Laboratory Study of Hydraulic Characteristics of Density Current with Phase Space Theory Analysis
Subject Areas : Water and river engineeringMohammad Hosseini 1 , Mohammad Shabani 2
1 - Department of Civil Engineering, Meymand Center, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran
2 - دانشگاه آزاد اسلامی واحد شیراز
Keywords: Density current, Phase space, Trajectory, Absorber, Surrounding Dimension.,
Abstract :
Introduction: Density currents are created due to the entry of a heavier fluid into a lighter fluid due to the acceleration of the earth's gravity. Density current is non-linear in nature and is sensitive to initial conditions. The time evolution of the dense flow is expressed using trajectory lines in the phase space. The purpose of this research is to extract information hidden inside the system using the phase space theory regarding the evolution of the dense flow with changes in the hydraulic parameters of the dense flow input, so that the nature of the system and the formed pattern of the dense flow can be expressed in the bed of time. Materials and Methods: Density current was conducted with 28 different tests in a laboratory flume with a length of 8 m, a width of 35 cm and a height of 60 cm by changing the slope, concentration and inlet flow rate. In order to describe the system, using the phase space theory, the time signal was transmitted as an object in space, and the analysis in space replaced the analysis in time. Results and Discussion: With the increase of the slope, the data concentration is drawn on the semi-conductor and moves from top to bottom. When the inlet flow rate is 50 l/min, the concentration of the speed data is in the range of 2 cm/s to 4.5 cm/s, and it has taken on an absorbing state, and more chaos is seen in it. By changing the density from 1005 kg/m3 to 1010 kg/m3, the absorption of flow lines is reduced and the trajectories in the phase space diagram are more open and have more maxima. Conclusion: By increasing the slope from 1% to 3%, when the flume is without narrowing, its absorber is reduced and in fact it takes more time to reach the equilibrium and evolution of the phenomenon, but due to continuous or local narrowing, becomes more absorbent. In general, with the increase of the slope, the evolution of the flow occurs earlier. The system begins to saturate in the surrounding dimension of 13.8.
#Adab F. Karami H. Mousavi SF. Farzin S. Application of Chaos Theory in Modeling and Analysis of River Discharge under Different Time Scales (Case Study: Karun River). Physical Geography Research. 2018; 50(3): 443-457. https://doi.org/10.22059/JPHGR.2018.234491.1007061#
#Adenan N. Noorani M. Nonlinear Prediction of River Flow in Different Watershed Acreage. KSCE. 2014; 18(7): 2268-2274. https://doi.org/10.1007/s12205-014-0646-4#
#Delafrouz H. Ghaheri A. Ghorbani M. A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Comput. 2018; 22(1): 2205-2215. https://doi.org/10.1007/s00500-016-2480-8#
#Fahimfard S. Fattahi Mh. Shamsai A. Farzin S. Application of the Chaos Theory, the Reconstructed Phase Space and Correlation Dimensions in the Suspended Load Transport Patterns as Affected by a Dam: The Case of the Karaj River. Water Resources Engineering. 2016; 8(26): 89-100. https:// doi.org/20.1001.1.20086377.1394.8.26.7.7#
#Fahimfard S. Shamsai A. Fattahi MH. Farzin S. 2015. The effect of the dam on the dynamics of sediment transfer of river suspended load from the perspective of chaos theory" 10th International Congress of Civil Engineering, Faculty of Civil Engineering, Tabriz.#
#Farzin S. Sheikholeslami SR. Hasanzadeh Y. 2010. Analyzing the volatility of time series using phase space drawing and correlation dimension method of a case study of monthly rainfall in Lake ormia. The 4th Iran Water Resources Management Conference. Amir Kabir University of Technology.Tehran.#
#Fattahi MH. Tarahi M. chaotic monitoring of river flow using phase space reconstruction method. Iran- Water Resources Research. 2017; 13(2): 221-225. https://www.iwrr.ir/article_34197.html?lang=fa#
#Ghorbani MA. Karimi V. Ruskeepaa H. Sivakumar B. Pham Q. Mohammadi F. Yasamin N. Application of complex networks for monthly rainfall dynamics over central Vietnam. Stochastic Environmental Research and Risk Assessment. 2021; 35(1): 535-548. https://doi.org/10.1007/s00477-020-01962-2#
#Hong M. Wang D. Wang Y. Zeng X. Ge S. Yan H. Singh N. Mid- and long-term runoff predictions by animproved phase-space reconstruction model. Environmental Research. 2016; 148(1): 560–573.https://doi.org/10.1016/j.envres.2015.11.024#
#Hooshmandzade F. Yazdani M. Mousavi F. Chaotic Study and Reconstruction of the Dynamic Phase Space of Evaporation Using Chaos Theory (Case Study: Semnan Synoptic Station). JWSS. 2022; 26(1): 117-129. https://doi.org/10.47176/jwss.26.1.4393#
#Hosseini M. Zakemoshfegh M. Comparison between phase space-based local chaotic models for riverflow forecasting. Tarbiat Modares University Journals System. 2015; 15(3): 13-24. http://mcej.modares.ac.ir/article-16-4895-en.html#
#Jiang J. Tang S. Liu R. Sivakumar B. Wu X. Tianrui P. A hybrid wavelet-Lyapunov exponent model for river water quality forecast. Journal of Hydroinformatics. 2021; 23(4): 864-878. https://10.2166/hydro.2021.023.#
#Jiang Y. Bao X. Hao S. Zhao H. Li X. Wu X. Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction. Water Resources Management. 2020; 34(1): 3515-3531. https://doi.org/10.1007/s11269-020-02631-3#
#Jin Y. Li X. Zhao M. Liu X. Li H. A mathematical model of fluid flow in tight porous media based on fractal assumptions. Journal of Heat and Mass Transfer. 2017; 108(1): 1078–1088. https://doi.org/10.1016/j.ijheatmasstransfer.2016.12.096#
#Khatibi R. Sivakumar B. Ghorbani M.A. Kisi O. Kocak K. Farsadizadeh D. Investigating chaos in river stage and discharge time series. Journal of Hydrology. 2012; 414(1): 108–117. https://doi.org/10.1016/j.jhydrol.2011.10.026#
#Major J. Zheng S. Mosbrucker A. Spicer K. Christianson T. Thorme C. Multidecadal Geomorphic Evolution of a Profoundly Disturbed Gravel Bed River System—A Complex, Nonlinear Response and Its Impact on Sediment Delivery. Journal of Geophysical Research: Earth Surface. 2019; 124(5): 1281-1309. https://doi.org/10.1029/2018JF004843#
#Rezaei H. Garebahi P. Khani Z. Mirabbasi R. Monthly flow analysis of Sefidrood River using Chaos theory. Water and Soil Management and Modeling. 2021; 2(1): 27-41. https:// doi.org/10.22098/MMWS.2021.9431.1043#
#Rolim LZ. Souzafilho F. Exploring spatiotemporal chaos in hydrological data: evidence from Ceará, Brazil. Stochastic Environmental Research and Risk Assessment. 2023; 37(1): 4513-4537. https://doi.org/10.1007/s00477-023-02501-5#
#Sivakumar B. Jayawardena AW. Femando T. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology. 2022; 265(1): 225-245. https://doi.org/10.1016/S0022-1694(02)00112-9#
#Tao H. Sulaiman S. Yaseen Z. Asadi H. Mehram S. Ghorbani M. What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale. Water Resources Management. 2018; 32(1): 3935-3959. https://doi.org/10.1007/s11269-018-2028-z#
#Uuyang Q. Lu W. Xin X. Zhang Y. Cheng W. Yu T. Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction. Water Resources Management. 2016; 30(1): 2311-2325. https://doi.org/10.1007/s11269-016-1288-8#
#Zounemat M. Amirkhani Kh. Efficiency Assessment of Local Prediction Method Considering Reconstruction of Phase Space and Artificial Neural Network Model for Prediction of Runoff (Case Study: Pole-Kohneh Station, Kermanshah). Ferdowsi Civil Engineering. 2016; 28(2): 91-108. https://doi.org/10.22067/CIVIL.V28I2.41135#