Advancements in Computational Analysis for Jet Engine Optimization: A Review of CFD, Structural Analysis, and Multidisciplinary Approaches
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Abstract
The evolution of jet engine technology has been profoundly influenced by advancements in computational analysis, particularly in Computational Fluid Dynamics (CFD), structural analysis, and multidisciplinary optimization. This review explores state-of-the-art computational techniques applied to jet engine analysis, emphasizing their applications, benefits, and inherent challenges. CFD has become an essential tool, enabling detailed simulations of complex aerodynamic and combustion processes. Methods such as Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) have provided deeper insights into turbulence and combustion dynamics, leading to improved efficiency and reduced emissions. However, these high-fidelity simulations entail significant computational costs, driving the development of more efficient algorithms and high-performance computing resources. The integration of structural analysis with aerodynamic simulations has facilitated the design of components capable of withstanding extreme operational conditions, thereby enhancing engine reliability and safety. Multidisciplinary Design Optimization (MDO) frameworks have further transformed engine design by simultaneously evaluating multiple performance metrics, resulting in configurations that balance efficiency, weight, and durability. Despite these advances, challenges remain in accurately modeling complex physical phenomena such as combustion instabilities and material behavior under high temperatures. The incorporation of machine learning techniques offers promising solutions to address these issues by complementing traditional computational methods with data-driven insights. Looking ahead, the future of computational analysis in jet engine development lies in the seamless integration of high-fidelity simulations, real-time data analytics, and adaptive modeling, paving the way for more efficient, reliable, and sustainable propulsion systems.
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References
[1] J. D. Anderson, Computational Fluid Dynamics: The Basics With Applications. New York: McGraw-Hill, 2017. [Online]. Available: https://upsalesiana.ec/ing35ar3r1
[2] F. M. White, Fluid Mechanics, 9th ed. McGraw-Hill Education, 2018. [Online]. Available: https://upsalesiana.ec/ing35ar3r7
[3] J. D. Anderson, Modern Compressible Flow: With Historical Perspective, 2nd ed. New York: McGraw-Hill Education, 2017. [Online]. Available: https://upsalesiana.ec/ing35ar3r10
[4] H. Schlichting and K. Gersten, Boundary-Layer Theory. Springer Berlin Heidelberg, 2017. [Online]. Available: https://doi.org/10.1007/978-3-662-52919-5
[5] S. B. Pope, Turbulent Flows. Cambridge University Press, Aug. 2000. [Online]. Available: https://doi.org/10.1017/CBO9781316179475
[6] T. Colonius and S. Laizet, “Numerical simulation of turbulent flows: Advances and challenges,” Annual Review of Fluid Mechanics, vol. 53, pp. 365–391, 2021. [Online]. Available: https://upsalesiana.ec/ing35ar3r6
[7] P. A. Durbin and B. A. Pettersson Reif, Statistical Theory and Modeling for Turbulent Flows, 2nd ed. Wiley, 2010. [Online]. Available: https://upsalesiana.ec/ing35ar3r4
[8] C. J. Lagares-Nieves, J. Santiago, and G. Araya, “Turbulence modeling in hypersonic turbulent boundary layers subject to convex wall curvature,” AIAA Journal, vol. 59, no. 12, pp. 4935–4954, Dec. 2021. [Online]. Available: https://doi.org/10.2514/1.J060247
[9] M. A. Leschziner, Statistical Turbulence Modelling for Fluid Dynamics: Demystified. London: World Scientific Publishing / Imperial College Press, 2015. [Online]. Available: https://upsalesiana.ec/ing35ar3r8
[10] R. O. Fox, Computational Models for Turbulent Reacting Flows. Cambridge University Press, Oct. 2003. [Online]. Available: https://doi.org/10.1017/CBO9780511610103
[11] R. D. Blevins, Applied Fluid Dynamics Handbook. New York, NY, USA: Van Nostrand Reinhold Co., 1984. [Online]. Available: https://upsalesiana.ec/ing35ar3r15
[12] A. F. El-Sayed, Aircraft Propulsion and Gas Turbine Engines, 2nd ed. CRC Press, 2017. [Online]. Available: https://upsalesiana.ec/ing35ar3r16
[13] J. H. Ferziger, M. Perić, and R. L. Street, Computational Methods for Fluid Dynamics. Springer International Publishing, 2020. [Online]. Available: https://doi.org/10.1007/978-3-319-99693-6
[14] G. Tryggvason, R. Scardovelli, and S. Zaleski, Direct Numerical Simulations of Gas–Liquid Multiphase Flows. Cambridge University Press, Jan. 2001. [Online]. Available: https://doi.org/10.1017/CBO9780511975264
[15] R. Mittal and G. Iaccarino, “Immersed boundary methods,” Annual Review of Fluid Mechanics, vol. 37, no. 1, pp. 239–261, Jan. 2005. [Online]. Available: https://doi.org/10.1146/annurev.fluid.37.061903.175743
[16] T. C. Lieuwen and V. Yang, Combustion Instabilities In Gas Turbine Engines: Operational Experience, Fundamental Mechanisms, and Modeling. American Institute of Aeronautics and Astronautics, Jan. 2006, pp. I–Xiv. [Online]. Available: https://doi.org/10.2514/5.9781600866807.0000.0000
[17] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, 2019. [Online]. Available: https://upsalesiana.ec/ing35ar3r28
[18] J. R. R. A. Martins and A. Ning, Engineering Design Optimization. Cambridge University Press, Sep. 2013, vol. 51, no. 9. [Online]. Available: https://upsalesiana.ec/ing35ar3r29
[19] E. Benini, Advanced Gas Turbine Technology. Intechweb.org, 2020, 1st ed. [Online]. Available: https://upsalesiana.ec/ing35ar3r27
[20] Z.-H. Han and K.-S. Zhang, Surrogate-Based Optimization. InTech, Mar. 2012. [Online]. Available: https://doi.org/10.5772/36125
[21] R. Cant, U. Ahmed, J. Fang, N. Chakarborty, G. Nivarti, C. Moulinec, and D. Emerson, “An unstructured adaptive mesh refinement approach for computational fluid dynamics of reacting flows,” Journal of Computational Physics, vol. 468, p. 111480, Nov. 2022. [Online]. Available: https://doi.org/10.1016/j.jcp.2022.111480
[22] A. Jameson, “Computational aerodynamics for aircraft design,” Science, vol. 245, no. 4916, pp. 361–371, Jul. 1989. [Online]. Available: https://doi.org/10.1126/science.245.4916.361
[23] Y. Saad, Numerical Methods for Large Eigenvalue Problems: Revised Edition. Society for Industrial and Applied Mathematics, Jan. 2011. [Online]. Available: https://doi.org/10.1137/1.9781611970739
[24] L. N. Trefethen, Spectral Methods in MATLAB. Society for Industrial and Applied Mathematics (SIAM), 2000. [Online]. Available: https://doi.org/10.1137/1.9780898719598
[25] F. O. Carta, Ed., Unsteady Flows in Jet Engines: Proceedings of a Workshop Held at United Aircraft Research Laboratories, 11 and 12 July 1974. Project SQUID Headquarters, Jet Propulsion Center, School of Mechanical Engineering, Purdue University, 1974. [Online]. Available: https://upsalesiana.ec/ing35ar3r34
[26] H. Holden and N. H. Risebro, Front Tracking for Hyperbolic Conservation Laws. Springer Berlin Heidelberg, 2002. [Online]. Available: https://doi.org/10.1007/978-3-642-56139-9
[27] A. R. Salem, I. Soliman, and R. S. Amano, “Heat transfer and crossflow investigations for jet impingement cooling applications,” International Journal of Gas Turbine, Propulsion and Power Systems, vol. 15, no. 1, pp. 15–23, 2024. [Online]. Available: https://doi.org/10.38036/jgpp.15.1_15
[28] B. Cockburn, “Discontinuous Galerkin methods for computational fluid dynamics,” Encyclopedia of Computational Mechanics Second Edition, pp. 1–63, Dec. 2017. [Online]. Available: https://doi.org/10.1002/9781119176817.ecm2053
[29] P. Moin and S. V. Apte, “Large-eddy simulation of realistic gas turbine combustors,” AIAA Journal, vol. 44, no. 4, pp. 698–708, Apr. 2006. [Online]. Available: http://doi.org/10.2514/1.14606
[30] H. Bijl, D. Lucor, S. Mishra, and C. Schwab, Uncertainty Quantification in Computational Fluid Dynamics. Springer International Publishing, 2013. [Online]. Available: https://doi.org/10.1007/978-3-319-00885-1
[31] J. D. Denton, “The 1993 igti scholar lecture: Loss mechanisms in turbomachines,” Journal of Turbomachinery, vol. 115, no. 4, pp. 621–656, Oct. 1993. [Online]. Available: http://doi.org/10.1115/1.2929299
[32] H. Pitsch, “Large-eddy simulation of turbulent combustion,” Annual Review of Fluid Mechanics, vol. 38, no. 1, pp. 453–482, Jan. 2006. [Online]. Available: https://doi.org/10.1146/annurev.fluid.38.050304.092133
[33] L. Wang, W. K. Anderson, E. J. Nielsen, P. S. Iyer, and B. Diskin, “Wall-modeled largeeddy simulation method for unstructured-grid navier–stokes solvers,” Journal of Aircraft, vol. 61, no. 6, pp. 1735–1760, Nov. 2024. [Online]. Available: https://doi.org/10.2514/1.C037847
[34] P. Brandão, V. Infante, and A. Deus, “Thermo-mechanical modeling of a high pressure turbine blade of an airplane gas turbine engine,” Procedia Structural Integrity, vol. 1, pp. 189–196, 2016. [Online]. Available: http://doi.org/10.1016/j.prostr.2016.02.026
[35] D. Lee, I. Shin, Y. Kim, J.-M. Koo, and C.-S. Seok, “A study on thermo mechanical fatigue life prediction of ni-base superalloy,” International Journal of Fatigue, vol. 62, pp. 62–66, May 2014. [Online]. Available: http://doi.org/10.1016/j.ijfatigue.2013.10.011
[36] A. Fedorov and A. Tumin, “High-speed boundarylayer instability: Old terminology and a new framework,” AIAA Journal, vol. 49, no. 8, pp. 1647–1657, Aug. 2011. [Online]. Available: https://doi.org/10.2514/1.J050835
[37] L. Saucedo-Mora and T. J. Marrow, “Multi-scale damage modelling in a ceramic matrix composite using a finite-element microstructure meshfree methodology,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2071, p. 20150276, Jul. 2016. [Online]. Available: http://doi.org/10.1098/rsta.2015.0276
[38] Z. Y. Wang and W. W. Zhang, “A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers,” Fluid Dynamics, 2022. [Online]. Available: https://doi.org/10.48550/arXiv.2211.00601
[39] H. Farooq, A. Saeed, I. Akhtar, and Z. Bangash, “Neural network-based model reduction of hydrodynamics forces on an airfoil,” Fluids, vol. 6, no. 9, p. 332, Sep. 2021. [Online]. Available: https://doi.org/10.3390/fluids6090332
[40] A. Thelen, X. Zhang, O. Fink, Y. Lu, S. Ghosh, B. D. Youn, M. D. Todd, S. Mahadevan, C. Hu, and Z. Hu, “A comprehensive review of digital twin – part 1: Modeling and twinning enabling technologies,” Computational Engineering, Finance, and Science, 2022. [Online]. Available: https://doi.org/10.48550/arXiv.2208.14197
[41] Z. Meng and Y. Yang, “Quantum computing of fluid dynamics using the hydrodynamic Schrödinger equation,” Physical Review Research, vol. 5, no. 3, p. 033182, Sep. 2023. [Online]. Available: https://doi.org/10.1103/PhysRevResearch.5.033182