CN112507566A - Method for calculating parameters of throttling device - Google Patents
Method for calculating parameters of throttling device Download PDFInfo
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- CN112507566A CN112507566A CN202011520692.4A CN202011520692A CN112507566A CN 112507566 A CN112507566 A CN 112507566A CN 202011520692 A CN202011520692 A CN 202011520692A CN 112507566 A CN112507566 A CN 112507566A
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Abstract
The invention discloses a method for calculating parameters of a throttling device, which comprises the following steps: A. selecting a calibration model closest to the geometric shape of the throttling device to be calculated; B. performing iterative computation by using the outflow coefficient of the calibration model as an iteration starting point; if the iterative process has divergence problems, turning to the step C; C. randomly selecting the outflow coefficient of another calibration model as an iteration starting point to carry out iterative computation; D. selecting a plurality of iteration nodes in the iteration process of the step C, and sequentially accessing the divergent iteration process in the step B to the iteration nodes for continuous iteration; if the iteration process has at least one convergent iteration process, fitting by using the convergent iteration process; and if the iterative process does not have a convergent iterative process, returning to the step C to reselect the calibration model. The method can overcome the defects of the prior art, and expands the application range of determining the outflow coefficient of the throttling device by using an iteration method.
Description
Technical Field
The invention relates to the technical field of petrochemical industry, in particular to a method for calculating parameters of a throttling device.
Background
The petrochemical industry basically adopts pipeline transportation, the most common metering method of pipeline flow is throttling device metering, the metering mode of the throttling device needs to calculate various parameters of the throttling device, and the outflow coefficient is one of the most important parameters. In the prior art, the calculation of the outflow coefficient by using an iteration method is a commonly used mode. However, the iterative process may have a problem that an iterative result diverges, so that the determination of the outflow coefficient cannot be performed by using an iterative method in some cases.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for calculating parameters of a throttling device, which can solve the defects of the prior art, solve the problem that the outflow coefficient cannot be obtained due to divergence in the iteration process, and expand the application range of determining the outflow coefficient of the throttling device by using an iteration method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A method for throttle device parameter calculation, comprising the steps of:
A. selecting a calibration model closest to the geometric shape of the throttling device to be calculated;
B. performing iterative computation by using the outflow coefficient of the calibration model as an iteration starting point to obtain the outflow coefficient of the throttling device to be computed; if the iterative process has divergence problems, turning to the step C;
C. randomly selecting the outflow coefficient of another calibration model as an iteration starting point to carry out iterative computation;
D. selecting a plurality of iteration nodes in the iteration process of the step C, and sequentially accessing the divergent iteration process in the step B to the iteration nodes for continuous iteration; if at least one convergent iterative process exists in the iterative process, fitting by using the convergent iterative process to obtain an outflow coefficient of the throttling device to be calculated; and if the iterative process does not have a convergent iterative process, returning to the step C to reselect the calibration model.
Preferably, in step D, selecting the iteration node comprises the following steps,
d11, in the iteration process, comparing the latest iteration result with the iteration process result before the current iteration process;
d12, if the similarity of the latest iteration result and the iteration process result before the current iteration process meets the preset condition, marking the latest iteration result as a preselected iteration node;
d13, continuing the iteration process, and if the deviation ratio of the iteration result of the preselected iteration node and the result average value of the current iteration process is greater than a set threshold value, deleting the mark of the preselected iteration node;
d14, after the iteration is finished, taking the remaining preselected iteration nodes as iteration nodes.
Preferably, in step D12, the preset conditions are,
the deviation rate of the latest iteration result and the last iteration result is less than 5%, and the deviation rate of the latest iteration result and other iteration results is less than 10%.
Preferably, in the step D, sequentially accessing the iterative process that diverges in the step B to the iterative node for continuing iteration includes the following steps,
d21, averaging the results of the last three iterations of the iterative process in which divergence occurs;
d22, comparing the average value obtained in the step D21 with the original iteration result of the iteration node to be accessed, and correcting;
d23, inputting the iteration node by using the corrected iteration result, and continuing the subsequent iteration process.
Preferably, in step D22, the linear correlation between the average value obtained in step D21 and the original iteration result of the iteration node to be accessed is compared, if the linear correlation is lower than the set threshold, the number of iteration results averaged in step D21 is increased until the linear correlation is not lower than the set threshold, and if the linear correlation is still lower than the set threshold after all iteration results are averaged, the average result with the highest linear correlation is selected as the correction result.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: the invention adopts the existing calibration model to carry out re-iteration processing on the divergent iteration process, thereby solving the problem that the outflow coefficient cannot be calculated due to the iterative divergence problem. In order to improve the accuracy of the reiteration process, the invention further improves the data processing process of the reiteration, thereby expanding the application range of determining the outflow coefficient of the throttling device by using an iteration method.
Drawings
FIG. 1 is a schematic diagram of one embodiment of the present invention.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes the steps of:
A. selecting a calibration model closest to the geometric shape of the throttling device to be calculated;
B. performing iterative computation by using the outflow coefficient of the calibration model as an iteration starting point to obtain the outflow coefficient of the throttling device to be computed; if the iterative process has divergence problems, turning to the step C;
C. randomly selecting the outflow coefficient of another calibration model as an iteration starting point to carry out iterative computation;
D. selecting a plurality of iteration nodes in the iteration process of the step C, and sequentially accessing the divergent iteration process in the step B to the iteration nodes for continuous iteration; if at least one convergent iterative process exists in the iterative process, fitting by using the convergent iterative process to obtain an outflow coefficient of the throttling device to be calculated; and if the iterative process does not have a convergent iterative process, returning to the step C to reselect the calibration model.
In step D, selecting an iteration node comprises the following steps,
d11, in the iteration process, comparing the latest iteration result with the iteration process result before the current iteration process;
d12, if the similarity of the latest iteration result and the iteration process result before the current iteration process meets the preset condition, marking the latest iteration result as a preselected iteration node;
d13, continuing the iteration process, and if the deviation ratio of the iteration result of the preselected iteration node and the result average value of the current iteration process is greater than a set threshold value, deleting the mark of the preselected iteration node;
d14, after the iteration is finished, taking the remaining preselected iteration nodes as iteration nodes.
In step D12, the preset condition is,
the deviation rate of the latest iteration result and the last iteration result is less than 5%, and the deviation rate of the latest iteration result and other iteration results is less than 10%.
In the step D, the iterative process which is diverged in the step B is sequentially accessed to the iterative nodes for continuous iteration, which comprises the following steps,
d21, averaging the results of the last three iterations of the iterative process in which divergence occurs;
d22, comparing the average value obtained in the step D21 with the original iteration result of the iteration node to be accessed, and correcting;
d23, inputting the iteration node by using the corrected iteration result, and continuing the subsequent iteration process.
In step D22, the linear correlation between the average value obtained in step D21 and the original iteration result of the iteration node to be accessed is compared, if the linear correlation is lower than the set threshold, the number of iteration results averaged in step D21 is increased until the linear correlation is not lower than the set threshold, and if all the iteration results are averaged and the linear correlation is still lower than the set threshold, the average result with the highest linear correlation is selected as the correction result.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A method for throttling device parameter calculation, comprising the steps of:
A. selecting a calibration model closest to the geometric shape of the throttling device to be calculated;
B. performing iterative computation by using the outflow coefficient of the calibration model as an iteration starting point to obtain the outflow coefficient of the throttling device to be computed; if the iterative process has divergence problems, turning to the step C;
C. randomly selecting the outflow coefficient of another calibration model as an iteration starting point to carry out iterative computation;
D. selecting a plurality of iteration nodes in the iteration process of the step C, and sequentially accessing the divergent iteration process in the step B to the iteration nodes for continuous iteration; if at least one convergent iterative process exists in the iterative process, fitting by using the convergent iterative process to obtain an outflow coefficient of the throttling device to be calculated; and if the iterative process does not have a convergent iterative process, returning to the step C to reselect the calibration model.
2. The method for throttle parameter calculation of claim 1, wherein: in step D, selecting an iteration node comprises the following steps,
d11, in the iteration process, comparing the latest iteration result with the iteration process result before the current iteration process;
d12, if the similarity of the latest iteration result and the iteration process result before the current iteration process meets the preset condition, marking the latest iteration result as a preselected iteration node;
d13, continuing the iteration process, and if the deviation ratio of the iteration result of the preselected iteration node and the result average value of the current iteration process is greater than a set threshold value, deleting the mark of the preselected iteration node;
d14, after the iteration is finished, taking the remaining preselected iteration nodes as iteration nodes.
3. The method for throttle parameter calculation of claim 2, wherein: in step D12, the preset condition is,
the deviation rate of the latest iteration result and the last iteration result is less than 5%, and the deviation rate of the latest iteration result and other iteration results is less than 10%.
4. A method for throttle device parameter calculation in accordance with claim 3, characterized by: in the step D, the iterative process which is diverged in the step B is sequentially accessed to the iterative nodes for continuous iteration, which comprises the following steps,
d21, averaging the results of the last three iterations of the iterative process in which divergence occurs;
d22, comparing the average value obtained in the step D21 with the original iteration result of the iteration node to be accessed, and correcting;
d23, inputting the iteration node by using the corrected iteration result, and continuing the subsequent iteration process.
5. The method for throttle parameter calculation of claim 4, wherein: in step D22, the linear correlation between the average value obtained in step D21 and the original iteration result of the iteration node to be accessed is compared, if the linear correlation is lower than the set threshold, the number of iteration results averaged in step D21 is increased until the linear correlation is not lower than the set threshold, and if all the iteration results are averaged and the linear correlation is still lower than the set threshold, the average result with the highest linear correlation is selected as the correction result.
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Application publication date: 20210316 |