Computational Modelling of Fluid Dynamics for Real-world Applications
DOI:
https://doi.org/10.63876/ijtm.v2i2.117Keywords:
Computational Fluid Dynamics (CFD), Navier–Stokes Equations, Turbulent Flow, Fluid-Structure Interaction, Heat TransferAbstract
This study presents an innovative computational framework for modelling fluid dynamics in real-world applications. The proposed approach effectively simulates turbulent flows, fluid-structure interactions, and heat transfer processes by integrating advanced numerical methods with optimised algorithms. The model developed through adaptations of the Navier–Stokes equations, was rigorously validated using comprehensive experimental trials. The experimental results demonstrated that the simulations achieved an accuracy within 5% of the observed measurements, confirming the model’s reliability in replicating complex physical phenomena. These findings not only enhance our fundamental understanding of fluid behaviour but also provide valuable insights for design optimisation and system management across various industrial sectors.
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C. Kappelt and R. Rzehak, “Investigation of Fluid-dynamics and Mass-transfer in a bubbly mixing layer by Euler-Euler simulation,” Chem. Eng. Sci., vol. 264, p. 118147, Dec. 2022, doi: https://doi.org/10.1016/j.ces.2022.118147.
A. C. B. Silva, G. Batista, M. N. Esperança, A. C. Badino, and R. Béttega, “Analysis of the interfacial force effect on simulated oxygen transfer of a bubble column using computational fluid dynamics,” Digit. Chem. Eng., vol. 5, p. 100061, Dec. 2022, doi: https://doi.org/10.1016/j.dche.2022.100061.
L. Wang, X. Guan, X. Yang, X. Zhan, X. Cai, and B. Shi, “Thermal-fluid behavior and microstructure morphology during laser melting deposition of TiC/Ti6Al4V functionally graded materials,” J. Mater. Res. Technol., vol. 27, pp. 3214–3230, Nov. 2023, doi: https://doi.org/10.1016/j.jmrt.2023.10.107.
F. A. P. Morales, R. Serfaty, J. M. Vedovotto, A. Cavallini, M. M. Villar, and A. da Silveira Neto, “Fluid–structure interaction with a Finite Element–Immersed Boundary approach for compressible flows,” Ocean Eng., vol. 290, p. 115755, Dec. 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.115755.
H. Norouzi and D. Younesian, “Fluid-structure interactions in nonlinear plates subjected to sub and supersonic airflow: A review,” Thin-Walled Struct., vol. 192, p. 111144, Nov. 2023, doi: https://doi.org/10.1016/j.tws.2023.111144.
K. Zeng et al., “Numerical study of obstacle effect on atomic behavior of argon fluid flow inside a nanochannel with molecular dynamics approach,” J. Mol. Liq., vol. 363, p. 119954, Oct. 2022, doi: https://doi.org/10.1016/j.molliq.2022.119954.
A. Borkar, M. S. Nagmode, and D. Pimplaskar, “Real Time Abandoned Bag Detection Using OpenCV,” Int. J. Sci. Eng. Res., vol. 4, no. 11, pp. 2229–5518, 2013.
F. Jiang, Y. Liu, H. Wang, G. Qi, P. Nkomazana, and X. Li, “Effect of particle characteristics on particle collision behaviors and heat transfer performance in a down-flow circulating fluidized bed evaporator,” Powder Technol., vol. 399, p. 116954, Feb. 2022, doi: https://doi.org/10.1016/j.powtec.2021.10.062.
S. Karuppasamy, S. Krishnan, and K. B., “Assessment of the flow behavior of power-law fluids in spinnerets,” Chem. Eng. Res. Des., vol. 176, pp. 134–145, Dec. 2021, doi: https://doi.org/10.1016/j.cherd.2021.09.029.
J. Zhou, G. Jin, T. Ye, and X. Wang, “Fluid-induced vibration analysis of centrifugal pump including rotor system based on Computational Fluid Dynamics and Computational Structural Dynamics coupling approach,” Ocean Eng., vol. 288, p. 115993, Nov. 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.115993.
M. Tom, H. Wang, F. Ou, S. Yun, G. Orkoulas, and P. D. Christofides, “Computational fluid dynamics modeling of a discrete feed atomic layer deposition reactor: Application to reactor design and operation,” Comput. Chem. Eng., vol. 178, p. 108400, Oct. 2023, doi: https://doi.org/10.1016/j.compchemeng.2023.108400.
A. J. Ferreira–Martins and R. da Rocha, “Generalized Navier–Stokes equations and soft hairy horizons in fluid/gravity correspondence,” Nucl. Phys. B, vol. 973, p. 115603, Dec. 2021, doi: https://doi.org/10.1016/j.nuclphysb.2021.115603.
D. Wang and P. L.-F. Liu, “An ISPH with k–ε closure for simulating turbulence under solitary waves,” Coast. Eng., vol. 157, p. 103657, Apr. 2020, doi: https://doi.org/10.1016/j.coastaleng.2020.103657.
M.-J. Xiao, T.-C. Yu, Y.-S. Zhang, and H. Yong, “Physics-informed neural networks for the Reynolds-Averaged Navier–Stokes modeling of Rayleigh–Taylor turbulent mixing,” Comput. Fluids, vol. 266, p. 106025, Nov. 2023, doi: https://doi.org/10.1016/j.compfluid.2023.106025.
M. Romanelli, S. Beneddine, I. Mary, H. Beaugendre, M. Bergmann, and D. Sipp, “Data-driven wall models for Reynolds Averaged Navier–Stokes simulations,” Int. J. Heat Fluid Flow, vol. 99, p. 109097, Feb. 2023, doi: https://doi.org/10.1016/j.ijheatfluidflow.2022.109097.
K. Sreekesh, D. K. Tafti, and S. Vengadesan, “The combined effect of coriolis and centrifugal buoyancy forces on internal cooling of turbine blades with modified ribs using Large Eddy Simulation (LES),” Int. J. Therm. Sci., vol. 182, p. 107797, Dec. 2022, doi: https://doi.org/10.1016/j.ijthermalsci.2022.107797.
M. Abdi, M. O. Rouiss, M. A. Yahia, and A. O. Mohamed, “Large eddy simulation investigation of Reynolds number effects on rheological behavior of Ostwald–de Waele fluids,” Desalin. Water Treat., vol. 279, pp. 168–172, Dec. 2022, doi: https://doi.org/10.5004/dwt.2022.29104.
J. Xu, J. Xia, L. Wang, E. J. Avital, H. Zhu, and Y. Wang, “An improved Eulerian method in three-dimensional direct numerical simulation on the local scour around a cylinder,” Appl. Math. Model., vol. 110, pp. 320–337, Oct. 2022, doi: https://doi.org/10.1016/j.apm.2022.06.002.
A. Hernandez-Aguirre, E. Hernandez-Martinez, F. López-Isunza, and C. O. Castillo, “Framing a novel approach for pseudo continuous modeling using Direct Numerical Simulations (DNS): Fluid dynamics in a packed bed reactor,” Chem. Eng. J., vol. 429, p. 132061, Feb. 2022, doi: https://doi.org/10.1016/j.cej.2021.132061.
K. Wijesooriya, D. Mohotti, A. Amin, and K. Chauhan, “Wind loads on a super-tall slender structure: A validation of an uncoupled fluid-structure interaction (FSI) analysis,” J. Build. Eng., vol. 35, p. 102028, Mar. 2021, doi: https://doi.org/10.1016/j.jobe.2020.102028.
P.-N. Sun, D. Le Touzé, G. Oger, and A.-M. Zhang, “An accurate FSI-SPH modeling of challenging fluid-structure interaction problems in two and three dimensions,” Ocean Eng., vol. 221, p. 108552, Feb. 2021, doi: https://doi.org/10.1016/j.oceaneng.2020.108552.
P. A. Mirzaei, “CFD modeling of micro and urban climates: Problems to be solved in the new decade,” Sustain. Cities Soc., vol. 69, p. 102839, Jun. 2021, doi: https://doi.org/10.1016/j.scs.2021.102839.
C. C. Kang, J. D. Tan, M. Ariannejad, M. A. S. Bhuiyana, Z. N. Ng, and S. C. H. Yong, “Smart sensor controller for HVAC system,” Energy Reports, vol. 9, pp. 60–63, Nov. 2023, doi: https://doi.org/10.1016/j.egyr.2023.09.113.
B. Chaudhury, A. Varma, Y. Keswani, Y. Bhatnagar, and S. Parikh, “Let’s HPC: A web-based platform to aid parallel, distributed and high performance computing education,” J. Parallel Distrib. Comput., vol. 118, pp. 213–232, Aug. 2018, doi: https://doi.org/10.1016/j.jpdc.2018.03.001.
N. Morozova, F. X. Trias, R. Capdevila, C. D. Pérez-Segarra, and A. Oliva, “On the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control,” Build. Environ., vol. 184, p. 107144, Oct. 2020, doi: https://doi.org/10.1016/j.buildenv.2020.107144.
M. Kurz, P. Offenhäuser, D. Viola, O. Shcherbakov, M. Resch, and A. Beck, “Deep reinforcement learning for computational fluid dynamics on HPC systems,” J. Comput. Sci., vol. 65, p. 101884, Nov. 2022, doi: https://doi.org/10.1016/j.jocs.2022.101884.
H. Zhu, J. Shen, K. Y. Lee, and L. Sun, “Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler,” Control Eng. Pract., vol. 101, p. 104484, Aug. 2020, doi: https://doi.org/10.1016/j.conengprac.2020.104484.
M. R. Varkey, J. Ali, and N. C. Lapinel, “The chicken and the egg dilemma: A case of disseminated MAC with Hodgkin’s lymphoma,” Respir. Med. Case Reports, vol. 31, p. 101253, 2020, doi: https://doi.org/10.1016/j.rmcr.2020.101253.
O. Giannopoulou, A. Colagrossi, A. Di Mascio, and C. Mascia, “Chorin’s approaches revisited: Vortex Particle Method vs Finite Volume Method,” Eng. Anal. Bound. Elem., vol. 106, pp. 371–388, Sep. 2019, doi: https://doi.org/10.1016/j.enganabound.2019.05.026.
H. Elzaabalawy, G. Deng, L. Eça, and M. Visonneau, “Assessment of solving the RANS equations with two-equation eddy-viscosity models using high-order accurate discretization,” J. Comput. Phys., vol. 483, p. 112059, Jun. 2023, doi: https://doi.org/10.1016/j.jcp.2023.112059.
C.-Y. Xu, Z. Sun, Y.-T. Zhang, and J.-H. Sun, “Improvement of the scale-adaptive simulation technique based on a compensated strategy,” Eur. J. Mech. - B/Fluids, vol. 81, pp. 1–14, May 2020, doi: https://doi.org/10.1016/j.euromechflu.2020.01.002.
B. Xue, S.-P. Wang, Y.-X. Peng, and A.-M. Zhang, “A novel coupled Riemann SPH–RKPM model for the simulation of weakly compressible fluid–structure interaction problems,” Ocean Eng., vol. 266, p. 112447, Dec. 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.112447.
T. Fabbri, G. Balarac, V. Moureau, and P. Benard, “Design of a high fidelity Fluid–Structure Interaction solver using LES on unstructured grid,” Comput. Fluids, vol. 265, p. 105963, Oct. 2023, doi: https://doi.org/10.1016/j.compfluid.2023.105963.
M. C. Sadino-Riquelme, A. Donoso-Bravo, F. Zorrilla, E. Valdebenito-Rolack, D. Gómez, and F. Hansen, “Computational fluid dynamics (CFD) modeling applied to biological wastewater treatment systems: An overview of strategies for the kinetics integration,” Chem. Eng. J., vol. 466, p. 143180, Jun. 2023, doi: https://doi.org/10.1016/j.cej.2023.143180.
P. Cheng, Y. Lu, Y. Du, and Z. Chen, “Tiered data management system: Accelerating data processing on HPC systems,” Futur. Gener. Comput. Syst., vol. 101, pp. 894–908, Dec. 2019, doi: https://doi.org/10.1016/j.future.2019.07.046.
O. Irigaray, Z. Ansa, U. Fernandez-Gamiz, A. Larrinaga, R. García-Fernandez, and K. Portal-Porras, “Adaptive mesh refinement (AMR) criteria comparison for the DrivAer model,” Heliyon, vol. 10, no. 11, p. e31966, Jun. 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e31966.
X. Dong et al., “Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods,” Energy, vol. 311, p. 133309, Dec. 2024, doi: https://doi.org/10.1016/j.energy.2024.133309.
K. Sato, K. Kawasaki, and S. Koshimura, “A numerical study of the MRT-LBM for the shallow water equation in high Reynolds number flows: An application to real-world tsunami simulation,” Nucl. Eng. Des., vol. 404, p. 112159, Apr. 2023, doi: https://doi.org/10.1016/j.nucengdes.2023.112159.
D. Mira, E. J. Pérez-Sánchez, R. Borrell, and G. Houzeaux, “HPC-enabling technologies for high-fidelity combustion simulations,” Proc. Combust. Inst., vol. 39, no. 4, pp. 5091–5125, 2023, doi: https://doi.org/10.1016/j.proci.2022.07.222.
