CN113887058A - Chloride ion deposition rate prediction method considering distance from coastline and wind speed influence - Google Patents

Chloride ion deposition rate prediction method considering distance from coastline and wind speed influence Download PDF

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CN113887058A
CN113887058A CN202111192123.6A CN202111192123A CN113887058A CN 113887058 A CN113887058 A CN 113887058A CN 202111192123 A CN202111192123 A CN 202111192123A CN 113887058 A CN113887058 A CN 113887058A
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deposition rate
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coastline
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CN113887058B (en
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马小兵
陈乾
蔡义坤
刘宇杰
上官奕阳
刘瀛潞
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Beihang University
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Abstract

The invention provides a chloride ion deposition rate prediction method considering the distance from a coastline and the influence of wind speed, which is realized by the following steps: the method comprises the following steps: acquiring data of a chlorine ion deposition rate measuring point; step two: acquiring environmental factors corresponding to the measuring points; step three: fitting a chloride ion deposition rate prediction model; step four: predicting regional chloride ion deposition rate distribution; on the basis of a traditional single-factor deposition rate prediction model, the invention provides a prediction model integrating a plurality of factors including the distance from a coastline and the wind speed, and the application range of the model is widened; the method has higher precision, and can realize more accurate prediction of the chloride ion deposition rate; the method can be used for predicting the regional chloride ion deposition rate by combining with a historical environment database, and the result can be further used for product anticorrosion design, so that the method has a wide application prospect.

Description

Chloride ion deposition rate prediction method considering distance from coastline and wind speed influence
Technical Field
The invention provides a chloride ion deposition rate prediction method considering the influence of the distance from a coastline and the wind speed, and relates to a regional chloride ion deposition rate prediction method, which is based on the principle that a prediction model is constructed on the basis of a chloride ion deposition rule, and regional deposition rate numerical prediction is carried out by combining an environmental factor database; aiming at a small amount of historical data of the chloride ion deposition rate obtained by measurement of an environment data station, the method takes the distance between a main environmental factor influencing the deposition rate and a coastline and the wind speed as prediction factors, combines the change rule of the historical data, proposes a prediction model and carries out fitting verification, then obtains the prediction factor data to substitute the model on the basis of the existing environment data base, and finally predicts regional chloride ion deposition rate distribution; belongs to and is suitable for the fields of environmental corrosivity evaluation, product anticorrosion design, material selection and the like.
Background
The chloride ions are used as the main component of the atmospheric salt spray, the corrosion caused by the chloride ions can cause serious safety risk and economic loss, and the research on the distribution of the deposition rate of the chloride ions has important significance on the research on the corrosion degree and reliability of the structure; however, for the acquisition of the chloride ion deposition rate, the direct measurement has the defects of long period, high cost and hysteresis, and a more economic and reasonable method is used for constructing an empirical model of the chloride ion deposition rate for prediction;
the existing research shows that most prediction models of the chloride ion deposition rate are prediction models considering single environmental factors, and the prediction precision is low due to less model consideration factors; environmental factors considered by the existing model include the distance from the coastline, the wind speed, the temperature, the humidity, the rainfall and the like; the two factors of the distance from the coastline and the wind speed are main environmental factors influencing the deposition rate, but a prediction model comprehensively considering the two factors is less;
based on the above, the invention provides a chloride ion deposition rate prediction method considering the distance from the coastline and the influence of the wind speed, which is a chloride ion deposition rate prediction model considering the distance from the coastline and the wind speed, and the model is well verified on historical data and has higher fitting accuracy; furthermore, the method carries out regional deposition rate prediction on the basis of fully utilizing the existing database.
Disclosure of Invention
(1) The purpose of the invention is as follows: aiming at a small amount of discrete historical chlorine ion deposition rate data obtained by measurement of an environment data station, the invention aims to provide a chlorine ion deposition rate prediction method considering the influence of the distance from a coastline and the wind speed.
(2) The technical scheme is as follows:
the invention establishes the following basic settings:
setting 1 the historical measurement data of the deposition rate to be obtained by the same measurement mode; according to research, the deposition rate measurement data obtained by different measurement modes have larger difference, and the original data are required to be obtained by the same measurement mode in order to reduce the original data error caused by different measurement modes;
based on the above assumptions, the method for predicting the chloride ion deposition rate considering the distance from the coastline and the influence of the wind speed is realized by the following steps:
the method comprises the following steps: acquiring data of a chlorine ion deposition rate measuring point;
the original data of the chloride ion deposition rate is the basis for constructing and verifying a prediction model; the original data of the deposition rate can be obtained from historical measurement data of an environment measuring station, and field measurement can also be performed in a targeted manner; the measured point data needs to contain longitude and latitude, measuring time and deposition rate data corresponding to the measured point, and is stored in the following vector form:
(Loni,Lati,ti,Di)
wherein i is the number of the measuring point, LoniLongitude, Lat of point iiFor measuring the latitude, t, of point iiMeasuring time for point i, DiDeposition rate in mg.m for point i-2·d-1；
Step two: acquiring environmental factors corresponding to the measuring points;
constructing a deposition rate prediction model, wherein environmental factors corresponding to the measuring points, including the distance from the coast line and the wind speed, need to be acquired;
the distance from the coastline can be directly obtained through measuring point historical data, and if the historical data is lacked, the distance can be realized by combining GSHHG high-precision coastline data and a distance function in Matlab; finding out the distances from the station to all coastline data points through a distance function, and selecting the minimum value as the distance data between the station and the coastline;
the GSHHG High-precision coastline data is totally called as 'A Global Self-consistent, High-resolution geographic Database', and is a Global Self-consistent, layered and High-resolution geographic Database; the database provides global coastline, river and boundary data, and has different resolutions to choose from; in the invention, coastline data with the highest longitude is selected for processing;
the distance function in Matlab is commercial mathematical software produced by MathWorks company in America, and is used in the fields of data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robots, control systems and the like; the distance function is a calculation function used for solving the distance between two points on a sphere in Matlab, and the specific implementation mode is as follows:
[arclen,az]＝distance(lat1,lon1,lat2,lon2)
wherein lat1, lon1, lat2 and lon2 are longitude and latitude coordinates of the two points, arclen is a function solution to obtain the distance between the two points, the unit is km, and az is an azimuth angle of the second point relative to the first point, namely an angle of intersection of an arc on a meridian line containing the first point;
the wind speed can be directly obtained through measuring point historical data, and if the historical data is lacked, the wind speed can be obtained from the existing global environment database; the existing environment databases such as WDCC, NCDC and the like provide global meteorological data based on longitude and latitude positioning; in the invention, the point with the closest latitude in the existing database is used as the wind speed data of the measuring point;
the WDCC is a Climate database established by international support and provides service for the scientific community, and is totally called as World Data Center for the client; the system collects, stores and transmits earth system data, and focuses on climate simulation data and climate related data products;
the NCDC is a climate Data database in the largest running world and provides various environmental Data;
after the environmental data of the measuring points are obtained in the step, the measuring point data are stored in the form of the following vectors:
(Loni,Lati,ti,Di,vi,di)
wherein v isiWind speed at point i in m/s, diThe distance from a measuring point i to the coastline is km;
step three: fitting a chloride ion deposition rate prediction model;
for the obtained measuring point data, fitting is performed by using the formula (1):
D＝D0·d-n·exp(m·v) (1)
wherein D is deposition rate data of the measuring point and the unit is mg.m-2·d-1V is the corresponding wind speed of the measuring point, the unit is m/s, D is the distance between the measuring point and the coastline, the unit is km, D0N and m are undetermined constants and are obtained through fitting; the specific fitting process is obtained by solving by utilizing software such as Matlab and the like;
step four: predicting regional chloride ion deposition rate distribution;
according to a deposition rate prediction formula obtained by fitting in the third step and by combining with the existing environmental data, the method can be used for predicting regional chloride ion deposition rate distribution; obtaining environmental data values corresponding to each grid point by carrying out grid division on the longitude and latitude of the target area and combining the wind speed and the distance from the coastline obtaining mode in the step two, and substituting the environmental data values into a deposition rate prediction formula obtained by fitting in the step three to obtain regional chlorine ion deposition rate distribution; the accuracy of the prediction result is determined by the mesh partitioning accuracy;
through the steps, on the basis of a traditional deposition rate prediction model considering a single factor, the influence of a plurality of environmental factors such as the distance from a coastline and the wind speed is integrated, and an empirical prediction model considering a plurality of environmental factors and having a wider application range is provided; the prediction model for various environmental factors can realize more accurate prediction of the chloride ion deposition rate; the prediction method can be used for predicting the deposition rate in a regional range by combining with a historical database, and the result can be further used for product anticorrosion design; the method is scientific, has good manufacturability and has wide application prospect.
(3) The advantages and the effects are as follows: the invention relates to a chloride ion deposition rate prediction method considering the distance from a coastline and the influence of wind speed, which has the advantages that:
firstly, on the basis of a traditional deposition rate prediction model considering a single factor, the influence of two environmental factors, namely the distance from a coastline and the wind speed, is integrated, and a double-factor prediction model with a wider application range is provided;
the two-factor prediction model provided by the invention can realize more accurate prediction of the chloride ion deposition rate;
the prediction method can be used for predicting the deposition rate in a regional range by combining with a historical database, and the result can be further used for product anticorrosion design, so that the prediction method has wide application prospect.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a graph of the results of fitting the chloride ion deposition rate prediction model of the example embodiment.
FIG. 3 is a graph showing the deposition rate distribution of the regional chlorine ions of the example embodiment.
Detailed Description
The present invention will be described in further detail with reference to examples.
The invention relates to a chloride ion deposition rate prediction method considering the distance from a coastline and the influence of wind speed, which is to collect chloride ion deposition rate distribution data of Brazilian regions from documents, process the data and predict the deposition rate distribution of local Brazilian regions, and the concrete implementation mode is as follows as shown in figure 1.
The method comprises the following steps: acquiring data of a chlorine ion deposition rate measuring point;
acquiring data of a Brazil part region from a document, wherein the data comprises measurement data obtained by five measuring points at different times, and the measuring points already comprise historical data of distance from a coastline and wind speed; the detailed data are shown in table 1:
TABLE 1 summary of deposition Rate survey points for Brazilian areas
Figure BDA0003301605800000041
Figure BDA0003301605800000051
Figure BDA0003301605800000061
Step two: acquiring environmental data of a measuring point corresponding to the chloride ion deposition rate;
according to the invention, when the measuring points contain historical environmental data, the historical data is preferentially selected as the actual result of the environmental data; in the case of the scheme, environmental factor data in a document is selected as environmental data corresponding to a measuring point;
step three: fitting a chloride ion deposition rate prediction model;
substituting the sorted data into a model D ═ D0·d-nExp (m · v), fitting by Matlab, obtaining the fitting results of the model parameters as shown in fig. 2, and the parameter fitting results are reported in table 2:
TABLE 2 model parameter fitting results
Parameter(s) D0 m n R2
Value taking 2.49 0.5921 0.7467 0.9551
Step four: predicting regional chloride ion deposition rate distribution;
selecting a prediction area as a longitude range (33W,39W) and a latitude range (4S,10S) to predict the deposition rate distribution; firstly, obtaining an environmental data value of a grid point according to the solving mode of two environmental factors of the wind speed and the distance from the coastline, which are provided in the step two, substituting the environmental data value into a prediction formula to obtain a deposition rate corresponding to the grid point, wherein the result is shown in figure 3;
in conclusion, the invention relates to a chloride ion deposition rate prediction method considering the distance from a coastline and the wind speed, which is an empirical prediction model considering various environmental factors and has higher prediction precision; the method comprises the following specific steps: firstly, acquiring measuring point data of the chloride ion deposition rate; acquiring environment data corresponding to the measuring points; thirdly, fitting a chloride ion deposition rate prediction model; fourthly, predicting the distribution of the regional chloride ion deposition rate; the invention is suitable for the fields of environmental corrosivity evaluation, product anticorrosion design, material selection and the like, and has higher practical value.

Claims (1)

1. A method for predicting the deposition rate of chloride ions by considering the distance from a coastline and the influence of wind speed establishes the following basic settings:
setting 1: historical measurement data of the deposition rate is obtained in the same measurement mode; according to research, the deposition rate measurement data obtained by different measurement modes have large difference, and in order to reduce the original data error caused by different measurement modes, the original data are required to be obtained by the same measurement mode;
the method is characterized in that: the method is realized by the following steps:
the method comprises the following steps: acquiring data of a chlorine ion deposition rate measuring point;
the original data of the chloride ion deposition rate is the basis for constructing and verifying a prediction model; the original data of the deposition rate can be obtained from historical measurement data of an environment measuring station, and field measurement can be performed in a targeted manner; the measured point data needs to contain longitude and latitude, measuring time and deposition rate data corresponding to the measured point, and is stored in the following vector form:
(Loni,Lati,ti,Di)
wherein i is the number of the measuring point, LoniLongitude, Lat of point iiFor measuring the latitude, t, of point iiMeasuring time for point i, DiDeposition rate in mg.m for point i-2·d-1；
Step two: acquiring environmental factors corresponding to the measuring points;
constructing a deposition rate prediction model, wherein environmental factors corresponding to the measuring points, including the distance from the coast line and the wind speed, need to be acquired;
the distance from the coastline can be directly obtained through measuring point historical data, and if the historical data is lacked, the distance can be realized by combining GSHHG high-precision coastline data and a distance function in Matlab; finding out the distances from the station to all coastline data points through a distance function, and selecting the minimum value as the distance data between the station and the coastline;
for the wind speed, historical data can be directly obtained through measuring points, and if the historical data is lacked, the wind speed can be obtained from the existing global environment database; WDCC and NCDC in the existing environment database provide global meteorological data based on longitude and latitude positioning; the point with the closest latitude in the existing database is used as the wind speed data of the measuring point;
after the environmental data of the measuring points are obtained in the step, the measuring point data are stored in the form of the following vectors:
(Loni,Lati,ti,Di,vi,di)
wherein v isiWind speed at point i in m/s, diThe distance from a measuring point i to the coastline is km;
step three: fitting a chloride ion deposition rate prediction model;
for the obtained measuring point data, fitting is performed by using the formula (1):
D＝D0·d-n·exp(m·v)·············(1)
wherein D is deposition rate data of the measuring point and the unit is mg.m-2·d-1V is the corresponding wind speed of the measuring point, the unit is m/s, D is the distance between the measuring point and the coastline, the unit is km, D0M and n are undetermined constants and are obtained through fitting; the specific fitting process is obtained by solving by using Matlab software;
step four: predicting regional chloride ion deposition rate distribution;
according to a deposition rate prediction formula obtained by fitting in the third step, the method can be used for predicting regional chloride ion deposition rate distribution by combining with the existing environmental data; obtaining environmental data values corresponding to each grid point by carrying out grid division on the longitude and latitude of the target area and combining the wind speed and the distance from the coastline obtaining mode in the step two, and substituting the environmental data values into a deposition rate prediction formula obtained by fitting in the step three to obtain regional chlorine ion deposition rate distribution; the accuracy of the prediction result is determined by the mesh partitioning accuracy.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN109557017A (en) * 2018-11-15 2019-04-02 北京科技大学 Tropical marine atmospheres Environmental Concrete structural steel muscle corrosion resistance experimental method
CN112115598A (en) * 2020-09-09 2020-12-22 南京大学 Data and model coupling analysis method for marine drift trajectory prediction

Patent Citations (4)

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Publication number Priority date Publication date Assignee Title
CN104697908A (en) * 2015-03-12 2015-06-10 国家海洋局天津海水淡化与综合利用研究所 Method for monitoring drifting salt deposition of seawater cooling tower
CN107992975A (en) * 2017-12-14 2018-05-04 北京交通大学 A kind of load forecasting method that becomes more meticulous for merging historical data and Real Time Effect factor
CN109557017A (en) * 2018-11-15 2019-04-02 北京科技大学 Tropical marine atmospheres Environmental Concrete structural steel muscle corrosion resistance experimental method
CN112115598A (en) * 2020-09-09 2020-12-22 南京大学 Data and model coupling analysis method for marine drift trajectory prediction

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117313307A (en) * 2023-06-26 2023-12-29 北京航空航天大学 Climate model simulation temperature data correction method integrating space-time environment information

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