Karkheh Storage Dam Cutoff Wall Analysis and Design

Karkheh dam cut-off wall with an area of about 150000 m2 is the largest plastic concrete cut-off wall ever built in the world. The wall was designed on the basis of seepage and material behavior analysis. The design and construction of the wall was a great challenge in which in different stages, major design modifications were made based on existing construction facilities, updated geological conditions and cost optimization. The philosophy of “design as you go” was tried to be perfectly accomplished in this project. The cut-off wall connections to the dam body and other appurtenant structures such as power tunnels; diversion culvert and spillway were designed and constructed to provide a tight and deformable interface. In this paper, the analysis results are briefly described. The technical specification of execution and material is also summarized

Performance enhancement of a solar-driven DCMD system using an air-cooled condenser and oil: Experimental and machine learning investigations

Solar-driven direct contact membrane distillation systems (DCMD) are disadvantaged by low freshwater productivity and low gain-output-ratio (GOR). Consequently, this study aims to achieve two primary objectives: i) improving the solar DCMD performance, and ii) harnessing machine learning models for precise and straightforward modeling of the solar DCMD system. To achieve these goals, a novel solar DCMD system powered with oil-filled heat pipe evacuated tube collectors (HP-ETCs) and equipped with an air-cooled condenser was used for the first time. The system was evaluated under eight different scenarios covering both its energy and economic performances. The performance prediction of three different machine learning models including ANN, SVR and RF was assessed for the proposed system. The results showed that integrating an air-cooled condenser and oil-filled HP-ETCs into the solar DCMD system significantly improved the performance and reduced freshwater cost, resulting in: a 35.39–37 % increase in freshwater productivity; a 30.64–31.57 % enhancement in GOR; a 35–38 % rise in daily efficiency; and a 20 % decrease in freshwater cost. The results demonstrate that ANN and SVR have excellent performance for modeling the solar-driven DCMD system, achieving MAPEtest values of approximately 1 % and 4 % for predicting permeate flux and GOR, respectively

Experimental investigation of the angle effect of the cylindrical bridge group piers relative to the flow direction on the maximum scour depth of the piers

Bridges are one of the important structures in the field of land communication. With the construction of these structures in the river, a flow pattern with a three-dimensional structure is formed in the vicinity of its piers, and as a result of increasing the flow speed and the formation of horseshoe vortex and wake vortices, part of the sediments around the piers and foundation will be washed away, and if the sufficient depth of foundation is not taken into account, the destruction of the bridge will result especially during floods. Road or railway crossing over the rivers is limited to the particular reach of the rivers which is determined by the general direction of the road or railway. Moreover, the general direction of the road or railway determines the position of the bridge over the river. Selection of the bridge path angle relative to the river flow direction is very important.  Sometimes, due to the geographical conditions of the region and the general direction of the road or railway, the bridge crossing directly perpendicular to the flow direction is impossible. In this case, the bridge deck diagonally crosses over the river and the bridge group piers are angled relative to the flow direction. In such case, the distance between the piers, the flow direction relative to the piers and the piers submergence are very important parameters which affect the scour depth

Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset

Risk-Based Optimal Operation of Coordinated Natural Gas and Reconfigurable Electrical Networks with Integrated Energy Hubs

Abstract This paper elaborates on optimal scheduling of coordinated power and natural gas (NG) networks in the presence of interconnected energy hubs considering reconfiguration as a flexibility source. With regard to the energy hub system consisting of several generation units, storage and conversion technologies, as well as natural gas‐fired units, the high interdependency between gas and electricity carriers should be captured. The hourly reconfiguration capability is developed for the first time in a multi‐energy system to enhance the optimal power dispatch and gas consumption pattern. The realistic interdependency of electrical and NG grids is investigated by employing the steady‐state Weymouth equation and AC‐power flow model for power and gas networks, respectively. Furthermore, to handle the risk associated with strong uncertainty of wind power, load, and real‐time power price, the conditional value at risk approach is employed. The proposed model is implemented on the integrated test system and simulation results are presented for different cases. The impact of the risk aversion level on operating cost and optimal scheduling of controllable units is examined. Numerical results demonstrate that reconfigurable capability reduces the operational cost up to 7.82%

Designing a Robust Decentralized Energy Transactions Framework for Active Prosumers in Peer-to-Peer Local Electricity Markets

In this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution’s existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players’ private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method