Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 6 | Year 2026 | Article Id. IJCE-V13I6P108 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I6P108

Physics-Informed Mesh Optimization for Improving Computational Efficiency in Urban CFD Simulations

Shubham Kumar Verma, Rachit Manchanda, Rupesh Gupta, Abhijit Bhowmik, Sanjay Kumar, Vinod Balmiki

Received Revised Accepted Published
10 Mar 2026 09 Apr 2026 08 May 2026 30 Jun 2026

Citation :

Shubham Kumar Verma, Rachit Manchanda, Rupesh Gupta, Abhijit Bhowmik, Sanjay Kumar, Vinod Balmiki, "Physics-Informed Mesh Optimization for Improving Computational Efficiency in Urban CFD Simulations," International Journal of Civil Engineering, vol. 13, no. 6, pp. 102-116, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I6P108

Abstract

Computational fluid dynamics (CFD) is extensively utilized to assess the thermal behaviour of pedestrians and mitigate the effects of the urban heat island. However, such analysis are usually computationally intensive and thus limit their applicability in large-scale parametric analysis. From the green computing perspective, achieving accurate and precise results at a lower cost is considered essential. However, this aspect remains underexplored in urban CFD literature. This paper tries to fill this gap by examining how mesh structure can affect thermal accuracy and computing efficiency. The test case of this study is a hypothetical urban domain which is consisted of a 3x3 array of buildings. The study tested six mesh configurations to understand their effects. Thermal accuracy is measured by the pedestrian level canyon-averaged air temperature and the measures of evaluating computational efficiency are solver convergence behaviour and wall-clock time. A Mesh Performance Index (MPI) is proposed to enable the combined effect of both the thermal precision and the computational expense in order to support the integrated evaluation. The findings indicate that uniformly refined meshes increase computational cost without improving convergence or thermal accuracy, whereas coarse meshes reduce runtime but fail to adequately solve the pedestrian-level thermal gradients. Conversely, the optimized mesh achieved a thermal accuracy close to baseline at a reduction of about 74 percent in wall-clock time over the physics-informed mesh. Not only this, but optimized mesh has also listed MPI values which are 1.8 to 178.5 times higher than that of the other configurations. These findings suggest that optimization of mesh using physics can be used to considerably reduce the energy requirement and preserve the thermal fidelity. This method offers a viable way to more environmentally friendly and sustainable urban CFD simulations.

Keywords

Computational fluid dynamics, Energy efficiency, Mesh optimization, Pedestrian behaviour, Urban microclimate.

References

  1. G.Y. Cao et al., “Urban Growth in China: Past, Prospect, and its Impacts,” Population and Environment, vol. 33, pp. 137-160, 2011.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Rani Mughal, Javed Manzoor, and Rouf Ahmad Wagay, “Growing Urbanization in the Jammu Province of Jammu and Kashmir, India: an Environmental and Social Interpretation,” Journal of Bioresource Management, vol. 11, no. 2, pp. 174-185, 2024.
    [    Google Scholar] [Publisher Link]
  3. Arshad Ahmad Lone, G. M. Rather, and M. Sultan Bhat, Urbanization and Quality of Human Health in Srinagar City, Jammu and Kashmir, Environment and Health in Jammu and Kashmir and Ladakh, 1st ed., pp. 1-18, 2023.
    [    Google Scholar] [Publisher Link]
  4. Muhammad Waqar Younis, Edore Akpokodje, and Syeda Fizzah Jilani, “Urbanization and its Environmental Impact in Ceredigion County, Wales: A 20-Year Remote Sensing and GIS-Based Assessment (2003–2023),” Sensors, vol. 25, no. 17, pp. 1-21, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Kangning Huang et al., “Projecting Global Urban Land Expansion and Heat Island Intensification through 2050,” Environmental Research Letters, vol. 14, no. 11, pp. 1-12, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Svetlana Vujovic et al., “Urban Heat Island: Causes, Consequences, and Mitigation Measures with Emphasis on Reflective and Permeable Pavements,” CivilEng, vol. 2, no. 2, pp. 459-484, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Seonju Jang, Jinhyun Bae, and YouJoung Kim, “Street-level Urban Heat Island Mitigation: Assessing the Cooling Effect of Green Infrastructure using Urban IoT Sensor Big Data,” Sustainable Cities and Society, vol. 100, pp. 1-51, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Sahidul Islam et al., “Urban Heat Island Effect in India: A Review of Current Status, Impact and Mitigation Strategies,” Discover Cities, vol. 1, pp. 1-28, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Kyeongjoo Park et al., “Changes in Urban Heat Island Intensity with Background Temperature and Humidity and their Associations with Near-surface Thermodynamic Processes,” Urban Climate, vol. 58, pp. 1-18, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Mehrdad Karimimoshaver, Rezvan Khalvandi, and Mohammad Khalvandi, “The Effect of Urban Morphology on Heat Accumulation in Urban Street Canyons and Mitigation Approach,” Sustainable Cities and Society, vol. 73, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Hongyuan Huo et al., “Simulation of the Urban Space Thermal Environment Based on Computational Fluid Dynamics: A Comprehensive Review,” Sensors, vol. 21, no. 20, pp. 1-27, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Xiaoyue Xu, Zhi Gao, and Mingjie Zhang, “A Review of Simplified Numerical Approaches for Fast Urban Airflow Simulation,” Building and Environment, vol. 234, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Pier Francesco Melani, George Pechlivanoglou, and Alessandro Bianchini, “Using Multi-scale CFD Simulations to Improve Pedestrian Comfort within Built Areas in Complex Terrains,” Journal of Physics: Conference Series: ATI Annual Congress, Benevento, Italy, vol. 3143, pp. 1-11, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Paulo Ulisses da Silva, Gustavo Bono, and Marcelo Greco, “Assessing Pedestrian Comfort in Dense Urban Areas Using CFD Simulations: A Study on Wind Angle and Building Height Variations,” Fluids, vol. 10, no. 9, pp. 1-30, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Muhammad Zeeshan, and Zaib Ali, “Heat Stress Mitigation in Urban Streets Having Hot Humid Climatic Conditions: Strategies and Performance Results from a Real Scale Retrofitting Project,” Science and Technology for the Built Environment, vol. 28, no. 4, pp. 513-526, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Peng Cao, and Wenhui Li, “Evaluation and Optimization of Outdoor Wind Environment in Block Based on Space Syntax and CFD Simulation,” PLoS One, vol. 19, no. 3, pp. 1-17, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Lup Wai Chew, Negin Nazarian, and Leslie Norford, “Pedestrian-Level Urban Wind Flow Enhancement with Wind Catchers,” Atmosphere, vol. 8, no. 9, pp. 1-22, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. An-Shik Yang et al., “CFD Assessment of Wind Energy Potential: A Combined Framework of Urban Morphology and Design Modification of High-rise Buildings with Voids,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 271, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Sushobhan Sen, and Jeffery Roesler, “Wind Direction and Cool Surface Strategies on Microscale Urban Heat Island,” Urban Climate, vol. 31, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. José Galindo et al., “Impact of Mesh Resolution and Temperature Effects in Jet Ejector CFD Calculations,” Applied Sciences, vol. 15, no. 7, pp. 1-19, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. W.N.W. Yahya et al., “CFD Simulation on the Pressure Distribution for an Isolated Single-Story House with Extension: Grid Sensitivity Analysis,” IOP Conference Series: Earth and Environmental Science: 4th International Conference on Civil and Environmental Engineering for Sustainability, Langkawi, Malaysiam, vol. 140, pp. 1-9, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Zhenxing Li et al., “Evaluating the Impact of Road Layout Patterns on Pedestrian-Level Ventilation Using Computational Fluid Dynamics (CFD),” Atmosphere, vol. 16, no. 2, pp. 1-19, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Rukiye Cetin, and Ardeshir Mahdavi, “An Empirically-based Assessment of CFD Utility in Urban-level Air Flow Studies,” Proceedings of Building Simulation 2015: 14th Conference of IBPSA, Hyderabad, India, pp. 796-803, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Francisco Toja-Silva et al., “A Review of Computational Fluid Dynamics (CFD) Simulations of the Wind Flow Around Buildings for Urban Wind Energy Exploitation,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 180, pp. 66-87, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Ted Stathopoulos et al., “Urban Wind Energy: Some Views on Potential and Challenges,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 179, pp. 146-157, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Ruoping Chu, and Kai Wang, “CFD in Urban Wind Resource Assessments: A Review,” Energies, vol. 18, no. 10, pp. 1-21, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Kristina Kostadinović Vranešević et al., “Furthering Knowledge on the Flow Pattern around High-Rise Buildings: LES Investigation of the Wind Energy Potential,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 226, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Yu-Hsuan Juan et al., “CFD Assessment of Wind Energy Potential for Generic High-rise Buildings in Close Proximity: Impact of Building Arrangement and Height,” Applied Energy, vol. 321, pp. 1-22, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Yaxing Du, Cheuk Ming Mak, and Zhengtao Ai, “Modelling of Pedestrian Level Wind Environment on a High-Quality Mesh: A Case Study for the HKPolyU Campus,” Environmental Modelling & Software, vol. 103, pp. 105-119, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Qians Liang et al., “Simulating Microscale Urban Airflow and Pollutant Distributions Based on Computational Fluid Dynamics Model: A Review,” Toxics, vol. 11, no. 11, pp. 1-14, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  31. B. Blocken, W.D. Janssen, and T. Van Hooff, “CFD Simulation for Pedestrian wind Comfort and Wind Safety in Urban Areas: General Decision Framework and Case Study for the Eindhoven University Campus,” Environmental Modelling & Software, vol. 30, pp. 15-34, 2012.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Yoshihide Tominaga et al., “AIJ Guidelines for Practical Applications of CFD to Pedestrian Wind Environment Around Buildings,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 10-11, pp. 1749-1761, 2008.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. P.J. Richards, and S.E. Norris, “Appropriate Boundary Conditions for Computational Wind Engineering Models Revisited,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 99, no. 4, pp. 257-266, 2011.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. T. Van Hooff, and B. Blocken, “Coupled Urban Wind Flow and Indoor Natural Ventilation Modelling on a High-Resolution Grid: A Case Study for the Amsterdam ArenA Stadium,” Environmental Modelling & Software, vol. 25, no. 1, pp. 51-65, 2010.
    [    CrossRef] [Google Scholar] [Publisher Link]
  35. Guglielmo Vivarelli, Ning Qin, and Shahrokh Shahpar, “A Review of Mesh Adaptation Technology Applied to Computational Fluid Dynamics,” Fluids, vol. 10, no. 5, pp. 1-41, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  36. Hanen Amor, and Fayssal Benkhaldoun, “Adaptive Mesh Refinement for Flow and Transport Problem in Heterogeneous Porous Media,” Mathematics and Computers in Simulation, vol. 239, pp. 766-789, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  37. Amirhossein Fattahi et al., “Measuring Accuracy and Computational Capacity Trade-offs in an Hourly Integrated Energy System Model,” Advances in Applied Energy, vol. 1, pp. 1-22, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  38. Zohreh Moradinia, Hans Vandierendonck, and Adrian Murphy, “Navigating Speed–Accuracy Trade-Offs for Multi-Physics Simulations,” Meccanica, vol. 60, pp. 1583-1599, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  39. Aliasghar Azma, and Yakun Liu, “Enhancing CFD Computational Efficiency using Hybrid Data-driven and Physics-based Modeling,” Case Studies in Thermal Engineering, vol. 78, pp. 1-11, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  40. Xiaotong Luo, Jun Yin, and Simon Kuhn, “Implementation of a Data-driven Model for Mesh-induced Error Corrections in CFD Simulations of Stirred Tanks,” Chemical Engineering Research and Design, vol. 223, pp. 331-347, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  41. Azfarizal Mukhtar, Ahmad Shah Hizam Md Yasir, and Mohamad Fariz Mohamed Nasird, “A Machine Learning-based Comparative Analysis of Surrogate Models for Design Optimisation in Computational Fluid Dynamics,” Heliyon, vol. 9, no. 8, pp. 1-19, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  42. Kenza Tlales et al., “Machine Learning Mesh-adaptation for Laminar and Turbulent Flows: Applications to High-Order Discontinuous Galerkin Solvers,” Engineering with Computers, vol. 40, pp. 2947-2969, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  43. Kheir-Eddine Otmani et al., “Toward a Robust Detection of Viscous and Turbulent Flow Regions using Unsupervised Machine Learning” Physics of Fluids, vol. 35, no. 2, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  44. Paulo Sousa, Carlos Veiga Rodrigues, and Alexandre Afonso, “Enhancing CFD Solver with Machine Learning Techniques,” Computer Methods in Applied Mechanics and Engineering, vol. 429, pp. 1-17, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  45. Sumit Kumar Saurav et al., “Optimization and Scaling of OpenFOAM-based Urban Modeling Simulations,” 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  46. Mingyu Yang et al., “Multi-GPU-based Real-time Large-eddy Simulations for Urban Microclimate,” Building and Environment, vol. 245, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  47. Jumana Abdullah Kareema, and Turkan Ahmed Khaleel, “Green Computing for Sustainability,” Green Technologies and Sustainability, vol. 4, no. 2, pp. 1-8, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  48. Andrea Pazienza et al., “A Holistic Approach to Environmentally Sustainable Computing,” Innovations in Systems and Software Engineering, vol. 20, pp. 347-371, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  49. Salil Bharany et al., “A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing,” Sustainability, vol. 14, no. 10, pp. 1-89, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  50. Manmeet Singh, and Debra F. Laefer, “Recent Trends and Remaining Limitations in Urban Microclimate Models,” Open Urban Studies and Demography Journal, vol. 1, pp. 1-12, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  51. Naga Venkata Sai Kumar Manapragada, and Jonathan Natanian, “Urban Microclimate and Energy Modeling: A Review of Integration Approaches,” Sustainability, vol. 17, no. 7, pp. 1-17, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  52. Jorg Franke et al., “The COST 732 Best Practice Guideline for CFD Simulation of Flows in the Urban Environment: A Summary,” International Journal of Environment and Pollution, vol. 44, no. 1-4, pp. 419-427, 2011.
    [    CrossRef] [Google Scholar] [Publisher Link]
  53. Kiyoshi Uehara et al., “Wind Tunnel Experiments on How Thermal Stratification Affects Flow in and Above Urban Street Canyons,” Atmospheric Environment, vol. 34, no. 10, pp. 1553-1562, 2000.
    [    CrossRef] [Google Scholar] [Publisher Link]
  54. Sushobhan Sen, and Lev Khazanovich, “Limited Application of Reflective Surfaces can Mitigate Urban Heat Pollution,” Nature Communications, vol. 12, pp. 1-8, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  55. Xiaomin Xie, Chun-Ho Liu, and Dennis Y.C. Leung, “Impact of Building Facades and Ground Heating on Wind Flow and Pollutant Transport in Street Canyons,” Atmospheric Environment, vol. 41, no. 39, pp. 9030-9049, 2007.
    [    CrossRef] [Google Scholar] [Publisher Link]
  56. Jae-Jin Kim, and Jong-Jin Baik, “Urban Street-Canyon Flows with Bottom Heating,” Atmospheric Environment, vol. 35, no. 20, pp. 3395-3404, 2001.
    [    CrossRef] [Google Scholar] [Publisher Link]