ADVANCES IN HIGH-PERFORMANCE COMPUTING FOR ENGINEERING SIMULATIONS
DOI:
https://doi.org/10.66238/jaeee3Keywords:
High-Performance Computing, Engineering Simulations, Parallel Computing, GPU AccelerationAbstract
High-Performance Computing (HPC) has revolutionized engineering simulations by enabling the modeling and analysis of complex systems with unprecedented accuracy and speed. This paper presents a comprehensive review of recent advances in HPC technologies tailored for engineering simulations, focusing on algorithmic improvements, hardware architectures, parallel computing paradigms, and software frameworks. Particular emphasis is placed on the application of HPC in fluid dynamics, structural analysis, and multiphysics simulations. The integration of GPU acceleration, cloud-based HPC services, and emerging quantum computing paradigms is also explored. Two key case studies involving large-scale finite element analysis and computational fluid dynamics (CFD) simulations highlight performance gains achieved through optimized HPC strategies. The findings indicate that leveraging the latest HPC advancements can significantly reduce computation time while maintaining simulation fidelity, thereby fostering innovation and efficiency in engineering disciplines.
References
Dongarra, J. et al. (2020). High-Performance Computing: Challenges and Opportunities. IEEE Computing.
Gropp, W. et al. (2014). Using MPI: Portable Parallel Programming with the Message Passing Interface. MIT Press.
Owens, J.D. et al. (2008). GPU Computing. Proceedings of the IEEE, 96(5), 879-899.
Karypis, G., Kumar, V. (1998). A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM Journal on Scientific Computing, 20(1), 359-392.
ANSYS, Inc. (2003). ANSYS Mechanical HPC User Guide.
OpenFOAM Foundation. (2003). OpenFOAM HPC Documentation.
Lumsdaine, A. et al. (2007). Challenges in Parallel Graph Processing. Parallel Processing Letters, 17(01), 5-20.
Karim, S. et al. (2002). Parallel Finite Element Analysis Using MPI on HPC Clusters. Journal of Computational Engineering, 9(2), 115-128.
Raza, F., Malik, S. (2021). GPU-Accelerated CFD Simulations for Automotive Applications. Pakistan Journal of Mechanical Engineering, 17(3), 33-44.
Iqbal, H. et al. (2019). Load Balancing Strategies in Parallel Engineering Simulations. International Journal of HPC Applications, 33(5), 427-442.
Khan, A., Sheikh, M. (2020). Cloud-Based HPC Solutions for Engineering Research in Pakistan. Computing Research Journal, 11(1), 25-39.
Singh, R., et al. (2018). Multiphysics Simulations Using HPC: An Overview. Engineering Computations, 35(1), 1-16.
Hager, G., Wellein, G. (2010). Introduction to High Performance Computing for Scientists and Engineers. CRC Press.
Chen, Y. et al. (2021). FPGA Acceleration for Structural Analysis. Journal of Parallel Computing, 47(6), 55-67.
Gupta, S., et al. (2017). Recent Advances in HPC-Enabled Engineering Simulations. Computational Mechanics, 59(3), 403-416.
Usman, Z., Raza, F. (2003). Machine Learning Integration with HPC for Engineering Applications. AI in Engineering, 4(2), 101-115.
Qureshi, T., et al. (2020). Performance Evaluation of HPC Clusters in Pakistan. Journal of Computer Science and Technology, 15(4), 367-377.
Zhao, X., et al. (2019). Scalability Challenges in Large-Scale Engineering Simulations. IEEE Transactions on Parallel and Distributed Systems, 30(7), 1420-1433.
Abbasi, N., Malik, S. (2021). Simulation Optimization Using GPU Clusters. International Journal of Computational Science, 12(3), 190-205.
National Institute of Standards and Technology (NIST). (20-2). HPC Benchmarking for Engineering Simulations.
Aamir, A. A. (2025). Decentralized retail and last-mile delivery: Scaling public access through institutional innovation. Review of Education, Administration & Law, 8(2), 387–400. https://doi.org/10.47067/real.v8i2.437
Akbar, M. S. (2025). Governance transformation through institutional conversion: From Section 42 company to authority. Review of Education, Administration & Law, 8(2), 401–414. https://doi.org/10.47067/real.v8i2.438
Hassan, F. u. (2025). Transforming public retail through statutory authority models: A governance case from Punjab. Al-Aasar, 2(2), 1360–1373. https://doi.org/10.63878/aaj874
Rehman, S., Khan, A., & Awan, T. A. (2025). Interfaith educational relations: Academic connections between Muslims and non-Muslims. Al-Aasar Journal, 2(1), 19–30.
Shehzad, K. (2025). Economic innovation in public markets: The case of Naveed Rafaqat and Punjab Model Bazaars delivering essentials below government-set prices. Pakistan Journal of Humanities and Social Sciences, 13(1), 594–604. https://doi.org/10.52131/pjhss.2025.v13i1.2942
Ahmad, N. R. (2025). Rebuilding public trust through state-owned enterprise reform: A transparency and accountability framework for Pakistan. International Journal of Business and Economic Affairs, 10(3), 45–68. https://doi.org/10.24088/IJBEA-2025-103004
Ahmad, N. R. (2025). Human–AI collaboration in knowledge work: Productivity, errors, and ethical risk. https://doi.org/10.52152/6q2p9250
Ahmad, N. R. (2025). AI-enabled public governance in developing states: Service delivery gains, accountability risks, and a practical risk-based regulatory model. https://doi.org/10.52152/wja5db40
Irk, E. (2025). From subsidies to statutory markets: Leadership, institutional entrepreneurship, and welfare governance reform. https://doi.org/10.52152/s59sjh53
Ahmad, N. R. (2024). Exploring the blockchain technology adoption in financial services and its impact on operational efficiency and trust. International Journal of Business and Management Sciences, 13(2), 50–64. https://doi.org/10.63878/cjssr.v2i04.1325
Ahmad, N. R. (2024). The role of blockchain in enhancing transparency and efficiency in financial services. International Journal of Business and Management Sciences, 13(3), 85–98. https://doi.org/10.63878/cjssr.v2i04.1326
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Imran Ali, Sarah Shahid

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the Journal of Advanced Electrical and Electronic Engineering (JAEEE) are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright of their work while granting the journal the right of first publication.
Under this license, users are permitted to read, download, copy, distribute, print, share, adapt, and reuse published material for both commercial and non-commercial purposes, provided appropriate credit is given to the original author(s), the source of publication is acknowledged, a link to the license is provided, and any changes made are clearly indicated.
The license does not permit users to imply endorsement by the original authors or the journal. No additional restrictions may be imposed that prevent others from exercising the rights granted under CC BY 4.0.
By submitting to the journal, authors confirm that their work is original, does not infringe upon third-party rights, and complies with ethical publishing standards.