مجله هیدرولیک
سال هفدهم شماره 3 (پاییز 1401)

Most of the times, flow passing above, through or below hydraulic structures is in the form of jets, which can cause downstream soil material erosion. When the amount of clay in soil materials is more than 10%, they can be considered as cohesive soils. If the results of cohesionless sediment research are generalized to cohesive sediments, the scour values obtained will be more or less than the actual values. On the other hand, there are no specific conversion ratios to estimate the characteristics and temporal changes of the scouring of cohesive sediments from cohesionless sediments. The duration of reaching maximum scour depth in cohesive sediments has shown to be longer than that of cohesionless ones.Previous works on cohesive sediments are often performed on flumes or by using a submerged vertical jet device. However, the jets formed below the hydraulic structures are mostly horizontal or oblique which are examined in this paper.Based on dimensional analysis, it was determined that the parameters of nozzle diameter, jet drop height, jet angle, jet velocity, tailwater depth, fluid density, dynamic fluid viscosity, critical shear stress and gravity acceleration affect the scour of cohesive sediments caused by the jet which is studied here.

Experiments were carried out at the Hydraulic Lab of Tarbiat Modares University in a rectangular flume 0.6 m wide and 0.6 m high. A 0.2 m deep hole is created on the floor to place cohesive sediments. The laboratory channel is equipped with a 2m3 inlet tank, from which water is pumped into the jet tube. Froude numbers 3, 5, 7, and 9 are established based on common hydraulic structures and previous works.The experiments were performed using horizontal, oblique and vertical jets using tubes with nozzle diameters of 10, 15, 20 and 25 mm and with 3 tailwater depths of 5, 10 and 15 cm and 3 jet drop heights of 20, 50 and 60 cm. The cohesive sediments used were produced from a combination of fine sand with clay (including kaolin and bentonite at a ratio of 3 to 1). The amount of clay was considered to be 20% by weight of the total soil based on natural soils and previous works.Prior to the start of tests, tailwater was established on the sediment layer in order to allow it to saturate. After reaching equilibrium in the experiments, water was completely drained from the channel and the scouring hole and bed profiles were extracted by a laser distance meter device.

Erosion of cohesive sediments has the greatest scouring potential in the initial stage and in the later stages, the sediment bed becomes rougher and its resistance to scouring increases until equilibrium establishes.A sedimentary ridge is formed at the end of the scouring hole by horizontal and oblique jets and around the scouring hole by vertical jets. In horizontal and oblique jets, the maximum scour depth does not necessarily form on the centerline. The growth of the length and width of scouring hole stops almost simultaneously, but deepening of it continues after that. This is in accordance with findings of Mazurek et al. (2001), Ansari et al. (2002), and Mazurek et al. (2003).As the jet height rises, the time it takes to reach equilibrium increases and leads to maximum scouring occur at a greater distance from the jet nozzle. In horizontal jets, the location of the maximum scouring depth shifts in the early stages of scouring but stabilizes after approximately 2 hours. Increased shear stress due to jet flow increases the scouring rate.Increasing the ratio of tailwater depth to jet drop height (Yt / H) has a dual effect on the maximum relative scour depth. So that the maximum relative scour depth first increases with increasing Yt / H to about 0.3 for horizontal jets and about 0.35 for vertical jets, then the trend reversed and with increasing Yt / H ratio the scour rate relatively reduced. Increasing the Froude number increases the amount of scouring. Also, the amount of scouring at two angles of 0 and 30 degrees relative to the horizon, are very close to each other. At larger angles, except for the 90 degree angle, the scour depth increases as the jet angle increases. The 45 degree angle jet creates the maximum scouring depth.

As the jet height rises, the time it takes for the scouring to reach equilibrium increases. It also leads to maximum scouring to occur at a greater distance from the jet nozzle. Increasing shear stress by jet flow, increases the scouring rate.At lower values of Yt/H, with increasing this ratio, the maximum scour depth increases until it reaches the maximum value and then. the trend reverses.By increasing Froude number, scour rate increases. By steepening jet angle, the scour depth almost increases but when the jet becomes vertical, lower scour depths are observed.

The formation of vortices at the intake and the air entertainment into the intake duct is an important hydraulic phenomenon that usually occurs in the dam intakes and causes such problems as energy loss and reduction of intake discharge coefficient. Among different types of intakes exposed to the vortex phenomenon are hydropower intakes used to supply water to turbines and generate electricity. These intakes are mainly horizontal. To prevent the formation of strong surface vortices, their strength must be physically controlled. A practical solution for this is to use anti-vortex structures. These structures mainly eliminate the vortex by reducing the flow velocity near the intake, lengthening the flow path between the free water surface and the mouth of the intake, as well as energy dissipation. Some studies on the structural methods of vortex dissipation have been done by Amiri et all (2011), Tahershamsi et all (2012), Monshizadeh et all (2018), Taghvaei et all (2012). In this study, the effect of horizontal perforated plates on the dissipation of the strong vortices, the intake discharge coefficient and inlet loss coefficient of the intake is studied, so far no special studies have been done in this area.

In the present study, a physical model was used to investigate the performance of horizontal perforated plates. This model was designed to produce the strongest type of vortex with air core and different strengths. The main components of the experimental setup are: reservoir, intake duct, pump and electromotor speed controller device. The dimensions of reservoir is 1.3 m in wide, 3.5 m long and 2 m high. The mouth of intake extends 20 cm into the reservoir and is positioned so that the side walls and the bottom of the reservoir do not affect the flow conditions. The length of the intake pipe is 4.5 m and its diameter is 16 cm. At a distance of 2 m upstream of the intake in the reservoir, some blades are installed vertically that by changing their angle relative to the intake axis, the angle of inflow to the intake can be changed. This makes it possible to strengthen the upstream vorticity to reach stronger vortices. For modeling the perforated anti-vortex plates, some plastic mesh with different openings and different thicknesses were used. For each plate, the corresponding mesh was placed in a metal coil and this coil is screwed to the reservoir wall so that the perforated plate be placed on the mouth of the intake. By creating 36 types of strong vortices, the performance of 10 types of perforated plates with different dimensions, thicknesses and openings was tested.

Calibration tests showed that in the range of 1.5D to 2D (D is the diameter of the intake pipe) for submergence depth, flow discharges of 15 to 30 lit/s and blade angles of 0 to 20 degrees, the stable strong vortices are formed. A total of 36 strong vortices (three relative submergence depths, four flow discharges and three blade angles) were formed with different strengths in the model. In order to consider the appropriate confidence limit in this study, the performance of each of the anti-vortex plates in the model was considered so that it is able to dissipate vortex type-six or decrease to type-two vortices. Therefore, the conditions in which the strength of a type-six vortex was reduced by the relevant anti-vortex plate to a type-three (or higher) vortex are known as critical conditions. It should be noted that the type of vortex is determined based on its appearance. Finally with 360 tests it was concluded that the effect of opening of the plates to eliminate the vortex strength is more than the dimensions and the thickness of the plates. In addition, the effect of using perforated horizontal plates on discharge coefficient and inlet loss coefficient of the intake was investigated. It was concluded that the use of perforated anti-vortex plate with openings of 70%, 58% and 50% reduces the intake discharge coefficient by 5.9%, 10.5%, and 13.4%, respectively. It is also caused 12.9%, 24.7% and 33.5% for inlet loss coefficient of the intake, respectively.

The effect of submergence depth on the vortex strength is greater than the flow discharge and it is also greater than the geometric asymmetry. Dimensions of the plate have little effect on the vortex dissipation. The thickness of the plates has little effect on the vortex strength. The opening rate of the plates has a great effect on the vortex and a plate with 50% opening, was able to dissipate all strong vortices. The vortex strength has a direct relationship with the inflow angle and the flow discharge and is inversely proportional to the submergence depth. As the flow discharge increases, the discharge coefficient decreases and the inlet loss coefficient increases.

Flood routing is an important issue in river engineering. The flood routing methods are categorized into two groups of hydraulic and hydrologic methods. Hydraulic routing methods require considerable input data and time-consuming calculations. But hydrological methods need less input data and are less complicated in comparison with hydraulic routing methods. The hydrological routing methods are based on the continuity equation and a relationship between inflow/outflow values and flood storage. Linear Muskingum is a hydrological routing method commonly used in rivers routing. However, as the relation between channel storage and the inflow/outflow is nonlinear, the nonlinear form of this model is developed which has received special attention in recent years and several types of it have been proposed. Using the Muskingum method, while saving time, valuable information about the flood depth and hydrograph is obtained. However, the performance of these models is highly dependent on the optimal estimation of their parameters considering the study area characteristics.

Although the nonlinear Muskingum models have special advantages over the linear Muskingum model. The hydrologists avoid from the nonlinear Muskingum models, because of the difficulties in estimation of their parameters. Therefore, researchers have attempted to estimate these parameters using the optimization algorithms. In this research, the nonlinear Muskingum model type 5 (NL5) is considered for flood routing and Nonlinear programing (NLP) is used for estimation of the optimal values of model parameters. The results are compared with the metaheuristic optimization algorithms of genetic algorithm (GA), particle swarm optimization algorithm (PSO) and Gray wolf optimizer (GWO). The objective function of the optimization algorithms was set to minimize the sum of squares of the difference between the measured and simulated values of flows (SSR). Wilson flood hydrograph (first case study), Wy River flood hydrograph in England (second case study) and the hydrograph presented by Vatankhah (2014) (third case study) were used as the case studies of this research.

The performance of NL5 model was very good in the all considered cases. In the first case study, the maximum absolute error is less than seven percent. Also, in the second and third case studies, the maximum absolute errors are less than 20 percent and 10 percent, respectively. MARE, NSE, CC, DPO and DPOT measures were used to further evaluate the model performance. The closer values of MARE, DPO and DPOT to zero and the closer the values of the NSE and CC measures to one, show the better the performance of the model (Kult et al., 2014). In the first case study, the MARE values for NLP, GWO, PSO and GA algorithms are 0.011, 0.011, 0.012 and 0.012 m3/s, respectively. For the second case study, the MARE values are 0.104, 0.105, 0.103 and 0.104 m3/s, respectively; The values of this measure in the third case study for the mentioned optimization methods are 0.0301, 0.0301, 0.0301 and 0.0303 m3/s, respectively. The values of this measure show the perfect performance of NLP, GWO, PSO and GA techniques in estimation of NL5 parameters. DPO, DPOT, NSE and CC indices also show the same finding. SSR values in the first case study for NLP, GWO, PSO and GA optimization methods are 5.44, 5.44, 5.47 and 5.88, respectively. Also, SSR values for the second case study are 30837.6, 30848.2, 30880.1 and 30929.1, respectively. For the third case study, these values are 7356.7, 7432.1, 7391 and 7412.3. The simulation times for NLP, GWO, PSO and GA methods show that the processing time in the NLP method is much less than the other methods. The optimization methods are ranked based on their results accuracy and simulation time. NLP method is ranked first in the regard while is followed by GWO, PSO and GA in the next ranks, respectively. The comparison of the obtained SSR values in the current study and the previous studies which used the cases one and two, show that the NLP optimization method has better performance in estimation of NL5 model parameters. In this study, for the third case study (Vatankhah, 2014 data), which has not been routed by the Muskingum method previously, the results of routing with NL5 are compared with the results obtained with the Rang Kota method (Vatankhah, 2014). The SSR value when using NLP as the optimization tool for estimation of NL5 model parameters is 7356.8 m6/s2, while in the Rang Kota method it is 14441.3 m6/s2. Therefore, in this case study, the NL5 model has performed better than the Rang Kota method.

In the present study, NLP technique and powerful GWO algorithm were used to estimate the optimal values of NL5 model parameters and the results were compared with GA and PSO algorithms. The performance evaluation results indicate that the NLP method, in addition to being more accurate, also requires less time to estimate the optimal value of the parameters. The values of the objective function for the first case study for NLP, GWO, GA and PSO methods are 5.44, 5.44, 5.88 and 5.47 m6/s2, respectively, while these values for the second case study are 30837.6, 30848, 30929.1 and 3088.1 m6/s2 and for the third case study are 7356.7, 7432.1 7412.3 and 7391 m6/s2. NLP processing time is at least 10% less than the other considered optimization methods. Therefore, the NLP method is the best choice for estimating the optimal parameters of Muskingum type five by considering two factors of accuracy and speed of simulation even though all of the methods showed a very good performance. Also, in the third case study, which was not routed previously, by the Muskingum method, the results were compared with the Rang-Kota method, and the results showed that the NL5 model, which was solved by the NLP method, performed better. After NLP, GWO, PSO and GA methods had the better performance in estimating the NL5 parameters, respectively.

The ease of installing sluice gates and simplicity of their equations resulted in sluice gates as one of the most widely used hydraulic structures in regulating and controlling the water level. Several factors are discussed on the discharge coefficient of the sluice gate, including the effect of sill under the gate. The most important application of sill under sluice gate is to increase its discharge coefficient. Geometry and widths of sill is one of the important factors on discharge coefficient. also use of non-suppressed sills changes the flow pattern and the general equation of discharge coefficient. discharge coefficient of sluice gate with sill was studied by Jalil et al. (2016). In this study, the effect of sill under sluice gate was experimentally investigated on flow discharge coefficient. Results showed that the coefficient of discharge decreases with an increase of relative sill height to the head upstream. Rezavand (2018) investigated the effects of the hydraulic parameters on the flow discharge coefficient by Fluent software. Results showed that the sill under the gate has a positive effect on the flow discharge coefficient. The goal of this study is to investigate the geometry of sill with changes in its width on flow pattern and discharge coefficient in free-flow conditions. According to previous studies effect of sill width parameter with different geometric shapes on discharge coefficient and flow pattern has not been studied.

The experiments were performed in a hydraulic laboratory with flume dimensions of 5 m in length, 0.30 m in width, and 0.45 m in height. The walls are made from Plexiglass in order to provide good visibility. The inlet flow were measured by two rotameters with± 2% accuracy. Rotameters were installed at the outlet of the pump and measured with a point gage with an accuracy of 1 mm. a sluice gate with a 1 cm thickness is installed with the distance of 1.5 m away from the inlet of flow. The gate opening was fixed at 4 cm in all experiments. Sills including cylindrical, semicylindrical, pyramidal, and rectangular cubic were prepared in order to investigate the shape effect. All four sill shapes were prepared with widths of 5, 7.5, 10, 15, and 20 cm in order to study the effect of sill width under the gate. The height of all sills in this study was considered to be a fixed value of 3 cm. A total of 20 physical models were tested. In this study, flow discharge in the range of 475 to 700 liters per minute was applied to all models. A total of 200 experiments were performed in order to investigate the effect of sill shape and width on flow pattern and discharge coefficient in free conditions.

Results of sluice gate patterns with sill and without sill situations were investigated. The results of these experiments, similar to previous studies, show that a sluice gate with sill increases the discharge coefficient. The results showed that sills with different geometries affect flow under the gate. Also, using non-suppressed sills under the gate breaks the flow lines. As the downstream progress, v-shaped sections are formed. Investigation of flow patterns in cylindrical and semi-cylindrical and pyramidal sills showed pyramidal sill causes a significant uniformity flow lines compared to other geometric shapes due to its sloping side at downstream. while sill with rectangular cube geometry improves rotational flows at downstream of sill. The results of placing sill in different geometric shapes under sluice gate indicate that using semicy-lindrical sill compared to other shapes increases in discharge coefficient and the highest values of discharge coefficient after this sill are allocated to cylindrical, pyramidal and rectangular cubic sills, respectively. semicylindrical average discharge coefficient increased 19.1 percent compared with the gate without sill. According to the laboratory findings, it was observed that increased sill width with decreased gate opening increases the discharge coefficient. Placing a sill with a width of 20 cm in all geometric shapes increases the discharge coefficient by an average of 10% compared to a sill with a width of 5 cm.

The study of discharge coefficient in 20 physical models showed that the highest values of discharge coefficient after semicircular sill are allocated to circular, triangular, and square sills, respectively. This increase is expressed because the semicircular, circular, triangular, and square sills at the smallest width (b = 5 cm) increased discharge coefficient by 6.5, 5.6, 3.5, and 1.6% compared to non- sill state, respectively. Changing sill width from 5 to 20 cm showed that discharge coefficient of semi-cylindrical, cylindrical, pyramidal and rectangular cubic increased by an average of 19.1, 17.2, 14.7, and 12.1% compared to non- sill state.

The need to control floods and their dangers is not hidden from anyone. In addition, a wide range of economic, social and environmental issues are affected by this phenomenon. The first step in the design and optimal management of flood control methods is the correct identification of river behavior during floods. In most river engineering projects such as flood routing, determining the bed and river area, etc., calculating the average values of hydraulic parameters of the river section is sufficient. Today, the use of numerical and analytical methods in the study of fluid environment have grown and developed. Due to the production of reliable results, they have been able to be a good alternative to physical models. Today, with the rapid development of numerical models and increasing the speed of computer calculations, the use of 3D numerical models is preferred and also due to the fact that measuring the velocity distribution and shear stress in rivers is very time consuming and expensive, the results of 3D numerical models It will be valuable. On the other hand, the present studies show that comprehensive numerical research using FLOW-3D model has not been performed on composite sections, so a suitable ground for research is provided. Therefore, the innovation of the present study is the numerical study of the effects of parameters such as roughness on the status and hydraulic performance of the flow in non-prismatic composite sections, which are accompanied by divergent and convergent floodplains, which have received less attention numerically.

Younesi (2013) research has been used to validate the results of numerical simulation. In these experiments, first the hydraulic flow in composite prismatic and non-prismatic sections with fixed bed was examined and then, while maintaining the conditions, sediment transfer experiments were performed in prismatic and non-prismatic mode. The experiments were performed in a research channel 15 meters long. This canal is a composite canal with two symmetrical floodplains with a width of 400 mm with a flow rate that can be provided for recirculation in the system of 250 liters per second and a longitudinal slope of 0.0088 000. The depth of the main canal to the edge of the floodplain is equal to 0.18 meters and the width of the main canal is equal to 0.4 meters (Figure 1). In order to roughen the bed and walls of the main canal, sediments with an average diameter of 0.65 mm have been used and at each stage, the walls and bed of floodplains have been roughened by sediments with an average diameter of 0.65, 1.3 and 1.78 (mm). A triangular overflow is used to measure the inflow to the canal, upstream of the canal. In order to measure the flow velocity in experiments with relative depth of 0.15 and 0.25, a micromolina with a diameter of 14 mm and in experiments with relative depth of 0.35, a three-dimensional speedometer (ADV) was used. The water level was also taken by depth gauges with an accuracy of 0.1 mm.

In the present study, in order to validate the numerical model of water surface profile, average depth velocity distribution and boundary shear stress in the three sections at the beginning, middle and end of the divergence zone) in experiments 0.25-2-11.3-NP and 0.25-2-5.7-NP and Also, the 0.25-2-2 P test of the prismatic composite section has been evaluated. In Table (1) the values of RMSE and NRMSE indices related to the P.20-2-2-P test of the prismatic composite section, and also in Table (2) the values of the RMSE and NRMSE indices in the experiments 11.3-2-0.25-NP and -0.25. 2-5.7-NP is provided. The results related to the validation of the average depth velocity of the experiments 0.25-2-5.7- NP-11.3-2-0.25, NP and P.2.0-2-2-P are shown. In 0.25-2-5.7-NP experiment, the amount of NRMSE in elementary, middle and final grades was calculated to be 5.7, 11.8 and 10.3%, respectively, which is in the excellent grade in the elementary grade and good in the middle and final grades. Placed. As can be seen, the RMSE values are calculated as 0.026, 0.037 and 0.026, respectively. In the experiment 11.3-2-0.25, NP, the NRMSE values in the primary, middle and final levels were calculated as 7, 11.2 and 15.4%, respectively, which are in the excellent category in the primary level and in the good category in the middle and final levels. Take. As can be seen, the RMSE values are calculated as 0.032, 0.038 and 0.04, respectively. In the 0.25-2-P experiment, the NRMSE value was calculated to be 1.7%, which is in the excellent category. As can be seen, the RMSE value is also calculated to be 0.004. Regarding the medium-depth velocity distribution, it can be said that the numerical model has an acceptable compliance with the laboratory results and only a small error has been entered in the junction area, which can be considered as a result of the movement of secondary cells towards the corners.

in this research The flow pattern in waterways with composite prismatic and non-prismatic sections was investigated using Flow 3D software that is capable of three-dimensional flow analysis. For three different relative roughnesses (1, 2 and 2.74) as well as three relative depths (0.15, 0.25 and 0.35) and divergence angles of 5.7 and 11.3 degrees, changes in the longitudinal component of velocity, The average depth velocity distribution, the boundary shear stress distribution as well as the flow rate transmitted by the floodplains were investigated. The results showed that with increasing the width of floodplains along the canal, the amount of velocity decreases. Also, the study of the effect of roughness on the flow pattern showed that in general, with wall roughness, the amount of velocity has decreased in all sections and also the flow pattern at the junction of the main canal and floodplain is more affected by wall roughness. The results also showed that with increasing relative depth or decreasing relative roughness, the velocity gradient between the main channel and floodplains decreases

It is common in practice to partially drain the pipelines for inspection and repair. If not properly controlled, refilling of the pipeline may expose them to significant transient pressures which can compromise the integrity of the pipeline and associated joints. Implementing a safe filling protocol requires that the location and size of hydro-mechanical equipment are calculated. Such information can be obtained through analysis of different filling scenarios, but unfortunately, such a detailed analysis is usually ignored in the design stage, not surprising why pipe incidents usually happen during operation.With the aid of numerical explorations, this paper aims to shed some light on the key factors affecting the filling hydraulics. To this end, a numerical model is proposed to calculate the filling hydraulics. The model uses the method of characteristics to solve the water hammer equations and employs the Discrete Gas Cavity Model (DGCM) to treat column separation. The model is validated with the experiments. Extensive numerical explorations reveal that lack of a safe filling protocol as well as lack or inadequate sizing of the required hydro-mechanical equipment can result in water hammer pressures. The results also show that without a properly sized bypass and air valve, it is impossible to control transient pressures during filling.

Extensive numerical explorations are conducted with a hypothetical water pipeline to analyze the key factors affecting the transient pressures which occur during filling. The pipeline has an undulating profile with the diameter, length, and acoustic wave speed of 0.9 m, 15900 m, and 1000 m/s respectively. The pipeline is supplied by a reservoir with a constant water depth of 5 m which is located at the upstream end of the pipeline. It is assumed that the last 1600 m of the pipelined is drained and an air valve at the end of the pipeline allows air management during the filling. A bypass line located at the upstream end of the empty zone is also equipped with a flow control valve to control the filling flow rates. Several numerical solutions are conducted with the different sizes of the air valve, bypass line and different opening times of the flow control valve, and the maximum and minimum pressure heads induced during filling are recorded.

Analyzing the results obtained from the numerical explorations show that when the flow control valve opens, the empty pipeline starts being filled with a filing flow rate which depends on the size of the bypass and the rate of opening of the flow rate. A large bypass line and rapid opening of the flow control valve result in the rapid filling of the empty pipeline and a significant down surge on the upstream side of the bypass line. The progressing water column in the empty pipe serves as a piston and pushes the air out of the system through the air valve. If the outlet orifice of the air valve is large enough, the air pressure in the empty pipeline does not increase significantly otherwise higher air pressures are built up which can slow down the filling water column. When the last air is eventually released from the system, the water column comes to rest and significant water hammer pressures result. The magnitude of the resulting water hammer pressure depends on the velocity at which the water column hits the pipe’s end as well as the acoustic wave speed of the pipe. Numerical exploration shows that the maximum and minimum pressures induced during filling depend on the diameters of both the outlet orifice of the air valve and the bypass line as well as the opening time of the flow control valve. For this particular case study, it is found that the bypass line diameter = 0.2 m, the outlet orifice diameter = 1.5 cm, and the flow control valve opening time = 40 s can control maximum and minimum pressure head within the acceptable range without unreasonably prolonging the filling time.

• The proposed model can be successfully assisted in analyzing the hydraulics of filling and in designing a safe filling protocol• Without a proper filling protocol, the resulting transited pressures can be strong enough to rupture the pipeline • Without a proposedly sized bypass, it is impossible to control negative pressures in the pipeline • The rate of opening of the flow control valve might play an important role in controlling the induced maximum and minimum pressures during filling• Reducing the diameter of the outlet orifice of the air valve significantly reduces the resulting transient pressures but at the same time prolongs the filling. • An optimum filling protocol can be obtained through an iterative procedure in which the bypass and air valve diameters, as well as the opening time of the flow control valve, are determined in such a way that the induced maximum and minimum transient pressures remain within the acceptable level and the filling is performed as fast as possible

Population increasing along with the environmental crisis due to the use of fossil fuels has led humans to seek to use renewable energy sources. One of the most important sources of renewable energy is the waves of the seas and oceans, which can meet some of the human needs for energy resources. One of the key steps in the development of wave energy renewable technology is the design and validation of physical models. Although physical models can not accurately simulate all the details and performance of the original prototype, they can be a very valuable source of information for researchers, developers, and inventors in this area. Due to its simple mechanical structure, the oscillating water column has become one of the most common tools for converting wave energy in the world. The oscillating water column could be used as a breakwater on the shores in addition to generating energy from the waves. Due to the complexities related to the hydrodynamic conditions of air and airflow inside the system, it is necessary to use laboratory models to study it more precisely.

In the present study, laboratory flume model GUNT HM162 with a length of 12.5 m, width 0.31 m, and height 0.47 m with glass walls and the metal floor was used. A centrifugal pump with a flow rate of 165 m3/h and a height of 16 meters was used for the experiments. A wave generator with a frequency of 0.5 to 1.11 Hz was applied to create a wave in the laboratory flume. All experiments were performed at a constant flow depth of 200 mm. Three values were chosen for the distance of the OWC device from the water surface in the normal state (d), according to the chamber length (B). Therefore, distances of 10%, 25%, and 45% of the OWC chamber length were used as parameter d. To investigate the effect of back wall height (Z) on OWC efficiency, three physical models were made in three modes without back wall and with 5 and 10 cm back wall. In this research, the power generated by the wave inside the device was performed to evaluate the performance of the OWC. In addition, a two-way analysis of variance test was used to investigate the effect of independent parameters such as back wall height, the depth of the system, and the frequency of waves on the output power to determine the main and interaction effects.

The results show that with increasing the installation depth of the system, initially the amount of output power increased but then had a decreasing trend. Accordingly, the depth with the best performance must be considered for OWC. In this study, it was found that 0.25 B (chamber length) installation depth has better performance than the other two cases. Comparison of the effect of the back wall on the performance of the device at a depth of 0.25B shows that the models with the back wall have better performance comparing with the model without a back wall. The performance of the two back walls at frequencies less than 0.8 is similar, but for higher frequencies, the 10 cm back wall has better performance than another back wall. All the main effects have a significant influence on the output power, which the frequency of the waves and the height of the back wall have a higher effect. The results related to the interaction effects of independent parameters show that the interaction effects have a high influences on the amount of output power. Among the interaction effects, (Z × d) and (Z × Frequency) have a significant effect on the output power, which indicates the effect of the back wall on the total power. The results of the margin averages show that at the maximum frequency used, the 5 and 10 cm back wall were increased the efficiency of the OWC by 98% and 182%, respectively, compared to the model without a back wall.

Based on the result of the experiments, the presence of the back wall has a high effect on the OWC output power so that in the best installation depth (d =0.25B) and frequency of 1.1 Hz, the 5 and 10 cm back wall, increases the output power by 1.18 and 1.83, respectively. Two-way analysis of variance was used to investigate the effect of different parameters on OWC efficiency. The result of two-way ANOVA shows that the frequency of the waves and the back wall had the greatest effect on the output power. On the other hand, the interaction of the back wall with the frequency and installation depth also had a significant difference at the level of 0.01. The performance of the two back walls used at low frequencies was similar, but for the higher frequencies, the 10 cm back wall performed better. Accordingly, it can be concluded that the presence of a larger back wall cannot produce more power in all frequencies.

Understanding hydrological phenomena is essential for the optimal use of water resources. Surface runoff is an important part of the hydrological cycle. Accurate runoff estimation can make a significant role in water engineering and the proper utilization of resources for the various uses of agriculture, drinking, hydropower and the environment. Therefore, the use and development of accurate and reliable methods to model the runoff of the catchments are essential. One of the new methods of runoff calculations is cellular automata. Cellular automata is a fundamental method for simulating complex systems.

In cellular automata, the lattice space is divided into a number of cells and creates a cellular space (Fig2). A set of cells adjacent to the central cell is called a neighborhood (Fig1). In the runoff production process, the cell state is the water level, which is the sum of the cell height and water depth. The height of the cell is determined from the digital elevation model and the determination of water depth is controlled by the effective precipitation at the present time step and the balance between inlet and outlet flow at the last time step. The transition rules in the cellular automata model determine the behavior of cells at different time steps and define the future state of the cell. The first transition rule determines which neighboring cell can get water from the central cell at each time step (Fig3). The second transition rule is used to calculate the amount of flow to neighboring cells, in which the Manning equation is used. The first and second transition rule applies to all cells at each time step and as a result, the output flow from each central cell to its neighbors is determined. In the general view, each central cell is a neighbor of other cells, as a result, a third rule must be used for calculating the total flow for each cell. The evaluation of the cellular automata model is performed using the statistical indicators of correlation coefficient and root mean square error and Nash-Sutcliffe efficiency coefficient.

First, the runoff is simulated on a uniform rectangular surface and the results of the cellular automata model are compared with the results of the Akan analytical solution. In order to evaluate the efficiency and accuracy of the cellular automata, the statistical parameters of the models were calculated. The results showed that the cellular automata model has high accuracy and efficiency (Fig 5). Then runoff in the Con catchment is simulated. This catchment is located in the northwest of Spain. (Fig 6). The results showed that the cellular automata model has been able to simulate runoff well in the catchment surface (Fig 7). At the outlet, the discharge is calculated based on the cellular automata and compared with the observed discharge. The results of the cellular automata model are shown with three different time steps (Fig 8). So far, various mathematical models for rainfall-runoff estimation have been proposed. In integrated models, the whole catchment is considered as a unit. These models have a simple structure and appropriate computation time, but are accompanied by many assumptions and the spatial distribution of variables is not considered. Therefore, integrated models are not suitable for large catchments. In semi-distributed models, the catchment is divided into a number of sub-catchments. In these models, important features of the catchment are shown, but for each sub-basin, moderate data is considered and the exact spatial distribution of data is not considered. In distribution models, spatial distribution data is considered, but the time required for computation and modeling is high. Therefore, it seems necessary to develop methods that have a simple structure and high accuracy at the same time. Due to the accuracy of the results and the ability to access the required information anywhere in the catchment, the cellular automata model can be used to predict runoff.

The results showed that the cellular automata model has a high accuracy compared to the Akan analytical solution. Also, in simulating the runoff of the con catchment, the runoff network at the catchment surface was well simulated. Comparing the computational discharge results from the cellular automata model and observational data, the values of the correlation coefficient, mean the square root of error and Nash-Sutcliffe coefficient were 0.99, 0.11 and 0.97. As the result, due to the accuracy of the results and the ease of implementation, the cellular automation model can be used to predict runoff in catchment without data and reliable results can be achieved.