CN-121596756-B - Self-adaptive adjustment method and system for total temperature sensor module

CN121596756BCN 121596756 BCN121596756 BCN 121596756BCN-121596756-B

Abstract

The invention belongs to the technical field of automatic control, and provides a self-adaptive adjustment method and a self-adaptive adjustment system for a total temperature sensor module, wherein the method comprises the steps of constructing a three-dimensional model of an air inlet channel, simulating a fluid environment, collecting thermal response data, calculating a time constant, and judging whether hysteresis exists; if the historical fluid speed mutation point is delayed, the mutation heat data of the historical fluid speed mutation point is extracted, a dynamic association model is established to carry out compensation correction and verify qualification, upstream pressure and temperature gradient are collected in real time after the historical fluid speed mutation point is qualified, the mutation point is predicted and the total temperature value is corrected, the suitability of the model is estimated based on the correction value, and working condition partition correction is carried out when the model is insufficient, wherein the system comprises a hysteresis analysis module, a model construction module, a compensation correction module and an adaptation analysis module. The method is beneficial to optimizing the problem of total temperature measurement hysteresis in high-speed airflow.

Inventors

LIU BO
CHEN HAIGUANG

Assignees

哈工科讯(沈阳)智能工业技术有限公司

Dates

Publication Date: 20260508
Application Date: 20260130

Claims (8)

1. The self-adaptive adjustment method of the total temperature sensor module is characterized by comprising the following steps: calculating a thermal response time constant of the total temperature sensor based on the thermal response data, and judging whether the thermal response of the total temperature sensor is lagged or not according to the thermal response time constant; if the historical measured value of the historical mutation point is delayed, obtaining mutation heat data and mutation delay coefficients of the historical mutation point and establishing a dynamic association model; The compensation correction process comprises the following steps: constructing a dynamic association model; Acquiring a historical measured value and inputting the historical measured value into a dynamic correlation model to obtain a predicted actual total temperature value; Obtaining a sudden change hysteresis coefficient, calculating an absolute difference value between a predicted actual total temperature value and a historical measured value, and performing product processing on an absolute difference value calculation result, the sudden change hysteresis coefficient and a thermal response time constant to obtain a total temperature compensation value; Based on the obtained total temperature compensation value, summing the total temperature compensation value and the historical measurement value to obtain a total temperature correction value; the method for obtaining the mutation hysteresis coefficient is as follows: Obtaining the difference value between the historical actual total temperature value and the historical measured value of different historical mutation points to obtain a total temperature deviation value, and carrying out ratio processing on the total temperature deviation value and the fluid velocity mutation amplitude value to obtain a pre-mutation hysteresis coefficient; Calculating the average value of the pre-mutation hysteresis coefficients of m historical mutation points to obtain mutation hysteresis coefficients; If the fluid velocity is qualified, real-time collecting upstream fluid parameters of the total temperature sensor, predicting and analyzing real-time mutation points of the fluid velocity based on the upstream fluid parameters, and compensating and correcting total temperature measured values of the real-time mutation points to obtain real-time total temperature corrected values; and carrying out suitability analysis on the dynamic correlation model under different working conditions based on the real-time total temperature correction value, and entering a working condition partition correction flow if the suitability is insufficient.
2. The method for adaptively adjusting a total temperature sensor module according to claim 1, wherein the calculating the thermal response time constant is performed by: collecting thermal response data of a total temperature sensor in a thermal response experiment; And inputting the thermal response data into a thermal response time constant calculation formula to obtain the thermal response time constant.
3. The method for adaptively adjusting a total temperature sensor module according to claim 1, wherein the predictive analysis is performed by: acquiring upstream dynamic pressure data and temperature gradient data, and integrating the data into a dynamic pressure sequence and a temperature gradient sequence according to sampling time points; performing deviation analysis based on the dynamic pressure sequence and the temperature gradient sequence to respectively obtain dynamic pressure change rate and temperature gradient change rate; carrying out standardized dimensionality removal treatment on the obtained dynamic pressure change rate and the temperature gradient change rate; And (3) carrying out product calculation on the dynamic pressure change rate and the temperature gradient change rate, and carrying out square opening processing to obtain mutation judgment values of different sampling time points.
4. The method for adaptively adjusting a total temperature sensor module as set forth in claim 3, wherein said deviation analysis is performed by: Performing difference processing on dynamic pressure data of adjacent sampling time points of the dynamic pressure sequence to obtain dynamic pressure deviation values between the adjacent sampling time points, and performing ratio calculation on the dynamic pressure deviation values and the time intervals between the adjacent sampling time points to obtain dynamic pressure change rates; And carrying out difference processing on the temperature gradient values of adjacent sampling time points of the temperature gradient sequence to obtain temperature gradient deviation values of the adjacent sampling time points, and carrying out ratio calculation on the time intervals between the temperature gradient deviation values and the adjacent sampling time points to obtain the temperature gradient change rate.
5. The method for adaptively adjusting a total temperature sensor module as in claim 1, wherein said determining the fluid velocity discontinuity comprises the steps of: obtaining mutation judgment values of k sampling time points before occurrence of all mutation points in historical data; Averaging mutation judgment values of k sampling time points before each mutation point to obtain average mutation judgment values, arranging the average mutation judgment values of all mutation points in a descending order, and selecting the minimum average mutation judgment value as a mutation judgment threshold; and if the mutation judgment value is larger than or equal to the mutation judgment threshold value, judging the sampling time point as a mutation point at which the mutation of the fluid speed occurs.
6. The method for adaptively adjusting a total temperature sensor module according to claim 1, wherein the suitability analysis is performed by: Acquiring total temperature correction values and actual total temperature values of different mutation points in each working condition interval; calculating absolute difference values between the total temperature correction values of the abrupt points in all working condition intervals and the actual total temperature values to obtain compensation deviation values; carrying out averaging treatment on all the compensation deviation values under a single working condition to obtain a compensation deviation average value, and carrying out standard difference calculation to obtain a compensation standard difference value; Calculating the ratio of the compensation standard deviation value to the compensation deviation mean value to obtain a single-working-condition variation coefficient; and (3) comparing and analyzing based on the single-working-condition variation coefficient, and judging that the suitability of the dynamic association model to the working condition is insufficient.
7. The method for adaptively adjusting the total temperature sensor module according to claim 1, wherein the working condition partition correction is performed by the following steps: acquiring original experimental data of all mutation points in a defect working condition interval, including upstream streaming pressure data, temperature gradient data, total temperature measured values and actual total temperature values, and screening abnormal data points through a data quality evaluation matrix; optimizing and correcting the dynamic association model based on the screened abnormal data points; And carrying out cross verification on the corrected dynamic association model in a defective working condition interval, evaluating the robustness of the model by adopting a leave-one-out method, and carrying out suitability analysis on different working conditions on the corrected dynamic association model again.
8. An adaptive adjustment system for a total temperature sensor module for implementing an adaptive adjustment method for a total temperature sensor module according to any one of claims 1 to 7, comprising the following modules: the hysteresis analysis module is used for carrying out a thermal response experiment on the total temperature sensor and collecting thermal response data, calculating a thermal response time constant of the total temperature sensor based on the thermal response data, and judging whether the thermal response of the total temperature sensor is hysteresis or not according to the thermal response time constant; The model construction module is used for acquiring the mutation heat data and mutation hysteresis coefficient of the historical mutation points and establishing a dynamic association model if the mutation heat data and the mutation hysteresis coefficient of the historical mutation points are lagged; The compensation correction module is used for acquiring upstream fluid parameters of the high-temperature sensor in real time if the fluid parameters are qualified, predicting and analyzing real-time mutation points of the fluid speed based on the upstream fluid parameters, and compensating and correcting total temperature measured values of the real-time mutation points to obtain real-time total temperature correction values; And the adaptation analysis module is used for carrying out adaptation analysis on the dynamic association model under different working conditions based on the real-time total temperature correction value, and entering a working condition partition correction flow if the adaptation is insufficient.

Description

Self-adaptive adjustment method and system for total temperature sensor module Technical Field The invention belongs to the technical field of automatic control, and particularly relates to a self-adaptive adjustment method and system for a total temperature sensor module. Background In a high-speed high-temperature airflow environment of an aeroengine air inlet channel, a total temperature sensor is easy to generate obvious thermal response lag due to material thermal inertia, a thermal-flow-force multi-field coupling effect and fluid temperature mutation, so that a measured value and an actual total temperature have dynamic deviation; The measurement deviation is further amplified when the flow field is suddenly changed, the real-time performance of the total temperature monitoring of the air inlet duct is seriously affected, and even the measurement distortion possibly caused by thermal hysteresis induces the safety risks of engine surge, overtemperature and the like, a method for performing self-adaptive compensation correction is needed to quantify the thermal response time constant, locate the hysteresis degree and realize real-time calibration, so that the dynamic accuracy of total temperature measurement and the running safety of a system are improved; therefore, the invention provides a self-adaptive adjustment method and a self-adaptive adjustment system for a total temperature sensor module. Disclosure of Invention In order to overcome the deficiencies of the prior art, at least one technical problem presented in the background art is solved. The invention solves the technical problems by adopting the technical scheme that the self-adaptive adjusting method of the total temperature sensor module comprises the following steps: calculating a thermal response time constant of the total temperature sensor based on the thermal response data, and judging whether the thermal response of the total temperature sensor is lagged or not according to the thermal response time constant; if the historical measured value of the historical mutation point is delayed, obtaining mutation heat data and mutation delay coefficients of the historical mutation point and establishing a dynamic association model; If the fluid velocity is qualified, real-time collecting upstream fluid parameters of the total temperature sensor, predicting and analyzing real-time mutation points of the fluid velocity based on the upstream fluid parameters, and compensating and correcting total temperature measured values of the real-time mutation points to obtain total temperature corrected values; and carrying out suitability analysis on the dynamic correlation model under different working conditions based on the total temperature correction value, and entering a working condition partition correction flow if the suitability is insufficient. Further, the method for calculating the thermal response time constant is as follows: collecting thermal response data of a total temperature sensor in a thermal response experiment; And inputting the thermal response data into a thermal response time constant calculation formula to obtain the thermal response time constant. Further, the compensation correction is performed by: constructing a dynamic association model; Acquiring a historical measured value and inputting the historical measured value into a dynamic correlation model to obtain a predicted actual total temperature value; Obtaining a sudden change hysteresis coefficient, calculating an absolute difference value between a predicted actual total temperature value and a historical measured value, and performing product processing on an absolute difference value calculation result, the sudden change hysteresis coefficient and a thermal response time constant to obtain a total temperature compensation value; And summing the total temperature compensation value with the history measured value based on the obtained total temperature compensation value to obtain a total temperature correction value. Further, the method for obtaining the mutation hysteresis coefficient is as follows: Obtaining the difference value between the historical actual total temperature value and the historical measured value of different historical mutation points to obtain a total temperature deviation value, and carrying out ratio processing on the total temperature deviation value and the fluid velocity mutation amplitude value to obtain a pre-mutation hysteresis coefficient; and calculating the average value of the pre-mutation hysteresis coefficients of the m historical mutation points to obtain the mutation hysteresis coefficients. Further, the process of performing the predictive analysis is: acquiring upstream dynamic pressure data and temperature gradient data, and integrating the data into a dynamic pressure sequence and a temperature gradient sequence according to sampling time points; performing deviation analysis based on the dynamic pressure sequence and the