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

Year : 2025 | Volume : 15 | Issue : 02 | Page : 26-35
    By

    Pallavi Sindhu,

  • Shiv Kumar Rajak,

  • Raj kumar,

  1. .Assistant Professor, Department of Mechanical Engineering, Government Engineering College, Sheikhpura, Bihar, India
  2. .Assistant Professor, Department of Mechanical Engineering, Government Engineering College, Sheikhpura, Bihar, India
  3. Assistant Professor, Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

Abstract

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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: Finite element analysis (FEA), computational fluid dynamics (CFD), mechanical design, advanced simulation techniques, structural mechanics, fluid flow simulation, heat transfer analysis, aerodynamics, design optimization, engineering simulation, thermal stress analysis, vibration analysis

[This article belongs to Journal of Aerospace Engineering & Technology ]

How to cite this article:
Pallavi Sindhu, Shiv Kumar Rajak, Raj kumar. The Impact of High-Performance Computing on FEA and CFD Simulations. Journal of Aerospace Engineering & Technology. 2025; 15(02):26-35.
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Pallavi Sindhu, Shiv Kumar Rajak, Raj kumar. The Impact of High-Performance Computing on FEA and CFD Simulations. Journal of Aerospace Engineering & Technology. 2025; 15(02):26-35. Available from: https://journals.stmjournals.com/joaet/article=2025/view=0


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References

1. Nath D, Ankit, Neog DR, Gautam SS. Application of machine learning and deep learning in finite element analysis: a comprehensive review. Arch Comput Methods Eng. 2024; 31(5): 2945–84.https://doi.org/10.1007/s11831-024-10063-0
2. Raizer A, Elias EP, da Silva JV, da Luz MVF, Coelho VL, Telló M. A comparative analysis of grounding in different structures: Operational vs. finite element method models. Electr Power Syst Res. 2024; 230: 110243. https://doi.org/10.1016/j.epsr.2024.110243
3. Hafeez MB, Krawczuk M. Fractional Spectral and Fractional Finite Element Methods: A Comprehensive Review and Future Prospects. Arch Comput Methods Eng. 2024; 31(6): 3443–54. https://doi.org/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 Sci Eng. 2024;6:113–23. 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. Appl Ocean Res. 2024;150: 104075. https://doi.org/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. https://doi.org/10.3390/su17020480
7. Hajdukiewicz M, Gallero FJG, Mannion P, Loomans MG, Keane MM. A narrative review to credible computational fluid dynamics models of naturally ventilated built environments. Renew Sustain Energy Rev. 2024; 198: 114404. https://doi.org/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. J Eng Sci Technol Trends. 2024; 1(2): 69–75. https://doi.org/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. J Cardiovasc Dev Dis. 2025; 12(2): 70. https://doi.org/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. Appl Sci. 2025; 15(1): 424. https://doi.org/10.3390/app15010424
11. Barbhuiya S, Jivkov A, Das BB. A review of multi-scale modelling of concrete deterioration: Fundamentals, techniques and perspectives. Constr Build Mater. 2023; 406: 133472. https://doi.org/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. https://doi.org/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. Mater Today Proc. 2021; 38: 2515–24. https://doi.org/10.1016/j.matpr.2020.07.546
14. Radgolchin M, Anbarsooz M. Fatigue failure of centrifugal compressor impellers: a comprehensive review. Eng Fail Anal. 2023; 153: 107592. https://doi.org/10.1016/j.engfailanal.2023.107592

15. Müzel SD, 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. https://doi.org/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. https://doi.org/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). Environ Sci Pollut Res. 2022; 29(26): 38673–714. https://doi.org/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. Arch Comput Methods Eng.2023; 30(2): 1187–230. https://doi.org/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. https://doi.org/10.3390/pharmaceutics14081625
21. Reddy MV, Hemasunder B, Ramana SV, Babu PR, Thejasree P, Joseph J. State of art on FEM approach in inverse heat transfer problems for different materials. Mater Today Proc. 2023. https://doi.org/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. Appl Sci. 2023; 14(1): 398. https://doi.org/10.3390/app14010398
23. Szpicer A, Bińkowska W, Wojtasik-Kalinowska I, Salih SM, Półtorak A. Application of computational fluid dynamics simulations in food industry. Eur Food Res Technol. 2023; 249(6):1411–30. https://doi.org/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. Chem Eng Process. 2022; 181: 109155. https://doi.org/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. Int J Adv Manuf Technol. 2022; 119(7): 4159–75. https://doi.org/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. https://doi.org/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. Appl Sci. 2023; 13(22): 12502. https://doi.org/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 Comput Appl. 2022; 34(20): 17207–29. https://doi.org/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. Med Eng Phys. 2020; 78: 1–13. https://doi.org/10.1016/j.medengphy.2020.01.008
30. Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SM, et al. Latest developments in adapting deep learning for assessing TAVR procedures and outcomes. J Clin Med. 2023; 12(14): 4774. https://doi.org/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. Prog Nucl Energy. 2022; 152: 104374. https://doi.org/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. Struct Heart. 2022; 6(2): 100032. https://doi.org/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. Arch Comput Methods Eng. 2023; 30(3): 1495–552. https://doi.org/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. https://doi.org/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. Renew Sustain Energy Rev. 2022; 168: 112785. https://doi.org/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. Appl Mater Today. 2021; 23: 101040. https://doi.org/10.1016/j.apmt.2021.101040

Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 15/04/2025
Accepted 07/05/2025
Published 22/05/2025
Publication Time 37 Days
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