Skip to main content
Log in

A numerical study on the effect of static magnetic field on the hemodynamics of magnetic fluid in biological porous media

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

High interstitial fluid pressure in the tumor is among the most important barriers to drug delivery. The use of the static magnetic field is one of the treatment modalities for cancers and tumors. In this study, the effect of magnetic field on pressure and magnetized interstitial fluid preservation is discussed. The simulation is solved in two-dimensional space using the COMSOL Multiphysics 5.4.0 software. The geometry includes the porous space of healthy tissue and tumor, as well as the parent vessel as the source and the lymphatic system as the sink modeled based on the biology of the body. Governing coupled equations solved in this simulation include fluid flow in the porous space, which are solved using the law of mass and momentum conservation. The magnetic field equations are solved independently and finally the term of the volume force is coupled to the serial flow equations affecting the interstitial fluid pressure. The concentration distribution is the product of Fick’s equations, which is solved in porous media by the magnetic field according to the bolus injection method. Simulation results show that by applying the static magnetic field generated from permanent magnet to the homogenous magnetic fluid, the magnetized interstitial fluid pressure in the tissue and especially in the tumor region decreases, leading to the higher concentration of the drug that reaches the tumor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Abbreviations

\(P_{\text{V}}\) :

Intravascular pressure (Pa)

\(P_{\text{i}}\) :

Interstitial pressure (Pa)

\(P_{\text{L}}\) :

Hydrostatic pressure of lymphatic (Pa)

\(V_{\text{i}}\) :

Interstitial fluid velocity (m s−1)

\(L_{\text{P}}\) :

Hydrostatic conductivity of the vessel wall (m Pa−1 s−1)

\(F\) :

Volume force (N)

\(C\) :

Solute Concentration (Kg m−3)

\(C^{*}\) :

Dimensionless concentration

\(P\) :

Diffusive permeability (m s−1)

\(C_{\text{P}}\) :

Plasma concentration of the solute (Kg m−3)

\(\frac{S}{V}\) :

Surface area per unit volume of tissue for transport in the interstitium (1 m−1)

\(\frac{{L_{\text{PL}} S_{\text{L}} }}{V}\) :

The lymphatic filtration coefficient (1 Pa−1 s−1)

\({\text{Pe}}\) :

Peclet number

\(D_{\text{eff}}\) :

Effective diffusion (m2 s−1)

\(H\) :

Magnetic field vector (A m−1)

\(B\) :

Magnetic flux density (T)

\(J\) :

Current density vector (A m−2)

\(B_{\text{rem}}\) :

Remanent magnetic flux density (A m−1)

\(M\) :

Magnetization vector in the blood stream (A m−1)

\(X\) :

Magnetic susceptibility

\(K\) :

Permeability of the porous media (m2)

\(L_{\text{PL}}\) :

Hydrostatic conductivity of the lymphatic vessel wall (m/Pa−1 s−1)

\(r\) :

Length (m)

\(r^{*}\) :

Dimensionless length

\(v_{\text{vas}}\) :

Fluid inlet velocity from blood vascular (m s−1)

\(v_{\text{L}}\) :

Fluid outlet velocity to lymphatic vessel (m s−1)

\(j_{\text{vas}}\) :

Inward flux density of fluid (mol m−2 s−1)

\(j_{\text{L}}\) :

Outward flux density of fluid (mol/m−2 s−1)

J :

Electric current density vector (A m−2)

i:

Interstitial medium

L:

Lymphatic drainage

vas:

Blood vasculature

eff:

Effective

P:

Plasma

\(\rho\) :

Interstitial fluid density (Kg m−3)

\(\mu\) :

Interstitial fluid viscosity (Pa s)

\(\kappa\) :

Hydraulic conductivity of the interstitium (m2 Pa−1 s−1)

\(\sigma_{\text{f}}\) :

Solvent-drag reflection coefficient

\(\pi_{\text{V}}\) :

Osmotic pressure of the plasma (Pa)

\(\pi_{\text{i}}\) :

Osmotic pressure of the interstitial fluid (Pa)

\(\tau\) :

Drug half-life in plasma (h)

\(\mu_{0}\) :

Magnetic permeability of vacuum (N A−2)

\(\mu_{\text{r}}\) :

Relative magnetic permeability of the permanent magnet (N A−2)

\(\sigma\) :

Average osmotic reflection coefficient for the plasma proteins

References

  1. Hadim H, Vafai K. Overview of current computational studies of heat transfer in porous media and their applications–forced convection and multiphase heat transfer. In: Minkowycz WJ, Sparrow EM, editors. Advances in numerical heat transfer, vol 2. Routledge; 2018. p. 291–329.

  2. Vafai K. Handbook of porous media. Boca Raton: CRC Press; 2015.

    Google Scholar 

  3. Vafai K, Tien CL. Boundary and inertia effects on flow and heat transfer in porous media. Int J Heat Mass Transf. 1981;24(2):195–203.

    CAS  Google Scholar 

  4. Siavashi M, Joibary SMM. Numerical performance analysis of a counter-flow double-pipe heat exchanger with using nanofluid and both sides partly filled with porous media. J Therm Anal Calorim. 2019;135(2):1595–610.

    CAS  Google Scholar 

  5. Khanafer K, Vafai K. Applications of nanofluids in porous medium. J Therm Anal Calorim. 2019;135(2):1479–92.

    CAS  Google Scholar 

  6. Rashidi S, Esfahani JA, Rashidi A. A review on the applications of porous materials in solar energy systems. Renew Sustain Energy Rev. 2017;73:1198–210.

    CAS  Google Scholar 

  7. Rashidi S, Kashefi MH, Kim KC, Samimi-Abianeh O. Potentials of porous materials for energy management in heat exchangers—a comprehensive review. Appl Energy. 2019;243:206–32.

    Google Scholar 

  8. Sheikholeslami M. New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media. Comput Methods Appl Mech Eng. 2019;344:319–33. https://doi.org/10.1016/j.cma.2018.09.044.

    Article  Google Scholar 

  9. Sheikholeslami M, Sheremet MA, Shafee A, Tlili I. Simulation of nanoliquid thermogravitational convection within a porous chamber imposing magnetic and radiation impacts. Physica A. 2020. https://doi.org/10.1016/j.physa.2019.124058.

    Article  Google Scholar 

  10. Vafai K, Tien C. Boundary and inertia effects on convective mass transfer in porous media. Int J Heat Mass Transf. 1982;25(8):1183–90.

    Google Scholar 

  11. Ai L, Vafai K. A coupling model for macromolecule transport in a stenosed arterial wall. Int J Heat Mass Transf. 2006;49(9–10):1568–91.

    CAS  Google Scholar 

  12. Yang N, Vafai K. Modeling of low-density lipoprotein (LDL) transport in the artery—effects of hypertension. Int J Heat Mass Transf. 2006;49(5–6):850–67.

    CAS  Google Scholar 

  13. Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science. 2005;307(5706):58–62.

    CAS  PubMed  Google Scholar 

  14. Ilami M, Ahmed RJ, Petras A, Beigzadeh B, Marvi H. Magnetic needle Steering in Soft phantom tissue. Sci Rep. 2020;10(1):1–11.

    Google Scholar 

  15. Jang SH, Wientjes MG, Lu D, Au JL-S. Drug delivery and transport to solid tumors. Pharm Res. 2003;20(9):1337–50.

    CAS  PubMed  Google Scholar 

  16. Blakeslee S. Impenetrable tumors found to block even the newest cancer agents. The New York Times. 1989.

  17. Goldacre R, Sylven B. On the access of blood-borne dyes to various tumour regions. Br J Cancer. 1962;16(2):306.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Sefidgar M, Soltani M, Raahemifar K, Bazmara H, Nayinian SMM, Bazargan M. Effect of tumor shape, size, and tissue transport properties on drug delivery to solid tumors. J Biol Eng. 2014;8(1):12.

    PubMed  PubMed Central  Google Scholar 

  19. Soltani M, Chen P. Numerical modeling of interstitial fluid flow coupled with blood flow through a remodeled solid tumor microvascular network. PLoS ONE. 2013;8(6):e67025.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Stylianopoulos T, Jain RK. Combining two strategies to improve perfusion and drug delivery in solid tumors. Proc Natl Acad Sci. 2013;110(46):18632–7.

    CAS  PubMed  Google Scholar 

  21. Jain R. Delivery of molecular medicine to solid tumors: lessons from in vivo imaging of gene expression and function. J Controll Release. 2001;74(1–3):7–25.

    CAS  Google Scholar 

  22. Pozrikidis C. Numerical simulation of blood and interstitial flow through a solid tumor. J Math Biol. 2010;60(1):75–94.

    CAS  PubMed  Google Scholar 

  23. Baxter LT, Jain RK. Transport of fluid and macromolecules in tumors. I. Role of interstitial pressure and convection. Microvasc Res. 1989;37(1):77–104.

    CAS  PubMed  Google Scholar 

  24. Baxter LT, Jain RK. Transport of fluid and macromolecules in tumors. II. Role of heterogeneous perfusion and lymphatics. Microvasc Res. 1990;40(2):246–63.

    CAS  PubMed  Google Scholar 

  25. Baxter LT, Jain RK. Transport of fluid and macromolecules in tumors: III. Role of binding and metabolism. Microvasc Res. 1991;41(1):5–23.

    CAS  PubMed  Google Scholar 

  26. Saltzman WM, Radomsky ML. Drugs released from polymers: diffusion and elimination in brain tissue. Chem Eng Sci. 1991;46(10):2429–44.

    CAS  Google Scholar 

  27. Goh Y-MF, Kong HL, Wang C-H. Simulation of the delivery of doxorubicin to hepatoma. Pharm Res. 2001;18(6):761–70.

    CAS  PubMed  Google Scholar 

  28. Tan WHK, Lee T, Wang CH. Simulation of intratumoral release of etanidazole: effects of the size of surgical opening. J Pharm Sci. 2003;92(4):773–89.

    CAS  PubMed  Google Scholar 

  29. Tan WHK, Wang F, Lee T, Wang CH. Computer simulation of the delivery of etanidazole to brain tumor from PLGA wafers: comparison between linear and double burst release systems. Biotechnol Bioeng. 2003;82(3):278–88.

    CAS  PubMed  Google Scholar 

  30. Teo CS, Tan WHK, Lee T, Wang C-H. Transient interstitial fluid flow in brain tumors: effect on drug delivery. Chem Eng Sci. 2005;60(17):4803–21.

    CAS  Google Scholar 

  31. Wang C-H, Li J. Three-dimensional simulation of IgG delivery to tumors. Chem Eng Sci. 1998;53(20):3579–600.

    CAS  Google Scholar 

  32. Wang C-H, Li J, Teo CS, Lee T. The delivery of BCNU to brain tumors. J Controll Release. 1999;61(1–2):21–41.

    CAS  Google Scholar 

  33. Zhao J, Salmon H, Sarntinoranont M. Effect of heterogeneous vasculature on interstitial transport within a solid tumor. Microvasc Res. 2007;73(3):224–36.

    CAS  PubMed  Google Scholar 

  34. Soltani M, Chen P. Numerical modeling of fluid flow in solid tumors. PLoS ONE. 2011;6(6):e20344.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Chauhan VP, Stylianopoulos T, Martin JD, Popović Z, Chen O, Kamoun WS, et al. Normalization of tumour blood vessels improves the delivery of nanomedicines in a size-dependent manner. Nat Nanotechnol. 2012;7(6):383.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Welter M, Rieger H. Interstitial fluid flow and drug delivery in vascularized tumors: a computational model. PLoS ONE. 2013;8(8):e70395.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Secomb TW, Alberding JP, Hsu R, Dewhirst MW, Pries AR. Angiogenesis: an adaptive dynamic biological patterning problem. PLoS Comput Biol. 2013;9(3):e1002983.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Chaplain MA, McDougall SR, Anderson AR. Blood flow and tumour-induced angiogenesis: dynamically adapting vascular networks. In: Jackson T, editor. Modeling tumor vasculature. New York: Springer; 2012. p. 167–212.

  39. Sefidgar M, Soltani M, Bazmara H, Mousavi M, Bazargan M, Elkamel A. Interstitial flow in cancerous tissue: effect of considering remodeled capillary network. J Tissue Sci Eng Sci. 2014;4:2.

    Google Scholar 

  40. Sefidgar M, Raahemifar K, Bazmara H, Bazargan M, Mousavi S, Soltani M, editors. Effect of remodeled tumor-induced capillary network on interstitial flow in cancerous tissue. In: 2nd Middle East conference on biomedical engineering; 2014: IEEE.

  41. Sefidgar M, Soltani M, Raahemifar K, Sadeghi M, Bazmara H, Bazargan M, et al. Numerical modeling of drug delivery in a dynamic solid tumor microvasculature. Microvasc Res. 2015;99:43–56.

    CAS  PubMed  Google Scholar 

  42. Dejam M. Dispersion in non-Newtonian fluid flows in a conduit with porous walls. Chem Eng Sci. 2018;189:296–310.

    CAS  Google Scholar 

  43. Dejam M. Derivation of dispersion coefficient in an electro-osmotic flow of a viscoelastic fluid through a porous-walled microchannel. Chem Eng Sci. 2019;204:298–309.

    CAS  Google Scholar 

  44. Forbes ZG, Yellen BB, Halverson DS, Fridman G, Barbee KA, Friedman G. Validation of high gradient magnetic field based drug delivery to magnetizable implants under flow. IEEE Trans Biomed Eng. 2008;55(2):643–9.

    PubMed  Google Scholar 

  45. Manshadi MK, Saadat M, Mohammadi M, Shamsi M, Dejam M, Kamali R, et al. Delivery of magnetic micro/nanoparticles and magnetic-based drug/cargo into arterial flow for targeted therapy. Drug Deliv. 2018;25(1):1963–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Shapiro B. Towards dynamic control of magnetic fields to focus magnetic carriers to targets deep inside the body. J Magn Magn Mater. 2009;321(10):1594–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Cherry EM, Eaton JK. A comprehensive model of magnetic particle motion during magnetic drug targeting. Int J Multiph Flow. 2014;59:173–85.

    CAS  Google Scholar 

  48. Oldenburg CM, Borglin SE, Moridis GJ. Numerical simulation of ferrofluid flow for subsurface environmental engineering applications. Transp Porous Media. 2000;38(3):319–44.

    CAS  Google Scholar 

  49. Morega A, Dobre A, Morega M. Magnetic Field-Flow Interactions in Drug Delivery through an Arterial System. Revue Roumaine des Sciences Techiques—Electrotechnique et Energetique. 2011;56:199–208.

    Google Scholar 

  50. Beigzadeh B, Halabian M. Effect of static magnetic field on the hemodynamic properties of blood flow containing magnetic substances. J Comput Appl Mech. 2016;47(2):181–94.

    Google Scholar 

  51. Shamsi M, Sedaghatkish A, Dejam M, Saghafian M, Mohammadi M, Sanati-Nezhad A. Magnetically assisted intraperitoneal drug delivery for cancer chemotherapy. Drug Deliv. 2018;25(1):846–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Shojaee P, Niroomand-Oscuii H, Sefidgar M, Alinezhad L. Effect of nanoparticle size, magnetic intensity, and tumor distance on the distribution of the magnetic nanoparticles in a heterogeneous tumor microenvironment. J Magn Magn Mater. 2020;498:166089.

    Google Scholar 

  53. Beigzadeh B, Rasaeifard A. Homotopy-based solution of Navier–Stokes equations for two-phase flow during magnetic drug targeting. J Mol Liq. 2017;238:11–8.

    CAS  Google Scholar 

  54. Pishko GL, Astary GW, Mareci TH, Sarntinoranont M. Sensitivity analysis of an image-based solid tumor computational model with heterogeneous vasculature and porosity. Ann Biomed Eng. 2011;39(9):2360.

    PubMed  PubMed Central  Google Scholar 

  55. Rasouli SS, Jolma IW, Friis HA. Impact of spatially varying hydraulic conductivities on tumor interstitial fluid pressure distribution. Inform Med Unlocked. 2019;16:100175.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Majid Siavashi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseinikhah, S.M., Beigzadeh, B., Siavashi, M. et al. A numerical study on the effect of static magnetic field on the hemodynamics of magnetic fluid in biological porous media. J Therm Anal Calorim 141, 1543–1558 (2020). https://doi.org/10.1007/s10973-020-09703-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-020-09703-x

Keywords

Navigation