Numerical study of energy recovery from the wakes of moving vehicles on highways by using a vertical axis wind turbine

Tian, Wenlong; Mao, Zhaoyong; An, Xinyu; Zhang, Baoshou; Wen, Hao

doi:10.1016/j.energy.2017.07.172

Cited by 49 publications

(30 citation statements)

References 16 publications

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“…In a normal day, the wind velocity may not sufficient enough to bend to stems or trees, however, once a vehicle moves at high speed creates vortices and generates two distinct regions. A weak wind region at the downstream directly at the rear of the vehicle and other one is a strong wind region at the downstream side surface of the vehicles (Tian et al, 2017). This strong wind velocity may increase up to 30 m/s and produce significant amount energy with the rotation of the rotor blade.…”

Section: Tested In Wind Tunnelmentioning

confidence: 99%

A Conceptual Design of Energy Harvest from Roadside Trees of a Highway

Ahmed

Minar

Rana

et al. 2019

EUROPEAN J SUSTAINAB DEV

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An experimental setup is constructed to harvest energy from wind by means of oscillating of a body, structure or even trees. A vertical stem which can oscillate or bend is connected with a rack and pinion and finally pinion is connected with generator to harvest the electrical energy. Once the force is applied on the top of the stem, the connecting wire pulls the rack and pinion is in rotary motion and generates the power. The experiments are carried out for both applied static force and wind assist dynamic force. The experimental results show that the forces exerted on the stem are very much influenced by the stem height, the rack length for power generation. The results from wind assist experiments reveals that the sail size and the materials of both stem and sails play vital role on power generation. The constructed setup shows a good prospect on power generation from wind specially the places like highways, urban areas or any other places where the tree or any other object or structure oscillate.

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Section: Tested In Wind Tunnelmentioning

confidence: 99%

A Conceptual Design of Energy Harvest from Roadside Trees of a Highway

Ahmed

Minar

Rana

et al. 2019

EUROPEAN J SUSTAINAB DEV

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“…As shown in Figure 3, it can be seen from the figure that the center of UMP is placed at a distance from the left boundary of the computational domain to 15 and on the symmetry axis of the of the top and bottom boundary. The overall domain is split into seven subdomains, including two external static domains, two dynamic domains for updating the grid with layering method [22,23], two inner stationary domains 5 and 6, and an oscillating domain. This inner stationary domain 7 contains the cells around the UMP.…”

Section: Computational Domain and Boundary Conditionsmentioning

confidence: 99%

Structure Optimization of a Vibration Suppression Device for Underwater Moored Platforms Using CFD and Neural Network

Mao

Zhao

2017

Complexity

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We only consider the underwater mooring platform (UMP) and the plate moving in the transverse direction, and the plate can be relative to the UMP free rotation. In the case of constant flow rate ( = 1 m/s), the effect of different dimensionless plate length ( / ) and damping value ( ) on the UMP was studied. We get the sample data point set by computational fluid dynamics (CFD) simulation with changing the dimensionless plate length ( / = 0.3, 0.5, 0.75, 1.0, 1.25, 1.5) and damping value ( = 50, 75, 100, 125, 175, 250, 300 (N × s/m)). The optimal value of the vibration suppression rate is obtained by backpropagation (BP) neural network and genetic algorithm. The optimal vibration suppression rate is = 0.9878 and the corresponding variable value is / = 1.0342, = 57.9631 (N × s/m). In order to verify the accuracy of the optimization, we perform the CFD numerical simulation with the optimized parameters and compare the theoretical optimization results with the CFD simulation result. The absolute error between CFD simulation and optimal is only 0.0037. Finally, we compare the results of CFD simulation based on optimal parameter with the bare UMP and analyze their dimensionless amplitude, wake structure, and lift coefficient. It is shown that BP neural network and generic algorithm are effective.

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“…The SST k-turbulence model is able to model the transport of turbulent shear stress and gives accurate predictions on the onset and amount of flow separation under adverse pressure gradients and has been successfully used in the CFD simulation of complex flows [11,[19][20][21][22].…”

Section: Governing Equationsmentioning

confidence: 99%

Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

et al. 2017

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This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a backpropagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

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Numerical study of energy recovery from the wakes of moving vehicles on highways by using a vertical axis wind turbine

Cited by 49 publications

References 16 publications

A Conceptual Design of Energy Harvest from Roadside Trees of a Highway

A Conceptual Design of Energy Harvest from Roadside Trees of a Highway

Structure Optimization of a Vibration Suppression Device for Underwater Moored Platforms Using CFD and Neural Network

Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

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