The hydrodynamic behavior of approaching Flow and the amount of sediment entry into right-angled lateral intakes in diversion dams were investigated using a laboratory channel by experimental measurement and observation. The velocity field upstream of the intake in the main channel and before it at the sluice gate was measured at different elevations of Flow at different discharge rates of the river, intake and sluice gate. Velocity profiles were also measured from upstream to downstream for the intake and the amount of sediment entry into it. Analysis of the velocity data showed that discharge at the sluice gate strongly affected velocity profiles and the mechanism of sediment entry. All velocity profiles where the sluice was closed had an inflection point, where the Flow direction changed, leading to a return velocity near the bed. The elevation of this point was a function of the intake discharge and approximately equal to the height of the entrance sill. Observation showed that sediment entered the intake in whirlpool vortices. The strength and frequency of the vortices depended on the intake and sluice gate discharges. Analysis of sedimentary data showed that the amount of sediment entering the intake increases with an increase in intake discharge. In addition, for any given intake discharge rate, an increase in the sluice gate discharge caused an increase in sediment entry into the intake.

In case of poor-quality oil refining in the oil pipeline, water accumulations are formed, increasing hydraulic losses during transportation and contributing to corrosion processes. Hydrodynamic cleaning, which uses pumped oil Flow, has been investigated due to its cost-effectiveness and adaptability for pipelines of varying diameters. This study develops a finite element hydrodynamic model to simulate the removal of water accumulations from inclined pipelines (inclination angle α = 45°). The model reveals a clear relationship between inlet velocity and multiphase Flow patterns, demonstrating transitions from stratified Flow (ST) at velocities below 0.1 m/s, to stratified with mixing (ST&MI) at 0.1–0.2 m/s, and finally to a dispersed water-in-oil (DW/O) pattern beyond 0.2 m/s. These velocity transitions are achieved in controlled steps: a steady increase to 0.1 m/s within 20 seconds, followed by acceleration phases reaching 0.25 m/s by 100 seconds. The DW/O regime exhibits the highest cleaning efficiency, reducing water volume from 660 ml to 273.29 ml over 125 seconds—a 58.5% reduction. The analysis further shows an initial rise in pressure gradient within the ST regime, peaking during the first plateau (0.1 m/s) before stabilizing and significantly declining in the DW/O regime at velocities exceeding 0.25 m/s. These findings emphasize the importance of optimizing Flow velocity to achieve effective water removal while minimizing hydraulic losses. The study also highlights limitations in existing experimental setups, which predominantly use small diameters (<50 mm), and underscores the need for larger-scale experiments to validate these findings in real-world pipeline operations.

ABSTRACT Despite a wide range of components and criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan in Tehran, Iran, as the old, conventional, and new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered that factors such as car ownership, dependence and pro-liking for private cars, density and access to educational centers and parks, access to medical and service centers, and variety and density of retail stores had been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, and pro-liking for other than a private car, having a license, number of children under five years old, and age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding factors affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including land use, along with demographic-economic characteristics, travel behavior, and attitudes of people, has been raised by urban planners. Studies seek to find factors affecting travel behavior, especially land use characteristics. Despite presenting a wide range of components and criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical and non-physical characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical and experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, and walking as the travel behavior. According to the research's purpose, indicators and criteria affecting travel behavior were extracted after reviewing the theoretical and experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan as the old, conventional, and new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of land use, travel habits, and access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered which factors affecting travel behavior were due to the differences in study areas and which factors affect travel behavior regardless of development patterns.   Results and discussion This research aims to identify the factors affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, and pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of factors affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence and pro-liking for personal cars, variety and density of retail stores, density and access to educational units and parks, access to medical and service centers, and car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical and non-physical factors (the same indicators in the same study areas) with travel behavior, the factors affecting travel behavior owing to different development patterns were identified. Factors such as car ownership, dependence and pro-liking for private cars, density and access to educational units and parks, access to medical and service centers, and variety and density of retail stores have been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, and pro-liking for other than a private car, having a certificate, number of children under five years old, and age have influenced on travel behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the factors affecting travel behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the factors affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior and finding the main components affecting it, considering the extent of variables and data, can pave the way for professionals in transportation planning and urban planning, in addition to providing detailed methods and criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Flooding of rivers is accompanied with a threat on the population living on their floodplains and on the neighboring settlements. Accurate modeling of such Flows is thus imperative to assess flood risks, perform real-time flood routing, or estimate the impact of a mitigation schema. The converging of the compound channel causing the Flow become even more complicated. The Flow patterns in converging compound channel and free-surface profile has been simulated by using RSM turbulence model and VOF method, respectively. The comparison of the experimental results including longitudinal free surface profiles, depthaveraged velocity distribution and the ratio between floodplains and total discharge confirmed that the numerical simulation can be used to model the Flow pattern in converging compound channel. Furthermore, Absolute Percentage Error (APE) for each of these parameters was amounted to 3. 25%, 4. 66% and 9. 72%, respectively. Respectto the numerical simulation capability in anticipating the Flow field parameters, we investigated the effect of relative depth on the Flow patterns in a converging compound channel. Moreover, the Flow parameters including velocity distribution, depth-average velocity, secondary Flows, ratio between floodplain and total discharge, bed sheer stress and energy dissipation were investigated in different relative depths h* (0. 1, 0. 2, 0. 3, 0. 4 and 0. 5). By evaluating and comparing the Flow results in the different relative depths, we came to conclusion that the longitudinal-average velocity in the main channel increased as the cross section was narrowed. However, the longitudinal-average velocity in the floodplains decreased in the relative depths of 0. 1 and 0. 2 as the cross section was narrowed. In contrast, this parameter increased in the relative depths of 0. 3, 0. 4 as well as 0. 5 the floodplains narrowed. Velocity gradient between the main channel and floodplains in the relative depth of 0. 1 was strong and in the relative depth of 0. 5 was insignificant. In the smaller shallow depth, this velocity gradient has been resulted in secondary Flow in the cross section of converging compound channel. Accordingly, in the relative depth of 0. 1 and 0. 2 four cells of secondary Flows were formed and in the relative depth of 0. 3 just two cells of secondary Flowswere formed. The secondary Flows in the relative depths of 0. 4 and 0. 5 was eliminated. Convergence in the length of main channel get the discharge conveyance copacity of floodpland to decrease. furthemore by decreasing the relative depth the capability of floodplains to conveance the discharge was significantly dicreasead. This decrease was evident in the depth of 0. 1 in which the ratio between floodplain and total discharge was amounted to 2. 52%. Sheer stress in channel bed increased when the relative depth of the main channel increased and the maximum amount of bed shear stress was happened at end of the channel. On the other hand, in the floodplains, this parameter decreased along with the converging in the lower relative depths (0. 1, 0. 2) and for the other relative depths the bed sheer stress increased along with converging. In channel inlet the maximum and minimum amount of energy dissipation was resulted at the relative depth of 0. 1 and 0. 5 repectively.

Two-phase Flow happens widely in the industrial plants and certain equipment. This paper attempts to study the characteristics of two-phase Flow in a vertical piping system. This was achieved by comparing the void fraction, in the working fluid, by employing Constant Electric Current Method (CECM) with the actual observation using high-speed camera. The experiment requires a complete set of two-phase Flow system information and was conducted based on various Flow conditions. In order to carry out this experiment, the two-phase Flow loop was constructed using a specific experimental apparatus and components. The Flow channels were constructed using three pipes with three different inner diameters of 21. 0 mm, 47. 0 mm and 95. 0 mm. The Flow direction was vertical upward co-current Flow with liquid superficial velocity range of 0. 025 m/s to 3. 0 m/s and gas superficial velocity range of 0. 025 m/s to 3. 0 m/s, depending on the size of the pipe. The Flow pattern investigation focuses on experimental work, which was based on systematic observation and measurements using a high-speed camera and some measuring apparatus. The void fraction measurement using the CECM sensor was integrated into two-phase Flow system with constant electric current running in the pipe and data acquisition system controlled virtually via LabVIEW software. Both result of the Flow pattern and void fraction graph were then compared to determine the type of Flow pattern from the void fraction graph. Information from the previous studies and experiments were collected and the assumption of any theoretical simplifications were used as a reference. According to the result, the Flow pattern in pipe can be easily determined using CECM.

Spurs are hydraulic structures used to protect riverbanks from erosion and to concentrate the Flow on the river axis. As the Flow approach the spurs, discontinuous in the streamlines are occurred and vortices form before the spurs. By placing the spurs in an open channel, scours occur before and after the spurs due to changes in Flow conditions and velocities. Determination of velocity distributions around the spur accurately is extremely important for spur design. For a sustainable and hydraulically efficient spur design, Flow velocities, Flow conditions, sediment characteristics and stream morphology around the spur should be well investigated. In this study, the changing Flow velocities and Flow patterns on the upstream side of two spurs with were investigated. Two spur which have same geometry placed in an open channel system at an angle of 90o with the side wall. For 2 different discharge values (Q=17.66 lt/sn and Q=15.27 lt/sn), 3-Dimensional velocity measurements were made with ADV (Acoustic Doppler Velocimetry) at 20 different points on the upstream side of the spurs. Velocity values obtained from the experiments were compared with the numerical models and gave consistent results with the numerical models. With these obtained velocity profiles, Flow patterns are investigated.