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Journal of Aerospace Engineering & Technology Review Article

The Impact of High-Performance Computing on FEA and CFD Simulations

  1. Pallavi Sindhu Department of Mechanical Engineering, Government Engineering College, Sheikhpura
  2. Shiv Kumar Rajak Department of Mechanical Engineering, Government Engineering College, Sheikhpura
  3. Raj Kumar Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur

Abstract

The integration of advanced simulation techniques, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), has transformed mechanical design by enabling engineers to develop optimized, efficient, and reliable systems. FEA is widely applied to analyze structural mechanics, thermal stresses, and vibrations, offering detailed insights into material behavior and design performance. On the other hand, CFD focuses on simulating fluid flow, heat transfer, and aerodynamic performance, making it indispensable across industries like aerospace, automotive, and energy. Together, these tools facilitate comprehensive analysis and validation of mechanical systems, significantly reducing development time and costs. This study explores the principles, applications, and advantages of FEA and CFD in mechanical design, emphasizing their role in improving innovation and precision. Key processes such as modeling, meshing, and simulation are discussed, alongside best practices for achieving accurate and reliable results. Challenges like computational requirements and proper boundary condition settings are also examined. Real-world examples highlight the impact of these techniques, demonstrating how they drive the creation of cutting-edge technologies. As the need for high-performance and sustainable designs grows, FEA and CFD continue to advance engineering by enabling efficient and effective solutions for complex mechanical systems.

Keywords

References (36)

  1. Nath D, Ankit, Neog DR, Gautam SS. Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review. Archives of Computational Methods in Engineering. 2024;31(5):2945-2984. doi:10.1007/s11831-024-10063-0
  2. Raizer A, Elias EP, da Silva JV, da Luz MVF, Coelho VL, Raupp VMA, et al. A comparative analysis of grounding in different structures: Operational vs. finite element method models. Electric Power Systems Research. 2024;230:110243. doi:10.1016/j.epsr.2024.110243
  3. Hafeez MB, Krawczuk M. Fractional Spectral and Fractional Finite Element Methods: A Comprehensive Review and Future Prospects. Archives of Computational Methods in Engineering. 2024;31(6):3443-3454. doi:10.1007/s11831-024-10083-w
  4. Raouf I, Gas P, Kim HS. Advances in Finite Element Analysis for Cancer Therapy Focusing on Magnetic Nanoparticle Hyperthermia. Multiscale Science and Engineering. 2024;6(2-3):113-123. doi:10.1007/s42493-024-00116-8
  5. Zhang W, Calderon-Sanchez J, Duque D, Souto-Iglesias A. Computational Fluid Dynamics (CFD) applications in Floating Offshore Wind Turbine (FOWT) dynamics: A review. Applied Ocean Research. 2024;150:104075. doi:10.1016/j.apor.2024.104075
  6. Li Z, Zhou T, Lu W, Yang H, Li Y, Liu Y, et al. Computational Fluid Dynamics (CFD) Technology Methodology and Analysis of Waste Heat Recovery from High-Temperature Solid Granule: A Review. Sustainability. 2025;17(2):480. doi:10.3390/su17020480
  7. Hajdukiewicz M, González Gallero FJ, Mannion P, Loomans MGLC, Keane MM. A narrative review to credible computational fluid dynamics models of naturally ventilated built environments. Renewable and Sustainable Energy Reviews. 2024;198:114404. doi:10.1016/j.rser.2024.114404
  8. Sarwari S, Qiao Y, Rafi MA. A Short Review Note on Finite Element Method for Hydraulic Structural Engineering. Journal of Engineering, Science and Technological Trends. 2024;1(2):69-75. doi:10.48112/jestt.v1i2.826
  9. Dave A, Santos RD, Siddiqi U, Dharia A, Li W, Siddiqi U, et al. Applications of Computational Fluid Dynamics in Congenital Heart Disease: A Review. Journal of Cardiovascular Development and Disease. 2025;12(2):70. doi:10.3390/jcdd12020070
  10. Szpicer A, Bińkowska W, Stelmasiak A, Zalewska M, Wojtasik-Kalinowska I, Piwowarski K, et al. Computational Fluid Dynamics Simulation of Thermal Processes in Food Technology and Their Applications in the Food Industry. Applied Sciences. 2025;15(1):424. doi:10.3390/app15010424
  11. Barbhuiya S, Jivkov A, Das BB. A review of multi-scale modelling of concrete deterioration: Fundamentals, techniques and perspectives. Construction and Building Materials. 2023;406:133472. doi:10.1016/j.conbuildmat.2023.133472
  12. Akbari M, Asadi P, Sadowski T. A Review on Friction Stir Welding/Processing: Numerical Modeling. Materials. 2023;16(17):5890. doi:10.3390/ma16175890
  13. Singh S, Kharub M, Singh J, Singh J, Jangid V. Brief survey on mechanical failure and preventive mechanism of turbine blades. Materials Today: Proceedings. 2021;38:2515-2524. doi:10.1016/j.matpr.2020.07.546
  14. Radgolchin M, Anbarsooz M. Fatigue failure of centrifugal compressor impellers: A comprehensive review. Engineering Failure Analysis. 2023;153:107592. doi:10.1016/j.engfailanal.2023.107592
  15. David Müzel S, Bonhin EP, Guimarães NM, Guidi ES. Application of the Finite Element Method in the Analysis of Composite Materials: A Review. Polymers. 2020;12(4):818. doi:10.3390/polym12040818
  16. Shen B, Chen Y, Li C, Wang S, Chen X. Superconducting fault current limiter (SFCL): Experiment and the simulation from finite-element method (FEM) to power/energy system software. Energy. 2021;234:121251. doi:10.1016/j.energy.2021.121251
  17. Gupta N, Bhardwaj N, Khan GM, Dave V. Global trends of computational fluid dynamics to resolve real world problems in the contemporary era. Curr Biochem Eng. 2020; 6(3): 136–55. https://doi.org/10.1007/s11356-022-18952-2
  18. Purnachandrakumar D, Mittal G, Sharma RK, Singh DB, Tiwari S, Sinhmar H. Review on performance assessment of solar stills using computational fluid dynamics (CFD). Environmental Science and Pollution Research. 2022;29(26):38673-38714. doi:10.1007/s11356-022-18952-2
  19. Gupta V, Jameel A, Verma SK, Anand S, Anand Y. An Insight on NURBS Based Isogeometric Analysis, Its Current Status and Involvement in Mechanical Applications. Archives of Computational Methods in Engineering. 2022;30(2):1187-1230. doi:10.1007/s11831-022-09838-0
  20. Yan Q, Shen S, Wang Y, Weng J, Wan A, Yang G, et al. The Finite Element Analysis Research on Microneedle Design Strategy and Transdermal Drug Delivery System. Pharmaceutics. 2022;14(8):1625. doi:10.3390/pharmaceutics14081625
  21. Venkateswar Reddy M, Hemasunder B, Ramana SV, Ramesh Babu P, Thejasree P, Joseph J. State of art on FEM approach in inverse heat transfer problems for different materials. Materials Today: Proceedings. 2023. doi:10.1016/j.matpr.2023.06.323
  22. Gbagba S, Maccioni L, Concli F. Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures. Applied Sciences. 2023;14(1):398. doi:10.3390/app14010398
  23. Szpicer A, Bińkowska W, Wojtasik-Kalinowska I, Salih SM, Półtorak A. Application of computational fluid dynamics simulations in food industry. European Food Research and Technology. 2023;249(6):1411-1430. doi:10.1007/s00217-023-04231-y
  24. Harris M, Wu H, Zhang W, Angelopoulou A. Overview of recent trends in microchannels for heat transfer and thermal management applications. Chemical Engineering and Processing - Process Intensification. 2022;181:109155. doi:10.1016/j.cep.2022.109155
  25. Miroir M, Laniel R, Brient A, Kerbrat O. Water jet incremental sheet metal forming: a critical state-of-the-art review and a proposal for technological windows. The International Journal of Advanced Manufacturing Technology. 2022;119(7-8):4159-4175. doi:10.1007/s00170-021-08635-6
  26. Song Y, Zhou T, Bai R, Zhang M, Yang H. Review of CFD-DEM Modeling of Wet Fluidized Bed Granulation and Coating Processes. Processes. 2023;11(2):382. doi:10.3390/pr11020382
  27. Kasaei A, Yang W, Wang Z, Yan J. Advancements and Applications of Rim-Driven Fans in Aerial Vehicles: A Comprehensive Review. Applied Sciences. 2023;13(22):12502. doi:10.3390/app132212502
  28. Sachdeva I, Ramesh S, Chadha U, Punugoti H, Selvaraj SK. Computational AI models in VAT photopolymerization: a review, current trends, open issues, and future opportunities. Neural Computing and Applications. 2022;34(20):17207-17229. doi:10.1007/s00521-022-07694-4
  29. Hirschhorn M, Tchantchaleishvili V, Stevens R, Rossano J, Throckmorton A. Fluid–structure interaction modeling in cardiovascular medicine – A systematic review 2017–2019. Medical Engineering & Physics. 2020;78(1):1-13. doi:10.1016/j.medengphy.2020.01.008
  30. Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, et al. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. Journal of Clinical Medicine. 2023;12(14):4774. doi:10.3390/jcm12144774
  31. Khalid SU, Hasnain S, Ali HM, Bano S, Nasir MA, Abbas N. Experimental investigation of aluminum fins on relative thermal performance of sintered copper wicked and grooved heat pipes. Progress in Nuclear Energy. 2022;152:104374. doi:10.1016/j.pnucene.2022.104374
  32. Esmailie F, Razavi A, Yeats B, Sivakumar SK, Chen H, Samaee M, et al. Biomechanics of Transcatheter Aortic Valve Replacement Complications and Computational Predictive Modeling. Structural Heart. 2022;6(2):100032. doi:10.1016/j.shj.2022.100032
  33. Evans LM, Sözümert E, Keenan BE, Wood CE, du Plessis A. A Review of Image-Based Simulation Applications in High-Value Manufacturing. Archives of Computational Methods in Engineering. 2023;30(3):1495-1552. doi:10.1007/s11831-022-09836-2
  34. Li A, Yuen ACY, Wang W, De Cachinho Cordeiro IM, Wang C, Chen TBY, et al. A Review on Lithium-Ion Battery Separators towards Enhanced Safety Performances and Modelling Approaches. Molecules. 2021;26(2):478. doi:10.3390/molecules26020478
  35. Diao K, Sun X, Bramerdorfer G, Cai Y, Lei G, Chen L. Design optimization of switched reluctance machines for performance and reliability enhancements: A review. Renewable and Sustainable Energy Reviews. 2022;168:112785. doi:10.1016/j.rser.2022.112785
  36. Nyamekye P, Nieminen P, Bilesan MR, Repo E, Piili H, Salminen A. Prospects for laser based powder bed fusion in the manufacturing of metal electrodes: A review. Applied Materials Today. 2021;23:101040. doi:10.1016/j.apmt.2021.101040
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