–517
Parker JC, Lenhard RJ (1987) A model for hysteretic constitutive relations governing multiphase flow. 1. Saturation-pressure relations. Water Resour Res 23:2187–2196
Parker JC, Lenhard RJ (1990) Determining three-phase permeability saturation-pressure relations from two-phase measurements. J Pet Sci Eng 4:57–65
Perfahl H, Byrne HM, Chen T, Estrella V, Alarcòn T, Lapin A, Gatenby RA, Gillies RJ, Lloyd MC, Maini PK, Reuss M, Owen MR (2011) Multiscale modelling of vascular tumor growth in 3D: the roles of domain size and boundary conditions. PLoS ONE 6(4):e14790
Peterson JW, Carey GF, Knezevic DJ, Murray BT (2007) Adaptive finite element methodology for tumour angiogenesis modeling. Int J Numer Methods Eng 69:1212–1238
Pettet G, Please C, Tindall M, McElwain D (2001) The migration of cells in multicell tumor spheroids. Bull Math Biol 63:231–257
Plank MJ, Sleeman BD (2004) Lattice and non-lattice models of tumor angiogenesis. Bull Math Biol 66:1785–1819
Podio-Guidugli P (2006) Models of phase segregation and diffusion of atomic species on a lattice. Ric Mat 55:105–118
Preziosi L, Ambrosi D, Verdier C (2010) An elsto-visco-plastic model of cell aggregates. J Theor Biol 262:35–47
Preziosi L, Farina A (2001) On Darcy’s law for growing porous media. Int J Non-Linear Mech 37:485–491
Preziosi L, Tosin A (2009) Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications. J Math Biol 58(4–5):625–656
Preziosi L, Vitale G (2011) A multiphase model of tumour and tissue growth including cell adhesion and plastic re-organisation. Math Models Methods Appl Sci 21(9):1901–1932
Rank E, Katz C, Werner H (1983) On the importance of the discrete maximum principle in transient analysis using finite element methods. Int J Numer Methods Eng 19:1771–1782
Roose T, Chapman SJ, Maini PK (2007) Mathematical models of avascular tumor growth. SIAM Rev 49(2):179–208
Sarntinoranont M, Rooney F, Ferrari M (2003) Interstitial stress and fluid pressure within a growing tumor. Ann Biomed Eng 31(3):327–335
Sciumè G, Shelton SE, Gray WG, Miller CT, Hussain F, Ferrari M, Decuzzi P, Schrefler BA (2013) A multiphase model for three dimensional tumor growth. New J Phys 15:015005. doi:10.1088/1367-2630/15/1/015005
Sciumè G, Shelton SE, Gray WG, Miller CT, Hussain F, Ferrari M, Decuzzi P, Schrefler BA (2012) Tumor growth modeling from the perspective of multiphase porous media mechanics. Mol Cell Biomech 9(3):193–212
Shelton S (2011) Mechanistic modeling of cancer tumor growth using a porous media approach. Master thesis, Department of Environmental Sciences and Engineering, University of North Carolina at Chaper Hill
Stamper IJ, Byrne HM, Owen MR, Maini PK (2007) Modelling the role of angiogenesis and vasculogenesis in solid tumor growth. Bull Math Biol 69:2737–2772
Stephanou A, McDougall SR, Anderson ARA, Chaplain MAJ (2005) Mathematical modelling of flow in 2d and 3d vascular networks: applications to anti-angiogenic and chemotherapeutic drug strategies. Math Comput Model 41:1137–1156
Stephanou A, McDougall SR, Anderson ARA, Chaplain MAJ (2006) Mathematical modeling of the influence of blood rheological properties upon adaptive tumor-induced angiogenesis. Math Comput Model 44:96–123
Stone HL (1970) Probability model for estimating three-phase relative permeability. Trans SPE AIME 249:214–218
Stone HL (1973) Estimation of three-phase relative permeability and residual oil data. J Can Pet Technol 12:53–61
Sun S, Wheeler MF, Obeyesekere M, Patrick CW Jr (2005) A deterministic model of growth factor-induced angiogenesis. Bull Math Biol 67:313–337
Sun S, Wheeler MF, Obeyesekere M, Patrick CW Jr (2005) Multiscale angiogenesis modeling using mixed finite element methods. Multiscale Model Simul 4:1137–1167
Tosin A, Ambrosi D, Preziosi L (2006) Mechanics and chemotaxis in the morphogenesis of vascular networks. Bull Math Biol 68:1819–1836
Turska E, Wisniewski K, Schrefler BA (1994) Error propagation of staggered solution procedures for transient problems. Comput Methods Appl Mech Eng 144:177–188
van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44:892–898
van Genuchten MT, Rolston DE, German PF (1990) Transport of water and solutes in macropores. Geoderma 46(1–3):1–297
Wise SM, Lowengrub JS, Frieboes HB, Cristini V (2008) Three-dimensional multispecies nonlinear tumor growth model and numerical method. J Theor Biol 253:524–543
Zavarise G, Wrigger P, Schrefler BA (1995) On augmented Lagrangian algorithms for thermomechanical contact problems with friction. Int J Numer Methods Eng 38:2929–2949
Zhao G, Wu J, Xu S, Collins MW, Long Q, Koenig CS, Jiang Y, Wang J, Padhani AR (2007) Numerical simulation of blood flow and interstitial fluid pressure in solid tumor microcirculation based on tumor-induced angiogenesis. Mech Sin 23:477–483
Zheng X, Wise S, Cristini V (2005) Nonlinear simulation of tumor necrosis, neovascularization and tissue invasion via an adaptive finite element/level-set method. Bull Math Biol 67:211–259
Zienkiewicz OC, Taylor RL (2000) The finite element method. Solid mechanics, vol 2. Butterworth Heinemann, Oxford
Acknowledgements
G.S. and B.S. acknowledge partial support from the Strategic Research Project “Algorithms and Architectures for Computational Science and Engineering”—AACSE (STPD08JA32-2008) of the University of Padova (Italy) and the partial support of Università Italo Francese within the Vinci Program. W.G. acknowledges partial support from the US National Science Foundation Grant ATM-0941235 and the US Department of Energy Grant DE-SC0002163. P.D. and M.F. acknowledge partial support from the NIH/NCI grants U54CA143837 and U54CA151668. M.F. acknowledges the Ernest Cockrell Jr. Distinguished Endowed Chair.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix A: Linear Momentum Balance Equation for a Fluid Phase
The general conservation of momentum Eq. (15) will be denoted for the fluid phase using the letter f as a qualifier. In the paper the f will be specified as either l, h or t.
where \(\mathbf{g}^{\overline{f}}\) is the body force, \(\mathop{M_{v}}\limits^{i\kappa \to if}\mathbf{v}^{\overline{f}}\)represents the momentum exchange from the κ to the f phase due to mass exchange of species i, \(\mathop{\mathbf{T}}\limits^{\kappa \to f}\) is the interaction force between phase f and the adjacent interfaces, and \(\mathbf{t}^{\overline{\overline{f}}}\) is the stress tensor. If the inertial terms are considered to be negligible, as is the case for slow flow in a porous medium, the first two terms in Eq. (A.1) can be neglected. Additionally, the momentum exchange due to mass transfer, \(\mathop{M_{v}}\limits^{i\kappa \to if}\mathbf{v}^{\overline{f}}\)may also be considered small since this term is of the same order of magnitude as the inertial terms. Thus the momentum equation simplifies to
The TCAT method of closure involves arranging terms in the entropy inequality into force-flux pairs. At equilibrium each member of the force-flux pair will be zero. This equilibrium constraint guides closure of the conservation system for near equilibrium situations. In the case here where the flows are slow, the near-equilibrium state assumption is appropriate. Based on the TCAT procedure, the elements of the entropy inequality relating to flow velocity that arise in the entropy inequality are
In this equation, \(\theta^{\overline{\overline{f}}}\) is the macroscale temperature of the f phase, \(\psi^{\overline{f}}\) is the gravitational potential, \(\varsigma^{\overline{f}}\) is the chemical potential, p f is the fluid pressure, \(\mathbf{v}^{\overline{s}}\) is the velocity of the solid phase and \(\mathbf{d}^{\overline{\overline{f}}}\) the rate of strain tensor of the phase \(f (\mathbf{d}^{\overline{\overline{f}}} = \frac{1}{2} [ \nabla \mathbf{v}^{\overline{f}} + ( \nabla \mathbf{v}^{\overline{f}} )^{T} ])\). All of these quantities are macroscale averages.
Consider the variability in volume fraction of the f phase to be small. For this situation, \(\nabla \psi^{\overline{f}} + \mathbf{g} = 0\). Additionally, consider an isothermal case such that the Gibbs-Duhem equation provides \(\rho^{f}\nabla \varsigma^{\overline{f}} - \nabla p^{f} = 0\). Application of these two conditions to Eq. (A.3) reduces it to
This equation contains two independent force-flux products. The stipulation that both elements of each product pair must be zero at equilibrium and the requirement that the grouping of terms must be non-negative suggests the linear relations
and
In the first relation, R f is a symmetric, positive, semi-definite tensor accounting for the resistance to flow. In the second relation, A f is fourth order tensor that accounts for the dependence of the stress tensor on the rate of strain. At the macroscale for slow flow, this tensor is taken to be zero such that
is the resulting form of the stress tensor. We note that this does not imply that the fluid is inviscid. The effects of viscosity are accounted for at the macroscale by the momentum exchange term \(\mathop{\mathbf{T}}\limits^{\kappa \to f}\).
Substitution of the closure relations Eqs. (A.5) and (A.7) into Eq. (A.2) provides the momentum equation in the form
Typically this relation is expressed as
where K f=(ε f)2(R f)−1is called the hydraulic conductivity.
The hydraulic conductivity depends on the properties of both the flowing fluid and the solid porous material. For an isotropic medium, K f=K f 1. The morphology and topology of the solid media are important in determining the hydraulic conductivity of the cellular solid phases. The conductivity is influenced by the cell size distribution, shape of the cells, tortuosity of passages, specific surface area, and porosity (the sum of the fluid volume fractions). It also depends on the density and viscosity of the fluid. Neglecting gravity in Eq. (A.8) yields Eq. (17).
Appendix B: Coefficients of the Matrices Appearing in Eq. (42)
In the following equations K s is the Bulk modulus of the solid skeleton and \(\frac{\partial \mathbf{e}_{sw}^{s}}{\partial t} = \frac{\mathbf{1}}{3K^{s}}\frac{\partial p^{s}}{\partial t}\)
Rights and permissions
About this article
Cite this article
Sciumè, G., Gray, W.G., Ferrari, M. et al. On Computational Modeling in Tumor Growth. Arch Computat Methods Eng 20, 327–352 (2013). https://doi.org/10.1007/s11831-013-9090-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11831-013-9090-8