Comprehensive test system for hydrogen valves based on vacuum leak detection method

Abstract

The present invention relates to the field of leakage detection technology, and further to a comprehensive test system for hydrogen valves based on a vacuum leak detection method. The system includes: a modeling part, an analysis and processing part, a real-time control part, and a predictive control part; the modeling part is used to establish a gas state equation in a vacuum chamber and a thermodynamic balance equation of the vacuum chamber according to the initial parameters of the test environment; the analysis and processing part is used to consider the influence of temperature changes on the vacuum degree and calculate the leakage flux; the real-time control part is used to adjust the hydrogen flow rate; the predictive control part is used to perform Kalman filtering on the system temperature, pressure, and hydrogen flow rate to ensure the tracking of the set hydrogen flow rate. The present invention provides an efficient, stable, and reliable technical solution for hydrogen valve performance evaluation and flow regulation.

Description

Technical Field

[0001] The invention belongs to the technical field of leakage detection, and in particular relates to a comprehensive testing system for hydrogen valves based on a vacuum leak detection method. Background Art

[0002] In the comprehensive test of hydrogen valves, detecting valve leakage and regulating hydrogen flow is a key task. In many industrial application scenarios, such as hydrogen transportation, fuel cell systems, chemical processes, etc., the sealing performance and flow control of hydrogen valves are directly related to the safety and operation efficiency of the system. Therefore, it is of great practical value to accurately manage the leakage detection and flow control of hydrogen valves to ensure the sealing performance and flow adjustability of the valves. However, there are still many challenges in the existing technology, which makes the comprehensive testing of hydrogen valves difficult and imprecise. This article will elaborate on the existing technology and point out its shortcomings.

[0003] In the prior art, traditional methods for valve leakage detection are usually based on pressure decay detection, soap liquid detection, and direct mass flow meter detection. These methods rely on determining whether the valve has a leak by measuring the pressure changes on both sides of the valve, observing the leak with soap liquid, or using a flow meter to monitor the gas flow. However, these traditional detection methods often have certain limitations. For example, the pressure decay detection method requires a long wait to observe the pressure changes on both sides of the valve, and its detection sensitivity is low, making it difficult to accurately identify small leaks; soap liquid detection requires manual operation, which is not only inefficient, but also the accuracy and stability of the detection depend on the operator's experience; and although the mass flow meter detection can directly measure the flow change, the accuracy and response time of the flow meter may be limited in a high vacuum environment, and the detection sensitivity is often not high for hydrogen, a gas with a small molecular weight and strong permeability. Therefore, these traditional detection methods are limited in the application of high-precision and rapid detection of hydrogen valve leaks. In order to solve the above-mentioned detection sensitivity and efficiency problems, the helium mass spectrometer leak detector has become an important tool for leak detection. Since helium molecules are small in size, diffuse quickly, are chemically inert, and have a low content in the air, they are used as tracer gases for leak detection with extremely high sensitivity and accuracy. Helium mass spectrometer leak detectors are able to detect trace helium leaks and infer the leakage of hydrogen valves by analyzing the helium flux. However, in the application of helium mass spectrometer leak detectors, there are still some technical problems that need to be solved. First, the flow characteristics of helium are complex and are easily affected by temperature, vacuum, and valve structural characteristics, which makes it difficult to directly convert helium leakage flux into hydrogen leakage flux, resulting in limited leak detection accuracy. Secondly, the leakage flux detected by the helium mass spectrometer leak detector needs to be combined with the pressure and flow status of the system to accurately determine the leak location and degree, which places higher demands on the system's dynamic analysis capabilities. Summary of the invention

[0004] The main purpose of the present invention is to provide a comprehensive hydrogen valve test system based on the vacuum leak detection method, which realizes the accurate detection and control of hydrogen valve leakage and flow. The invention improves the sensitivity and accuracy of detection, ensures the tracking of flow, and effectively reduces the cumulative impact of leakage flux, providing an efficient, stable and reliable technical solution for hydrogen valve performance evaluation and flow regulation.

[0005] In order to solve the above problems, the technical solution of the present invention is achieved as follows:

[0006] A comprehensive hydrogen valve test system based on a vacuum leak detection method, the system comprising: a modeling part, an analysis and processing part, a real-time control part and a predictive control part; the modeling part is used to establish a gas state equation in a vacuum chamber and a thermodynamic balance equation of the vacuum chamber according to initial parameters of a test environment; the analysis and processing part is used to consider the influence of temperature changes on the vacuum degree, use fluid dynamics equations to describe the pressure changes during the vacuuming process, and calculate the leakage flux in combination with the helium flux detected by a helium mass spectrometer leak detector; the real-time control part is used to adjust the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and system temperature; the predictive control part is used to perform Kalman filtering on the system temperature, pressure and hydrogen flow rate, obtain a predicted hydrogen leakage rate according to the result after Kalman filtering, establish an optimization problem based on the predicted hydrogen leakage rate, the goal of the optimization problem is to minimize the flow deviation, solve the optimization problem to obtain an optimization result, and design a feedback controller based on the optimization result to adjust the hydrogen valve opening to ensure the tracking of the set hydrogen flow rate.

[0007] Furthermore, the initial parameters of the test environment include: heat capacity of the vacuum chamber, number density of gas molecules in the vacuum chamber, initial pressure, volume of the vacuum chamber, temperature of the vacuum chamber, external heat input value, inherent leakage flux of the system and heat dissipation coefficient.

[0008] Furthermore, in the modeling part, the formula of the gas state equation in the vacuum chamber is established as follows:

[0009] ;

[0010] in, for The number density of gas molecules in the vacuum chamber at the moment; is the initial pressure; is the volume of the vacuum chamber; is the Boltzmann constant; for The vacuum chamber temperature at the time; is the inherent leakage flux of the system; is the coefficient of thermal expansion.

[0011] Furthermore, in the modeling part, the formula of the thermodynamic balance equation in the vacuum chamber is as follows:

[0012] ;

[0013] in, is the heat capacity of the vacuum chamber; is the heat dissipation coefficient; for External heat input value at the moment; is the specific heat capacity of the gas.

[0014] Furthermore, the analysis and processing part considers the influence of temperature change on vacuum degree through the following formula, and uses fluid dynamics equation to describe the pressure change during vacuuming:

[0015] ;

[0016] in, for The pressure during the vacuuming process at all times; is the air flow rate of the vacuum pump; For the injection of helium pressure; is the gas constant;

[0017] Furthermore, the analysis and processing part calculates the leakage flux by combining the helium flux detected by the helium mass spectrometer leak detector through the following formula:

[0018] ;

[0019] in, is the leakage flux; For helium mass spectrometry Helium flux at the moment; is the leakage area.

[0020] Furthermore, the real-time control part adjusts the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and the system temperature:

[0021] ;

[0022] in, is the adjusted hydrogen flow rate; for The pressure difference on both sides of the hydrogen valve at the moment; is the valve damping coefficient.

[0023] Furthermore, the process of performing Kalman filtering on the system temperature, pressure and hydrogen flow rate in the predictive control part includes: taking the system temperature, pressure and hydrogen flow rate as vector elements to construct a state vector , filtered through a Kalman filter, the state equation of the Kalman filter is as follows:

[0024] ;

[0025] in, is the state vector after filtering; is the predicted state vector; is the Kalman gain; is the observation matrix; according to the result after Kalman filtering, the predicted hydrogen leakage rate is obtained through the following formula: :

[0026] According to the flow after Kalman filtering and pressure , predict hydrogen leakage rate for:

[0027] ;

[0028] in, is the predicted hydrogen flow rate in the predicted state vector; is the pressure in the predicted state vector.

[0029] Furthermore, based on the predicted hydrogen leakage rate, the optimization problem established is as follows:

[0030] ;

[0031] in, To optimize the objective function; for The set flow rate at the time; for Valve opening at the time; is a pre-set weight parameter; The total test time.

[0032] Furthermore, based on the optimization results, a feedback controller is designed to adjust the valve opening To ensure the set flow The controller model is:

[0033] ;

[0034] in is the first controller gain; is the second controller gain; is the integration variable.

[0035] The hydrogen valve comprehensive testing system based on the vacuum leak detection method of the present invention has the following beneficial effects:

[0036] The present invention realizes high-precision real-time estimation of key variables such as system temperature, pressure and hydrogen flow rate by introducing Kalman filtering technology. In the process of detecting the leakage of hydrogen valves, the fluctuation of environmental conditions such as temperature and pressure will significantly affect the accurate detection of leakage flux, and the Kalman filter can recursively optimize the estimation of these variables by using the system state model and observation data. In actual detection, the system uses temperature, pressure and flow as state vectors and uses Kalman filtering for real-time processing to effectively filter out noise and improve the accuracy of state estimation. This filtering technology can make adaptive adjustments to unpredictable interference and dynamic changes in the system, thereby ensuring accurate tracking of hydrogen leakage flux and flow state, and providing reliable data support for subsequent optimization control and feedback regulation. The advantages of Kalman filter in processing dynamic uncertainty and noise interference have greatly improved the detection accuracy and stability of the present invention.

[0037] Another beneficial effect of the present invention is that it achieves the coordinated optimization of leak detection and flow control. The helium mass spectrometer leak detector provides a highly sensitive leak detection capability and can detect the leakage flux of trace helium. On this basis, the present invention combines the helium flux with factors such as system temperature, pressure and hydrogen flow, corrects and estimates it through Kalman filtering, and predicts the actual hydrogen leakage rate. Then, the leakage rate is introduced into the objective function through optimization control, and the valve opening is comprehensively adjusted in combination with the flow deviation. In this way, the system can not only accurately detect leaks, but also adjust the hydrogen flow in real time according to the leakage situation to achieve the dual goals of minimizing leakage and tracking flow. This collaborative optimization strategy makes leak detection and flow control complement each other, and the system can maintain efficient and stable operation in a complex dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic structural diagram of an automatic control system for a comprehensive hydrogen valve testing system based on a vacuum leak detection method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that shown or described herein.

[0040] Example 1, reference Figure 1:A comprehensive hydrogen valve test system based on vacuum leak detection method, the system includes: a modeling part, an analysis and processing part, a real-time control part and a predictive control part; the modeling part is used to establish the gas state equation in the vacuum chamber and the thermodynamic balance equation of the vacuum chamber according to the initial parameters of the test environment; the analysis and processing part is used to consider the influence of temperature change on the vacuum degree, use the fluid dynamics equation to describe the pressure change during the vacuum pumping process, and calculate the leakage flux in combination with the helium flux detected by the helium mass spectrometer leak detector; the real-time control part is used to adjust the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and system temperature; the predictive control part is used to perform Kalman filtering on the system temperature, pressure and hydrogen flow rate, and obtain the predicted hydrogen leakage rate based on the result after Kalman filtering, and establish an optimization problem based on the predicted hydrogen leakage rate. The goal of the optimization problem is to minimize the flow deviation, and the optimization result is obtained by solving the optimization problem. Based on the optimization result, a feedback controller is designed to adjust the hydrogen valve opening to ensure the tracking of the set hydrogen flow rate.

[0041] Specifically, in the vacuum chamber environment, the gas state and temperature changes are dynamic, so establishing a state equation that can describe the gas behavior in the vacuum chamber in real time is the primary task of modeling. Since the movement of gas in the vacuum chamber follows certain physical laws, in the comprehensive test of hydrogen valves, we need to fully consider the relationship between gas pressure, volume, temperature, flow rate and other parameters, which are dynamically changing and affecting each other. The primary goal of modeling is to construct a dynamic equation group reflecting the gas state based on these initial conditions and change laws. In the modeling process, we first need to start with the initial conditions of the vacuum chamber and analyze the gas state in the chamber. In the case of vacuuming, the pressure of the gas in the chamber decreases rapidly over time, and the temperature in the chamber will also change due to the vacuuming process and the influence of the external environment. Therefore, the modeling part needs to establish two main equations: one is the state equation that describes the gas state, and the other is the thermodynamic equilibrium equation that describes the thermodynamic equilibrium of the system. The state equation is used to describe the relationship between the pressure, volume and temperature of the gas in the vacuum chamber at different time points. It is an overall description of the internal state of the system. The thermodynamic balance equation is used to describe the temperature changes in the cabin caused by factors such as gas expansion and external ambient temperature transfer during the vacuum extraction process. This equation can reflect the heat flow and temperature distribution in the cabin, thereby providing a complete thermodynamic perspective on the changes in gas state.

[0042] In the application of vacuum leak detection, hydrogen leakage is indirectly inferred by detecting helium flux, which means that the physical properties of helium and hydrogen need to be fully considered in modeling. Due to the extremely high sensitivity of the helium mass spectrometer leak detector, any tiny helium leak can be captured and used to evaluate the sealing of the hydrogen valve. In this case, the modeling part needs to use helium as a tracer gas to establish a fluid dynamics equation to describe the change of gas pressure in the vacuum chamber and the leakage flux. Considering that the flow of gas in the chamber is nonlinear and highly dependent on temperature, a series of differential equations need to be used in modeling to capture these dynamic characteristics, especially from the perspective of fluid dynamics, to describe the flow state and pressure distribution of helium in a vacuum environment. This process involves analyzing the flow velocity, diffusion rate and flux changes of the gas under different temperature and pressure conditions. In the establishment of the state equation and the thermodynamic equilibrium equation, the effect of temperature on the gas properties needs to be fully considered, because the change in temperature will directly affect the key parameters such as gas density, viscosity and diffusion coefficient, thereby affecting the leakage characteristics of the vacuum chamber. Therefore, the modeling part needs to combine the thermodynamic properties of the gas and use the state equation of the gas to reflect the changing relationship between temperature, pressure and volume in real time. In addition, the system also needs to model the heat transfer process, including the analysis of various heat transfer modes such as heat conduction, convection heat transfer and radiation heat transfer of the cabin wall, to ensure that the temperature field distribution in the vacuum chamber can be accurately described. In actual modeling, the established set of equations is not independent, but mutually coupled, which means that the gas state equation and the thermodynamic balance equation need to be solved collaboratively in order to accurately predict the gas state and temperature changes in the vacuum chamber. The processing of this coupling relationship is very critical in modeling, because the leakage behavior of the gas is not only affected by the pressure and volume in the cabin, but also closely related to the change of temperature. Therefore, in order to achieve high-precision hydrogen valve leakage detection, the modeling part needs to numerically solve these equations, update the gas state and temperature data in real time, and ensure that the system can dynamically reflect the actual state of the vacuum chamber.

[0043] Due to the characteristics of the vacuum leak detection method, during the vacuum extraction process, the pressure of the gas in the cabin will gradually decrease, and the temperature may change due to the external environment and the expansion or compression of the internal gas. The helium mass spectrometer leak detector can detect the flux of helium molecules in real time, which directly reflects the degree of leakage in the vacuum chamber. However, the flux of helium is closely related to the vacuum degree in the cabin, and the vacuum degree is also significantly affected by the temperature change in the cabin. Therefore, one of the core tasks of the analysis and processing part is to capture the effect of temperature on the vacuum degree in real time to ensure that the changes in pressure and leakage flux can accurately reflect the leakage of the hydrogen valve. In order to achieve this goal, the analysis and processing part first needs to model and analyze the pressure changes in the cabin during the vacuum extraction process. This usually involves the study of gas fluid dynamics. Since the flow of gas in a vacuum environment is non-steady, the rate of pressure change depends not only on the pumping rate, but also on factors such as gas temperature, gas molecular weight and cabin structure. Therefore, by establishing a fluid dynamics equation to describe the change law of the pressure in the cabin over time, the dynamic behavior during the vacuum extraction process can be better understood. This process needs to consider physical parameters such as gas density, viscosity, flow rate at different temperatures, as well as the resistance of the cabin structure to gas flow. By integrating these factors, the fluid dynamics equation can provide a comprehensive pressure change model for the system and provide a basis for the calculation of leakage flux.

[0044] On the basis of the fluid dynamics equation, the analysis and processing part needs to further combine the helium flux detected by the helium mass spectrometer leak detector and associate it with the gas pressure and temperature in the vacuum chamber. As a tracer gas, the change in the flux of helium directly reflects the degree of hydrogen leakage, because under high vacuum conditions, the mobility of helium molecules is extremely strong and can sensitively reflect any tiny leak. When the helium mass spectrometer leak detector detects the helium flux, the analysis and processing part needs to convert the detection data into leakage flux to describe the rate and magnitude of gas leakage. This conversion process needs to be combined with the pressure level in the vacuum chamber, the temperature in the chamber, and the diffusion characteristics of helium to ensure that the calculation of the leakage flux accurately reflects the actual hydrogen leakage. Since the temperature has a significant impact on the gas state in the vacuum chamber, the analysis and processing part also needs to conduct a detailed analysis of the relationship between temperature change and vacuum degree. In the actual detection process, the temperature in the chamber may change due to the expansion or compression of the gas during the pumping process, and may even be affected by the ambient temperature or the heat of the equipment itself. Therefore, the analysis and processing part needs to establish a temperature-vacuum degree correlation model to capture the impact of temperature changes on the gas pressure in the chamber. The establishment of this model helps to correct the temperature influence in the detection data of the helium mass spectrometer leak detector, making the calculation of the leakage flux more accurate. In the analysis and processing part, the flow characteristics of helium in a vacuum environment also need to be considered. Since helium is a light inert gas, its molecules move fast and have strong permeability in a vacuum environment, so its flux can well reflect the leakage situation. In the calculation of the leakage flux, it is necessary to fully consider the molecular diffusion and flow pattern of helium, especially under high vacuum conditions, the free path of helium is relatively long, and the flow is closer to the molecular flow state. The analysis and processing part needs to accurately calculate the leakage flux based on the diffusion model of helium and the detection data of the helium mass spectrometer leak detector, and correspond it to the actual leakage situation in the cabin.

[0045] In the process of valve control, the real-time control part needs to take into account the nonlinear relationship between leakage flux and valve opening. Since hydrogen, as a light gas, has extremely high diffusion rate and permeability, when the valve opening changes, the flow rate of hydrogen will also show a complex dynamic response. Therefore, the real-time control part must dynamically adjust the valve opening based on the real-time feedback of the leakage flux to achieve precise regulation of the hydrogen flow rate. This real-time regulation needs to fully consider the current temperature and pressure of the system, because these two parameters directly affect the density and flow characteristics of hydrogen, thus having a significant impact on the regulation of flow. Therefore, in the process of valve control, the real-time control part will continuously monitor the deviation between the actual value and the target value of the hydrogen flow rate, and adjust the valve opening in real time according to this deviation to minimize the flow deviation and ensure that the flow rate always remains within the predetermined range. Another key task of the real-time control part is to regulate the system temperature, because the temperature has a direct impact on the flow characteristics and leakage behavior of hydrogen. In a vacuum environment, changes in temperature will cause changes in the density and pressure of the gas in the cabin, thereby affecting the flow rate and leakage flux of hydrogen. Therefore, the real-time control part needs to dynamically adjust the valve opening according to the real-time changes in the system temperature to ensure the stability of the hydrogen flow. When the temperature is high, the molecular motion of hydrogen intensifies and the flow rate increases, so the valve opening may need to be reduced accordingly to maintain a stable flow; when the temperature is low, the flow rate of hydrogen decreases, and the valve opening needs to be increased to compensate for the reduction in flow. Through real-time monitoring of temperature and dynamic adjustment of valve opening, the real-time control part can ensure that the hydrogen flow remains stable under temperature changes, thereby improving the accuracy and reliability of leak detection.

[0046] In addition, the real-time control part also needs to consider the influence of system pressure. In the vacuum leak detection method, the change of cabin pressure has an important influence on the regulation of hydrogen flow rate. Since the flow of hydrogen is driven by pressure, when the cabin pressure decreases, the flow rate of hydrogen will also decrease, resulting in a change in flow rate. Therefore, the real-time control part needs to dynamically adjust the valve opening according to the real-time state of the cabin pressure to compensate for the flow fluctuation caused by pressure changes. In order to achieve this goal, the real-time control part usually constructs a real-time pressure-flow regulation model, based on which the opening of the hydrogen valve is regulated in real time so that the hydrogen flow rate is always kept within the target range. This regulation method based on the pressure-flow model can effectively cope with the fluctuation of cabin pressure and ensure that the hydrogen valve can maintain a stable leak detection effect under different pressure conditions. In the entire real-time control process, the feedback mechanism of leakage flux plays a vital role. The helium flux detected by the helium mass spectrometer leak detector reflects the actual leakage of hydrogen. This flux data will be transmitted to the control system in real time as an important reference for valve opening and flow regulation. When an increase in leakage flux is detected, the real-time control part will determine that there may be a risk of leakage and reduce the hydrogen flow rate by adjusting the valve opening to reduce the possibility of leakage; when the leakage flux decreases, the system will appropriately increase the valve opening to ensure that the hydrogen flow rate can be maintained at the desired level. This real-time feedback control mechanism ensures that the opening and flow of the hydrogen valve can be dynamically adjusted according to changes in the leakage situation, thereby achieving precise control of the leakage.

[0047] In the predictive control part, Kalman filter plays a vital role as the core prediction tool. Kalman filter is a recursive minimum mean square error estimation method used to estimate and predict the dynamic state of the system. Since the state parameters of the system (such as temperature, pressure and hydrogen flow) are in a constantly changing state during the comprehensive test of hydrogen valves, and there is a complex coupling relationship between these parameters, the advantage of Kalman filter is that it can handle the random dynamic characteristics of this multivariable system and provide the best linear unbiased estimation of the system state. In practical applications, Kalman filter will continuously update the prediction of the state at the next moment based on the system state and measurement data at the current moment, forming an accurate estimate of temperature, pressure and hydrogen flow. The workflow of the predictive control part usually includes two main stages: prediction and correction. In the prediction stage, Kalman filter uses the state transition model of the system to predict the state of the system at the next moment based on the current state information. This prediction process takes into account the influence of temperature on the gas state, the regulation of pressure on the hydrogen flow, and the leakage flux information fed back by the helium mass spectrometer leak detector. This means that the prediction stage not only needs to consider the current dynamic characteristics of the system, but also needs to model the interaction between different variables to ensure that the prediction results can accurately reflect the actual changes of the system. After the prediction phase is completed, the Kalman filter will correct the prediction results in combination with real-time measurement data in the correction phase to ensure the accuracy of the system state estimation. In this process, the Kalman gain is used to adjust the weight between prediction and measurement to achieve the best estimate of the system state.

[0048] Based on the prediction results of the Kalman filter, the predictive control part further calculates the hydrogen leakage rate. The prediction of hydrogen leakage rate is the key to the system's optimization control, because the leakage rate directly reflects the sealing performance and safety of the hydrogen valve. By predicting the leakage rate, the system can identify possible leakage risks in advance, so as to take measures to prevent and control them. In this process, the predictive control part will model and analyze the trend of hydrogen leakage based on parameters such as leakage flux, temperature and pressure to ensure that the prediction of leakage conditions can maintain high accuracy under different conditions. This prediction of the leakage rate helps to optimize the system's regulation of hydrogen valves and ensure accurate control of the flow rate. Another core task of the predictive control part is to establish and solve the optimization problem to minimize the deviation of hydrogen flow. In actual operation, the regulation of hydrogen flow directly affects the accuracy of leak detection and the stability of the system. Therefore, the predictive control part needs to design an optimization target to minimize the deviation between the actual flow and the target flow. This optimization process usually involves constructing an objective function based on the predicted leakage rate, and by solving the objective function, determining the optimal valve opening and flow regulation strategy. When solving the optimization problem, the predictive control part needs to consider a variety of factors, including the changing trend of the leakage flux, the impact of temperature and pressure on the flow rate, and the deviation between the current state of the system and the target state. This comprehensive optimization design ensures that the system can achieve accurate control of the hydrogen flow rate in a complex multivariable environment. After solving the optimization problem, the predictive control part will use the optimization results to design a feedback controller to adjust the opening of the hydrogen valve. The feedback controller is a key component for the system to achieve real-time regulation and stable operation. It can adjust the hydrogen flow rate in real time according to the prediction and optimization results, thereby ensuring that the system's flow tracking and leak detection are always maintained at a high-precision state. When designing the feedback controller, the predictive control part will combine the real-time state of the system and the optimization results to dynamically adjust the parameters of the controller so that the valve opening can quickly respond to changes in the system state and achieve accurate tracking and control of the flow.

[0049] Embodiment 2: The initial parameters of the test environment include: the heat capacity of the vacuum chamber, the number density of gas molecules in the vacuum chamber, the initial pressure, the volume of the vacuum chamber, the temperature of the vacuum chamber, the external heat input value, the inherent leakage flux of the system and the heat dissipation coefficient.

[0050] Specifically, the heat capacity of the vacuum chamber reflects the response ability of the gas in the vacuum chamber and the chamber itself to heat changes. When the heat capacity is large, the temperature of the system will change relatively slowly, which has a direct impact on the stability of temperature control and the accuracy of leak detection. Because temperature changes will directly affect the molecular motion and leakage rate of the gas, the system needs to comprehensively consider the impact of heat capacity on the temperature distribution and thermal balance in the chamber, and adjust the valve opening and flow rate during real-time control and predictive control to ensure the accuracy of leak detection. The gas molecule number density determines the number of molecules per unit volume of gas in the vacuum chamber, which is very important for the sensitivity of detection and the calculation of leakage flux. The higher the gas molecule number density, the more frequent the collision and flow of the gas in the chamber, and the shorter the time required for leak detection. But at the same time, higher density may also make the system more sensitive and susceptible to interference from temperature and pressure changes. Therefore, in the modeling process, this parameter, together with other parameters such as pressure and temperature, constitutes an important part of the system state equation. The initial pressure is the initial pressure state of the gas in the vacuum chamber, which has a direct impact on the dynamic behavior of the entire vacuum process. Before starting the leak detection, the system usually pumps the vacuum chamber into a high vacuum to ensure that the helium mass spectrometer leak detector can respond sensitively to the tiny helium flux during the leak detection process. The initial pressure is closely related to the vacuum extraction rate, the gas distribution in the chamber and the leakage behavior. In modeling and real-time control, the initial pressure is the starting condition for calculating the gas state equation, and is also the reference benchmark for subsequent analysis of pressure changes and prediction of leakage flux. The volume of the vacuum chamber is the spatial range of the gas distribution and movement in the vacuum chamber, which directly affects the calculation of the gas molecule number density and leakage flux. When the volume is larger, the gas in the chamber is more diluted, the intermolecular collision is reduced, and the sensitivity of leak detection may be reduced; while a smaller volume may improve the accuracy of leak detection, but it will also cause faster changes in temperature and pressure. Therefore, the volume parameter works together with temperature, pressure and leakage flux in the modeling process to ensure that the system can achieve accurate leak detection under different volume conditions. The temperature of the vacuum chamber has a direct impact on the movement of gas molecules, pressure changes and leakage behavior. In the predictive control part, the system will dynamically adjust the valve opening and flow rate according to the change of the vacuum chamber temperature to ensure the accuracy of leak detection. When the temperature is high, the gas molecules move more actively and the leakage flux may increase; when the temperature is low, the molecular motion slows down and the leakage flux will be relatively reduced. Therefore, real-time monitoring and adjustment of temperature are the key to maintaining stability and high-precision detection of the system. The external heat input value is an important factor affecting the temperature change in the vacuum chamber. It may come from the external environment, the operation of the equipment, or other heat sources in the chamber. External heat input will cause the temperature of the vacuum chamber to rise, thereby affecting the gas molecule density and leakage behavior. Therefore, when modeling and analyzing, the system needs to take external heat input into account in the thermodynamic balance equation to accurately describe the effect of temperature on leak detection.In real-time control, this parameter is also one of the bases for adjusting the valve opening and hydrogen flow rate to ensure that the system can still operate stably under different external heat input conditions. The inherent leakage flux of the system is an inevitable small leak in the test environment. It is different from the actual leakage flux of the hydrogen valve and is a characteristic parameter of the test system itself. In the actual detection process, it is necessary to distinguish the inherent leakage flux from the leakage flux detected by the helium mass spectrometer leak detector to ensure that the leakage detection of the hydrogen valve is not affected by the inherent leakage of the system itself. Therefore, during analysis and real-time control, the system will compensate and correct the inherent leakage flux to ensure the accuracy of leak detection. The heat dissipation coefficient reflects the heat exchange capacity between the vacuum chamber and the external environment. When the heat dissipation coefficient is high, the vacuum chamber can exchange heat with the outside world faster, and the temperature change is relatively small; when the heat dissipation coefficient is low, the temperature in the chamber changes slowly and more evenly. Therefore, the heat dissipation coefficient affects the stability of the temperature in the chamber and the sensitivity of leak detection. In the modeling process, the heat dissipation coefficient is incorporated into the thermodynamic balance equation to describe the heat dissipation characteristics of the vacuum chamber to ensure that the system can achieve stable leak detection under different heat dissipation conditions.

[0051] Embodiment 3: In the modeling part, the formula of the gas state equation in the vacuum chamber is established as follows:

[0052] ;

[0053] in, for The number density of gas molecules in the vacuum chamber at the moment; is the initial pressure; is the volume of the vacuum chamber; is the Boltzmann constant; for The vacuum chamber temperature at the time; is the inherent leakage flux of the system; is the coefficient of thermal expansion.

[0054] Specifically, the initial pressure of the vacuum chamber is an important initial condition when the system starts leak detection, which reflects the initial concentration of the gas in the chamber. During the vacuum extraction process, as time goes by, the gas in the chamber is gradually extracted and the pressure decays exponentially, so the formula contains a The term describes the exponential change trend of pressure over time. This exponential decay reflects the process of gradual discharge of gas from the cabin and reduction of density under vacuum, ensuring that the system can capture small changes in hydrogen leakage at different time points. is another important parameter that affects gas density. The larger the volume, the lower the number density of gas molecules per unit volume, the higher the fluidity, and the lower the sensitivity of leak detection; while the smaller the volume, the higher the density, and the higher the system's sensitivity to leak detection. Therefore, in the formula, The number density of gas molecules in the vacuum chamber at the time Vacuum chamber volume This shows that as the volume of the vacuum chamber increases, the molecular density of the gas in the chamber will decrease. The formula also reflects the effect of temperature on the molecular density of the gas. Vacuum chamber temperature at time It is a key factor affecting the speed of gas molecule movement and leakage behavior. According to the principle of thermodynamics, when the temperature rises, the speed of gas molecules increases, resulting in a decrease in the number density of gas molecules in the cabin. Therefore, in the formula, the number density of gas molecules is inversely proportional to the temperature, that is, This relationship ensures that during the detection process, as the temperature changes, the system can promptly adjust its estimate of the gas state, thereby more accurately monitoring hydrogen leaks. System inherent leakage flux Indicates the small leakage that is inevitable due to the material and structural characteristics of the vacuum chamber. The existence of this parameter means that the number density of gas molecules in the chamber will change over time even in the absence of external leakage. The term describes the linear cumulative effect of the inherent leakage flux on the molecular number density. As time goes by, the inherent leakage flux will gradually accumulate, resulting in an increase in the molecular density of the gas in the cabin. Therefore, when leak testing, the influence of this inherent flux must be considered and separated from the actual leakage flux of the hydrogen valve to ensure the accuracy of the test results. Thermal expansion coefficient Reflects the sensitivity of the system to temperature changes. Since gases change in volume and pressure when heated or cooled, Used to describe the impact of this thermal expansion effect on system pressure. The exponential term in the formula This means that as time goes by, the gas pressure in the cabin will decay exponentially, and the decay rate depends on the thermal expansion coefficient. Larger A higher value means that the system responds more significantly to temperature changes, resulting in faster changes in the state of the gas in the cabin. It is an important constant that connects the macroscopic state of gas with the microscopic molecular motion, and plays a role in linking the system temperature with the number density of gas molecules in the formula. This constant ensures that the formula can describe the state change of gas in a thermodynamic and statistical mechanical way, and reflects the relationship between the average kinetic energy of gas molecules and the system temperature.

[0055] Embodiment 4: In the modeling part, the formula of the thermodynamic balance equation in the vacuum chamber is as follows:

[0056] ;

[0057] in, is the heat capacity of the vacuum chamber; is the heat dissipation coefficient; for External heat input value at the moment; is the specific heat capacity of the gas.

[0058] Specifically, the left side of the formula It represents the rate of change of the temperature in the vacuum chamber over time. It reflects the dynamic change of the temperature in the chamber during the leak detection process. In the system, temperature changes not only affect the movement rate and molecular density of gas molecules, but also have a direct impact on the leakage flux and hydrogen flow rate. Therefore, accurate modeling of temperature changes is the key to ensuring detection accuracy and control system stability. It reflects the system's ability to respond to heat changes. The larger the heat capacity, the slower the system temperature responds to heat changes, which means that the temperature changes in the cabin will be more gradual. Therefore, in the denominator of the equation, The temperature change rate of the entire system is adjusted so that the system can reflect the temperature change in real time according to the thermodynamic properties of the gas in the vacuum chamber. Describes the combined effects of heat dissipation in the system and external heat input. is the heat dissipation coefficient, which reflects the heat exchange capacity between the system and the external environment. The larger the heat dissipation coefficient, the faster the heat exchange between the vacuum chamber and the outside world, and the more obvious the temperature change. During the test, the vacuum chamber will release the excess heat in the chamber to the outside world through heat dissipation, so this item describes the effect of heat dissipation on reducing temperature. express The external heat input value at the moment can come from the external environment, equipment operation or other heat sources inside the vacuum chamber. In the actual detection process, the external heat input will cause the temperature inside the vacuum chamber to rise, thereby affecting the gas molecule number density and leakage flux. Therefore, represents the balance between the external heat and the heat inside the cabin, and This balance is then converted into an effect on temperature change, ensuring that the system can accurately describe the decay or increase of temperature over time. The second term in the equation Describes the contribution of the gas itself to the temperature in the vacuum chamber. Here, for The molecular density of the gas in the vacuum chamber at a given moment, which reflects the concentration of the gas. As the molecular density of the gas increases, the contribution of the gas to the heat in the chamber will also increase. The specific heat capacity of the gas is the heat capacity of a gas at a constant volume, which indicates the degree to which the temperature of the gas changes when it absorbs or releases heat. The term represents the contribution of the gas in the cabin to the temperature increase after absorbing heat, which means that when the number density of gas molecules increases or the specific heat capacity increases, the rate of increase of the system temperature will increase accordingly. Overall, the thermodynamic balance equation describes the temperature change process in the vacuum chamber, integrating the effects of heat dissipation, external heat input and gas characteristics. During the detection process, the change in temperature directly affects the state and leakage behavior of the gas. Therefore, this equation provides a physical model to describe the evolution of the temperature in the cabin, so that the system can predict the dynamic changes of temperature in real time according to external conditions and the state of the cabin. This is crucial for leak detection of hydrogen valves, because changes in temperature not only affect the movement and pressure of gas molecules, but may also cause changes in leakage flux. Therefore, through this thermodynamic balance equation, the system can maintain accurate modeling of temperature changes under different detection conditions, thereby optimizing the leak detection and control of hydrogen valves.

[0059] Embodiment 5: The analysis and processing part considers the influence of temperature change on vacuum degree through the following formula, and uses fluid dynamics equation to describe the pressure change during vacuuming:

[0060] ;

[0061] in, for The pressure during the vacuuming process at all times; is the air flow rate of the vacuum pump; For the injection of helium pressure; is the gas constant.

[0062] Specifically, the pressure in the formula It is the core state parameter of the system during the vacuum process. It changes with time and is determined by the gradual extraction of gas from the vacuum chamber, as well as the combined effects of temperature, leakage, and external influences. As the gas in the vacuum chamber is gradually extracted, the pressure decreases. However, the rate of change of pressure is not constant, but is affected by the thermodynamic properties of the gas, the physical environment in the chamber, and the dynamic characteristics of the vacuum process itself. Therefore, the structure of the formula reflects these complex physical interactions. First, the vacuum flow rate It is one of the main factors affecting the change of vacuum chamber pressure. During the vacuum extraction process, the vacuum system pump discharges the gas in the chamber, which reduces the number density of gas molecules and thus reduces the pressure in the chamber. In the formula, the air extraction flow is divided by the volume of the vacuum chamber. , reflects the effect of the pumping flow rate relative to the space inside the chamber, which shows that the effect of the pumping rate will vary with the volume. The pressure of a smaller vacuum chamber will drop more rapidly at the same pumping rate because the amount of gas pumped out is a larger proportion relative to the volume. Therefore, the formula The term accurately describes the negative effect of pumping on pressure, that is, the pressure decreases as the gas is pumped out. At the same time, the other part of the formula takes into account the effect of temperature and gas thermodynamic properties on the pressure change. Initial pressure It represents the gas pressure in the vacuum chamber before the vacuum is evacuated. During the vacuum extraction process, the pressure does not drop linearly because the gas exhibits complex behavior under the influence of temperature changes and leakage. Reflects the thermal expansion coefficient Effects on stress. Over time As the temperature increases, the gas in the cabin will expand or contract due to the temperature change, causing the gas state to change. This exponential decay term reflects that the pressure will gradually decrease over time according to the law of thermal expansion, and the thermal expansion coefficient determines the speed and magnitude of this change. The formula also includes a term , used to describe the effect of helium injection on the cabin pressure. Indicates the pressure of injected helium, which is usually higher than the initial pressure , so this part describes the pressure increase caused by the injection of helium. This item takes into account the volume of the vacuum chamber, the helium injection pressure, the gas constant and Vacuum chamber temperature at time The relationship between them ensures that the injected helium can be correctly simulated and controlled during the actual detection process. Vacuum chamber temperature at time The gas constant plays a key role in the formula because temperature directly affects the molecular motion and pressure of a gas. As a universal physical constant, it links the state of the gas to the temperature, ensuring that pressure changes can correctly reflect thermodynamic processes. This description is crucial in the leak detection process, because the detection sensitivity and accuracy of the system depend largely on the precise control and understanding of the pressure state in the cabin. This formula not only allows the system to monitor the changes in vacuum degree over time in real time, but also optimizes the control strategy of the hydrogen valve according to the state of pressure, volume and temperature to ensure the accuracy, timeliness and stability of leak detection. Ultimately, through this precise modeling, the comprehensive test system based on the vacuum leak detection method can achieve efficient detection of hydrogen valve leaks under complex physical conditions.

[0063] Embodiment 6: The analysis and processing part calculates the leakage flux by combining the helium flux detected by the helium mass spectrometer leak detector through the following formula:

[0064] ;

[0065] in, is the leakage flux; For helium mass spectrometry Helium flux at the moment; is the leakage area.

[0066] Specifically, the helium mass spectrometer in the formula Helium flux at time The leak detector is a helium mass spectrometer. The helium flow rate measured at all times. As a tracer gas for leak detection, helium has the characteristics of small molecular size, fast diffusion speed, and chemical inertness, making it an extremely sensitive detection gas in vacuum leak detection. In actual operation, the helium mass spectrometer leak detector can accurately detect tiny helium leaks, thereby providing direct leakage information. However, the detected helium flux cannot be directly used as the hydrogen leakage flux, and needs to be corrected and calculated in combination with other conditions in the vacuum chamber. The helium flux term in the formula is the basis, and further analysis of it can obtain the actual leakage information of the valve. Leakage flux is the key parameter we want to obtain, which indicates the gas flow rate through a unit leakage area per unit time. This indicator directly reflects the sealing performance of the hydrogen valve and is also an important data used by the test bench to evaluate the valve leakage status. In order to accurately calculate the leakage flux, the denominator in the formula includes the leakage area , which describes the size of the leak aperture. The larger the leak area, the higher the flux; while a smaller leak area means that the flux detection will be more sensitive. , the formula first calculates the helium flux per unit area. However, helium does not flow constantly during the leakage process. It is affected by the pressure, volume and temperature in the vacuum chamber. Therefore, the formula also introduces a correction factor to ensure that the calculation of the leakage flux can fully reflect the impact of these factors on the helium flow. This correction factor includes multiple variables, the first of which is the volume of the vacuum chamber. , which determines the distribution space and flow characteristics of the gas in the cabin. A larger cabin volume may lead to dilution of the leakage flux, while a smaller volume makes the flow more concentrated, so It plays a role in balancing the flow distribution in the formula. Pressure is also a key factor affecting leakage flux. The pressure during the vacuuming process It will change as the vacuum process progresses. When the pressure is high, the gas molecules move more violently and the leakage flux may increase; while at lower pressure, the gas flow slows down and the flux may decrease. Therefore, a pressure correction term is introduced into the formula , ensuring that the leakage flux calculation can reflect the effect of pressure on gas flow. Here, is the reference pressure of the injected helium gas, which serves as a pressure reference. The pressure during the vacuuming process Comparison provides a measure of the relative extent of leakage. It shows that the leakage flux will be affected by the cumulative effect over time. During the test, the leakage of helium may be a dynamic process, and the flux will gradually stabilize or change as time goes by. Therefore, Item It is a time scale factor that reflects the time-varying characteristics of the leakage process. Vacuum chamber temperature at time is another key factor that affects helium flux and leakage behavior. Since temperature directly affects the kinetic energy and movement rate of gas molecules, changes in system temperature will significantly affect the leakage flux. When the temperature increases, the movement of gas molecules increases, and the leakage flux may increase; while when the temperature decreases, the leakage flux may decrease accordingly. Therefore, the formula The term introduces the regulating effect of temperature on pressure and leakage behavior, ensuring that the leakage flux calculation is sensitive to changes in cabin temperature.

[0067] Embodiment 7: The real-time control part adjusts the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and the system temperature:

[0068] ;

[0069] in, is the adjusted hydrogen flow rate; for The pressure difference on both sides of the hydrogen valve at the moment; is the valve damping coefficient.

[0070] Specifically, the first key parameter involved in the formula is the leakage flux , which reflects the gas flow rate leaking from the hydrogen valve per unit time. This flux is calculated from the helium flux detected by the helium mass spectrometer leak detector and is a direct reflection of the system leakage status. During the leak detection process, the leakage flux may change dynamically due to factors such as temperature changes, vacuum environment pressure fluctuations, and valve opening changes. Since the leakage flux is directly related to the safety and sealing of the system, the real-time control system needs to respond quickly and make corresponding adjustments based on this parameter. The formula converts the leakage flux As one of the core factors of flow adjustment, the system's adjustment strategy will be closely centered around the change in leakage, so as to compensate or control the flow of hydrogen in a timely manner, thereby ensuring that the valve's leakage detection remains accurate under various conditions. It is also a key factor affecting the hydrogen flow rate. The pressure difference is the driving force for the hydrogen flow and directly affects the flow rate of hydrogen on both sides of the valve. Since one side of the valve is at high pressure (usually the hydrogen storage side) and the other side is at a lower vacuum state, the change in the pressure difference on both sides will significantly affect the leakage flux and flow characteristics of hydrogen. In the formula, the hydrogen flow rate It is positively correlated with the pressure difference. When the pressure difference increases, the flow rate also increases; when the pressure difference decreases, the flow rate decreases. Therefore, in actual control, when the pressure difference on both sides of the valve is detected to change, the system will balance the effect of the pressure difference on the flow rate by adjusting the valve opening to ensure that the hydrogen flow rate can remain stable within the set range.

[0071] The formula also introduces the valve damping coefficient , which reflects the resistance characteristics of the valve to the flow of hydrogen. The damping coefficient depends on factors such as the valve design, material properties and current opening, and is a quantitative representation of the resistance encountered by hydrogen when it flows through the valve. In the formula, the damping coefficient is in the denominator, indicating that it has a reducing effect on the flow. The larger the damping coefficient, the smaller the hydrogen flow through the valve; the smaller the damping coefficient, the larger the flow. It can be seen that when controlling the hydrogen flow, the system must not only consider the leakage flux and pressure difference, but also adjust the valve opening according to the actual value of the damping coefficient to ensure that the valve's resistance to the flow of hydrogen remains at an appropriate level, so that the flow can be accurately controlled under different leakage conditions. From the entire formula, the real-time control system has a good control over the hydrogen flow under the joint action of leakage flux, pressure difference and damping coefficient. Dynamic adjustment is performed by real-time monitoring of the leakage flux , the system can respond to the leakage information fed back by the helium mass spectrometer leak detector in a timely manner. When the leakage flux increases, it indicates that the sealing of the valve has deteriorated. The system will reduce the hydrogen flow rate by reducing the valve opening to reduce leakage; conversely, when the leakage flux decreases, it indicates that the sealing of the valve is better. The system can increase the valve opening to compensate for the demand for hydrogen flow. In this way, the real-time control of the system can effectively balance the relationship between leakage flux and flow, so that the hydrogen valve always remains in a state where the leakage is controllable and the flow is appropriate.

[0072] On the other hand, the pressure difference on both sides of the valve The positive correlation with flow rate enables the system to use pressure changes to regulate hydrogen flow. By adjusting the valve opening, the real-time control system can adjust the flow rate of hydrogen according to the current pressure difference state, ensuring that the valve operation can maintain flow stability and detection accuracy under different pressure conditions. At the same time, the valve damping coefficient The introduction of ensures that the system can effectively manage the resistance in the flow of hydrogen. Since the damping coefficient is related to factors such as valve structure and opening, the system needs to comprehensively consider the resistance characteristics of the valve when regulating the hydrogen flow, and achieve effective control of the flow through fine adjustment of the opening. Especially in the case of large changes in temperature and pressure, the role of the damping coefficient is particularly important, because it ensures that the system can maintain a stable flow regulation under different working conditions. The design of the real-time control part enables the system to cope with complex dynamic environments. During the hydrogen leak detection process, parameters such as leakage flux, pressure difference, temperature and damping coefficient may change with time and changes in external conditions. This change places extremely high demands on the accuracy of hydrogen flow and leak detection. By real-time monitoring and feedback adjustment of each parameter in the formula, the system can quickly respond to any changes in leakage flux, adjust the hydrogen flow, and ensure the accuracy and stability of the test results. At the same time, by dynamically adjusting the valve opening, the system can also maintain a balance between hydrogen flow and leakage, so that the valve's leak detection and flow control are always in the best state.

[0073] Embodiment 8: The process of performing Kalman filtering on the system temperature, pressure and hydrogen flow rate in the predictive control part includes: taking the system temperature, pressure and hydrogen flow rate as vector elements to construct a state vector , filtered through a Kalman filter, the state equation of the Kalman filter is as follows:

[0074] ;

[0075] in, is the state vector after filtering; is the predicted state vector; is the Kalman gain; is the observation matrix; according to the result after Kalman filtering, the predicted hydrogen leakage rate is obtained through the following formula: :

[0076] According to the flow after Kalman filtering and pressure , predict hydrogen leakage rate for:

[0077] ;

[0078] in, is the predicted hydrogen flow rate in the predicted state vector; is the pressure in the predicted state vector.

[0079] Specifically, in order to process in Kalman filtering, the system uses temperature, pressure, and hydrogen flow as state vectors elements to construct a complete system state description. This state vector can dynamically capture the system's temperature, pressure, and hydrogen flow at every moment. The changes in the state reflect the dynamic behavior of the entire vacuum chamber system. The working principle of the Kalman filter is to recursively estimate these variables based on the state space model. By integrating the prior prediction of the system state and the actual observation data, the uncertainty and noise of the system are gradually reduced, thereby obtaining a more accurate system state. For hydrogen valve leak detection, this accurate state estimation is crucial for the accurate prediction of the leakage flux and the dynamic control of the system. In the execution of the Kalman filter, the filter first estimates the state of the current moment based on the system model and the state of the previous moment. Make predictions and form a predicted state vector The prediction in this step is based on the physical model of the system and its dynamic characteristics, including the prediction of hydrogen flow, cabin pressure and temperature. After the prediction, the Kalman filter fuses this predicted state with the actual observed data to generate a more accurate filter state vector This filtered state is based on a weighted average of real-time observations and predictions of the system, forming an optimal estimate of the system state.

[0080] The state update process of the Kalman filter is described by a correction formula, where the predicted state vector and observations The error between This means that the Kalman filter calculates an optimal weight based on the dynamic characteristics of the system and the current observation data to minimize the observation error, thereby achieving an optimal estimate of the system state. It is a key parameter that determines the weight balance between prediction and observation. It is adjusted according to the noise of observation and the uncertainty of the system. When the accuracy of the observation data is high and the noise is low, the Kalman gain tends to rely more on the observation value; on the contrary, when the system model is accurate and the observation noise is large, the gain will rely more on the prediction value. By dynamically adjusting , Kalman filtering can optimize the estimation of system state under different detection conditions. After the system state is optimized by Kalman filtering, the filtered hydrogen flow rate can be extracted and cabin pressure The changes in these two variables directly affect the prediction of hydrogen leakage rate. Considering that the main driving forces of leakage are changes in cabin pressure and changes in hydrogen flow, the formula combines the time derivatives of these two variables to ensure that the calculation of leakage rate can accurately reflect the dynamic behavior of the system. Temperature is an important parameter that affects hydrogen flow and pressure, so it is also fully considered in the calculation of leakage rate.

[0081] Since the leakage flux is driven by both pressure change and flow change, the two derivative terms in the formula reflect the impact of these two factors on the leakage rate. Kalman filtering ensures that these derivative terms can provide accurate estimates in a noisy and dynamically changing environment, thereby ensuring the reliability and accuracy of the leakage rate prediction. Gas Constant and cabin temperature It also plays a key role in the prediction formula. Because the state equation of the gas and its flow characteristics are affected by temperature and gas constant, when predicting the leakage rate, the formula takes these two parameters into account to ensure that the prediction of the leakage rate can correctly reflect the actual thermodynamic state in the system. In particular, temperature , which significantly affects the speed of movement and leakage behavior of hydrogen molecules, appears as a denominator in the formula to ensure that the prediction results are sensitive to temperature changes and adjust accordingly. By combining the time rate of change of pressure and flow with the gas constant and temperature, the prediction formula can predict the leakage rate of the system. Dynamic prediction. Kalman filtering provides a powerful real-time filtering and state estimation tool, ensuring that the time derivatives of flow and pressure are estimated based on the optimal state, thereby greatly improving the accuracy of leakage rate prediction. This predictive control method enables the system to quickly respond to changes in pressure and flow in a complex and changing environment, and adjust the opening of the hydrogen valve and the leakage control strategy in real time.

[0082] Example 9: Based on the predicted hydrogen leakage rate, the optimization problem established is as follows:

[0083] ;

[0084] in, To optimize the objective function; for The set flow rate at the time; for Valve opening at the time; is a pre-set weight parameter; The total test time.

[0085] Specifically, the objective function It consists of two parts. The first part Expresses the actual hydrogen flow in the system With set flow This deviation term reflects the difference between the actual flow rate and the target flow rate, and the goal of the optimization problem is to make this difference as small as possible. By minimizing this term, the system ensures that the actual flow rate As close as possible during the entire test The flow rate set at the time , to achieve precise control of flow. The second part Focus on hydrogen leakage rate The cumulative effect during the entire test. It is a pre-set weight parameter used to balance the impact of hydrogen flow deviation and leakage flux on the optimization target. It is calculated from the time derivative of pressure and flow after Kalman filtering, which reflects the actual leakage of hydrogen valve during operation. The second part of the optimization problem is introduced by To control the impact of leakage flux, to a certain extent, the system allows flow control while minimizing the accumulation of leakage flux during the entire test process. It plays a key role in the optimization process, which determines the trade-off between the optimization problem and the flow deviation and leakage flux. A smaller value means that the system pays more attention to suppressing the leakage flux, and even allows a certain deviation between the actual flow rate and the target flow rate in exchange for a lower leakage rate when necessary. A value of 0 indicates a higher priority for flow control, and the system will try to ensure the actual flow With set flow The deviation is minimized while allowing a certain amount of leakage. , the system can flexibly respond to different testing requirements and achieve a balance between flow control and leak suppression.

[0086] Optimization variables Represents the hydrogen valve at time The opening degree at the moment. The opening degree adjustment directly affects the flow rate of hydrogen and the leakage characteristics of the valve, and is the core control method of the optimization problem. The two integral terms in the objective function are related to the opening degree. are closely related, so through The system can ensure the optimal adjustment during the entire test time. The change of valve opening will directly affect the hydrogen flow rate. The adjustment affects the flow deviation term and the leakage rate The optimization problem is solved by changing Continuous adjustment to find the objective function Minimize the optimal opening change path. Overall, this optimization problem organically combines hydrogen flow control and leakage flux suppression to ensure that under different test conditions and operating conditions, the system can accurately track the hydrogen flow and effectively control the leakage flux. The design of the objective function takes into account the actual operating requirements of the system. By finding an optimal balance between flow accuracy and leakage suppression, it ensures that the system can operate stably under various conditions and accurately monitor and control hydrogen leaks. Total test time Represents the time span of the entire test, which determines the scope of the solution to the optimization problem. The system will monitor the hydrogen flow, leakage flux and pressure in real time and adjust the valve opening to , ensuring that the minimization requirements of the objective function can be met throughout the entire test cycle.

[0087] Example 10: Based on the optimization results, a feedback controller is designed to adjust the valve opening To ensure the set flow The controller model is:

[0088] ;

[0089] in is the first controller gain; is the second controller gain; is the integration variable.

[0090] Specifically, the design of the feedback controller is based on two core parts: proportional control and integral control. First, the proportional control part calculates the set flow rate in real time. and the actual flow after filtering The instantaneous difference between , to generate the adjustment signal for the valve opening. The first controller gain Determines the system's immediate response to flow deviation. When the flow deviation is large, the proportional term will introduce a larger control signal , in order to quickly reduce the error, so that the actual flow rate quickly approaches the set flow rate. Correspondingly, when the deviation is small, the signal introduced by the proportional control will also decrease, ensuring smooth system adjustment. However, relying solely on proportional control may not completely eliminate the deviation, especially when there is a steady-state error or continuous disturbance in some systems. For this reason, the feedback controller introduces an integral control term to cumulatively correct the flow deviation. The integral control term calculates the flow deviation over time. To the current time The integral within , to accumulate the error generated by the system during operation. Integral control gain Determines the system's response to the accumulated error. The introduction of this term ensures that even if the system has a small and continuous deviation (steady-state error), the integral term will gradually increase until the deviation is eliminated.

[0091] The first controller gain in the feedback controller and the second controller gain are key parameters of controller performance, which determine the dynamic response characteristics and steady-state error correction capability of the system. A large value can speed up the system's response to deviations, but may cause overshoot and oscillation of the controller; a small value can speed up the system's response to deviations, but may cause overshoot and oscillation of the controller. The system response will be smoother, but the adjustment speed will be slower. Similarly, a higher A small value can quickly eliminate steady-state errors, but may also cause overshoot and oscillation in the system; while a small The value will slow down the correction of steady-state errors. and , the system can achieve the best control effect and ensure the valve opening It can smoothly adjust the hydrogen flow rate to always follow the set target. During the test, the system monitors the flow deviation and its cumulative value in real time and dynamically adjusts the valve opening. . When the system detects a large deviation between the actual flow and the set flow, the proportional controller will respond immediately to increase or decrease the valve opening to quickly correct the flow deviation. At the same time, the integral controller gradually accumulates the flow deviation to ensure that even if the deviation is small but persists, the system can still eliminate the error by adjusting the valve opening. This strategy that combines proportional and integral control ensures precise control of the hydrogen flow rate and enables the system to effectively compensate for external disturbances and parameter changes. Through this feedback control method, the hydrogen valve comprehensive test system can achieve high-precision flow control, ensuring that the actual flow rate remains consistent with the set flow rate throughout the test cycle, thereby optimizing the real-time monitoring and control of leak detection. The design of the feedback controller closely combines the optimization results with real-time control to achieve all-round management of hydrogen valve flow and leakage. Finally, by adjusting the valve opening, the feedback controller can effectively control the flow rate of hydrogen valves and the leakage of hydrogen valves. With intelligent adjustment, the system can adapt to complex and changeable test environments, ensure accurate monitoring and control of hydrogen leaks, and make the performance evaluation and leak detection process of hydrogen valves more efficient and stable.

[0092] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive hydrogen valve test system based on vacuum leak detection method, characterized in that: The system comprises: a modeling part, an analysis and processing part, a real-time control part and a predictive control part; the modeling part is used to establish a gas state equation in a vacuum chamber and a thermodynamic balance equation of the vacuum chamber according to the initial parameters of the test environment; the analysis and processing part is used to consider the influence of temperature change on the vacuum degree, use the fluid dynamics equation to describe the pressure change during the vacuum pumping process, and calculate the leakage flux in combination with the helium flux detected by the helium mass spectrometer leak detector; the real-time control part is used to adjust the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and the system temperature; the predictive control part is used to perform Kalman filtering on the system temperature, pressure and hydrogen flow rate, obtain the predicted hydrogen leakage rate according to the result after Kalman filtering, establish an optimization problem based on the predicted hydrogen leakage rate, the goal of the optimization problem is to minimize the flow deviation, solve the optimization problem to obtain the optimization result, and design a feedback controller based on the optimization result to adjust the hydrogen valve opening to ensure the tracking of the set hydrogen flow rate; In the modeling part, the formula of the gas state equation in the vacuum chamber is as follows: ； in, for The number density of gas molecules in the vacuum chamber at the moment; is the initial pressure; is the volume of the vacuum chamber; is the Boltzmann constant; for The vacuum chamber temperature at the time; is the inherent leakage flux of the system; is the coefficient of thermal expansion; In the modeling part, the formula of the thermodynamic balance equation in the vacuum chamber is as follows: ； in, is the heat capacity of the vacuum chamber; is the heat dissipation coefficient; for External heat input value at the moment; is the specific heat capacity of the gas; The analysis and processing part considers the influence of temperature change on vacuum degree through the following formula, and uses fluid dynamics equation to describe the pressure change during vacuuming: ； in, for The pressure during the vacuuming process at all times; is the air flow rate of the vacuum pump; For the injection of helium pressure; is the gas constant; The analysis and processing part calculates the leakage flux by combining the helium flux detected by the helium mass spectrometer leak detector through the following formula: ； in, is the leakage flux; For helium mass spectrometry Helium flux at the moment; is the leakage area; According to the flow after Kalman filtering and pressure , predict hydrogen leakage rate for: ； in, is the predicted hydrogen flow rate in the predicted state vector; is the pressure in the predicted state vector; Based on the predicted hydrogen leakage rate, the optimization problem is established as follows: ； in, To optimize the objective function; for The set flow rate at the time; for Valve opening at the time; is a pre-set weight parameter; is the total test time; Based on the optimization results, a feedback controller is designed to adjust the valve opening To ensure the set flow The controller model is: ； in is the first controller gain; is the second controller gain; is the integration variable.

2. The hydrogen valve comprehensive testing system based on vacuum leak detection method as claimed in claim 1, characterized in that: The initial parameters of the test environment include: the heat capacity of the vacuum chamber, the number density of gas molecules in the vacuum chamber, the initial pressure, the volume of the vacuum chamber, the temperature of the vacuum chamber, the external heat input value, the inherent leakage flux of the system and the heat dissipation coefficient.

3. The hydrogen valve comprehensive testing system based on vacuum leak detection method as claimed in claim 1, characterized in that: The real-time control part adjusts the hydrogen valve opening and the hydrogen flow rate according to the leakage flux and system temperature: ； in, is the adjusted hydrogen flow rate; for The pressure difference on both sides of the hydrogen valve at the moment; is the valve damping coefficient.

4. The hydrogen valve comprehensive testing system based on vacuum leak detection method as claimed in claim 3, characterized in that: The process of performing Kalman filtering on the system temperature, pressure and hydrogen flow rate in the predictive control part includes: taking the system temperature, pressure and hydrogen flow rate as vector elements to construct a state vector , filtered through a Kalman filter, the state equation of the Kalman filter is as follows: ； in, is the state vector after filtering; is the predicted state vector; is the Kalman gain; is the observation matrix; according to the result after Kalman filtering, the predicted hydrogen leakage rate is obtained through the following formula: .

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