Method for determining data average wind speed value point in cross section of air duct in non-uniform wind field
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Solution Overview
Problem
Existing air volume measurement devices fail to accurately measure air volume in non-uniform wind fields, particularly in the inlet ducts of coal-fired boilers, due to geometric averaging methods that do not represent actual wind speeds, leading to inaccuracies and potential blockages.
Innovation Solution
A method using a big data air volume dynamic sensing device with uniformly distributed preset points in the cross section of the air duct, measuring wind speed at each point and calculating a data average value through a controlling, monitoring, and analysis unit, adjusting error values until the preset points align with the data average wind speed range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If geometric average wind speed measurement is used in non-uniform wind fields, then measurement simplicity is maintained, but measurement precision deteriorates
Solution Approach 1:
The air duct cross-section is divided into multiple preset points that are uniformly distributed. Wind speed is measured at each individual point rather than using a single geometric average measurement, allowing the non-uniform wind field to be segmented and characterized more accurately.
Solution Approach 2:
The measurement approach transitions from a single-point geometric average (one-dimensional simplification) to multi-point spatial distribution (two-dimensional cross-section coverage). This dimensional expansion captures the spatial variability of the non-uniform wind field.
2Device complexity
If multiple pairs of sampling holes are uniformly set in straight pipe segment, then device complexity is reduced, but measurement precision deteriorates in non-uniform wind fields
Solution Approach 1:
The system dynamically identifies the data average wind speed value point position based on actual measurement data, rather than using a fixed geometric position. This dynamic adaptation allows the measurement system to adjust to varying non-uniform wind field conditions.
Solution Approach 2:
The patent replaces the mechanical/geometric averaging approach with a data-driven computational method. Big data analysis and statistical processing substitute for the physical geometric arrangement of sampling holes.
3Measurement precision
If Venturi tube air volume flowmeter is used for air volume measurement, then measurement precision is improved in uniform wind fields, but device complexity increases and it cannot ensure accuracy in non-uniform wind fields
Solution Approach 1:
A controlling, monitoring, and analysis unit serves as an intermediary between the multiple air volume flowmeters and the final air volume calculation. This intermediary processes data from multiple simple measurements to achieve accurate air volume measurement without requiring complex single-point devices.
4Measurement precision
If data average wind speed value point is determined through big data analysis, then measurement precision is improved in non-uniform wind fields, but device complexity and data processing requirements increase
Solution Approach 1:
The system uses its own measurement data from multiple preset points to automatically identify the data average wind speed value point position. The measurement system serves itself by using its collected data to optimize its own configuration without requiring external intervention.
Data Source
AI summary
A method for determining a data average wind speed value point in the cross section of the air duct. The method includes using, by a controlling, monitoring, and analysis unit A, the air volume flowmeter set in a big data air volume measurement dynamic sensing device in the cross section of the air duct, uniformly distributing preset points in the cross section of the air duct in all-around, measuring the air volume under a monitored load value for each preset point, and calculating an data average value under the monitored load value. The method includes accumulating the wind speed values of all preset points, and dividing by the number of the preset points; and amplifying a data average wind speed error value of a set air duct, until at least one preset point falls within a range of the data average wind speed error value of the set air duct.