Method for predicting heat flux density of boiler water wall based on cfd and temperature difference data

Abstract

This invention discloses a method for predicting the heat flux density of a boiler water-cooled wall based on CFD and temperature difference data. Multiple temperature difference heat flux meters are installed on the boiler water-cooled wall to acquire the time-varying characteristics of temperature and heat flux density at measuring points, as well as the measured load data of the boiler, resulting in measured temperature and heat flux density datasets at the measuring points. Boiler operating condition data is designed, and CFD numerical simulations are performed on the boiler load levels to obtain predicted heat flux density and temperature values ​​at the measuring points under different load levels. The predicted heat flux density and temperature values ​​under different load levels are used to correct the measured temperature and heat flux density datasets corresponding to the load levels, resulting in the actual measured heat flux density and temperature values ​​at measuring point i at any given time. Finally, the actual measured heat flux density value at any point within the coverage area of ​​the temperature difference heat flux meter to be tested is obtained using the actual measured heat flux density and the predicted heat flux density.

Description

Technical Field

[0001] This invention belongs to the field of thermal power generation technology, specifically a method for predicting the heat flux density of boiler water-cooled walls based on CFD and temperature difference data. Background Technology

[0002] The safety of water-cooled walls in ultracritical (supercritical) boilers is crucial to the safe operation of power plants. In recent years, with the increasing share of new energy sources, power plant boilers have been operating under wide loads for extended periods, significantly deviating from boiler design parameters. This operational instability has severely impacted the safety of boiler water-cooled walls. Accurate measurement of the heat flux density distribution in boiler water-cooled walls is a critical prerequisite for ensuring their safety. However, due to the high-temperature and high-pressure conditions under which boiler water-cooled walls operate, and the large dimensionality and numerous tube bundles in their geometry, an effective method for measuring and predicting the heat flux density distribution in boiler water-cooled walls is currently lacking.

[0003] The difficulty in accurately measuring the heat flux density distribution of water-cooled walls lies mainly in two aspects: First, due to the harsh operating conditions, large number of tubes, and large geometric dimensions of water-cooled walls, it is difficult to measure the characteristic parameters of the entire water-cooled wall region from a global perspective; second, the heat flux density of water-cooled walls is affected by multiple variables such as combustion in the furnace, ash and slag formation on the wall surface, and working fluid inside the water-cooled wall tubes, and existing monitoring methods are unable to obtain the above parameters, let alone the interrelationships between the above parameters. For example, existing differential heat flux meters or heat collectors can accurately measure the temperature or heat flux density of local locations in the furnace water-cooled walls. However, due to limitations in installation cost and boiler operation requirements, they cannot cover the entire furnace area, or even the critical operating zones, and therefore cannot obtain the heat flux density distribution of the furnace water-cooled walls. Existing industrial control cameras can be installed inside the furnace to visually obtain the overall characteristics of the flame inside the furnace, and can even monitor the slagging of local walls. However, this method has high installation costs and short camera lifespan, making it difficult to achieve full coverage monitoring of the water-cooled wall area. Acoustic wave measurement technology for water-cooled wall temperature is fast and convenient, but this method also cannot cover the entire furnace water-cooled wall area. The measured parameters are only relevant parameters on the fire-facing side inside the furnace, and none of the above methods can directly and quantitatively obtain the heat flux density distribution of the boiler water-cooled walls. In summary, there is currently no effective means to measure the heat flux density distribution of boiler water-cooled walls, and there is an urgent need to develop relevant methods and devices. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for predicting the heat flux density of boiler water-cooled walls based on CFD and temperature difference data, which solves the problems of harsh operating conditions of water-cooled walls, inability of measurement equipment to cover the entire area, and inability to measure the heat flux density of the entire water-cooled wall area.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the heat flux density of a boiler water-cooled wall based on CFD and temperature difference data, the specific steps of which are as follows:

[0006] S1 installs multiple differential heat flux meters on the boiler water-cooled wall, obtains the temperature and heat flux density at measuring point i of the differential heat flux meters as a function of time, obtains the measured load data of the boiler, and obtains the measured temperature dataset and measured heat flux density dataset at measuring point i respectively.

[0007] S2 designs boiler operating condition data, divides boiler load into levels, performs CFD numerical simulation, and obtains the predicted heat flux density and temperature at measuring point i of the temperature difference heat flux density meter under different load levels.

[0008] S3 uses the predicted heat flux density and temperature at point i of the differential heat flux density meter under different load conditions to correct the measured temperature dataset and measured heat flux density dataset corresponding to the load, so as to obtain the actual measured heat flux density and actual measured temperature at point i at any time.

[0009] S4 uses the actual measured heat flux density at any point i at any time and the predicted heat flux density to obtain the measured heat flux density value at any point within the coverage area of ​​the differential thermal heat flux density meter to be tested at any time.

[0010] Furthermore, in S1, the temperature difference heat flux density meter is installed on the back-fire side water-cooled wall of the front wall, rear wall, or side wall water-cooled walls of the boiler, and the number of installations is not less than 4.

[0011] In S4, the coverage area of ​​the differential temperature heat flux meter is the rectangular area enclosed by the midpoint of the line connecting it to the adjacent differential temperature heat flux meter, or the rectangular area enclosed by the midpoint of the line connecting it to the adjacent differential temperature heat flux meter, or the intersection area of ​​the adjacent differential temperature heat flux meters.

[0012] Furthermore, in S1, the heat flux density is a function of the temperature at measuring point i of the differential heat flux meter.

[0013] Furthermore, in S2, the boiler load is divided into 20%, 40%, 60%, 80%, and 100%, and CFD numerical simulations are performed under BMCR conditions respectively.

[0014] Furthermore, in S3, the measured temperature dataset and the measured heat flux density dataset at measuring point i are divided according to the load range [10%~30%], (30%~50%], (50%~70%], (70%~90%], (90%~100%), to obtain subsets of measured temperature data and subsets of measured heat flux density data;

[0015] The subsets of measured temperature data and measured heat flux density data in the above load ranges correspond to the boiler load grading in CFD numerical simulation.

[0016] Furthermore, in S3, the measured temperature data and measured heat flux density data within each load range are sorted by numerical value. The average value of the data in the 80% to 98% range in ascending order is calculated to obtain the measured average temperature value and the measured average heat flux density value. The measured temperature value is corrected using the measured average temperature value and the predicted temperature value, and the measured heat flux density value is corrected using the measured average heat flux density value and the predicted heat flux density value.

[0017] Furthermore, in S3, the heat flux density correction coefficient is obtained by using the predicted heat flux density value and the measured average heat flux density value, and the measured heat flux density value is corrected by the heat flux density correction coefficient to obtain the true measured heat flux density value.

[0018] A temperature correction factor is obtained by using the predicted temperature value and the average value of the measured temperature. The measured temperature value is then corrected using the temperature correction factor to obtain the true measured temperature value.

[0019] Furthermore, in S3, the heat flux density correction factor is the ratio of the predicted heat flux density to the measured average heat flux density; the temperature correction factor is the ratio of the predicted temperature to the measured average temperature.

[0020] Furthermore, in S4, the cleaning factor is obtained by using the actual measured value of heat flux density at measuring point i at any time and the predicted value of heat flux density. The final heat flux density value at any point within the coverage area of ​​the temperature difference heat flux density meter at any time is obtained by using the cleaning factor and the predicted value of heat flux density.

[0021] Furthermore, in S4, the cleanliness factor is the ratio of the actual measured value of heat flux density at measuring point i at any time to the predicted value of heat flux density.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects:

[0023] This invention proposes a method for predicting the heat flux density of boiler water-cooled walls based on CFD and temperature difference data. This method involves conducting CFD numerical simulations of the boiler furnace under different load conditions to obtain the heat flux density distribution characteristics across the entire furnace under various operating conditions. Based on this, a temperature difference heat flux meter is installed on the boiler water-cooled wall to obtain real-time operating data at the meter's measuring points. The measured temperature and heat flux density data at the temperature difference heat flux meter measuring points are used for dual correction based on the CFD prediction data. Furthermore, bidirectional calibration is performed using the measured heat flux density data and the CFD prediction data. This effectively combines the advantages of CFD calculations covering the entire water-cooled wall area to provide predictive data and the high accuracy of measurement data, thus solving the problems of harsh operating conditions for water-cooled walls, the inability of measurement equipment to fully cover the entire water-cooled wall area, and the inability to measure the heat flux density across the entire water-cooled wall area. Moreover, the temperature difference heat flux meter has the advantages of low installation cost, high measurement accuracy, and fast response. Attached Figure Description

[0024] Figure 1 Temperature cloud maps and outlines of the two walls on both sides of the boiler, where 1, 2, 3, and 4 are the installation locations of the differential temperature heat flux density meter.

[0025] Figure 2 A schematic diagram of the coverage area of ​​a differential temperature heat flux density meter in a boiler;

[0026] Figure 3 Schematic diagram of heat flux density distribution in the water-cooled walls of the entire furnace. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0028] This invention provides a method for predicting the heat flux density of boiler water-cooled walls based on CFD and temperature difference data, the specific steps of which are as follows:

[0029] 1) Install a differential heat flux meter on the water-cooled wall of the boiler, such as... Figure 1 As indicated by the asterisk, the differential temperature heat flux meter can be installed on the back-fire side water-cooled wall of the boiler's front wall, rear wall, or both side water-cooled walls. The specific installation location can be determined based on actual needs and boiler structural characteristics. The number of meters installed should not be less than four, with an optimal number of 20 to 200 recommended. The temperature T(i) and heat flux H(i) at measuring point i are obtained on-site using the differential temperature heat flux meter, showing their time-varying characteristics as T(i,t) and H(i,t). Simultaneously, the boiler load data C(t) is obtained. A database is established using the measured data and the load data over time for subsequent data analysis, resulting in the measured temperature dataset T(i,t,C) and the measured heat flux dataset H(i,t,C) at measuring point i.

[0030] Wherein, the heat flux density H(i) is a function of the temperature T(i) at the measuring point i of the temperature difference heat flux density meter, satisfying H(i,t,C)=f(T(i,t,C)).

[0031] 2) CFD numerical simulations were conducted on the boiler structure under load cc of 20%, 40%, 60%, 80%, and 100% BMCR conditions. The CFD boundary conditions were determined based on the boiler design coal type and design parameters to obtain the predicted values ​​of heat absorption and water-cooled wall temperature under the design conditions. The predicted heat flux density at point i of the thermodiffraction heat flux meter under load cc is Hp(i, cc), and the predicted temperature is Tp(i, cc).

[0032] 3) Take 5 load intervals [10%~30%], (30%~50%], (50%~70%], (70%~90%], (90%~100%], and divide the measured datasets T(i,t,C) and H(i,t,C) into 5 subsets according to the above load intervals, and correspond them to the 20%, 40%, 60%, 80% and 100% load conditions calculated by CFD, respectively. Take the measured temperature data subset Hm(i,cc) and measured heat flux density data subset Tm(i,cc) of the i-th measuring point in the cc load interval and in a certain operating time period (denoted as the training time period).

[0033] 4) After sorting the data in the subset of measured temperature data and subset of measured heat flux density data in each load interval according to the numerical value, calculate the average value of the data in the 80% to 98% range in ascending order of numerical value to obtain the measured average value of heat flux density Hclean(i,cc) and the measured average value of temperature Tclean(i,cc).

[0034] Specifically, due to the inherent uncertainty of the data, in order to eliminate this uncertainty and obtain a relatively stable reference value for the clean wall surface (i.e., the reference value should not fluctuate too much), the average value of the data in the ascending order of the values ​​between 80% and 98% is selected as the reference value.

[0035] 5) Due to positioning errors and water-cooled wall structure errors during the installation of the temperature difference heat flux meter, the measured values ​​at each measuring point may have uncertain deviations. Theoretically, CFD calculations can reasonably determine the heat flux density value of a clean water-cooled wall. Therefore, it is necessary to calculate a correction factor for the CFD. This correction factor can eliminate the uncertainty in the actual measurement of the device. Specifically:

[0036] The heat flux density correction factor Hfc(i,cc) is the ratio of the predicted heat flux density value Hp(i,cc) to the measured average heat flux density value Hclean(i,cc); the temperature correction factor Tfc(i,cc) is the ratio of the predicted temperature value Tp(i,cc) to the measured average temperature value Tclean(i,cc), as follows:

[0037] Hfc(i,cc)=Hp(i,cc) / Hclean(i,cc);

[0038] Tfc(i,cc)=Tp(i,cc) / Tclean(i,cc);

[0039] 6) Using correction coefficients, correct the measured heat flux density values ​​and measured temperature values ​​of the i-th measuring point in the cc load range and a certain operating time period (denoted as the training time period), to obtain the true measured heat flux density Htrue(i,t) and the true measured temperature Ttrue(i,t) of measuring point i at any time, specifically:

[0040] Htrue(i,t)=Hm(i,cc,t)*Hfc(i,cc);

[0041] Ttrue(i,t)=Tm(i,cc,t)*Tfc(i,cc);

[0042] 7) Using the actual measured heat flux density Htrue(i,t) at any time point i and the predicted heat flux density Hp(i,cc), the cleanliness factor CC(i,t) is obtained as follows:

[0043] CC(i,t)=Htrue(i,cc,t) / Hp(i,cc);

[0044] 8) Method for determining the final heat flux density value H(i,x,y) at any point within the coverage area of ​​the fluid density meter of the boiler to be tested:

[0045] Using the cleaning factor CC(i,t) and the predicted heat flux density Hp(i,cc), the final heat flux density value at any point within the coverage area of ​​the differential temperature heat flux meter at any given time can be obtained as follows:

[0046] H(i,x,y)=Hp(i,x,y)*CC(i,t);

[0047] Preferably, the load grading can be arbitrarily graded cc according to user needs and the actual operating load range of the device. Correspondingly, the measured data grading interval in step 3 can be defined as cc±dc, where dc is half of the difference between the two grading intervals.

[0048] Preferably, data cleaning can be performed before step 4, with methods such as box plots and data averaging recommended. If data cleaning is not performed, the range of the maximum value in step 4 is not limited to (80%–98%), but can be any range with a lower limit of 80%–90% and an upper limit of (95%–99%). However, considering the possibility of noise in the measured data, the upper limit cannot be 100% (as the upper limit may contain noise); if other data cleaning methods are used, the upper limit can be (95%–100%).

[0049] Preferably, in step 8, the coverage area of ​​each differential temperature heat flux meter is defined as the rectangular area enclosed by the midpoint of the line connecting it to the adjacent differential temperature heat flux meter, or a circle with a radius of 5 meters centered on its location, or the intersection of both, which can be determined according to user needs. Figure 2 The area shown by the dashed line is as follows.

[0050] The method of this invention can be applied to predicting the heat flux density distribution of water-cooled walls in all boilers. Figure 3 The schematic diagram of the heat flux density distribution of the entire furnace water-cooled wall is shown. It is preferably applied to high-parameter thermal power generating units with membrane water-cooled wall structures, such as the furnace water-cooled wall of ultra-supercritical thermal power generating units.

Claims

1. A method of predicting heat flux density of boiler water wall based on CFD and temperature difference data, characterized in that, The specific steps are as follows: S1 installs multiple differential heat flux meters on the boiler water-cooled wall, obtains the temperature and heat flux density at measuring point i of the differential heat flux meters as a function of time, obtains the measured load data of the boiler, and obtains the measured temperature dataset and measured heat flux density dataset at measuring point i respectively. S2 designs boiler operating condition data, divides boiler load into levels, performs CFD numerical simulation, and obtains the predicted heat flux density and temperature at measuring point i of the temperature difference heat flux density meter under different load levels. S3 uses the predicted heat flux density and temperature at point i of the differential heat flux density meter under different load conditions to correct the measured temperature dataset and measured heat flux density dataset corresponding to the load, so as to obtain the actual measured heat flux density and actual measured temperature at point i at any time. S4 uses the actual measured value of heat flux density at measuring point i at any time and the predicted value of heat flux density to obtain the measured heat flux density value at any point within the coverage area of ​​the differential temperature heat flux density meter to be tested at any time. In S3, the measured temperature dataset and measured heat flux density dataset at measuring point i are divided according to the load range [10%~30%], (30%~50%], (50%~70%], (70%~90%], (90%~100%], to obtain subsets of measured temperature data and subsets of measured heat flux density data; The subsets of measured temperature data and measured heat flux density data in the above load ranges correspond to the boiler load grading in CFD numerical simulation. In S3, the measured temperature data and measured heat flux density data within each load range are sorted by numerical value. The average value of the data in the 80%~98% range in ascending order is calculated to obtain the measured average temperature value and the measured average heat flux density value. The measured temperature value is corrected using the measured average temperature value and the predicted temperature value, and the measured heat flux density value is corrected using the measured average heat flux density value and the predicted heat flux density value. In S3, the heat flux density correction coefficient is obtained by using the predicted heat flux density value and the average value of the measured heat flux density. The measured heat flux density value is then corrected using the heat flux density correction coefficient to obtain the true measured heat flux density value. The temperature correction coefficient is obtained by using the predicted temperature value and the average value of the measured temperature. The measured temperature value is then corrected using the temperature correction coefficient to obtain the true measured temperature value. In S3, the heat flux density correction factor is the ratio of the predicted heat flux density to the measured average heat flux density; the temperature correction factor is the ratio of the predicted temperature to the measured average temperature. In S4, the cleaning factor is obtained by using the actual measured value of heat flux density at measuring point i at any time and the predicted value of heat flux density. The final heat flux density value at any point within the coverage area of ​​the temperature difference heat flux density meter at any time is obtained by using the cleaning factor and the predicted value of heat flux density. In S4, the cleanliness factor is the ratio of the actual measured value of heat flux density at measuring point i at any time to the predicted value of heat flux density.

2. A method of predicting heat flux density of boiler water wall based on CFD and temperature difference data as claimed in claim 1 wherein, In S1, the temperature difference heat flux density meter is installed on the back-fire side water-cooled wall of the front wall, rear wall or side wall water-cooled wall of the boiler, and the number of installations shall not be less than 4. In S4, the coverage area of ​​the differential temperature heat flux meter is the rectangular area enclosed by the midpoint of the line connecting it to the adjacent differential temperature heat flux meter, or the rectangular area enclosed by the midpoint of the line connecting it to the adjacent differential temperature heat flux meter, or the intersection area of ​​the adjacent differential temperature heat flux meters.

3. A method of predicting heat flux density of water wall of a boiler based on CFD and temperature difference data as claimed in claim 1 wherein, In S1, the heat flux density is a function of the temperature at point i of the thermoelectric heat flux density meter.

4. The method of predicting heat flux density of boiler water wall based on CFD and temperature difference data according to claim 1, characterized in that, In S2, the boiler load is divided into 20%, 40%, 60%, 80%, and 100%, and CFD numerical simulations are performed under BMCR conditions respectively.

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