CN-121997626-A - Thermal expansion stress monitoring method for thermal energy storage system

CN121997626ACN 121997626 ACN121997626 ACN 121997626ACN-121997626-A

Abstract

The invention provides a thermal expansion stress monitoring method of a thermal energy storage system, which belongs to the technical field of thermal energy storage systems, and aims to solve the technical problems that thermal expansion stress and other load coupling stress cannot be accurately separated and predicted in the operation process of the thermal energy storage system by deploying a multi-physical-field coupling sensor array, adopting an empirical mode decomposition algorithm to separate thermal expansion stress components from other load stress components, establishing a self-adaptive grid refinement finite difference thermal stress prediction model to combine a Kalman filtering algorithm to realize transient thermal shock stress prediction, simplifying stress component matrix calculation through spectral decomposition and diagonalization frames, establishing a stress threshold calculation equation set, and realizing intelligent switching of a standard operation mode, a stress monitoring enhancement mode and a stress self-adaptive adjustment mechanism according to a stress concentration coefficient and a threshold comparison result.

Inventors

ZHANG LEI
XU MEI
ZHANG CHENXI
WANG ZEZHONG
ZHU CHANG
WEI FEI
BAI DINGRONG

Assignees

鄂尔多斯实验室
清华大学

Dates

Publication Date: 20260508
Application Date: 20251203

Claims (10)

1. The thermal expansion stress monitoring method of the thermal energy storage system is characterized by comprising the steps of deploying a multi-physical field coupling sensor array at key nodes on the wall surface of a heat storage container of the thermal energy storage system; the method comprises the steps of establishing a multi-load response characteristic library of a thermal energy storage system, acquiring a multi-load response characteristic library data set by applying known thermal load, mechanical load and gravity load to a heat storage container, acquiring corresponding strain response signals, temperature change signals and vibration signals, acquiring multi-physical field signals in the operation process of the heat storage container in real time, preprocessing the acquired signals to obtain preprocessed multi-physical field monitoring data, adopting an empirical mode decomposition algorithm to conduct signal decomposition on the preprocessed multi-physical field monitoring data, separating thermal expansion stress components, mechanical load stress components and gravity load stress components, realizing source identification and separation of multi-load coupling stress, obtaining a decomposed stress component matrix, establishing a self-adaptive grid refinement finite difference thermal stress prediction model, combining a Kalman filtering algorithm to correct transient thermal shock model parameters in real time, calculating transient stress predicted values, obtaining a thermal expansion stress distribution cloud chart and a stress concentration coefficient, establishing a spectral decomposition and diagonalization framework of the stress component matrix, calculating a diagonalization power degree and function simplification matrix, calculating a thermal expansion stress evaluation index, adopting a stress threshold calculation equation set to obtain an operation mode switching threshold and an early warning threshold and a control threshold and an early warning threshold and comparing an operation threshold and an early warning threshold and an operation threshold and controlling a comparison result of the thermal expansion threshold and an operation mode switching threshold.
2. The method of claim 1, wherein the multi-physical field coupling sensor array comprises a strain gauge sensor, a temperature sensor, and a vibration acceleration sensor, the sensor arrangement pitch is 1/10 of the characteristic length of the heat storage container, and the sensor sampling frequency is set to 1000Hz.
3. The method for monitoring thermal expansion stress of thermal energy storage system according to claim 2, wherein the step of preprocessing the collected signals comprises denoising filtering, signal calibration and data synchronization.
4. A thermal expansion stress monitoring method for a thermal energy storage system according to claim 3, wherein the multi-load response characteristic library refers to a database storing system response characteristics under different load conditions, and is used as a reference basis for load identification and stress separation.
5. The method of claim 4, wherein the decomposed stress component matrix is a matrix data structure including a thermal expansion stress component, a mechanical load stress component, and a gravitational load stress component, which is obtained by processing the decomposed stress component matrix by an empirical mode decomposition algorithm.
6. The method of claim 5, wherein the stress concentration factor is a dimensionless parameter indicative of a degree of non-uniformity of stress distribution, and is defined as a ratio of a maximum stress to an average stress.
7. The method of claim 6, wherein the spectral decomposition and diagonalization framework refers to a mathematical method of eigenvalue decomposition and diagonalization of the decomposed stress component matrix for simplifying matrix operations and extracting the principal stress features.
8. The method of claim 7, wherein the system of stress threshold calculation equations includes a stability criteria threshold calculation equation for determining an upper limit of a stress concentration coefficient for normal operation of the system, the input includes a thermal storage container material yield strength, a safety coefficient, a temperature correction coefficient, the output is a stability criteria threshold, and the risk alert threshold calculation equation for determining a stress concentration coefficient threshold for initiating a stress adaptive adjustment mechanism, the input includes a thermal storage container material ultimate strength, a fatigue correction coefficient, an ambient temperature coefficient, and the output is a risk alert threshold.
9. The method of claim 8, wherein the standard operation mode is maintained when the stress concentration coefficient is less than the stability standard threshold, and the stress monitoring enhancement mode is started when the stress concentration coefficient e [ stability standard threshold, hazard pre-warning threshold), and the sampling frequency is increased to 2000Hz.
10. The method for monitoring thermal expansion stress of a thermal energy storage system according to claim 9, wherein when the stress concentration coefficient is greater than or equal to a danger early warning threshold value, a stress self-adaptive adjustment mechanism is started, and stress level control is achieved by adjusting the flow rate of a heat storage medium, reducing the working temperature and starting a stress compensation mechanism.

Description

Thermal expansion stress monitoring method for thermal energy storage system Technical Field The invention belongs to the technical field of thermal energy storage systems, and particularly relates to a thermal expansion stress monitoring method of a thermal energy storage system. Background The thermal energy storage system is used as a key technology in the field of new energy, a heat storage container of the thermal energy storage system bears complex multi-load coupling action in the operation process, a traditional stress monitoring method mainly adopts a single parameter monitoring and static threshold judging mechanism, stress state evaluation of a single point or a local area is carried out by arranging strain gauges or temperature sensors, and thermal stress distribution is calculated based on an empirical formula or a simplified model. In the prior art, because complex coupling effects of various loads such as thermal load, mechanical load, gravity load and the like exist in the operation process of the thermal energy storage system, the traditional monitoring method cannot effectively separate stress components generated by different load sources, so that the thermal expansion stress evaluation precision is insufficient, the prediction model parameter is solidified and lacks self-adaptive adjustment capability, and the response of the monitoring system is lagged. Under the condition of facing transient thermal shock and complex load coupling, the traditional technology is difficult to realize accurate identification and separation of thermal expansion stress, can not provide reliable stress early warning and adjustment basis for safe operation of a heat storage system, and has the technical problem that the thermal expansion stress and other load coupling stress can not be accurately separated and predicted. Disclosure of Invention In view of the above, the invention provides a method for monitoring thermal expansion stress of a thermal energy storage system, which can solve the technical problem that thermal expansion stress and other load coupling stress cannot be accurately separated and predicted in the operation process of the thermal energy storage system in the prior art. The invention provides a method for monitoring thermal expansion stress of a thermal energy storage system, which comprises the steps of deploying a plurality of physical field coupling sensor arrays on key nodes on the wall surface of a heat storage container of the thermal energy storage system; the method comprises the steps of establishing a multi-load response characteristic library of a thermal energy storage system, acquiring a multi-load response characteristic library data set by applying known thermal load, mechanical load and gravity load to a heat storage container, acquiring corresponding strain response signals, temperature change signals and vibration signals, acquiring multi-physical field signals in the operation process of the heat storage container in real time, preprocessing the acquired signals to obtain preprocessed multi-physical field monitoring data, adopting an empirical mode decomposition algorithm to conduct signal decomposition on the preprocessed multi-physical field monitoring data, separating thermal expansion stress components, mechanical load stress components and gravity load stress components, realizing source identification and separation of multi-load coupling stress, obtaining a decomposed stress component matrix, establishing a self-adaptive grid refinement finite difference thermal stress prediction model, combining a Kalman filtering algorithm to correct transient thermal shock model parameters in real time, calculating transient stress predicted values, obtaining a thermal expansion stress distribution cloud chart and a stress concentration coefficient, establishing a spectral decomposition and diagonalization framework of the stress component matrix, calculating a diagonalization power degree and function simplification matrix, calculating a thermal expansion stress evaluation index, adopting a stress threshold calculation equation set to obtain an operation mode switching threshold and an early warning threshold and a control threshold and an early warning threshold and comparing an operation threshold and an early warning threshold and an operation threshold and controlling a comparison result of the thermal expansion threshold and an operation mode switching threshold. The multi-physical field coupling sensor array comprises strain gauge sensors, temperature sensors and vibration acceleration sensors, the arrangement space of the sensors is 1/10 of the characteristic length of the heat storage container, and the sampling frequency of the sensors is set to be 1000Hz. The step of preprocessing the acquired signals specifically comprises denoising filtering, signal calibration and data synchronization. The multi-load response characteristic library refers to a database for stori