Hasil untuk "Hydraulic engineering"

Oodi Shahin, Khoshkonesh Alireza, Okhravi Saeid et al.

Catastrophic floods triggered by a dam-break pose significant hazards to infrastructure due to transient flows and concentrated structural loads. These hazards are intensified where in-channel structures or variable topography redirect momentum and magnify impacts on downstream infrastructures. This study aimed to numerically predict the dam-break transient flow characteristics around a bed depression, as a specific topographic condition, in the downstream channel. The numerical model was optimized and validated against experimental results reported in the literature. Subsequently, the effects of bed depression dimensions and location along the channel on transient flow were examined in twelve cases. The validation results demonstrated that the model accurately reproduced the evolution of the free surface, flow velocity, formation of the impact jet, and impact loads. The bed depression has increased plunge-pool dissipation and reduced the peak force to 16.6–18.6 N. Accordingly, the supercritical flow was characterized by a maximum Froude number (Fr) of approximately 5.4 around this depression. Increasing the depression distance attenuated the wave front and decreased the post-depression flow depth from 0.16 m to 0.03 m within approximately 2 seconds. Overall, the framework captured sharp interfaces and transient regime shifts, enabling the prediction of jetting, nappe stabilization and impact-load envelopes. The study has implications for evaluating in-channel structures and the effects of channel topography on rapid flood hazard screening and emergency planning.

Rohit Dhakate, Christian Brommer, Christoph Böhm et al.

This article presents an entirely data-driven approach for autonomous control of redundant manipulators with hydraulic actuation. The approach only requires minimal system information, which is inherited from a simulation model. The non-linear hydraulic actuation dynamics are modeled using actuator networks from the data gathered during the manual operation of the manipulator to effectively emulate the real system in a simulation environment. A neural network control policy for autonomous control, based on end-effector (EE) position tracking is then learned using Reinforcement Learning (RL) with Ornstein-Uhlenbeck process noise (OUNoise) for efficient exploration. The RL agent also receives feedback based on supervised learning of the forward kinematics which facilitates selecting the best suitable action from exploration. The control policy directly provides the joint variables as outputs based on provided target EE position while taking into account the system dynamics. The joint variables are then mapped to the hydraulic valve commands, which are then fed to the system without further modifications. The proposed approach is implemented on a scaled hydraulic forwarder crane with three revolute and one prismatic joint to track the desired position of the EE in 3-Dimensional (3D) space. With the emulated dynamics and extensive learning in simulation, the results demonstrate the feasibility of deploying the learned controller directly on the real system.

YANG Wenqi, WANG Liying, ZHANG Jiale et al.

In order to study the transient flow field characteristics of large centrifugal pumps with adjustable guide vanes during startup and shutdown, this paper numerically simulated the transient flow field of the pump under startup and shutdown conditions based on the overlapping grid method and using the SST k-ω model. The results show that during the startup and shutdown of the pump, there are a large number of vortices in the guide vane area when the adjustable guide vane opening is small, at which time the hydraulic loss is large, and the pressure difference before and after the adjustable guide vane is great. During the startup process, as the guide vane opening increases, the vortex bands at the outlet of the suction chamber and the inlet of the runner gradually disappear, and the hydraulic loss in the guide vane area decreases; the pressure after the adjustable guide vane increases, and the flow state in the adjustable guide vane area stabilizes earlier than that in the fixed guide vane area. During the shutdown process, as the adjustable guide vane closes, the flow state in the fixed guide vane area becomes disordered earlier than that in the adjustable guide vane area. When the guide vane opening is small, a large number of vortices appear in the guide vane area, at which time the hydraulic loss is large, and the pressure after the adjustable guide vane decreases; vortices begin to appear at the outlet of the suction chamber and the inlet of the runner. During the startup and shutdown, the pressure at the inlet of the runner is relatively low, and the pressure gradually increases along the flow direction from the inlet to the outlet of the runner. During the startup process, when the guide vane opening is less than 14°, the flow state in the guide vane area is disordered, and the head of the unit fluctuates sharply. Due to the lag effect of the water flow, during the shutdown process, the flow state in the guide vane area becomes disordered, and the head of the unit begins to fluctuate sharply only after the guide vane opening is less than 12°.

LUO Gang, XIAO Xiao, WU Di, JISHA Ri-fu, LU Jun

[Objective] This study aims to investigate the dramatic changes in water-sediment processes within the Jinsha River reservoir area following the impoundment and operation of the Wudongde and Baihetan cascade hydropower stations. Using multi-source observational data, the study reveals the variation patterns of water and sediment fluxes between the two dams, the spatiotemporal distribution characteristics of riverbed erosion and deposition, and their driving mechanisms. The findings provide scientific support for reservoir safety operation, navigation channel management, and ecological conservation. [Methods] The study was conducted using runoff-sediment transport data from 2015 to 2023 at the Wudongde and Baihetan hydrological stations, fixed cross-sectional topographic surveys from 2016 to 2023, and hydrodynamic measurements collected downstream of the Wudongde Dam in 2023. Water-sediment relationship analysis was employed to examine the response patterns between runoff and sediment transport. Erosion and deposition volumes were calculated using the cross-sectional method, with 825 m water level as the reference and the channel storage volume estimated via the frustum formula. Spatial variations of erosion and deposition were quantified by overlaying thalweg line and comparing morphological changes of typical cross-sections (JC199, JC153, JC126). [Results] 1) Water-sediment flux variations: Annual runoff exhibited a slight decrease, 2% at the Wudongde station and 17.8% at the Baihetan station. Annual sediment transport plummeted by more than 90%, primarily due to the “cumulative sediment retention effect” of upstream reservoirs. Intra-annual runoff distribution demonstrated a “peak-shaving and valley-filling” pattern, with a 22%-48% increase in December and a 16%-38% decrease in July. Sediment transport was concentrated from June to October (accounting for over 63%), yet monthly averages dropped by more than 95%. A progressive downstream sedimentation trend was observed in September. 2) Spatiotemporal evolution of erosion and deposition: erosion dominated during dry season (October-May), while deposition dominated the wet season (May-October). From 2021 to 2023, a net deposition volume reached 12.63 million m3, showing an overall cumulative trend. Spatially, a strong erosion zone formed at the reservoir tail driven by the high-kinetic-energy discharges from the Wudongde Dam. The core deposition area in the main reservoir was found 25-75 km upstream of the dam. In the tributary-affected zone, the Heishui River confluence showed prominent deposition. 3) Driving mechanisms of erosion and deposition: In terms of hydrodynamic forces, erosion was triggered by high flow velocities and strong sediment-carrying capacities within 20 km downstream of the Wudongde Dam, while beyond this zone, deposition was promoted by slower flows and weaker sediment-carrying capacities. Regarding tributary replenishment, tributaries such as the Pudu River, Xiaojiang River, and Heishui River contributed an average annual sediment transport of 5.73 million tons (2011-2022), accounting for over 46% of the deposition volume in the reservoir area. [Conclusions] The operation of cascade hydropower stations has restructured the water-sediment process. Although the runoff volume decreased slightly, its intra-annual redistribution was significant, and the sediment transport plummeted by 96% due to the “cumulative sediment retention effect”, with sediment being concentrated in flood season. The erosion and deposition in the reservoir area exhibit a spatial pattern of “erosion at the tail and deposition in the head”. The reservoir tail is eroded by the discharged flow, while the main reservoir experiences deposition due to reduced flow velocity and tributary replenishment, with the confluence of the Heishui River being a key source of deposition. A clear long-term deposition trend is observed, and it is necessary to focus on the high-risk deposition zone 25-75 km upstream of the dam and the sections with drastic morphological changes at tributary estuaries. These findings provide a quantitative basis for the joint operation of cascade reservoirs, navigation channel maintenance, and sediment management.

B. Barboza, Bin Chen, Chenfeng Li

Abstract Proppant transport is a critical physical process in hydraulic fracturing which has been extensively used for reservoir stimulation in petroleum engineering. Proppants injected together with fracturing fluid provide structural support to the stimulated fracture network and prevent them from closing after flowback. Hence, the final proppant distribution in fracture networks affects directly the effectiveness of hydraulic fracturing. Owing to the limitation and high cost of well logging, computational modelling has been increasingly used to study proppant transport, where different assumptions and numerical models have been employed often without rigorous validation or justification. This work presents a comprehensive review on proppant transport modelling, from relevant physics to numerical approaches, aiming to provide an unbiased global picture of the-state-of-the-art studies, inspire new insights, and promote the development of innovative and reliable computational models for proppant transport.

F. Gong, Xuefeng Si, Xi-bing Li et al.

Qi Wang, Zhenhua Jiang, B. Jiang et al.

Abstract The excavation of deep mining roadways with traditional mining methods leads to various issues, such as the occurrence of large deformations, the waste of coal resources, complex conditions and the high cost of advanced support measures. In this study, we examined the roadway damage mechanism of the traditional gob-side entry driving method with coal pillars using the Suncun Coal Mine, the deepest mine in China, as an engineering case study. A method for the automatic formation of roadways in deep mining areas by roof cutting with high-strength bolt-grouting was proposed, and several quantitative evaluation indicators, namely, the roadway deformation control rate, anchor cable strength utilization ratio, and hydraulic prop strength utilization ratio, were established. The impacts of controlling the deformation of the surrounding rock were comparatively analysed in consideration of numerous factors, such as the mechanical parameters of the surrounding rock, roof cutting height, roof cutting angle, grouting anchor cable length, and hydraulic prop spacing. On this basis, the proposed method was applied in the field. The results showed that the automatically formed roadway cross-section exhibited decreased convergence deformation; moreover, the roof deformation was effectively controlled, and the passive hydraulic prop supports could be effectively replaced with the active support of grouting anchor cables, verifying the effect of the proposed method.

B. Amadei, O. Stephansson

Zhiyu Zhang, Chenkaixiang Lu, Wenchong Tian et al.

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks, which regards recent hydraulic states as initial conditions, and future runoff and control policy as boundary conditions. To incorporate hydraulic constraints and physical relationships into drainage modelling, physics-guided mechanisms are designed on top of the surrogate model to restrict the prediction variables with flow balance and flooding occurrence constraints. According to case results in a stormwater network, the GNN-based model is more cost-effective with better hydraulic prediction accuracy than the NN-based model after equal training epochs, and the designed mechanisms further limit prediction errors with interpretable domain knowledge. As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling. Simultaneously, the surrogate model accelerates the predictive modelling of urban drainage networks for real-time use compared with the physics-based model.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

Jian Du, Haochong Li, Qi Liao et al.

The high-pressure transportation process of pipeline necessitates an accurate hydraulic transient simulation tool to prevent slack line flow and over-pressure, which can endanger pipeline operations. However, current numerical solution methods often face difficulties in balancing computational efficiency and accuracy. Additionally, few studies attempt to reform physics-informed learning architecture for pipeline transient simulation with magnitude different in outputs and imbalanced gradient in loss function. To address these challenges, a Knowledge-Inspired Hierarchical Physics-Informed Neural Network is proposed for hydraulic transient simulation of multi-product pipelines. The proposed model integrates governing equations, boundary conditions, and initial conditions into the training process to ensure consistency with physical laws. Furthermore, magnitude conversion of outputs and equivalent conversion of governing equations are implemented to enhance the training performance of the neural network. To further address the imbalanced gradient of multiple loss terms with fixed weights, a hierarchical training strategy is designed. Numerical simulations demonstrate that the proposed model outperforms state-of-the-art models and can still produce accurate simulation results under complex hydraulic transient conditions, with mean absolute percentage errors reduced by 87.8\% and 92.7 \% in pressure prediction. Thus, the proposed model can conduct accurate and effective hydraulic transient analysis, ensuring the safe operation of pipelines.

F. G. Acién Fernándéz, Cintia Gómez-Serrano, J. M. Fernández-Sevilla

In this chapter the relevance of microalgae-based processes for the recovery of nutrients contained in wastewaters is reviewed. The fundamentals of the process are discussed from the biological and engineering standpoints and it is shown that on this type of processes the nutrient recovery capacity is mainly a function of solar radiation availability. If adequately designed and operated up to 450 tCO2, 25 tN and 2.5 tP per hectare and per year can be fixed, producing up to 200 t/year of valuable microalgae biomass. The utilization of microalgae-based processes reduces to half the energy consumption of conventional wastewater treatment and allows recovering up to 90% of the nutrients contained into wastewater. Still the technology available (photobioreactors, harvesting, downstream) must be improved to reduce the land requirement and the hydraulic retention time, but current technology is ready to be demonstrated at large scale, so that the first initial facilities based on this technology have been recently developed. Moreover, this technology must be adapted to the different wastewater types, from sewage to manure. The major advantage of microalgae-based processes is the production of large amounts of valuable biomass, useful for the production of biofuels but much more interesting for animal feeding and agriculture uses, thus enhancing the productivity and sustainability of foods production.

Xingdong Lv, Yuqiang Lin, Xia Chen et al.

Abstract This paper discusses the utilization of iron ore tailing aggregates (IOTA) as a replacement for normal aggregates (NA) in dam concrete for hydraulic engineering applications. The durability performance of concrete prepared using IOTA and NA, including resistance to chloride-ion permeability, sulfate corrosion resistance, and abrasion-erosion resistance, are investigated in this study. Based on it, the interfacial transition zone (ITZ) characteristics of IOTA and NA samples are examined by scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and microscopic nanoindentation. Besides, the economic and environmental evaluations of the IOTA utilization in real hydraulic engineering are proposed. The results show the concrete prepared with IOTA has more superior durability than NA. The abrasion resistance of concrete prepared with IOTA is higher than that of the NA sample by 1.32–2.90 h m2/kg. Since the IOTA raw rock has higher compressive strength, lower saturated-surface-dry (SSD) water absorption, and angular shape, the concrete prepared with IOTA has more superior ITZ characteristics and durability performance than NA. Significant economic and environmental benefits of the utilization of IOTA in hydraulic engineering are obtained. The investments into the IOTA utilization in two practical hydraulic engineering applications can save 8.98 × 106 USD. It can also reduce the occupied land space, CO2 emission, and water consumption by 151 900 m2, 8157.0 t, and 942 000 m3. The relative reductions of CO2 emission and water consumption, as compared to NA, are 15.7 and 27.4%.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Max Rudolf Muskananfola, Nurul Latifah, Agus Hartoko et al.

The CO2 characteristics of the Semarang Bay system on the Northern coast of Java were investigated to understand the roles in carbon dioxide flux. The flux of carbon dioxide at the sea surface between seawater and atmosphere is essential for mitigating atmospheric CO2.  The aims of this study were to assess the variation of CO2 partial pressure seawater (pCO2sea) and CO2 flux and to analyse the water quality variable which has the larger influence on CO2 flux in the waters of Semarang Bay. The variables in this study were temperature, pH, salinity, and DO. Different methods were adopted in the analysis of water samples: spectrophotometric to measure chlorophyll-a and phosphate and titration method to measure Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC). A CO2 meter was used to measure the CO2atm. The lowest CO2 flux value was (1.86 mmol CO2 m-2 day-1) during the morning period while the pCO2sea reached 461.04 μatm and CO2 flux 83.79 mmol CO2 m-2 day-1 at night. At noon time, with increased temperature, pCO2sea was 461.04 μatm, and CO2 flux was 83.79 mmol CO2 m-2 day-1. The high concentration of chlorophyll-a ranged between 3.55–4.11 mg.L-1.  This chlorophyll-a concentration has a negative correlation with CO2 flux, and it was found that TA and DIC concentrations have no relationship with CO2 flux. Based on PCA analysis, it was found that the variability of CO2 flux in Semarang Bay is influenced by the variability of pCO2sea, sea surface temperature and kwa (affected by wind speed).

Tao Chen, Miqing Li

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

A. Möri, B. Lecampion

Hydraulic fractures propagating at depth are subjected to buoyant forces caused by the density contrast between fluid and solid. This paper is concerned with the analysis of the transition from an initially radial towards an elongated buoyant growth -- a critical topic for understanding the extent of vertical hydraulic fractures in the upper Earth crust. Using fully coupled numerical simulations and scaling arguments, we show that a single dimensionless number governs buoyant hydraulic fracture growth: the dimensionless viscosity of a radial hydraulic fracture at the time when buoyancy becomes of order one. It quantifies if the transition to buoyancy occurs when the growth of the radial hydraulic fracture is either still in the regime dominated by viscous flow dissipation or is already in the regime where fracture energy dissipation dominates. A family of fracture shapes emerge at late time from finger-like (toughness regime) to inverted elongated cudgel-like (viscous regime). 3D toughness dominated buoyant fractures exhibit a finger-like shape with a constant volume toughness dominated head and a viscous tail having a constant uniform horizontal breadth: there is no further horizontal growth past the onset of buoyancy. However, if the transition to buoyancy occurs while in the viscosity dominated regime, both vertical and horizontal growths continue to match scaling arguments. As soon as the fracture toughness is not strictly zero, horizontal growth stops when the dimensionless horizontal toughness becomes of order one. The horizontal breadth follows the predicted scaling.

J. Aberle, J. Järvelä

Feras A. Batarseh, Rasika Mohod, Abhinav Kumar et al.

The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and maintenance), it becomes clear that multiple AI paradigms (such as neural networks, machine learning, knowledge-based systems, natural language processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. This survey chapter is a review of the most commonplace methods of AI applied to SE. The review covers methods between years 1975-2017, for the requirements phase, 46 major AI-driven methods are found, 19 for design, 15 for development, 68 for testing, and 15 for release and maintenance. Furthermore, the purpose of this chapter is threefold; firstly, to answer the following questions: is there sufficient intelligence in the SE lifecycle? What does applying AI to SE entail? Secondly, to measure, formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly, this chapter aims to provide serious questions to challenging the current conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call for action, and to redefine the path forward.

Alexander Temnykh, Ivan Temnykh

In the compact variable-gap undulator with Hydraulic-Assist Driver, miniature hydraulic cylinders compensate for ~90% of magnetic forces and mechanical actuators are used to accurately control the undulator gap. This method of hydraulic assist enables design simplicity and compactness of the undulator. Proof-of-concept experiments and test results of the undulator prototype in step-by-step motion mode indicated excellent accuracy and stability properties of the driver. In this note we present test results of hydraulic-assist driver in continuous motion mode. Results indicate that hydraulic-assist driver can provide smooth continuous undulator gap change, keeping the undulator harmonic energy variation within the required 1eV/sec, and speed and RMS normalized energy errors at 1.8e-4 level (or less). That satisfies any application planned at CHESS.