Multi-objective optimization of diversion tunnel under the water conservancy project

Research Article
Open access

Multi-objective optimization of diversion tunnel under the water conservancy project

Junjin Yan 1*
  • 1 Xi’an University of Finance and Economics    
  • *corresponding author 397942293@qq.com
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/9/20230108
ACE Vol.9
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-007-3
ISBN (Online): 978-1-83558-008-0

Abstract

Tunnel diversion is a method of diverting upstream water through a diversion tunnel where the river valley is narrow and not conducive to the placement of an open channel, and where the geological conditions of the riverbank hills are favourable for the excavation of a tunnel. The construction of a tunnel diversion is limited by topographical conditions, construction budget, and construction scale. Therefore, the choice of a reasonable solution for the construction of a tunnel diversion has a certain influence on the cost, efficiency, and risk of construction A comprehensive consideration of how to construct a tunnel diversion can achieve a multi-objective optimization to reduce costs and increase efficiency. This paper incorporates risk as a constraint, with the radius of the upper part of the tunnel half arch and the lower tunnel elevation as optimizable variables for optimization. Based on the analysis of actual hydropower project data, Matlab is used to derive the most optimizable solution considering different possible scenarios of water flow, and the obtained results, show that the comprehensive objectives of shorter construction time and lower cost can be effectively achieved by analyzing the variables under different scenarios where the risk is a constraint.

Keywords:

diversion tunnels, tunnel diversion, hydraulic engineering, multi-objective optimization, overflow.

Yan,J. (2023). Multi-objective optimization of diversion tunnel under the water conservancy project. Applied and Computational Engineering,9,259-264.
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References

[1]. Barkhordary, A. (1994). Optimizing River Diversion Under Hydraulic And Hydrologic Uncertainties. Journal of Water Resources Planning and Management 120(1), 36-47.

[2]. Su, Y. Y. (2018). Multi-objective optimization model for open channel diversion in hydropower engineering. Wuhan University.

[3]. Zhang, W. M., Dong, Z. C., Zhu, C. T. and Qian, W. (2008). Research on multi-objective optimization of hydrological model parameters based on particle swarm algorithm. Journal of Water Resources (05), 528-534.

[4]. Chen, B. L. (2005). Optimization theory and algorithm. Beijing: Tsinghua University Press.

[5]. Jain, A., Bhattacharjya, R. K. and Sanaga, S. (2004). Optimal Design of Composite Channels Using Genetic Algorithm. Journal of Irrigation and Drainage Engineering-asce, 130, 286-295.

[6]. Liu, T., Lv, S., Wu, D. and Xing, J. Y. (2022). Experimental study on optimization of deflector cave outlet at Dongzhuang water conservancy hub. Hydropower Energy Science 40(07), 156-158+179.

[7]. Dong, Z. Y., Shi, K. B., Bai, X. J. and Diao, H. P. (2021). Multi-objective optimization of overwater cofferdam-tunnel diversion scheme based on construction capacity. Journal of Water Resources and Water Engineering 32(01), 151-157.

[8]. Jin, J. L., Yang, X. H. and Ding, J. (2001). An improved solution to the standard genetic algorithm--accelerated genetic algorithm. Systems Engineering Theory and Practice (04), 8-13.

[9]. Yanmaz, A. M. (2000). Overtopping risk assessment in river diversion facility design. Canadian Journal of Civil Engineering 27, 319-326.

[10]. Zhong, D. H. and Liu, D. H. (2000). Genetic algorithm-based optimization of construction inflow buildings. Systems Engineering Theory and Practice (10), 126-133.

Cite this article

Yan,J. (2023). Multi-objective optimization of diversion tunnel under the water conservancy project. Applied and Computational Engineering,9,259-264.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems

ISBN：978-1-83558-007-3(Print) / 978-1-83558-008-0(Online)
Editor：Seyed Ghaffar, Alan Wang
Conference website: https://2023.confmss.org/
Conference date: 24 June 2023
Series: Applied and Computational Engineering
Volume number: Vol.9
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Barkhordary, A. (1994). Optimizing River Diversion Under Hydraulic And Hydrologic Uncertainties. Journal of Water Resources Planning and Management 120(1), 36-47.

[2]. Su, Y. Y. (2018). Multi-objective optimization model for open channel diversion in hydropower engineering. Wuhan University.

[3]. Zhang, W. M., Dong, Z. C., Zhu, C. T. and Qian, W. (2008). Research on multi-objective optimization of hydrological model parameters based on particle swarm algorithm. Journal of Water Resources (05), 528-534.

[4]. Chen, B. L. (2005). Optimization theory and algorithm. Beijing: Tsinghua University Press.

[5]. Jain, A., Bhattacharjya, R. K. and Sanaga, S. (2004). Optimal Design of Composite Channels Using Genetic Algorithm. Journal of Irrigation and Drainage Engineering-asce, 130, 286-295.

[6]. Liu, T., Lv, S., Wu, D. and Xing, J. Y. (2022). Experimental study on optimization of deflector cave outlet at Dongzhuang water conservancy hub. Hydropower Energy Science 40(07), 156-158+179.

[7]. Dong, Z. Y., Shi, K. B., Bai, X. J. and Diao, H. P. (2021). Multi-objective optimization of overwater cofferdam-tunnel diversion scheme based on construction capacity. Journal of Water Resources and Water Engineering 32(01), 151-157.

[8]. Jin, J. L., Yang, X. H. and Ding, J. (2001). An improved solution to the standard genetic algorithm--accelerated genetic algorithm. Systems Engineering Theory and Practice (04), 8-13.

[9]. Yanmaz, A. M. (2000). Overtopping risk assessment in river diversion facility design. Canadian Journal of Civil Engineering 27, 319-326.

[10]. Zhong, D. H. and Liu, D. H. (2000). Genetic algorithm-based optimization of construction inflow buildings. Systems Engineering Theory and Practice (10), 126-133.

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