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RISS 인기검색어
- 검색키워드
- 저자 : Hojat Karami (검색결과 3 건)
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Hojat Karami,Hossein Hosseinjanzadeh,Khosrow Hosseini,Abdollah Ardeshir 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.1
Installing collar on abutment is an applicable method to protect the abutment against scouring. In this study, effect of using collar on reduction of scour depth at short vertical-wall abutment (ratio of abutment length to flow depth of less than 1, La/y < 1) under clear-water conditions and uniform bed materials was investigated experimentally. Different sizes and various elevations of installing collar were studied. Results showed that using collar with 2.25La in width, situated under the bed elevation, led to 88.9% scour depth reduction for threshold flow condition. Three-dimensional velocity components around the abutment, for both with/without collar conditions, were measured by Acoustic Doppler Velocimeter (ADV). Turbulent intensity components, Reynolds stresses and bed shear stress on a rough rigid bed were studied at different sections. A primary vortex, associated with the downflow, existed at upstream face of the abutment. In the downstream, the flow field was turbulent due to the vortex shedding, and wake vortices occurred near the bed level next to the abutment. Installing collar led to diminish the vortex power below the collar. Also, turbulent intensity at upstream face of the abutment and dimensionless shear stress was reduced by using collar. The data presented in this paper could be useful for development and validation of a flow field model.
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Armin Azad,Hojat Karami,Saeed Farzin,Amir Saeedian,Hamed Kashi,Fatemeh Sayyahi 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.7
Water quality management and control has high importance in planning and developing of water resources. This study investigatedapplication of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolution (DE)in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters ofGorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the qualityparameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DEwith R2 values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55,respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction ofSAR (R2 = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the bestperformance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentionedalgorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.
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Optimization of Reservoir Operation using New Hybrid Algorithm
Zaher Mundher Yaseen,Hojat Karami,Mohammad Ehteram,Nuruol Syuhadaa Mohd,Sayed Farhad Mousavi,Lai Sai Hin,Ozgur Kisi,Saeed Farzin,김성원,Ahmed El-Shafie 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.11
Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant’s objective function.
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