A method and system for estimating average flow velocity in coal-fired power plants based on turbulence numerical simulation and flow field characteristics analysis

Abstract

The present invention belongs to the technical field related to carbon monitoring in coal-fired power plants and discloses a method and system for estimating the average flow velocity of a coal-fired power plant based on turbulent numerical simulation and flow field characteristic analysis. The method includes the following steps: collecting geometric parameters and flue gas parameters of the chimney during operation of the coal-fired power plant, constructing a three-dimensional geometric model of the chimney, and performing meshing; obtaining the flow pattern of the flue gas under the flue gas parameters, and determining the factors that affect the measurement of the average flue gas flow velocity; determining the location and number of points in the chimney for initial measurement of the average flue gas flow velocity; establishing an optimization model using the relative root mean square (RMS) and relative error of the average flow velocity as evaluation indicators, and determining the optimal monitoring parameters based on the relative RMS meeting a preset threshold and the minimum relative error of the average flow velocity; and adding a correction factor to obtain the final average flue gas flow velocity. The present invention solves the problem of inaccurate flue gas flow velocity measurement caused by turbulent changes in large-diameter chimneys.

Description

Technical Field

[0001] The present invention belongs to the technical field related to carbon monitoring in coal-fired power plants, and more specifically, relates to a method and system for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis. Background Art

[0002] Coal-fired power plants remain a major source of carbon emissions. Online CO2 monitoring provides a real-time method for measuring carbon emissions by directly quantifying flue gas flow rate and CO2 concentration, overcoming the data lag and labor-intensive nature of traditional accounting methods. However, several issues can lead to errors of up to 30% in online monitoring methods. The largest source of error is inaccurate flue gas flow measurements caused by complex flue gas turbulence, resulting from large chimney diameters and varying coal load types in coal-fired power plants.

[0003] With the expected growth in the demand for flexible peak-shaving of China's thermal power plants, the load will experience rapid fluctuations, posing a significant challenge to the precise measurement of flue gas flow velocity. Current literature primarily focuses on the accuracy of velocity measurements at specific sections of the flow field, neglecting in-depth investigations into the impact of turbulent evolution on flow field uniformity and comprehensive analysis of the overall flow field characteristics. Furthermore, large chimneys in coal-fired power plants are difficult to model and study in a laboratory setting, and actual coal-fired power plants cannot arbitrarily adjust operating conditions and monitoring locations. Furthermore, a simpler and more systematic optimization strategy is needed for typical coal-fired power plants. Therefore, it is crucial to develop an optimal point placement scheme that considers multiple factors and an overall correction for turbulence errors using low-cost and easily implemented CFD numerical simulations. Summary of the Invention

[0004] In response to the above defects or improvement needs of the existing technology, the present invention provides a method and system for estimating the average flow velocity of a coal-fired power plant based on turbulent numerical simulation and flow field characteristic analysis, which solves the problem of inaccurate flue gas flow velocity measurement caused by turbulent changes in large-diameter chimneys.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis is provided. The method comprises the following steps:

[0006] Collecting geometric parameters and flue gas parameters of a chimney during operation of a coal-fired power plant, using the geometric parameters to construct a three-dimensional geometric model of the chimney and perform meshing;

[0007] Using the flue gas parameters, a turbulence numerical simulation is performed in the three-dimensional geometric model of the chimney to obtain a flow pattern of the flue gas under the flue gas parameters, and factors affecting the measurement of the average flow velocity of the flue gas are determined from the flow pattern of the flue gas;

[0008] The factors affecting the measurement of the average flow rate of flue gas are used as monitoring parameters, and the relative root mean square and relative error of the average flow rate of flue gas are established as evaluation indicators. The relative root mean square satisfies a preset threshold and the optimization goal of minimizing the relative error of the average flow rate is achieved. The corresponding monitoring parameter is the best monitoring parameter when the optimization goal is achieved.

[0009] The average flow velocity of the chimney cross section is estimated using the optimal monitoring parameters, and the relationship between the estimated average flow velocity of the chimney cross section and the actual average flow velocity is constructed, and the actual average flow velocity corresponding to the optimal monitoring parameters is calculated.

[0010] Further preferably, the flue gas parameters include flue gas flow rate, temperature in the chimney, pressure, humidity, density, thermal conductivity, viscosity, mass diffusivity, chimney height, power plant rated power, chimney diameter, ideal average flow velocity, and concentrations of water, oxygen, nitrogen, carbon dioxide and sulfur dioxide in the flue gas.

[0011] Further preferably, the flow law of the flue gas includes the generation, evolution and stabilization process of flue gas turbulence.

[0012] Further preferably, the factors affecting the measurement of the average flue gas flow rate include load, temperature, distribution point height, distribution point number and rotation angle of the measurement chord.

[0013] Further preferably, the relative root mean square relationship of the average flow rate is as follows:

[0014]

[0015] Among them, σ r is the relative RMS of the mean velocity, is the actual average flow rate of flue gas, M is the number of distribution points on the radius of each chimney cross section, m is the mth distribution point on the radius; N is the number of chords on a certain section of the chimney, 2N is the number of radii, n is the nth chord, η m is a dimensionless number, R is the radius of the chimney, r is the radial position of the mth point along the radius, θ n is the circumferential position of the nth chord, v z (η m ,θ n ) This parameter is at an angle of θ n The position on the chord is η m The speed at which the

[0016] Further preferably, the initial value of M is determined by using a log-linear method based on the collected chimney geometric parameters and flue gas parameters.

[0017] Further preferably, the relationship between the relative error of the average flow rate is as follows:

[0018]

[0019] Among them, E d is the relative error of the mean flow velocity, is the cross-sectional average velocity estimated by the log-linear method, is the ideal average flow rate set.

[0020] Further preferably, the relationship between the actual average flow rate is as follows:

[0021]

[0022] in, is the average velocity after adding the correction factor, k is the correction coefficient, k = 1 / (1 + E' d ), where E' d is the relative error of the fitted mean velocity, is the cross-sectional average velocity estimated by the log-linear method.

[0023] According to another aspect of the present invention, a system for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis is provided. The system includes an actuator for executing the above-mentioned method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis.

[0025] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0026] 1. This invention uses numerical turbulence simulation to obtain the general characteristics of the chimney flow field. It considers the effects of temperature, load, monitoring height, monitoring chord angle, and the number of monitoring points on average velocity estimation. Using simulated experimental conditions, the change in velocity estimation error and the relative root mean square of the average velocity are used as measurement factors. This comprehensive consideration yields an optimal solution for accurate average velocity estimation at a low cost. This approach provides an optimization reference for existing carbon monitoring facilities and offers substantial guidance for the design and development of carbon monitoring layout plans for coal-fired power plants.

[0027] 2. This invention uses a log-linear method to determine the location and number of measurement points. Compared with the traditional equal-area circular method, when the same number of measurement points is selected, the log-linear method can obtain more accurate flow velocity in both fully developed and non-fully developed turbulent areas.

[0028] 3. The present invention constructs an optimization model using the relative root mean square (RMS) and relative error of the average flow velocity as evaluation indicators. The relative RMS meets a preset threshold and the relative error of the average flow velocity is minimized, thereby determining the optimal monitoring parameters. The relative RMS of the average flow velocity in this method can measure the uniformity of the smoke distribution in the cross section at the monitoring height, ensuring that the smoke distribution in the cross section at the monitoring height is relatively uniform. The minimum relative error ensures that the estimated average flow velocity is closest to the actual average flow velocity.

[0029] 4. The present invention introduces a flow velocity turbulence correction factor into the optimization model to reduce the average flue gas flow velocity measurement error from the fluid side itself. This solution can greatly reduce the average flue gas flow velocity measurement error caused by turbulence changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis constructed according to a preferred embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of a chimney structure constructed according to a preferred embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of initial point distribution based on the logarithmic linear method constructed according to a preferred embodiment of the present invention;

[0033] Figure 4 3D streamlines of a single-air-inlet chimney constructed according to a preferred embodiment of the present invention, wherein (a) is an enlarged view of a small-scale vortex existing at the chimney corner, (b) is an enlarged view of a symmetrical vortex formed at the chimney corner, (c) is an enlarged view of the 3D streamlines viewed from bottom to top, and (d) is an overall view of the 3D streamlines of the chimney.

[0034] Figure 5 are velocity sections at y=0 and x=0 constructed according to a preferred embodiment of the present invention, wherein (a) is the velocity section at y=0, and (b) is the velocity section at x=0;

[0035] Figure 6 is the velocity variation at different heights on the string 2 constructed according to the preferred embodiment of the present invention;

[0036] Figure 7The cross-sectional flow velocity at different heights and the vortex change under the Q criterion; among them, (a) is the cross-sectional flow velocity at z = 1D, (b) is the cross-sectional flow velocity at z = 2D, (c) is the cross-sectional flow velocity at z = 3D, (d) is the cross-sectional flow velocity at z = 4D, (e) is the cross-sectional flow velocity at z = 5D, (f) is the cross-sectional flow velocity at z = 6D, (g) is the cross-sectional vortex change at z = 1D, (h) is the cross-sectional vortex change at z = 3D, (i) is the cross-sectional vortex change at z = 6D, and (j) is a schematic diagram of the cross-sectional position at different heights;

[0037] Figure 8 The velocity changes at different heights on chord 1 and chord 2 at a fixed flow rate constructed according to a preferred embodiment of the present invention are shown in FIG. (a) is the velocity change at different heights on chord 1, and (b) is the velocity change at different heights on chord 2.

[0038] Figure 9 is the velocity change of the representative point under different loads constructed according to the preferred embodiment of the present invention;

[0039] Figure 10 is constructed according to the preferred embodiment of the present invention r , where (a) is the change at different heights at a fixed flow rate, (b) is the change at different heights under different loads, (c) is the change at different loads when the height is 2D, and (d) is the change at different loads when the height is 3D;

[0040] Figure 11 E is constructed according to the preferred embodiment of the present invention d The change of E at different heights under a fixed flow rate is shown in Figure 2. d The change of value, (b) is E under different loads and different heights d (c) is the change of E under different loads at 2D height d (d) is the change of ΔE at different heights when the load changes from 6m / s to 12m / s d changes;

[0041] Figure 12 The effect of temperature changes on flow rate measurement constructed in accordance with the preferred embodiment of the present invention;

[0042] Figure 13 Schematic diagrams of chord rotation constructed according to a preferred embodiment of the present invention, wherein (a) is a schematic diagram of chord rotation geometry measured at a height of 4D, and (b) is a schematic diagram of chord rotation geometry measured at a height of 6D;

[0043] Figure 14 The effect of the angle change of the measuring chord on the flow velocity measurement constructed according to the preferred embodiment of the present invention is shown in FIG. 1 , wherein (a) is the effect of the angle change of the measuring chord on the flow velocity measurement when the height is 4D. r(b) is the effect of the chord angle on σ when the height is 6D. r (c) is the effect of the chord angle on E when the height is 4D. d (d) is the effect of the chord angle on E when the height is 6D. d the impact of;

[0044] Figure 15 The effect of the number of points on the flow rate measurement constructed according to the preferred embodiment of the present invention;

[0045] Figure 16 is the optimal number of points constructed according to the preferred embodiment of the present invention;

[0046] Figure 17 is a fitting curve of relative error between the height of the distribution points and the flow velocity constructed according to the preferred embodiment of the present invention;

[0047] Figure 18 It is a comparison between the average flow rate with the correction factor introduced according to the preferred embodiment of the present invention and the actual average flow rate. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0049] like Figure 1 As shown in FIG, a method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis includes:

[0050] First, basic structural information about the coal-fired power plant chimney is collected, including the chimney base structure, air intake method, chimney diameter, and chimney height. A three-dimensional geometric model of the chimney (e.g., SolidWorks) is constructed based on this basic structural information. This physical model is then input into meshing software, which divides the geometric model into small computational units (grids) for numerical calculations.

[0051] In one embodiment of the present invention, the flue gas parameters include flue gas flow rate, temperature in the chimney, pressure, humidity, density, thermal conductivity, viscosity, mass diffusivity, chimney height, power plant rated power, chimney diameter, actual average flow velocity, and concentrations of water, oxygen, nitrogen, carbon dioxide and sulfur dioxide in the flue gas.

[0052] Next, we selected the actual operating parameters of a coal-fired power plant under a certain stable load, such as the average flue gas velocity, temperature, pressure, and flue gas composition. The meshed geometric model and the actual operating parameters were imported into ANSYS Fluent software for turbulent numerical simulation.

[0053] Post-process the numerical simulation results, such as drawing streamline diagrams and velocity distribution diagrams of different cross sections, analyzing the evolution of flue gas turbulence, and summarizing the general flow patterns of flue gas under a single stable load, including the generation, evolution, and stabilization of flue gas turbulence;

[0054] From the flow law, we can know that the factors affecting the measurement of the average flue gas flow rate include load, temperature, distribution point height, distribution point number and the rotation angle of the measurement chord.

[0055] Finally, the number and method of initial point distribution based on the log-linear method were determined based on the chimney diameter and height. Using the relative error of flow velocity and the relative root mean square of flow velocity as evaluation indicators, the impact of various factors on the relative error and relative root mean square of the average flue gas velocity in the chimney were determined by changing the monitoring height (point distribution height), load, temperature, rotation angle of the measurement chord, and number of points. The optimal flow velocity monitoring parameters were determined based on the evaluation indicators and cost.

[0056] The height and number of points are the height and number of measurement points set in the chimney respectively. The initial number and height of points are determined by the geometric parameters and flue gas parameters of the chimney. In the process of obtaining the optimal monitoring parameters, the height and number of points need to be continuously adjusted.

[0057] In one embodiment of the present invention, the relative root mean square of the average flow rate is related as follows:

[0058]

[0059] Among them, σ r is the relative RMS of the mean velocity, is the actual average flow rate of flue gas, M is the number of distribution points on the radius of each chimney cross section, m is the mth distribution point on the radius; N is the number of chords on a certain section of the chimney, 2N is the number of radii, n is the nth chord, η m is a dimensionless number, R is the radius of the chimney, r is the radial position of the mth point along the radius, θ n is the circumferential position of the nth chord, v z (η m ,θ n ) This parameter is at an angle of θ n The position on the chord is η m The speed at which the

[0060] In one embodiment of the present invention, the relationship between the relative error of the average flow rate is as follows:

[0061]

[0062] Among them, E d is the relative error of the mean flow velocity, It is the cross-sectional average velocity estimated by the log-linear method, that is, the instantaneous velocity values ​​measured at each measuring point are added together and then divided by the number of measuring points to obtain the cross-sectional average velocity. is the ideal average flow rate set.

[0063] To address the problem of turbulence causing the flow rate to be too high, the relationship between turbulence error and monitoring height is established, and a turbulence correction factor is introduced. Finally, the average flue gas flow rate under the optimal monitoring parameters is calculated, and the turbulence correction factor is added to recalculate the average flue gas flow rate. This is then compared with the actual average flue gas flow rate to ensure that the relative error of the final average flow rate is minimized.

[0064] In one embodiment of the present invention, the relationship between the actual average flow rate is as follows:

[0065]

[0066] in, is the average velocity after adding the correction factor, k is the correction coefficient, is the average cross-sectional velocity estimated by the log-linear method, k = 1 / (1+E' d ), E' d It is based on the chimney monitoring height and E in the specific case. d The functional relationship between the fitting curve is calculated. Specifically, first calculate the E d The data is fitted with the distribution point height to obtain E d The logistic function relationship between the distribution point height and the distribution point height is finally obtained by directly substituting the distribution point height into the function relationship.

[0067] The present invention will be further described below with reference to specific embodiments.

[0068] Figure 2 The figure shows the meshing of a typical chimney structure. Structural data for a 660MW coal-fired power plant chimney was collected and a chimney model was created in SolidWorks software, including the inlet flue, chimney base, and chimney shaft. The chimney model was then imported into ICEM software for meshing, with the 90° bends refined.

[0069] Table 1 shows the flue gas parameters collected from a 660MW coal-fired power plant under normal operation. Flue gas enters the chimney from a horizontal flue, flows vertically upward through a 90° elbow, and exits the chimney at 238 meters.

[0070] Table 1

[0071]

[0072]

[0073] Figure 3 is the initial point distribution based on the log-linear method. When selecting the same number of measurement points, the log-linear method can obtain more accurate flow velocity in both fully and partially developed turbulent areas compared to the traditional equal-area circular point distribution method. The chord that passes through the vortex and turbulent core area is initially selected as the measurement chord. In order to more comprehensively capture the flow velocity information of the cross section, two vertical measurement chords are used. The circular cross section is evenly divided into several circular rings of equal area (the innermost is a circle). It is assumed that the flow velocity distribution on each circular ring satisfies the logarithmic model, as shown in formula (1):

[0074] v=Alny+By+C (1)

[0075] Where: y is the distance to the pipe wall; A, B, C are three arbitrary constants

[0076] Both the international standard ISO 3699 and the US EPA's Method 1 specify the number of points required for circular cross-sections of varying diameters. The standard recommends using a log-linear method. Considering the number of points typically deployed in coal-fired power plants, a "12 + 1" number of points was chosen for this study. It's worth noting that the center point was added to capture the flow velocity within the inner circle.

[0077] Figure 4 The three-dimensional velocity streamlines and velocity distribution of a single-inlet chimney are displayed. Numerical simulations using parameters from a specific operating condition of an actual 660MW coal-fired power plant provide a preliminary understanding of the flow patterns in a single-inlet chimney coal-fired power plant under certain operating conditions, laying the foundation for determining the optimal layout plan for the coal-fired power plant.

[0078] from Figure 4 As can be seen in (b) and (c), two symmetrical vortices are formed during the flue gas flow. This is mainly due to the influence of the chimney structure. After the flue gas enters the chimney horizontally from the flue, the flow direction changes by 90° under the guidance of the slope and enters the circular chimney with a larger volume. This redirection, coupled with the pressure difference and instantaneous volume expansion, guides the central airflow to flow upward, while the lateral airflow is diverted outward and upward, eventually forming a rotationally symmetrical airflow pattern. In addition, Figure 4(a) shows the presence of small-scale vortices at the chimney edges and corners, which increase the complexity of the flow field. The formation and development of two symmetrical vortices drive the changes in the flow field.

[0079] Figure 5 The cross-sectional velocity contours at y = 0 and x = 0 (actual average velocity = 11.3 m / s) are shown. Observed from the x = 0 direction, the symmetrical vortices result in an overall "W"-shaped velocity distribution. When the flue gas enters the center of the vortex, the rotational effect causes the flue gas to be subjected to centrifugal force, resulting in a lower velocity in the central region (<5 m / s). Subsequently, as the flue gas leaves the vortex center and moves toward the periphery, the velocity increases due to fluid inertia, forming a high-speed region (>18 m / s). The peripheries of the two symmetrical vortices interact to form a medium-speed region (>11 m / s), ultimately causing the velocity to change from a high-speed region (>18 m / s) to a low-speed region (<5 m / s), then to a medium-speed region (>11 m / s), and finally from a low-speed region (<5 m / s) back to a high-speed region (>18 m / s), forming a "W"-shaped distribution. Observed from the y = 0 direction, the flue gas flow path undergoes a sudden change at the corner, with a significant decrease in velocity at the corner. Due to the effects of viscosity and inertia, the fluid cannot turn closely to the boundary, so a low-speed area (<5m / s) is formed near the corner. Figure 4 In (a), it can be clearly seen that there are eddies in this area, making the flow field structure more complicated.

[0080] Figure 6 The velocity profile at different heights along chord 2 is shown. The velocity distribution along chord 2 becomes increasingly uniform at different heights, from 1D (where the velocity range is 4.3 m / s to 18.7 m / s) to 6D (where the velocity range narrows to 9.8 m / s to 11.6 m / s). It is noteworthy that there are two special points on chord 2 where the velocity remains constant and close to the actual average velocity. These special points are symmetrically distributed at ±2.55 degrees (r / R ≈ 0.73). By increasing the number and influence range of these special points, the number of required measurement points can be reduced while still accurately estimating the average velocity.

[0081] Figure 7 The flow velocity and vortex variation under the Q criterion at different heights. After the flue gas flows through the 90° bend, the flow velocity forms two symmetrical vortices. The velocity distribution gradually becomes uniform from 1D to 6D. The Q criterion is used to measure the vortex intensity. It can be seen that the vorticity decreases rapidly with decreasing height. The vorticity at the 6D cross section is approximately zero.

[0082] The flue gas velocity entering the tail duct from the flue gas desulfurization tower is generally 10 to 15 m / s. To study the effect of load variations on the chimney flow field, we selected six different flow rates. Specific parameters are shown in Table 2.

[0083] Table 2

[0084]

[0085] Figure 8 (a) and (b) show the velocity changes at different heights on chord 1 and chord 2 under a fixed flow rate. Figure 8 (a) shows that on chord 1, the normalized velocity varies greatly from 1.5 to 0.8, with no obvious regularity. Figure 8 (b) shows the regular and obvious velocity changes at each point on chord 2. The velocities can be divided into three parts: high-speed points (normalized velocity ranges from 1.5 to 1.0), low-speed points (normalized velocity ranges from -1.5 to 1.0), and special points (normalized velocity is approximately 1.0). After 4D, the normalized velocity at each point on chord 2 is approximately 1.0, indicating that the velocity changes on chord 2 are stable after 4D. According to the previous statement, the flow velocities at some special points are approximately equal to the actual average flow velocity. The relative positions of these points are r / R = ±0.73 of chord 2 and are named "representative points" here. By using a small number of "representative points", accurate flow velocities can be obtained at low cross-sections with high turbulence intensity.

[0086] exist Figure 9 The effect of load changes on flow velocity at "representative points" has been added to make these points more representative. When the average flow velocity ranges from 6 m / s to 16 m / s and the height ranges from 1D to 6D, the flow velocity at the representative points remains essentially constant as the load increases, and the difference between the normalized flow velocity and the actual average flow velocity is less than ±0.05. This demonstrates that when load changes, using representative points for average flow velocity estimation can reduce estimation errors and minimize the number of points.

[0087] In order to better quantify the impact of changes in different factors on the average flow velocity estimation, two evaluation factors are introduced:

[0088] (1) Relative RMS velocity (σ r ) is used to determine whether the flue gas flow is uniform.

[0089]

[0090] Where: σ r is the relative RMS of the mean velocity; is the actual average flue gas velocity (mass flow rate divided by density and cross-sectional area at the same simulated flow rate), m / s; M is the number of points on the radius of each chimney section, M is a variable; m is the mth point on the radius; N is the number of chords on a chimney section, 2N is the number of radii, and two radii separated by 180° form a chord; n is the nth chord; η m is a dimensionless number, R is the radius of the chimney, r is the radial position of the mth point along the radius; θn is the circumferential position of the nth chord, rad; v z (η m ,θ n ) This parameter is at an angle of θ n The position on the chord is η m Velocity at , m / s.

[0091] When the relative root mean square of the cross section is less than 0.15, the cross section can be considered to have uniform flow mixing and meet the requirements of the point cross section.

[0092] (2) Relative error of average velocity estimation (E d )

[0093] According to the discrete sampling theory, the average flow velocity is estimated based on the flow field distribution calculated by CFD simulation. The formula for estimating the relative error of the average flow velocity is as follows:

[0094]

[0095] Where: E d is the relative error of the mean flow velocity; is the cross-sectional average velocity estimated by the log-linear method, m / s; is the actual average flow velocity (mass flow rate divided by the density and cross-sectional area at the same simulated flow rate), m / s.

[0096] Figure 10 (a) shows the σ at different heights under a fixed flow rate. r The change of σ r ≤0.15 is considered to be uniform smoke distribution. σ calculated based on the average flow rate estimated at the "12+1" point r As the height increases from 1D to 8D, it decreases from 0.31 to 0.05. This is because under the same simulated flow rate, the lower the height, the greater the disturbance of the vortex, the higher the turbulence intensity, and the more uneven the fluid mixing. Figure 10 (b) and (d) show the σ at different heights under different loads. r When the average flow velocity increases from 6m / s to 16m / s, σ r The change range from 1D to 4D is between 0.002 and 0.004. This is because the flow field distribution under different loads is similar and has little effect on uniformity. At this time, the change of height and load has little effect on σ r In short, when the height is greater than 4D, σ r Keeping it below 0.15 indicates that the smoke is evenly distributed.

[0097] Figure 11 (a) shows the different heights E at a fixed flow rate. dE calculated based on the average velocity estimated at point "12+1" d It drops from 16% to about 0, with a maximum drop of 6% / D when the height increases from 1D to 8D. In actual power plants, the relative error between the average value of CMS velocity measurements and the average value of reference method velocity measurements should not exceed ±6%. In the simulation results, when the height of the points exceeds 4D, E d Less than 6%. However, after 6D, E d It remains basically unchanged at about 1%. Therefore, it is recommended to monitor the height above 6D. Figure 11 The red curve in (a) represents the E estimated based on the average flow velocity of all cells in the simulation cross section. d , the value dropped from 13% to about 0. Figure 11 (b) to (d) show the average velocity estimation error for different load heights from 6 m / s to 16 m / s. Figure 11 (b) shows that the height change is still the main factor affecting E d main factors. Figure 11 (c) further shows that E d Positively correlated with load changes. Figure 11 (d) shows Figure 11 ΔE in (b) d As the average flow velocity increases from 6m / s to 16m / s, ΔE d The change in E decreases from 0.12% at 1D to 0.01% at 6D. This shows that above 6D, the load change has little effect on E d The impact of E is small. d It is a positive value in all calculations, that is, the estimated average flow velocity is always greater than the actual average flow velocity, which proves that the existence of turbulence will make the estimated flow velocity larger.

[0098] Temperature is one of the main influencing parameters of flue gas flow. The general chimney inlet temperature is 40-60°C. In order to study the effect of temperature changes on the flow field, the flue gas inlet temperatures were set at 40, 45, 50, 55 and 60°C. The physical parameters and flow rates at different temperatures are shown in Table 3. Changes in temperature will indirectly change the magnitude of the flow rate. As the temperature increases, the flow rate will also increase slightly. However, it can be clearly seen from previous studies that an increase in flow rate will lead to an increase in water flow velocity. In order to eliminate the influence of flow rate changes on the results, the flow rate is divided by the actual flow rate and compared on this standard scale. A section with a height of 6D is selected for comparison, and the results are as follows: Figure 12 As shown in the figure, the error gradually increases with increasing temperature. However, the rate of change for a 5°C temperature change is only 1.4%, which is basically negligible compared to the effect of load changes on the flow field.

[0099] Table 3

[0100]

[0101]

[0102] Two vertical monitoring chords are used to capture cross-sectional flow velocity. The chords are rotated from 0° to 90°, and the average flow velocity is calculated every 10°. In order to more easily compare the similarities and differences between two sections at different heights under the same rotation conditions, 4D and 6D sections are selected to study the effect of different measurement angles on the average flow velocity estimation. Figure 13 Figures (c) and (d) illustrate the rotation of the measurement chord at different heights. The diagram shows the chords being rotated counterclockwise in 10-degree increments, with the 90° chord perpendicular to the flue gas inlet and the 0° chord parallel to the flue gas inlet serving as the reference lines. The two chords remain perpendicular while rotating counterclockwise in 10-degree increments.

[0103] Figure 14 The change in the rotation angle at heights 4D and 6D is the rotation angle of the chord with the horizontal coordinate being E. d and σ r , when the horizontal coordinates are the same, the closer the vertical coordinate is to 0, the better. Figure 14 (a) and (c) show that at 4D height, the 0° direction is the best among all rotation angles, σ r and E d Both are smallest. Figure 14 (b) and (d) show that when the height is 6D, the E d Minimum. At 6D height, σ r The value of is approximately 0.04, which is smaller than the standard value of 0.15. Therefore, the best solution is to set two mutually perpendicular chords at 0° and 90°.

[0104] To investigate the effect of increasing the number of measurement points on the average velocity estimation, we selected different numbers of measurement points for simulation experiments based on a log-linear measurement method. The number of measurement points per radius was increased (the final number of measurement points was multiplied by 4 and then added by 1). The total number of measurement points was 5, 9, 13, 17, and 21, respectively. The relative positions of the measurement points are shown in Table 4.

[0105] Table 4

[0106]

[0107] The best monitoring chord angles of 0° and 90° were selected, the number of points was changed, and the flow velocity at different height sections was measured. The results are as follows: Figure 15 According to the log-linear method, E d It will decrease as the number of points increases, and then tend to be stable. When the number of points exceeds 9, the E of all sections d The changes tend to be stable.

[0108] Figure 16 The optimal number of points for each section is shown, showing a stepped pattern. Nine points are recommended for heights less than 2D. Five points are recommended for heights between 3D and 5D. A single point is sufficient for heights greater than 6D. It is worth noting that the presence of turbulence results in a higher average velocity, and the magnitude of the deviation decreases with increasing height. This deviation is completely influenced by the turbulence intensity and cannot be reduced by increasing the number of points.

[0109] For single-inlet chimneys, the development pattern of turbulent flue gas is deterministic, and the error in the average velocity estimated using the log-linear method is directly related to the monitoring height. The estimated average velocity at each section obtained by simulation is always higher than the actual average velocity (under the same simulated flow rate) and varies with height. Increasing the number of monitoring points or changing the angle of the monitoring chord cannot reduce this error. Therefore, to further quantify the impact of height on the estimated average velocity, this study introduced a correction factor to improve the accuracy of the average velocity estimation.

[0110]

[0111] Where, is the average velocity after adding the correction factor, m / s; k is the correction coefficient, and the calculation equation is: k = 1 / (1 + E' d ), where E' d It is based on the chimney monitoring height and E in the specific case. d The functional relationship between the values ​​calculated by the fitting curve; is the average flow velocity in the cross section estimated by the log-linear method, in m / s.

[0112] The correction coefficient k is related to flow velocity, temperature, monitoring height and flow field characteristics. According to previous studies, the height of the monitoring point is obviously the main influencing factor of flow velocity measurement. In the load stability stage, the changes in load and temperature have little effect on the flow velocity measurement error. In order to improve the simplicity and applicability of the formula, only the relationship between the monitoring height and the average flow velocity estimation error is considered. Nonlinear curve fitting analysis shows that there is a logarithmic function relationship between the monitoring height and the estimation error, highlighting the importance of monitoring height for achieving accurate flow measurement, such as Figure 17 shown.

[0113] exist Figure 17Two fitting curves can be observed in the figure. The horizontal axis represents the monitoring height, and the vertical axis represents the relative error of the estimated average flow velocity. The sample includes 6 loads (6m / s, 8m / s, 10m / s, 12m / s, 14m / s, 16m / s) and 8 monitoring heights, with a total of 96 average flow velocity estimates (48 points each for curve 1 and curve 2). Various fitting functions were then used for fitting, and it was finally found that the best fitting effect was achieved under the logarithmic function (R 2 =0.99). Curve 1 represents the E calculated based on the average flow rate estimated at point "12+1". d Curve 2 represents the E calculated based on the average velocity estimated from the simulated cross-section of all cells. d The difference between the two mainly depends on the method and number of points.

[0114] After reaching a height of 6D or more, E of curve 1 d Always stay within the range of 0 to 1%. Therefore, when the height exceeds 6D, E d It can be regarded as a constant value of 1%. The fitting formula of curve 1 is as follows (in order to match E in formula (3) d Distinguish, use E' d express):

[0115]

[0116] If only a limited number of points are considered according to the log-linear method, then the height of E exceeding 8D d The fitting formula of curve 2 is as follows:

[0117]

[0118] In order to verify the correctness of the formula, other points with different heights, loads and temperatures were selected. Figure 18 As shown. The figure shows the comparison between the corrected flow rate and the actual flow rate. The verification results show that after correction using formula (6), the average flow rate estimation error is reduced from -0.2% to 14% to -0.1% to 0.3%. The correctness of the formula has been verified.

[0119] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis, characterized in that: The method comprises the following steps: Collecting geometric parameters and flue gas parameters of a chimney during operation of a coal-fired power plant, using the geometric parameters to construct a three-dimensional geometric model of the chimney and perform meshing; Using the flue gas parameters, a turbulence numerical simulation is performed in the three-dimensional geometric model of the chimney to obtain a flow pattern of the flue gas under the flue gas parameters, and factors affecting the measurement of the average flow velocity of the flue gas are determined from the flow pattern of the flue gas; The factors affecting the measurement of the average flow rate of flue gas are used as monitoring parameters, and the relative root mean square and relative error of the average flow rate of flue gas are established as evaluation indicators. The relative root mean square of the average flow rate satisfies a preset threshold and the relative error of the average flow rate is minimized. When the relative root mean square of the average flow rate meets the preset threshold and the relative error of the average flow rate is minimized, the corresponding monitoring parameter is the best monitoring parameter. The average flow velocity of the chimney cross section is estimated using the optimal monitoring parameters, and the relationship between the estimated average flow velocity of the chimney cross section and the actual average flow velocity is constructed, and the actual average flow velocity corresponding to the optimal monitoring parameters is calculated based on this.

2. The method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 1, characterized in that: The flue gas parameters include flue gas flow rate, temperature in the chimney, pressure, humidity, density, thermal conductivity, viscosity, mass diffusivity, chimney height, power plant rated power, chimney diameter, ideal average flow velocity, and concentrations of water, oxygen, nitrogen, carbon dioxide, and sulfur dioxide in the flue gas.

3. A method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 1 or 2, characterized in that: The flow law of the flue gas includes the generation, evolution and stabilization process of the flue gas turbulence.

4. The method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 3, characterized in that: The factors affecting the measurement of the average flue gas flow rate include load, temperature, distribution point height, distribution point number and the rotation angle of the measurement chord.

5. The method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 1 or 2, characterized in that: The relative root mean square relationship of the average flow rate is as follows: in, is the relative RMS of the mean velocity, is the actual average flow rate of flue gas, M is the number of points on the radius of each chimney cross section, m It is the radius m A distribution point, N is the number of chords on a certain section of the chimney, 2 N is the number of radii, n It is n String, is a dimensionless number, , R is the radius of the chimney, r m It is the radius m The radial position of a point along the radius, It is n The circumferential position of the chord, This parameter is at an angle of The position on the string is The speed at which the 6. The method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 5, characterized in that: described M The initial value of is determined by the log-linear method based on the collected chimney geometric parameters and flue gas parameters.

7. A method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 1 or 2, characterized in that: The relationship between the relative error of the average flow rate is as follows: in, is the relative error of the mean flow velocity, is the cross-sectional average velocity estimated by the log-linear method, is the actual average flow velocity of the flue gas.

8. The method for estimating average flow velocity in a coal-fired power plant based on turbulence numerical simulation and flow field characteristics analysis according to claim 7, characterized in that: The relationship between the actual average flow rate is as follows: in, is the average flow rate after adding the correction factor, k is the correction factor, ,in, is the relative error of the fitted mean velocity, is the cross-sectional average velocity estimated by the log-linear method.

9. A system for estimating average flow velocity in coal-fired power plants based on turbulence numerical simulation and flow field characteristics analysis, characterized in that: The system includes an actuator for executing the method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the average flow velocity of a coal-fired power plant based on turbulence numerical simulation and flow field characteristic analysis as described in any one of claims 1 to 8 is implemented.

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