A whole process model of an oscillating water column device based on CFD simulation and its efficiency prediction method

Abstract

The present invention discloses a full-process model of an oscillating water column device based on CFD simulation and an efficiency prediction method thereof, wherein the device comprises a numerical wave water tank and an air chamber arranged at one end of the numerical wave water tank, the side of the air chamber is connected to an air duct, a momentum source term region is arranged in the air duct, and the width of the momentum source term region is consistent with the blade width of the turbine in the device to be evaluated. The full-process model disclosed by the present invention can accurately introduce the damping effect of the air turbine into the air chamber, fully consider the dynamic coupling effect between the air turbine and the air chamber, can truly reflect the working state of the oscillating water column, and make a reasonable prediction of the full-process efficiency.

Description

Technical Field

[0001] The invention belongs to the field of development and utilization of wave energy, and particularly relates to a full-process model of an oscillating water column device based on CFD simulation and an efficiency prediction method thereof in the field. Background Art

[0002] Marine renewable energy such as wave energy is experiencing a period of rapid development. The oscillating water column wave energy power generation device is gradually becoming a hot spot for development and utilization due to its strong survivability and high conversion efficiency. The energy conversion of the device includes different stages. First, the waves drive the water column in the air chamber to oscillate up and down, thereby generating a reciprocating airflow above the air chamber, converting the wave energy into air kinetic energy; then the airflow drives the turbine in the air pipe to rotate, and the turbine is connected to the generator, thereby converting the air kinetic energy into shaft work and further converting it into electrical energy.

[0003] In traditional research methods, due to the limitation of research methods, orifice plate structures or porous media are usually used to replace the damping effect of air turbines. However, the relationship between the size of the substitute and the size and speed of the air turbine is completely ignored, and the mutual influence between the air chamber and the turbine during the operation of the device is not considered, resulting in the model being unable to accurately reflect the actual working state of the device, and a large error in efficiency prediction. In addition, most of the existing full-process models of oscillating water columns are based on Simulink simulation prediction or transient direct simulation. Although the Simulink model consumes less time for calculation, the result accuracy is poor; the transient direct simulation model has a high prediction accuracy for the efficiency of the device, but due to the large number of model grids and complex calculations, it often consumes huge computing resources and is inconvenient to use. Therefore, it is necessary to construct a full-process numerical model based on computational fluid dynamics (CFD) to improve the prediction accuracy of device performance under the condition of using less computing resources, and provide important guidance for the overall optimization of actual engineering devices, reasonable prediction of actual sea conditions working performance, and power generation output estimation. Summary of the invention

[0004] In order to solve the problems of poor accuracy in performance prediction of an oscillating water column device, inability to accurately reflect the working mechanism of the device, inability to provide accurate guidance for actual projects, insufficient accuracy of the existing full-process model, and excessive consumption of computing resources, the present invention provides a full-process model of an oscillating water column device based on CFD simulation and its efficiency prediction method.

[0005] The present invention adopts the following technical solution:

[0006] A full-process model of an oscillating water column device based on CFD simulation, the improvement of which lies in: comprising a numerical wave water tank and an air chamber arranged at one end of the numerical wave water tank, the side of the air chamber being connected to an air duct, a momentum source term area being arranged in the air duct, and the width of the momentum source term area being consistent with the blade width of the turbine in the device to be evaluated.

[0007] An efficiency prediction method is used to predict the above model, and its improvement lies in that it includes the following steps:

[0008] Step 1, using commercial software to establish the calculation area of ​​the whole process model of the oscillating water column device, constructing a momentum source term area in the middle of the air duct behind the air chamber, the width of which is consistent with the width of the turbine blade used in the required evaluation device;

[0009] Step 2: Divide the grid of the above model. The water tank and air chamber parts use hexahedral structured grids. The height and length of the grids are not more than 1 / 10 and 1 / 50 of the incident wave height and wavelength respectively. The grids are encrypted along the wave height near the free liquid surface. The grid encryption height is 1 / 20 of the wave height. The air duct part is created using tetrahedral unstructured grids. There is a transition from large to small grids from the two ends to the middle of the duct.

[0010] Step 3, setting boundary conditions for the above model, setting the wave-making boundary of the water tank to a stationary no-slip wall, setting the bottom and side walls of the water tank and the air chamber to stationary no-slip walls, setting the top of the water tank and the outlet of the air duct to the pressure outlet boundary (pressure out), and connecting the air chamber and the air duct with an interface (interface). After the setting is completed, export the model to a mesh file format (.msh);

[0011] Step 4, import the grid file into the software for solution setting, set the two-phase flow (VOF) to track the free liquid surface, and set the calculation water depth as needed. The k-epsilon RNG turbulence model is used for calculation and solution. The model uses the SIMPLE pressure-velocity coupling solution method. The momentum equation and turbulent kinetic energy both use the second-order upwind format. The user-defined function UDF is used to control the wave-making boundary of the water tank to generate the corresponding incident waves.

[0012] Step 5, setting the momentum source term region: The purpose of setting this region is to generate a certain pressure drop when the airflow passes through the region, so that the airflow velocity and pressure drop through the region present a linear relationship (Wells turbine) or a nonlinear relationship (impact turbine), thereby adding the damping effect of the real air turbine to the water tank calculation;

[0013] For this fluid region, the momentum source term S in the i direction i It is expressed as:

[0014]

[0015] Where μ is the dynamic viscosity, α is the permeability, v is the flow velocity, and v i is the flow velocity in the i direction, C2 is the inertial resistance coefficient, and ρ is the fluid density;

[0016] At the same time, the pressure drop Δp at the front and rear ends of this area is the momentum source term S i The product of the basin thickness Δn is:

[0017] Δp=-S i Δn (2)

[0018] The flow rate and pressure drop through this area are expressed as:

[0019] Δp=av 2 +bv (3)

[0020] For an air turbine with a certain speed, a and b are known parameters. Combining equations 1-3, we can obtain the permeability α and the inertial resistance coefficient C2.

[0021]

[0022] In the software, the permeability and inertial resistance coefficient can be customized through the porous zone, so as to accurately introduce the damping effect of the air turbine into the air chamber;

[0023] Step 6, set the corresponding physical parameter monitoring, including the free liquid level height in the air chamber, the pressure in the air chamber and the flow rate in the air duct, and set the calculation time step and calculation time, start the calculation, and after the calculation is completed, obtain the time series of the corresponding parameters. Since the torque output and flow rate of the air turbine also show a certain known relationship, the monitored air flow rate is brought into the torque-flow rate relationship to obtain the torque output result;

[0024] Step 7, substitute the result obtained in step 6 into the following formula: The incident wave power is: P W =0.5ρ w G JZ 2 C g L; air power is: The output power of the turbine is:

[0025] Among them, ρ w is the density of water, g is the acceleration due to gravity, A is the amplitude of the incident wave, C g is the wave speed, L is the width of the air chamber, T is the incident wave period, p(t) is the pressure in the air chamber, Q(t) is the flow rate in the air chamber, T0(t) is the turbine torque, and ω(t) is the turbine angular velocity;

[0026] The conversion efficiency η1 of the gas chamber is: η1=P P / P W ; The conversion efficiency of the air turbine η2 is: η2=P T / P P ; The energy conversion efficiency η of the entire process of the device is: η=η1η2.

[0027] Furthermore, the software used in step 1 is ANSYS-ICEM or Gambit.

[0028] Furthermore, the software used in step 4 and step 5 is ANSYS-Fluent.

[0029] The beneficial effects of the present invention are:

[0030] The full-process model disclosed in the present invention can accurately introduce the damping effect of the air turbine into the air chamber, fully consider the dynamic coupling effect between the air turbine and the air chamber, can truly reflect the working state of the oscillating water column, and make a reasonable prediction of the full-process efficiency.

[0031] The efficiency prediction method disclosed in the present invention has a relatively simple model grid structure. Compared with the transient direct simulation model, it can consume less computing resources while ensuring the prediction accuracy; and compared with the Simulink simulation model, the prediction accuracy is effectively improved. The whole process model is constructed based on CFD, which can be simulated at a prototype scale without being affected by the size of the device, effectively solving the problems of too small scale and inability to restore air compressibility in physical experiments. The CFD numerical simulation results can be post-processed to show the distribution of water and gas phases in the device and the flow field information in the air duct, which helps to further reveal the energy conversion mechanism of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the calculation area of ​​the whole process model;

[0033] Figure 2 It is a schematic diagram of the grid division and boundary condition setting of the calculation area;

[0034] FIG3(a) is a schematic diagram showing the relationship between the pressure drop at the front and rear ends of the turbine and the velocity of the air flowing through the turbine;

[0035] FIG3( b ) is a schematic diagram showing the relationship between turbine torque and air velocity;

[0036] Figure 4(a) is a time series of changes in the liquid level in the air chamber;

[0037] Figure 4(b) is the time series of the air pressure changes in the air chamber;

[0038] Figure 4(c) is the time series of turbine torque output. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] Example 1: This example discloses a method for predicting the efficiency of a whole-process model of an oscillating water column device based on CFD simulation, comprising the following steps:

[0041] Step 1: construct the calculation area of ​​the model in the commercial software Gambit, including the numerical wave tank, air chamber and air duct part, such as Figure 1 The water tank is 50 meters long, 1.2 meters high, and 0.8 meters wide, the air chamber is 0.6 meters long, and the width of the momentum source term area is consistent with the blade width of the turbine used in the device to be evaluated, which is 21.6 mm.

[0042] Step 2: Divide the computational model into grids, such as Figure 2 The water tank and air chamber parts use hexahedral structured grids, and the height and length of the grid are 1 / 10 and 1 / 50 of the incident wave height and wavelength respectively. The grid is encrypted along the wave height near the free liquid surface, and the encryption size is 1 / 20 of the wave height. The air duct part is created using tetrahedral unstructured grids, and the grid has a transition from large to small from the two ends to the middle of the duct.

[0043] Step 3, boundary condition setting: the bottom and side walls of the wave tank and the air chamber are set as stationary non-slip walls; the top of the wave tank and the outlet of the air duct are set as pressure outlet boundaries; the air chamber and the air duct are connected by an interface, such as Figure 2 After the settings are completed, export the model to a mesh file format.

[0044] Step 4: Import the grid file into ANSYS-Fluent software for solution setting. First, set the two-phase flow, i.e., gas phase and water phase, and set the water depth to 0.75 meters; select the k-epsilon RNG model as the turbulence model; select the SIMPLE method for pressure-velocity coupling solution, and use the second-order upwind format for the momentum equation and turbulent kinetic energy; import the user-defined function to control the wave-making boundary to generate incident waves.

[0045] Step 5, set the momentum source term area. First, determine the relationship between the pressure drop Δp at the front and rear ends of the turbine and the flow velocity v, and the relationship between the torque output T0 and the flow velocity v under a certain speed condition. This relationship is known through the previous turbine test, as shown in Figures 3(a) and 3(b). Then according to the formula: Δp=-S iΔn, Δp = av 2 +bv, we can find that the permeability α of the momentum source region at this speed is 5.7×10 -8 m 2 s 2 , the inertial drag coefficient C2 is 69.0m -1 , and set this value in the software.

[0046] Step 6: Set the monitoring of the free liquid level in the air chamber, the pressure in the air chamber, and the flow rate in the conduit; select the calculation time step as 0.01s, the calculation time as 50s, and start the calculation. After the calculation is completed, the time series of the free liquid level in the air chamber and the air pressure can be obtained, such as Figure 4(a) , 4(b) Then, the air flow rate is substituted into the torque-flow rate relationship shown in Figure 3(b) to obtain the torque output result of the turbine, as shown in Figure 4(c).

[0047] Step 7: Substitute the result obtained in step 6 into the following formula to calculate the incident wave power P W , air power P P and the turbine output power P T ,

[0048] P W =0.5ρ w G JZ 2 C g L,

[0049] Then calculate the energy conversion efficiency of each level of the device according to the following formula:

[0050] η1=P P / P W ,η2=P T / P P , η=η1η2

[0051] Finally, using this full-process model, it is predicted that the air chamber conversion efficiency η1 of the oscillating water column device is 45.7%, the conversion efficiency η2 of the air turbine is 14.8%, and the full-process energy conversion efficiency η is 6.8%.

Claims

1. An efficiency prediction method is used to predict a full-process model of an oscillating water column device based on CFD simulation, the model includes a numerical wave tank and an air chamber arranged at one end of the numerical wave tank, the side of the air chamber is connected to an air duct, and a momentum source term area is arranged in the air duct, the width of the momentum source term area is consistent with the blade width of the turbine in the device to be evaluated, and is characterized in that: The steps include: Step 1, using software to establish the calculation area of ​​the whole process model of the oscillating water column device; Step 2: Divide the grid of the above model. The water tank and air chamber parts use hexahedral structured grids. The height and length of the grids are not more than 1 / 10 and 1 / 50 of the incident wave height and wavelength respectively. The grids are encrypted along the wave height near the free liquid surface. The grid encryption height is 1 / 20 of the wave height. The air duct part is created using tetrahedral unstructured grids. There is a transition from large to small grids from the two ends to the middle of the duct. Step 3, setting boundary conditions for the above model, setting the wave-making boundary of the water tank to a stationary no-slip wall, setting the bottom and side walls of the water tank and the air chamber to stationary no-slip walls, setting the top of the water tank and the outlet of the air duct to pressure outlet boundaries, and connecting the air chamber and the air duct with an interface. After the setting is completed, export the model to a grid file format; Step 4, import the grid file into the software for solution setting, set the two-phase flow to track the free liquid surface and calculate the water depth, and use the k-epsilon RNG turbulence model for solution. The model uses the SIMPLE pressure-velocity coupling solution method. The momentum equation and turbulent kinetic energy both use the second-order upwind format. The user-defined function UDF is used to control the wave-making boundary of the water tank to generate the corresponding incident waves. Step 5, set the momentum source term area: For the fluid region, the momentum source term S in the i direction i It is expressed as: Where μ is the dynamic viscosity, α is the permeability, v is the flow velocity, and v i is the flow velocity in the i direction, C2 is the inertial resistance coefficient, and ρ is the fluid density; At the same time, the pressure drop Δp at the front and rear ends of this area is the momentum source term S i The product of the basin thickness Δn is: Δp=-S i Δn (2) The flow rate and pressure drop through this area are expressed as: Δp=off 2 +bv (3) For an air turbine with a certain speed, a and b are known parameters. Combining equations 1-3, we can obtain the permeability α and the inertial resistance coefficient C2. In the software, the permeability and inertial resistance coefficient can be customized through the porous zone, so as to accurately introduce the damping effect of the air turbine into the air chamber; Step 6, setting the corresponding physical parameter monitoring, including the free liquid level height in the air chamber, the pressure in the air chamber and the flow rate in the air duct, and setting the calculation time step and calculation time, starting the calculation, and obtaining the time series of the corresponding parameters after the calculation is completed; Step 7, substitute the result obtained in step 6 into the following formula: The incident wave power is: P W =0.5ρ w G JZ 2 C g L; air power is: The output power of the turbine is: Among them, ρ w is the density of water, g is the acceleration due to gravity, A is the amplitude of the incident wave, C g is the wave speed, L is the width of the air chamber, T is the incident wave period, p(t) is the pressure in the air chamber, Q(t) is the flow rate in the air chamber, T0(t) is the turbine torque, and ω(t) is the turbine angular velocity; The conversion efficiency η1 of the gas chamber is: η1=P P / P W ; The conversion efficiency of the air turbine η2 is: η2=P T / P P ; The energy conversion efficiency η of the entire process of the device is: η=η1η2.

2. The efficiency prediction method according to claim 1, characterized in that: The software used in step 1 is ANSYS-ICEM or Gambit.

3. The efficiency prediction method according to claim 1, characterized in that: The software used in steps 4 and 5 is ANSYS-Fluent.

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