CFD-based multi-objective optimization of impinging jet ventilation (IJV) systems to improve the local indoor air quality (IAQ) indexes

Shaker Taheri, Hamed; Sajadi, Behrang

doi:10.22059/ees.2025.2050536.1496

CFD-based multi-objective optimization of impinging jet ventilation (IJV) systems to improve the local indoor air quality (IAQ) indexes

Document Type : Research Paper

Authors

Hamed Shaker Taheri

Behrang Sajadi

School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22059/ees.2025.2050536.1496

Abstract

Impinging jet ventilation (IJV) systems, despite some advantages, encounter some weaknesses in delivering local thermal comfort and detachment of particles from the floor. This study leverages the NSGA-II algorithm to optimize the design parameters of IJV systems, aiming to achieve concurrently optimal conditions for thermal comfort and particle suspension. In this context, a surrogate model based on an artificial neural network (ANN) coupled with computation fluid dynamics (CFD) is employed for the problem space. The Jonson-Kendall-Roberts (JKR) model is also implemented using a UDF in Fluent software to simulate particle detachment from the ground level. The optimization variables encompass the dimensions of the air inlet nozzle, the nozzle height, the mass flow rate of the nozzle inlet, and the objective functions comprise the vertical temperature difference, draft index, and the total residence time of suspended particles within the room space. A factorial analysis was performed to evaluate the influence of design variables on the objective functions. The analysis revealed that all design variables have a significant impact on the performance of impinging jets in achieving the desired objective functions. Mass flow rate emerged as the most influential parameter, exerting the strongest effect on the optimized objective functions. After optimization, the points X=0.19, Z=0.23, H=1.36, ṁ=0.034 are chosen from the Pareto front as the consensus optimal point with more favorable objective functions compared to other options.

Keywords

Impingement Jet Ventilation (IJV)

Particle Detachment

Indoor Air Quality (IAQ)

Thermal Comfort

Artificial Neural Network (ANN)

Subjects

Optimization

[1] Karimipanah T, Awbi H. Theoretical and experimental investigation of impinging jet ventilation and comparison with wall displacement ventilation. Building and Environment. 2002;37(12):1329-42.

[2] Karimpanah T, Sandberg M, Awbi HB. A comparative study of different air distribution systems in a classroom. 2000.

[3] Ye X, Kang Y, Yang F, Zhong K. Comparison study of contaminant distribution and indoor air quality in large-height spaces between impinging jet and mixing ventilation systems in heating mode. Building and Environment. 2019;160:106159.

[4] Chen H, Janbakhsh S, Larsson U, Moshfegh B. Numerical investigation of ventilation performance of different air supply devices in an office environment. Building and Environment. 2015;90:37-50.

[5] Zuo B, Zhong K, Kang Y. An experimental study on particle resuspension in a room with impinging jet ventilation. Building and Environment. 2015;89:48-58.

[6] Haghshenaskashani S, Sajadi B. Evaluation of thermal comfort, IAQ and energy consumption in an impinging jet ventilation (IJV) system with/without ceiling exhaust. Journal of Building Engineering. 2018;18:142-53.

[7] Chen H, Moshfegh B, Cehlin M. Computational investigation on the factors influencing thermal comfort for impinging jet ventilation. Building and Environment. 2013;66:29-41.

[8] Chen H, Moshfegh B, Cehlin M. Investigation on the flow and thermal behavior of impinging jet ventilation systems in an office with different heat loads. Building and Environment. 2013;59:127-44.

[9] Ye X, Kang Y, Zuo B, Zhong K. Study of factors affecting warm air spreading distance in impinging jet ventilation rooms using multiple regression analysis. Building and Environment. 2017;120:1-12.

[10] Haghshenaskashani S, Sajadi B, Cehlin M, editors. Multi-objective optimization of impinging jet ventilation systems: Taguchi-based CFD method. Building Simulation; 2018: Springer.

[11] Wang C, Zhang X, Hu K, Liu Y. Geometric-parameter influence and orthogonal evaluation on the thermal environment for an impinging jet ventilation system inlet. Case Studies in Thermal Engineering. 2023;51:103573.

[12] Li T, Lei J, Luo H, Essah EA, Cheng Y. Parametric study and optimization on exhaled particle dispersion in a ward heated by impinging jet ventilation using orthogonal-based grey relational method. Journal of Building Engineering. 2024;91:109619.

[13] Fluent A. Ansys fluent theory guide. Ansys Inc, USA. 2011;15317:724-46.

[14] Soltani M, Ahmadi G. Particle detachment from rough surfaces in turbulent flows. The Journal of Adhesion. 1995;51(1-4):105-23,

Doi: 10.1080/00218469508009992.

[15] Johnson KL, Kendall K, Roberts A. Surface energy and the contact of elastic solids. Proceedings of the royal society of London A mathematical and physical sciences. 1971;324(1558):301-13.

[16] Soltani M, Ahmadi G. Particle detachment from rough surfaces in turbulent flows. The Journal of Adhesion. 1995;51(1-4):105-23,

[17] Fuller K, Tabor D. The effect of surface roughness on the adhesion of elastic solids. Proceedings of the Royal Society of London A Mathematical and Physical Sciences. 1975;345(1642):327-42.

[18] Greenwood JA, Williamson JP. Contact of nominally flat surfaces. Proceedings of the royal society of London Series a Mathematical and physical sciences. 1966;295(1442):300-19.

[19] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 2002;6(2):182-97.

[20] Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation. 1994;2(3):221-48.

[21] Haykin S. Neural networks: a comprehensive foundation: Prentice hall PTR; 1994.

[22] MacKay DJ. Bayesian interpolation. Neural computation. 1992;4(3):415-47.

[23] Foresee FD, Hagan MT, editors. Gauss-Newton approximation to Bayesian learning. Proceedings of international conference on neural networks (ICNN'97); 1997: IEEE.

[24] Cox DR, Reid N. The theory of the design of experiments: Chapman and Hall/CRC; 2000.

[25] ASHRAE (2010). ANSI/ASHRAE Standard 55-2004, Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating, and Air-conditioning Engineers, Atlanta

[26] Krus L. Multicriteria decision support in negotiations. Control and Cybernetics. 1996;25:1245-60.