Physics:Fluid–structure interaction

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Fluid–structure interaction (FSI) is the interaction of some movable or deformable structure with an internal or surrounding fluid flow.[1] Fluid–structure interactions can be stable or oscillatory. In oscillatory interactions, the strain induced in the solid structure causes it to move such that the source of strain is reduced, and the structure returns to its former state only for the process to repeat.

Propagation of a pressure wave through an incompressible fluid in a flexible tube

Examples

Fluid–structure interactions are a crucial consideration in the design of many engineering systems, e.g. automobile, aircraft, spacecraft, engines and bridges. Failing to consider the effects of oscillatory interactions can be catastrophic, especially in structures comprising materials susceptible to fatigue. Tacoma Narrows Bridge (1940), the first Tacoma Narrows Bridge, is probably one of the most infamous examples of large-scale failure. Aircraft wings and turbine blades can break due to FSI oscillations. A reed actually produces sound because the system of equations governing its dynamics has oscillatory solutions. The dynamic of reed valves used in two strokes engines and compressors is governed by FSI. The act of "blowing a raspberry" is another such example. The interaction between tribological machine components, such as bearings and gears, and lubricant is also an example of FSI.[2] The lubricant flows between the contacting solid components and causes elastic deformation in them during this process. Fluid–structure interactions also occur in moving containers, where liquid oscillations due to the container motion impose substantial magnitudes of forces and moments to the container structure that affect the stability of the container transport system in a highly adverse manner.[3][4][5][6] Another prominent example is the start up of a rocket engine, e.g. Space Shuttle main engine (SSME), where FSI can lead to considerable unsteady side loads on the nozzle structure.[7] In addition to pressure-driven effects, FSI can also have a large influence on surface temperatures on supersonic and hypersonic vehicles.[8]

Fluid–structure interactions also play a major role in appropriate modeling of blood flow. Blood vessels act as compliant tubes that change size dynamically when there are changes to blood pressure and velocity of flow.[9] Failure to take into account this property of blood vessels can lead to a significant overestimation of resulting wall shear stress (WSS). This effect is especially imperative to take into account when analyzing aneurysms. It has become common practice to use computational fluid dynamics to analyze patient specific models. The neck of an aneurysm is the most susceptible to changes in to WSS. If the aneurysmal wall becomes weak enough, it becomes at risk of rupturing when WSS becomes too high. FSI models contain an overall lower WSS compared to non-compliant models. This is significant because incorrect modeling of aneurysms could lead to doctors deciding to perform invasive surgery on patients who were not at a high risk of rupture. While FSI offers better analysis, it comes at a cost of highly increased computational time. Non-compliant models have a computational time of a few hours, while FSI models could take up to 7 days to finish running. This leads to FSI models to be most useful for preventative measures for aneurysms caught early, but unusable for emergency situations where the aneurysm may have already ruptured.[10][11][12][13]

Another real-world application of FSI involved using CFD and FEA to determine the technical feasibility of a cryogenic mixing tank. It was known that the mixing tank fabricated from a previous design buckled under the thermal stresses induced by its cooling jacket. The objective of this study was to simulate the thermal-induced stresses on the revised design to ascertain, before commencing fabrication, whether the modified and improved tank would buckle when the cryogenic fluid was fed. There were two parts to this study, namely: (1) A CFD study was performed on the fluid phase of the system (flow inside the cooling jacket) to obtain the transient temperatures induced by the fluid phase on the solid phase. (2) Using the transient temperature results from the CFD study as input, a stress and displacement analysis was performed on the solid phase (the tank) using the FEA method. This FSI study revealed the design weaknesses and enabled targeted design improvements. Although the benefits of using FSI are obvious, in reality, employing FSI technology to obtain accurate and meaningful results can be a challenge for many. FSI is advanced engineering and is considered a difficult subject by many, often, users of FSI had undertaken further study at postgraduate level and taken years of practice to master this technology.[14]

Analysis

Fluid–structure interaction problems and multiphysics problems in general are often too complex to solve analytically and so they have to be analyzed by means of experiments or numerical simulation. Research in the fields of computational fluid dynamics and computational structural dynamics is still ongoing but the maturity of these fields enables numerical simulation of fluid-structure interaction.[15] Two main approaches exist for the simulation of fluid–structure interaction problems:

  • Monolithic approach: the equations governing the flow and the displacement of the structure are solved simultaneously, with a single solver
  • Partitioned approach: the equations governing the flow and the displacement of the structure are solved separately, with two distinct solvers

The monolithic approach requires a code developed for this particular combination of physical problems whereas the partitioned approach preserves software modularity because an existing flow solver and structural solver are coupled. Moreover, the partitioned approach facilitates solution of the flow equations and the structural equations with different, possibly more efficient techniques which have been developed specifically for either flow equations or structural equations. On the other hand, development of stable and accurate coupling algorithm is required in partitioned simulations. In conclusion, the partitioned approach allows reusing existing software which is an attractive advantage. However, stability of the coupling method needs to be taken into consideration.

In addition, the treatment of meshes introduces other classifications of FSI analysis. For example，one can classify them as the conforming mesh methods and the non-conforming mesh methods.[16] Other classifications can be mesh-based methods and meshless methods.[17]

Numerical simulation

The Newton–Raphson method or a different fixed-point iteration can be used to solve FSI problems. Methods based on Newton–Raphson iteration are used in both the monolithic [18][19][20] and the partitioned [21][22] approach. These methods solve the nonlinear flow equations and the structural equations in the entire fluid and solid domain with the Newton–Raphson method. The system of linear equations within the Newton–Raphson iteration can be solved without knowledge of the Jacobian with a matrix-free iterative method, using a finite difference approximation of the Jacobian-vector product.

Whereas Newton–Raphson methods solve the flow and structural problem for the state in the entire fluid and solid domain, it is also possible to reformulate an FSI problem as a system with only the degrees of freedom in the interface’s position as unknowns. This domain decomposition condenses the error of the FSI problem into a subspace related to the interface.[23] The FSI problem can hence be written as either a root finding problem or a fixed point problem, with the interface’s position as unknowns.

Interface Newton–Raphson methods solve this root-finding problem with Newton–Raphson iterations, e.g. with an approximation of the Jacobian from a linear reduced-physics model.[24][25] The interface quasi-Newton method with approximation for the inverse of the Jacobian from a least-squares model couples a black-box flow solver and structural solver [26] by means of the information that has been gathered during the coupling iterations. This technique is based on the interface block quasi-Newton technique with an approximation for the Jacobians from least-squares models which reformulates the FSI problem as a system of equations with both the interface’s position and the stress distribution on the interface as unknowns. This system is solved with block quasi-Newton iterations of the Gauss–Seidel type and the Jacobians of the flow solver and structural solver are approximated by means of least-squares models.[27]

The fixed-point problem can be solved with fixed-point iterations, also called (block) Gauss–Seidel iterations,[22] which means that the flow problem and structural problem are solved successively until the change is smaller than the convergence criterion. However, the iterations converge slowly if at all, especially when the interaction between the fluid and the structure is strong due to a high fluid/structure density ratio or the incompressibility of the fluid.[28] The convergence of the fixed point iterations can be stabilized and accelerated by Aitken relaxation and steepest descent relaxation, which adapt the relaxation factor in each iteration based on the previous iterations.[29]

If the interaction between the fluid and the structure is weak, only one fixed-point iteration is required within each time step. These so-called staggered or loosely coupled methods do not enforce the equilibrium on the fluid–structure interface within a time step but they are suitable for the simulation of aeroelasticity with a heavy and rather stiff structure. Several studies have analyzed the stability of partitioned algorithms for the simulation of fluid-structure interaction[28][30] [31] .[32][33][34]

See also

Open source codes

Academic Codes

Commercial Codes

References

  1. ↑ Bungartz, Hans-Joachim; Schäfer, Michael, eds (2006). Fluid-structure Interaction: Modelling, Simulation, Optimization. Springer-Verlag. ISBN 978-3-540-34595-4. 
  2. ↑ Singh, Kushagra; Sadeghi, Farshid; Russell, Thomas; Lorenz, Steven J.; Peterson, Wyatt; Villarreal, Jaret; Jinmon, Takumi (2021-09-01). "Fluid–Structure Interaction Modeling of Elastohydrodynamically Lubricated Line Contacts" (in en). Journal of Tribology 143 (9). doi:10.1115/1.4049260. ISSN 0742-4787. https://asmedigitalcollection.asme.org/tribology/article/143/9/091602/1091936/Fluid-Structure-Interaction-Modeling-of. 
  3. ↑ Kolaei, Amir; Rakheja, Subhash; Richard, Marc J. (2016-01-25). "An efficient methodology for simulating roll dynamics of a tank vehicle coupled with transient fluid slosh". Journal of Vibration and Control 23 (19): 3216–3232. doi:10.1177/1077546315627565. ISSN 1077-5463. 
  4. ↑ Kolaei, Amir; Rakheja, Subhash; Richard, Marc J. (2015-09-01). "Three-dimensional dynamic liquid slosh in partially-filled horizontal tanks subject to simultaneous longitudinal and lateral excitations". European Journal of Mechanics B 53: 251–263. doi:10.1016/j.euromechflu.2015.06.001. Bibcode: 2015EJMF...53..251K. 
  5. ↑ Kolaei, Amir; Rakheja, Subhash; Richard, Marc J. (2014-01-06). "Range of applicability of the linear fluid slosh theory for predicting transient lateral slosh and roll stability of tank vehicles". Journal of Sound and Vibration 333 (1): 263–282. doi:10.1016/j.jsv.2013.09.002. Bibcode: 2014JSV...333..263K. 
  6. ↑ Kolaei, Amir; Rakheja, Subhash; Richard, Marc J. (2014-07-01). "Effects of tank cross-section on dynamic fluid slosh loads and roll stability of a partly-filled tank truck". European Journal of Mechanics B 46: 46–58. doi:10.1016/j.euromechflu.2014.01.008. Bibcode: 2014EJMF...46...46K. 
  7. ↑ Manuel, Frey (2001) (in de). Behandlung von Strömungsproblemen in Raketendüsen bei Überexpansion. doi:10.18419/opus-3650. http://elib.uni-stuttgart.de/handle/11682/3667. 
  8. ↑ Daub, Dennis; Esser, Burkard; Gülhan, Ali (April 2020). "Experiments on High-Temperature Hypersonic Fluid–Structure Interaction with Plastic Deformation" (in en). AIAA Journal 58 (4): 1423–1431. doi:10.2514/1.J059150. ISSN 0001-1452. 
  9. ↑ Taren, James A. (1965). "Cerebral Aneurysm". The American Journal of Nursing 65 (4): 88–91. doi:10.2307/3453223. ISSN 0002-936X. 
  10. ↑ Sforza, Daniel M.; Putman, Christopher M.; Cebral, Juan R. (June 2012). "Computational fluid dynamics in brain aneurysms". International Journal for Numerical Methods in Biomedical Engineering 28 (6–7): 801–808. doi:10.1002/cnm.1481. ISSN 2040-7939. PMID 25364852. 
  11. ↑ Khe, A K; Cherevko, A A; Chupakhin, A P; Bobkova, M S; Krivoshapkin, A L; Orlov, K Yu (June 2016). "Haemodynamics of giant cerebral aneurysm: A comparison between the rigid-wall, one-way and two-way FSI models". Journal of Physics: Conference Series 722 (1): 012042. doi:10.1088/1742-6596/722/1/012042. ISSN 1742-6588. Bibcode: 2016JPhCS.722a2042K. 
  12. ↑ Torii, Ryo; Oshima, Marie; Kobayashi, Toshio; Takagi, Kiyoshi; Tezduyar, Tayfun E. (2009-09-15). "Fluid–structure interaction modeling of blood flow and cerebral aneurysm: Significance of artery and aneurysm shapes". Computer Methods in Applied Mechanics and Engineering. Models and Methods in Computational Vascular and Cardiovascular Mechanics 198 (45): 3613–3621. doi:10.1016/j.cma.2008.08.020. ISSN 0045-7825. Bibcode: 2009CMAME.198.3613T. 
  13. ↑ Razaghi, Reza; Biglari, Hasan; Karimi, Alireza (2019-07-01). "Risk of rupture of the cerebral aneurysm in relation to traumatic brain injury using a patient-specific fluid-structure interaction model". Computer Methods and Programs in Biomedicine 176: 9–16. doi:10.1016/j.cmpb.2019.04.015. ISSN 0169-2607. 
  14. ↑ "FSI Analysis of Cryogenic Mixing Tank". https://www.jimmylea.com/fsi-analysis-services.html. Retrieved 10 November 2022. 
  15. ↑ J.F. Sigrist (2015). Fluid-Structure Interaction: An Introduction to Finite Element Coupling. Wiley (ISBN:978-1-119-95227-5)
  16. ↑ "Archived copy". http://www.global-sci.com/openaccess/v12_337.pdf. 
  17. ↑ Zhang, Chi; Rezavand, Massoud; Hu, Xiangyu (2021-03-15). "A multi-resolution SPH method for fluid-structure interactions". Journal of Computational Physics 429: 110028. doi:10.1016/j.jcp.2020.110028. 
  18. ↑ M. Heil (2004). "An efficient solver for the fully coupled solution of large-displacement fluid-structure interaction problems". Computer Methods in Applied Mechanics and Engineering 193 (1–2): 1–23. doi:10.1016/j.cma.2003.09.006. Bibcode: 2004CMAME.193....1H. 
  19. ↑ K.-J. Bathe; H. Zhang (2004). "Finite element developments for general fluid flows with structural interactions". International Journal for Numerical Methods in Engineering 60 (1): 213–232. doi:10.1002/nme.959. Bibcode: 2004IJNME..60..213B. 
  20. ↑ J. Hron, S. Turek (2006). H.-J. Bungartz. ed. A monolithic FEM/multigrid solver for ALE formulation of fluid-structure interaction with application in biomechanics. Lecture Notes in Computational Science and Engineering. Fluid–Structure Interaction – Modelling, Simulation, Optimisation. Springer-Verlag. pp. 146–170. ISBN 978-3-540-34595-4. 
  21. ↑ H. Matthies; J. Steindorf (2003). "Partitioned strong coupling algorithms for fluid-structure interaction". Computers and Structures 81 (8–11): 805–812. doi:10.1016/S0045-7949(02)00409-1. 
  22. ↑ 22.0 22.1 H. Matthies; R. Niekamp; J. Steindorf (2006). "Algorithms for strong coupling procedures". Computer Methods in Applied Mechanics and Engineering 195 (17–18): 2028–2049. doi:10.1016/j.cma.2004.11.032. Bibcode: 2006CMAME.195.2028M. 
  23. ↑ C. Michler; E. van Brummelen; R. de Borst (2006). "Error-amplification analysis of subiteration-preconditioned GMRES for fluid-structure interaction". Computer Methods in Applied Mechanics and Engineering 195 (17–18): 2124–2148. doi:10.1016/j.cma.2005.01.018. Bibcode: 2006CMAME.195.2124M. 
  24. ↑ J.-F. Gerbeau; M. Vidrascu (2003). "A quasi-Newton algorithm based on a reduced model for fluid-structure interaction problems in blood flows". ESAIM: Mathematical Modelling and Numerical Analysis 37 (4): 631–648. doi:10.1051/m2an:2003049. https://hal.inria.fr/inria-00071895/file/RR-4691.pdf. 
  25. ↑ J.-F. Gerbeau; M. Vidrascu; P. Frey (2005). "Fluid-structure interaction in blood flows on geometries based on medical imaging". Computers and Structures 83 (2–3): 155–165. doi:10.1016/j.compstruc.2004.03.083. 
  26. ↑ J. Degroote; K.-J. Bathe; J. Vierendeels (2009). "Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction". Computers and Structures 87 (11–12): 793–801. doi:10.1016/j.compstruc.2008.11.013. 
  27. ↑ J. Vierendeels; L. Lanoye; J. Degroote; P. Verdonck (2007). "Implicit coupling of partitioned fluid-structure interaction problems with reduced order models". Computers and Structures 85 (11–14): 970–976. doi:10.1016/j.compstruc.2006.11.006. 
  28. ↑ 28.0 28.1 P. Causin; J.-F. Gerbeau; F. Nobile (2005). "Added-mass effect in the design of partitioned algorithms for fluid-structure problems". Computer Methods in Applied Mechanics and Engineering 194 (42–44): 4506–4527. doi:10.1016/j.cma.2004.12.005. Bibcode: 2005CMAME.194.4506C. https://hal.inria.fr/inria-00071499/file/RR-5084.pdf. 
  29. ↑ U. Küttler; W. Wall (2008). "Fixed-point fluid-structure interaction solvers with dynamic relaxation". Computational Mechanics 43 (1): 61–72. doi:10.1007/s00466-008-0255-5. Bibcode: 2008CompM..43...61K. 
  30. ↑ J. Degroote; P. Bruggeman; R. Haelterman; J. Vierendeels (2008). "Stability of a coupling technique for partitioned solvers in FSI applications". Computers and Structures 86 (23–24): 2224–2234. doi:10.1016/j.compstruc.2008.05.005. https://biblio.ugent.be/publication/533350. 
  31. ↑ R. Jaiman; X. Jiao; P. Geubelle; E. Loth (2006). "Conservative load transfer along curved fluid-solid interface with non-matching meshes". Journal of Computational Physics 218 (1): 372–397. doi:10.1016/j.jcp.2006.02.016. Bibcode: 2006JCoPh.218..372J. 
  32. ↑ J. Vierendeels; K. Dumont; E. Dick; P. Verdonck (2005). "Analysis and stabilization of fluid-structure interaction algorithm for rigid-body motion". AIAA Journal 43 (12): 2549–2557. doi:10.2514/1.3660. Bibcode: 2005AIAAJ..43.2549V. https://zenodo.org/record/894358. 
  33. ↑ Christiane Förster; Wolfgang A. Wall; Ekkehard Ramm (2006). "The artificial added mass effect in sequential staggered fluid-structure interaction algorithms". European Conference on Computational Fluid Dynamics ECCOMAS CFD 2006. The Netherlands. 
  34. ↑ Christiane Förster; Wolfgang A. Wall; Ekkehard Ramm (2007). "Artificial added mass instabilities in sequential staggered coupling of nonlinear structures and incompressible viscous flows". Computer Methods in Applied Mechanics and Engineering 196 (7): 1278–1293. doi:10.1016/j.cma.2006.09.002. Bibcode: 2007CMAME.196.1278F. 



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