Abstract
Computational fluid dynamics (CFD) is a tool that has been used by engineers for over 50 years to analyse heat transfer and fluid flow phenomena. In recent years, there have been rapid developments in biomedical and health research applications of CFD. It has been used to evaluate drug delivery systems, analyse physiological flows (e.g. laryngeal jet flow), facilitate surgical planning (e.g. management of intracranial aneurysms), and develop medical devices (e.g. vascular stents and valve prostheses). Due to the complexity of these fluid flows, it demands an interdisciplinary approach consisting of engineers, computer scientists, and mathematicians to develop the computer programs and software used to solve the mathematical equations. Advances in technology and decreases in computational cost are allowing CFD to be more widely accessible and therefore used in more varied contexts. Cardiovascular medicine is the most common area of biomedical research in which CFD is currently being used, followed closely by upper and lower respiratory tract medicine. CFD is also being used in research investigating cerebrospinal fluid, synovial joints, and intracellular fluid. Although CFD can provide meaningful and aesthetically pleasing outputs, interpretation of the data can be challenging for those without a strong understanding of mathematical and engineering principles. Future development and evolution of computational medicine will therefore require close collaboration between experts in engineering, computer science, and biomedical research. This chapter aims to introduce computational fluid dynamics and present the reader with the basics of biological fluid properties, the CFD method, and its applications within biomedical research through published examples, in hope of bridging knowledge gaps in this rapidly emerging method of biomedical analysis.
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References
Ahookhosh K, Saidi M, Aminfar H et al (2020) Dry powder inhaler aerosol deposition in a model of tracheobronchial airways: validating CFD predictions with in vitro data. Int J Pharm 587:119599. https://doi.org/10.1016/j.ijpharm.2020.119599
Ameenuddin M, Anand M (2020) A mixture theory model for blood combined with low-density lipoprotein transport to predict early atherosclerosis regions in idealized and patient-derived abdominal aorta. J Biomech Eng 142:1–13. https://doi.org/10.1115/1.4047426
Arzani A, Dyverfeldt P, Ebbers T, Shadden SC (2012) In vivo validation of numerical prediction for turbulence intensity in an aortic coarctation. Ann Biomed Eng 40:860–870. https://doi.org/10.1007/s10439-011-0447-6
Basri EI, Basri AA, Riazuddin VN et al (2016) Computational fluid dynamics study in biomedical applications: a review. Int J Fluid Heat Transf 1:2–14
Bluestein D (2017) Utilizing computational fluid dynamics in cardiovascular engineering and medicine—what you need to know. Its translation to the clinic/bedside. Artif Organs 41:117–121. https://doi.org/10.1111/aor.12914
Can A, Du R (2016) Association of hemodynamic factors with intracranial aneurysm formation and rupture: systematic review and meta-analysis. Neurosurgery 78:510–519. https://doi.org/10.1227/NEU.0000000000001083
Clarke EC, Fletcher DF, Stoodley MA, Bilston LE (2013) Computational fluid dynamics modelling of cerebrospinal fluid pressure in Chiari malformation and syringomyelia. J Biomech 46:1801–1809. https://doi.org/10.1016/j.jbiomech.2013.05.013
De Zélicourt DA, Pekkan K, Wills L et al (2005) In vitro flow analysis of a patient-specific intraatrial total cavopulmonary connection. Ann Thorac Surg 79:2094–2102. https://doi.org/10.1016/j.athoracsur.2004.12.052
Dutta R, Spence B, Wei X et al (2020) CFD guided optimization of nose-to-lung aerosol delivery in adults: effects of inhalation waveforms and synchronized aerosol delivery. Pharm Res:37. https://doi.org/10.1007/s11095-020-02923-8
Feng Y, Zhao J, Chen X, Lin J (2017) An in silico subject-variability study of upper airway morphological influence on the airflow regime in a tracheobronchial tree. Bioengineering 4:90. https://doi.org/10.3390/bioengineering4040090
Fernández Tena A, Casan Clarà P (2015) Use of computational fluid dynamics in respiratory medicine. Arch Bronconeumol 51:293–298. https://doi.org/10.1016/j.arbr.2015.03.005
Hariharan P, D’Souza GA, Horner M et al (2017) Use of the FDA nozzle model to illustrate validation techniques in computational fluid dynamics (CFD) simulations. PLoS One 12:1–25. https://doi.org/10.1371/journal.pone.0178749
Ishimoto K, Gadêlha H, Gaffney EA et al (2018) Human sperm swimming in a high viscosity mucus analogue. J Theor Biol 446:1–10. https://doi.org/10.1016/j.jtbi.2018.02.013
Issa RI (1985) Solution of the implicitly discretised fluid flow equations by operator-splitting. J Comput Phys 62:40–65. https://doi.org/10.1080/10407782.2016.1173467
Karampatzakis A, Samaras T (2010) Numerical model of heat transfer in the human eye with consideration of fluid dynamics of the aqueous humour. Phys Med Biol 55:5653–5665. https://doi.org/10.1088/0031-9155/55/19/003
Katritsis D, Kaiktsis L, Chaniotis A et al (2007) Wall shear stress: theoretical considerations and methods of measurement. Prog Cardiovasc Dis 49:307–329. https://doi.org/10.1016/j.pcad.2006.11.001
Kurtcuoglu V, Jain K, Martin BA (2019) Modelling of cerebrospinal fluid flow by computational fluid dynamics. In: Biomechanics of the brain. Springer, Cham, pp 215–241
Liang L, Steinman DA, Brina O et al (2019) Towards the clinical utility of CFD for assessment of intracranial aneurysm rupture - a systematic review and novel parameter-ranking tool. J Neurointerv Surg 11:153–158. https://doi.org/10.1136/neurintsurg-2018-014246
Liu Y, Mitchell J, Chen Y et al (2018) Study of the upper airway of obstructive sleep apnea patient using fluid structure interaction. Respir Physiol Neurobiol 249:54–61. https://doi.org/10.1016/j.resp.2018.01.005
Liu QY, Tang XY, Chen DD et al (2020) Hydrodynamic study of sperm swimming near a wall based on the immersed boundary-lattice Boltzmann method. Eng Appl Comput Fluid Mech 14:853–870. https://doi.org/10.1080/19942060.2020.1779134
Liu B, Zheng J, Bach R, Tang D (2015) Influence of model boundary conditions on blood flow patterns in a patient specific stenotic right coronary artery. Biomed Eng Online 14:S6. https://doi.org/10.1186/1475-925X-14-S1-S6
López JM, Fortuny G, Puigjaner D et al (2018) A comparative CFD study of four inferior vena cava filters. Int j numer method biomed eng 34:1–14. https://doi.org/10.1002/cnm.2990
Maguire EM, Pearce SWA, Xiao Q (2019) Foam cell formation: a new target for fighting atherosclerosis and cardiovascular disease. Vasc Pharmacol 112:54–71. https://doi.org/10.1016/j.vph.2018.08.002
Malik J, Dholakia S, Spector BM et al (2020) Inferior meatus augmentation procedure (IMAP) normalizes nasal airflow patterns in empty nose syndrome patients via computational fluid dynamics (CFD) modeling. Int Forum Allergy Rhinol 11(5):1–8. https://doi.org/10.1002/alr.22720
Malve M, Gharib AM, Yazdani SK et al (2015) Tortuosity of coronary bifurcation as a potential local risk factor for atherosclerosis: CFD steady state study based on in vivo dynamic CT measurements. Ann Biomed Eng 43:82–93. https://doi.org/10.1007/s10439-014-1056-y
Marusic I, Broomhall S (2020) Leonardo da Vinci and fluid mechanics. Annu Rev Fluid Mech 14:1–25. https://doi.org/10.1086/sou.25.2.23208098
Mogilner A, Manhart A (2018) Intracellular fluid mechanics: coupling cytoplasmic flow with active cytoskeletal gel. Annu Rev Fluid Mech 50:347–370. https://doi.org/10.1146/annurev-fluid-010816-060238
Moore JE, Ku DN (1994) Pulsatile velocity measurements in a model of the human abdominal aorta under resting conditions. J Biomech Eng 116:337–346. https://doi.org/10.1115/1.2895740
Morris PD, Narracott A, von Tengg-Kobligk H et al (2016) Computational fluid dynamics modelling in cardiovascular medicine. Heart 102:18–28. https://doi.org/10.1136/heartjnl-2015-308044
Murayama Y, Fujimura S, Suzuki T, Takao H (2019) Computational fluid dynamics as a risk assessment tool for aneurysm rupture. Neurosurg Focus 47:E12. https://doi.org/10.3171/2019.4.FOCUS19189
Mylavarapu G, Mihaescu M, Fuchs L et al (2013) Planning human upper airway surgery using computational fluid dynamics. J Biomech 46:1979–1986. https://doi.org/10.1016/j.jbiomech.2013.06.016
Nanduri JR, Pino-Romainville FA, Celik I (2009) CFD mesh generation for biological flows: geometry reconstruction using diagnostic images. Comput Fluids 38:1026–1032. https://doi.org/10.1016/j.compfluid.2008.01.027
Narasimhan A, Sundarraj C (2013) Effect of choroidal blood perfusion and natural convection in vitreous humor during transpupillary thermotherapy (TTT). Int J Numer Method Biomed Eng 29:530–541. https://doi.org/10.1002/cnm.2538
Nowak N, Kakade PP, Annapragada AV (2003) Computational fluid dynamics simulation of airflow and aerosol deposition in human lungs. Ann Biomed Eng 31:374–390. https://doi.org/10.1114/1.1560632
Oakes JM, Marsden AL, Grandmont C et al (2015) Distribution of aerosolized particles in healthy and emphysematous rat lungs: comparison between experimental and numerical studies. J Biomech 48:1147–1157. https://doi.org/10.1016/j.jbiomech.2015.01.004
Ooi EH, Ng EYK (2011) Effects of natural convection within the anterior chamber on the ocular heat transfer. Int J Numer Method Biomed Eng 27:408–423. https://doi.org/10.1002/cnm.1411
Patankar SV, Spalding DB (1972) A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. Int J Heat Mass Transf 15:1787–1806. https://doi.org/10.1016/0017-9310(72)90054-3
Quadrio M, Pipolo C, Corti S et al (2014) Review of computational fluid dynamics in the assessment of nasal air flow and analysis of its limitations. Eur Arch Oto-Rhino-Laryngology 271:2349–2354. https://doi.org/10.1007/s00405-013-2742-3
Rahbar S, Shokooh-Saremi M (2018) Mathematical modeling of laser linear thermal effects on the anterior layer of the human eye. Opt Laser Technol 99:72–80. https://doi.org/10.1016/j.optlastec.2017.09.033
Sforza DM, Putman CM, Cebral JR (2009) Hemodynamics of cerebral aneurysms. Annu Rev Fluid Mech 41:91–107. https://doi.org/10.1146/annurev.fluid.40.111406.102126
Sievers HH, Schubert K, Jamali A, Scharfschwerdt M (2018) The influence of different inflow configurations on computational fluid dynamics in a novel three-leaflet mechanical heart valve prosthesis. Interact Cardiovasc Thorac Surg 27:475–480. https://doi.org/10.1093/icvts/ivy086
Siggers JH, Ethier CR (2012) Fluid mechanics of the eye. Annu Rev Fluid Mech 44:347–372. https://doi.org/10.1146/annurev-fluid-120710-101058
Sodré F, Santos AÁB, Tofaneli LA, Oliveira TD (2019) Computational fluid dynamics applied to atherosclerosis hemodynamics: a brief review. J Bioeng Technol Appl To Heal 2:15–20. https://doi.org/10.34178/jbth.v2i1.50
Støverud KH, Langtangen HP, Ringstad GA et al (2016) Computational investigation of cerebrospinal fluid dynamics in the posterior cranial fossa and cervical subarachnoid space in patients with Chiari I malformation. PLoS One 11:1–16. https://doi.org/10.1371/journal.pone.0162938
Subramaniam DR, Mylavarapu G, Fleck RJ et al (2017) Effect of airflow and material models on tissue displacement for surgical planning of pharyngeal airways in pediatric down syndrome patients. J Mech Behav Biomed Mater 71:122–135. https://doi.org/10.1016/j.jmbbm.2017.03.007
Takao H, Murayama Y, Otsuka S et al (2012) Hemodynamic differences between unruptured and ruptured intracranial aneurysms during observation. Stroke 43:1436–1439. https://doi.org/10.1161/STROKEAHA.111.640995
Tan L, Cai ZQ, Lai NS (2009) Accuracy and sensitivity of the dynamic ocular thermography and inter-subjects ocular surface temperature (OST) in Chinese young adults. Contact Lens Anterior Eye 32:78–83. https://doi.org/10.1016/j.clae.2008.09.003
Taylan M, Can OF, Cetincakmak MG, Ozbay M (2016) Effect of airway dynamics on the development of larynx cancer. Laryngoscope 126:1136–1142. https://doi.org/10.1002/lary.25645
Taylor CA, Hughes TJR, Zarins CK (1998) Finite element modeling of three-dimensional pulsatile flow in the abdominal aorta: relevance to atherosclerosis. Ann Biomed Eng 26:975–987. https://doi.org/10.1114/1.140
Thamboo A, Dholakia SS, Borchard NA et al (2020) Inferior meatus augmentation procedure (IMAP) to treat empty nose syndrome: a pilot study. Otolaryngol-Head Neck Surg (United States) 162:382–385. https://doi.org/10.1177/0194599819900263
Vinje V, Brucker J, Rognes ME et al (2018) Fluid dynamics in syringomyelia cavities: effects of heart rate, CSF velocity, CSF velocity waveform and craniovertebral decompression. Neuroradiol J 31:482–489. https://doi.org/10.1177/1971400918795482
Wan H, Ge L, Huang L et al (2019) Sidewall aneurysm geometry as a predictor of rupture risk due to associated abnormal hemodynamics. Front Neurol 10:1–7. https://doi.org/10.3389/fneur.2019.00841
Xi J, April Si X, Dong H, Zhong H (2018) Effects of glottis motion on airflow and energy expenditure in a human upper airway model. Eur J Mech B/Fluids 72:23–37. https://doi.org/10.1016/j.euromechflu.2018.04.011
Yamada S, Miyazaki M, Yamashita Y et al (2013) Influence of respiration on cerebrospinal fluid movement using magnetic resonance spin labeling. Fluids Barriers CNS 10:1–7. https://doi.org/10.1186/2045-8118-10-36
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Reid, L. (2021). An Introduction to Biomedical Computational Fluid Dynamics. In: Rea, P.M. (eds) Biomedical Visualisation. Advances in Experimental Medicine and Biology, vol 1334. Springer, Cham. https://doi.org/10.1007/978-3-030-76951-2_10
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