Browsing by Author "Kohandel Gargari, Mehrnoush"

Now showing 1 - 2 of 2

Flow and turbulence characteristics of bed load sediment transport for self-cleansing without deposition
(John Wiley and Sons Ltd, 2025) Mehrnoush Kohandel Gargari; Ilayda Keskin; Mir Jafar Sadegh Safari; Babak Vaheddoost; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Keskin, Ilayda; Kohandel Gargari, Mehrnoush
Investigating the structure of flow turbulence and bed load sediment transport is crucial as it provides insights into the functioning of aquatic environments where such variations can lead to changes in ecosystem dynamics. This study focuses on the impact of sediments on the hydraulic characteristics of flow at self-cleansing without deposition conditions of sediment transport. The self-cleansing without deposition is not only a mode of sediment transport in alluvial channels but it also serves as a criterion for the design of lined channels. Among the various design concepts for lined open channels such as sewers and drainage channels self-cleansing without deposition condition is implemented as the most conservative and reliable approach. However most of the conducted experimental studies on self-cleansing without deposition have focused on measuring the basic flow and sediment characteristics for modelling purposes and neglected the effect of bed load sediment size flow discharge and channel bed slope on turbulence characteristics. This study addresses this gap by examining the impact of bed load sediment size bed slope and discharge on turbulence characteristics through a series of experiments conducted in a 12.5 m flume with a rectangular cross-section equipped with an automatic control system (ACS) at the Hydraulic Laboratory of Yaşar University. The channel bed slope sediment discharge flow discharge and depth were adjusted and measured using ACS. Discharge and flow depth were measured using an ultrasonic flow-meter and depth sensors respectively. Flow characteristics were measured using a Vectrino profiler device. The study reveals that bed load sediment transport reduces streamwise velocity especially for coarse particles. Additionally at a constant bed slope velocity differences remain small at lower discharges but become more significant as discharge increases. Turbulence intensity rises with bed load motion more in the streamwise direction than vertically. At a constant bed slope increasing discharge enhances turbulence but the effect is more pronounced at lower slopes and less significant at steeper slopes. Reynolds shear stress increases with particle size and steeper slopes indicating greater shear production. These observations suggest critical implications for the design and optimization of open-channel systems emphasizing the need for detailed consideration of particle sizes and bed conditions in engineering practices. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Lq-norm multiple kernel fusion regression for self-cleansing sediment transport
(Springer Nature, 2024) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Mehrnoush Kohandel Gargari; Rahimzadeh Arashloo, Shervin; Gargari, Mehrnoush Kohandel; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Kohandel Gargari, Mehrnoush
Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m a width of 0.6 m a depth of 0.7 m and having an automatic control system for regulating channel slope discharge and sediment rate. Behind utilizing collected experimental data in this study existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental hydrological and hydraulic problems. © 2024 Elsevier B.V. All rights reserved.