Intelligent soft-sensing method for dynamic total pressure of high-temperature airflow based on multi-dynamics of micro-cavity

Abstract

The present invention discloses an intelligent soft measurement method for the dynamic total pressure of a high-temperature gas flow based on multi-dynamics of a micro-cavity, comprising the following steps: step 1, mathematical modeling of the dynamic process of total pressure decay in a micro-cavity in the flow field; step 2, mathematical modeling of the coupling process of temperature on dynamic total pressure in the flow field; step 3, design of a principle prototype of a short-tube piezoresistive intelligent pressure sensor; and step 4, design of an intelligent soft measurement method for the dynamic total pressure of a high-temperature gas. In the intelligent soft measurement method for the dynamic total pressure of a high-temperature gas flow based on a short-tube proposed in the present invention, the performance model is combined with an intelligent method to solve the scientific problems of the total pressure decay mechanism of gas in a micro-cavity and the coupling mechanism of temperature and pressure in a micro-cavity, a dynamic total pressure recovery model and a dynamic total pressure temperature correction model in a micro-cavity are established, a principle prototype of an intelligent total pressure sensor is designed, and an intelligent soft measurement method for dynamic total pressure is proposed based on the dynamic total pressure recovery model and the dynamic total pressure temperature correction model.

Description

Technical Field

[0001] The present invention relates to an intelligent soft measurement method for the dynamic total pressure of a high-temperature airflow based on micro-cavity multi-dynamics, and belongs to the field of aero-engine testing. Background Art

[0002] The dynamic total pressure measurement of high temperature airflow has always been a difficult problem in the aviation field. t3 This is a typical example of a pressure parameter that is difficult to measure accurately. Whether it is an aviation gas turbine equipped on a traditional fighter or a multi-electric / all-electric engine serving future hypersonic aircraft, the compressor is its core compression component, responsible for generating most of the engine's thrust. Its working efficiency and stability directly affect the performance and safety of the entire aircraft. t3 It is an important parameter to measure the engine's working capacity, determine the engine's stable operation and affect flight safety.

[0003] The total pressure at the compressor outlet determines its pressure ratio, and the high pressure ratio enables the turbine work to be efficiently transferred to the air to generate the main thrust. Therefore, the total pressure at the engine outlet determines the total mechanical energy of the gas in the engine, that is, the propulsion performance of the engine.

[0004] Compressor inlet total pressure affects flight safety through two channels: thrust and stability. First, for supersonic vehicles, since the compressor outlet total pressure determines thrust, any total pressure fluctuation will cause thrust pulsations, which will act on the vehicle and cause vibrations. This can lead to dynamic coupling between the aeroelastic system, the vehicle system, and the propulsion system, which can seriously endanger flight safety. To prevent or suppress this coupling, it is necessary to accurately and timely obtain the dynamic total pressure at the compressor outlet. Active compressor flow field control can be used to eliminate or mitigate total pressure and thrust fluctuations and actively suppress aeroelastic coupling. Second, while the compressor provides a high pressure ratio, it also limits the engine's stable operating range. To achieve the highest possible thrust, the maximum pressure ratio operating point on the speed curve of modern advanced fan / compressor systems generally falls on the stability boundary. When an engine is subjected to external disturbances (such as inlet flow field distortion), the engine's operating point may cross the stability boundary, resulting in instabilities such as rotating stall and surge. This can cause a sharp drop in engine performance and, in severe cases, engine stall or irreversible, fatal damage. To ensure proper compressor operation, a "stability margin" is typically set to keep the engine operating point a certain distance from the surge boundary. In this case, the compressor pressure ratio is not at its maximum. Compressor design constantly faces conflicts and tradeoffs between pressure ratio, efficiency, and stability margin. When rotating stall occurs, the compressor outlet total pressure exhibits high-frequency pulsations; during surge, the compressor outlet total pressure exhibits axial oscillations. Therefore, the steady-state value of the compressor outlet total pressure reflects the pressure ratio and the degree of engine surge, while its frequency and amplitude indicate transient engine stall or surge. In order to ensure stable operation of the compressor, the total pressure at the compressor outlet must be measured accurately and in real time, especially when the engine is in a high-performance state, so that the control system can take timely measures to suppress them in the early stages of stall / surge, or to stop the surge in time if they have already occurred.

[0005] Therefore, accurate sensing and measurement of the dynamic total pressure signal of high-temperature airflow is an urgent problem to be solved in engine performance analysis, air path fault diagnosis, flight / propulsion integrated active stability control, and surge prediction and control. Summary of the Invention

[0006] Purpose of the Invention: To overcome the shortcomings in the existing technologies and methods for measuring the dynamic total pressure of high-temperature airflows in aerospace propulsion systems and high-speed aircraft, this invention proposes an intelligent soft-measurement method for the dynamic total pressure of high-temperature airflows based on micro-cavity multi-dynamics. By establishing mathematical models for the dynamic process of total pressure decay within a micro-cavity in the flow field and the coupled process of temperature on the dynamic total pressure in the flow field, a prototype of a short-tube piezoresistive intelligent pressure sensor was designed. Using the concept of "series" model fusion and experimental data correction, an intelligent soft-measurement method for the dynamic total pressure of high-temperature gases was obtained based on these mathematical models, the sensor prototype, and experimental data.

[0007] The present invention adopts the following technical solutions:

[0008] The intelligent soft-sensing method for dynamic total pressure of high-temperature airflow based on micro-cavity multi-dynamics includes the following steps:

[0009] Step 1: Mathematical modeling of total pressure decay in a small cavity in the flow field:

[0010] Step 1.1: Based on the engine component-level aerodynamic thermodynamic model, a simplified compressor outlet flow field model is established;

[0011] In step 1.2, the flow in the microcavity is considered to be steady, compressible flow. A 3D flow model for the microcavity, consisting of a short, closed-end measuring tube, is established. This model is used to numerically simulate the stagnation process of steady, compressible flow in the cavity. The pressure distribution along the cavity during the stagnation process is determined, and the variations in the total and static pressures within the microcavity are determined as a function of the flow rate.

[0012] In step 1.3, considering the compressibility of the airflow in the tiny cavity, a volumetric dynamics model of the gas in the cavity is established to obtain the dynamic law of the pressure at the closed end of the cavity.

[0013] In step 1.4, based on the numerical simulation results of the 3D flow field calculation model and the closed end pressure dynamic data obtained by volumetric dynamics calculation, the extreme learning machine algorithm is used to establish a nonlinear mapping between the dynamic pressure at the measuring end (that is, the closed end of the tiny cavity) and the pressure at the measuring tube inlet (that is, the open end of the tiny cavity) in the flow field. This is the dynamic total pressure recovery model, which realizes soft measurement of the dynamic pressure at a certain point in the flow field without considering the influence of temperature.

[0014] Step 2: Mathematical modeling of the coupling between temperature and dynamic total pressure in the flow field:

[0015] The pressure and temperature in the tiny cavity calculated by the 3D flow field model are used as boundary constraints to establish a short measuring tube enhanced heat transfer model. The series principle is adopted to establish a temperature correction model for the pressure measurement signal based on the temperature volume dynamics model, dynamic correction model and steady-state correction model of the silicon resistor. That is, the output (temperature and pressure) of the temperature volume dynamics model of the silicon piezoresistor is used as the input of the dynamic correction model, and the output of the dynamic pressure correction model - the corrected dynamic pressure is used as the input of the steady-state correction model. The steady-state correction model is used to obtain the pressure after dynamic temperature and steady-state temperature correction. The silicon resistor measurement value and the silicon resistor correction value are superimposed to obtain the corrected silicon resistor output value, and the total gas pressure at the silicon resistor end is calculated to complete the temperature correction model modeling of the dynamic total pressure. The specific steps are as follows:

[0016] In step 2.1, using the pressure and temperature in the microcavity calculated from the 3D flow field model within the tube as boundary constraints, an enhanced heat transfer model for the short tube was established based on the thermodynamics of a multivariable gas, encompassing the environment, the short tube, and the gas within the tube. Based on this heat transfer model, the variations in temperature and gas parameters along the tube under enhanced heat transfer were calculated.

[0017] In step 2.2, the intelligent nonlinear fitting method of the neural network is used to establish a steady-state correction model of the temperature effect on the bias and sensitivity of the silicon resistor.

[0018] In step 2.3, a temperature dynamic correction model of the silicon resistor output is established using the transfer function method.

[0019] In step 2.4, considering the cavity effect, a temperature volume dynamics model of the silicon resistor end is established, and the dynamic change law of temperature is calculated based on the differential equation.

[0020] In step 2.5, a temperature correction model for the pressure measurement signal is established based on the series connection principle and the temperature-volume dynamics model, the dynamic correction model, and the steady-state correction model. That is, the output value of the model is calculated using the dynamic correction model, and the output value is used as the input value of the steady-state correction model. The correction value of the silicon resistor is calculated using the steady-state correction model.

[0021] In step 2.6, the measured silicon resistor value and the corrected silicon resistor value are superimposed to obtain the corrected silicon resistor output value. Based on the resistance-pressure characteristic of the silicon resistor, the pressure value acting on the silicon resistor is calculated from the silicon resistor output value, which is the total gas pressure at the silicon resistor end of the short measuring tube. This completes the temperature correction model for the dynamic total pressure, i.e., the mathematical modeling of the temperature-dynamic total pressure coupling process.

[0022] Step 3: Design of the prototype of the short-tube piezoresistive intelligent pressure sensor:

[0023] Based on the dynamic total pressure recovery model, dynamic total pressure temperature correction model, and micro-cavity enhanced heat exchange model established in steps 1 and 2, an intelligent sensing model of the dynamic total pressure at the compressor outlet based on the measured data is established. The calculation process of the intelligent sensing model of the dynamic total pressure at the compressor outlet is as follows: first, the measured pressure measurement signal and the temperature signal of the micro-cavity enhanced heat exchange model material are used as inputs of the dynamic total pressure temperature correction model to obtain the output of the model, that is, the dynamic total pressure signal after temperature correction; second, the temperature-corrected dynamic total pressure signal and the temperature output of the micro-cavity enhanced heat exchange model are used as inputs of the dynamic total pressure recovery model, and the dynamic total pressure of the airflow at the inlet of the short measuring tube in the flow field is obtained through the dynamic total pressure recovery model; the specific steps are as follows:

[0024] Step 3.1, design a short measuring tube with a single bend of equal diameter: the short measuring tube consists of a bend and a silicon piezoresistor. The bend is formed by connecting two perpendicular straight tubes. The inner and outer diameters of the bend are both equal diameter structures. The length of the bend is determined by the thickness of the aircraft engine compressor outlet casing and the thickness of the airflow boundary layer. Theoretically, the length should be greater than the sum of the casing thickness and the boundary layer thickness. The silicon piezoresistor is installed at one end of the short measuring tube and this end is sealed to prevent leakage of the measured gas. The silicon piezoresistor is connected to the pressure signal detection intelligent unit through a wire.

[0025] Step 3.2, design the pressure signal detection intelligent unit: The intelligent unit includes a central processing unit (CPU) based on ARM and FPGA, a measuring bridge, a shaping and filtering circuit, a signal acquisition circuit, a power supply circuit, a reset circuit, and a crystal oscillator circuit.

[0026] Step 3.3, design the pressure intelligent sensing software based on the compressor outlet pressure intelligent sensing model: mainly use the hardware description language Verilog and C language to write the embedded dynamic total pressure recovery model and the dynamic total pressure temperature correction model. The specific design process is: when there is no signal trigger, the design CPU is in an idle state; when the analog-to-digital conversion chip completes the conversion, the conversion completion enable signal triggers the CPU state transfer, so that the CPU enters the calculation state; after entering the calculation state, the CPU reads the reg type pressure signal and temperature signal, and then calculates the size of the dynamic pressure according to the dynamic total pressure recovery model, the dynamic total pressure temperature correction model and the short measuring tube enhanced heat exchange model calculation algorithm, and stores it in the reg type variable; after the calculation is completed, the CPU jumps to the sending state, which converts the dynamic pressure value into a binary quantity and outputs it as a wire type signal, and then the CPU state jumps back to the idle state.

[0027] Step 4: Design of intelligent soft-sensing method for high-temperature gas dynamic total pressure:

[0028] In step 4.1, based on the total pressure recovery model and the temperature correction model for the pressure measurement signal, an intelligent sensing model for the dynamic total pressure at the compressor outlet is established based on the measured data. Using a "series" approach, the output of the temperature correction model (i.e., the corrected pressure value) is passed to the dynamic total pressure recovery model as its input. This model then calculates the model output, which is the total pressure at the short measuring tube inlet.

[0029] In step 4.2, relying on the low-speed compressor test platform, simulate the compressor outlet flow field, use the short-tube piezoresistive intelligent pressure sensor principle prototype to measure the flow field pressure data, and use the temperature sensor to measure the flow field temperature data.

[0030] In step 4.3, based on the measured pressure and temperature data, a nonlinear fitting method is used to correct the total pressure attenuation model and the temperature correction model of the pressure measurement signal, namely, the dynamic total pressure recovery model and the dynamic total pressure temperature correction model. This forms an intelligent soft measurement method for the dynamic total pressure of high-temperature gas based on micro-cavity multi-dynamics.

[0031] The present invention has the following beneficial effects:

[0032] (1) The obtained dynamic total pressure recovery model in a micro-cavity, the dynamic total pressure temperature correction model, and the dynamic pressure intelligent soft measurement method can be used to measure the dynamic total pressure at the outlet of an aircraft engine compressor. After appropriate correction, these models and methods can also be used to measure the dynamic total pressure of high-temperature airflow in high-speed aircraft and other key sections of engines in the aerospace field.

[0033] (2) The numerical simulation methods and processes of temperature and pressure dynamics in micro-cavities based on basic theories and methods such as steady compressible flow gas dynamics, volumetric dynamics, and variable gas heat transfer can be used in the modeling of performance models of aircraft engine complete machines or cavity-type components.

[0034] (3) The intelligent processing unit architecture and related signal processing circuits in the design of the intelligent pressure sensor prototype can provide a useful reference for the design of intelligent processing units of other types of intelligent sensors with analog output, such as intelligent displacement sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is an intelligent soft measurement solution for the dynamic total pressure of high-temperature airflow.

[0036] Figure 2 It is an intelligent sensor solution for high-temperature airflow dynamic total pressure.

[0037] Figure 3 It is a technical approach to modeling the dynamic total pressure recovery model.

[0038] Figure 4 It is a technical approach to modeling the dynamic total pressure recovery model.

[0039] Figure 5 It is the hardware solution for the intelligent unit of the intelligent pressure sensor. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is described in detail below with reference to the accompanying drawings.

[0041] The technical solution of the present invention includes an intelligent soft measurement solution for the dynamic total pressure of high-temperature airflow ( Figure 1 ) and high temperature airflow intelligent pressure sensor solution ( Figure 2 ).

[0042] Figure 1 The intelligent soft-measurement method for the dynamic total pressure of high-temperature airflow shown in this paper primarily includes a dynamic total pressure recovery model, a micro-cavity enhanced heat transfer model, and a dynamic total pressure temperature correction model. The soft-measurement scheme uses a silicon resistor to obtain the dynamic pressure signal (i.e., dynamic total pressure) at section B of the short measuring tube. The enhanced heat transfer model then uses this signal to obtain the temperature signal at section B. This temperature signal is then passed to the dynamic pressure temperature correction model to obtain a temperature-corrected pressure measurement signal. The dynamic total pressure recovery model estimates (recovers) the dynamic total pressure at the inlet (section A) of the short measuring tube based on the corrected pressure and temperature signals.

[0043] like Figure 2 As shown in FIG, the intelligent pressure sensor is composed of a Microsoft measuring tube, a silicon resistor (pressure sensitive element) and an intelligent sensing unit. The short measuring tube is open at one end and closed at the other end. Figure 2 A silicon resistor is installed in section B of the flow field. During measurement, the open end of a short measuring tube is extended into section A of the flow field, with the small hole facing the incoming flow. High-temperature gas in the flow field enters the measuring tube and stagnates. The pressure sensor senses the gas pressure after stagnation (i.e., the total pressure of the outflow at section A) and transmits an output electrical signal to the intelligent unit. After processing and sampling the measured signal, the intelligent unit corrects it based on a dynamic total pressure recovery model and a temperature correction model to obtain the dynamic total pressure at section A within the flow channel.

[0044] In the mathematical modeling of pressure decay within a microcavity, to minimize the impact of a short measuring tube on the measured flow field, this paper establishes a three-dimensional fluid dynamics computational model encompassing the measured flow field and the short measuring tube. Using numerical simulation, this paper determines the influence of the tube's shape, characteristic geometric dimensions, installation position, length within the flow field, and total length on characteristic flow field parameters, particularly flow velocity, temperature, and pressure. With the goal of minimizing the change in these characteristic parameters, the paper optimizes and determines the tube's characteristic geometric dimensions, installation position, total length, lengths of each section, and length within the flow field.

[0045] In order to establish a mathematical model for the pressure decay from the open end to the closed end of a small cavity with one end open and the other closed, the stagnation process of steady compressible flow and the cavity effect and their mutual coupling are adopted. The specific technical approach is:

[0046] First, the modeling work of the 3D flow field calculation model and the volume dynamics model of the micro cavity is carried out in parallel. The airflow in the micro cavity is considered as a steady compressible flow, and a 3D flow field calculation model in the cavity is established. Based on this model, a numerical simulation of the stagnation process of the steady compressible flow in the cavity is carried out, and the pressure distribution along the cavity flow during the stagnation process is obtained. Considering the compressibility of the airflow in the micro cavity, a volume dynamics model of the gas in the cavity is established, and the closed end of the cavity ( Figure 3 Dynamic law of pressure (section B in the middle).

[0047] Secondly, based on the numerical simulation results and the closed end pressure dynamic data, the extreme learning machine algorithm is used to establish the dynamic pressure of the measuring end, that is, the closed end of the cavity, and the pressure at the measuring pipe inlet in the flow field ( Figure 3 The dynamic total pressure recovery model is a nonlinear mapping of the pressure at the open end of the cavity. On the one hand, it realizes the mathematical modeling of the dynamic total pressure attenuation. On the other hand, it uses the measured pressure at the closed end as input. Through this model, the total pressure of the outflow section A of the measuring tube inlet can be estimated, realizing the soft measurement of the dynamic pressure at a certain point in the flow field without considering the influence of temperature.

[0048] In order to establish a mathematical model of temperature and pressure coupling in a small cavity with one end open and the other closed, the present invention intends to establish a mathematical model of temperature and pressure coupling from the perspective of enhanced heat transfer, volume effect and the influence of temperature on the properties of silicon resistor materials. The technical path is as follows Figure 4 As shown. Along Figure 4 The technical approach shown here uses a method that includes establishing an enhanced heat transfer model based on a multi-variable gas thermodynamic process, including the environment, a short measuring tube, and the gas within the tube. This model calculates the variation patterns of the temperature and gas parameters along the gas within the tube under enhanced heat transfer. A steady-state correction model for the effects of temperature on silicon resistor bias and sensitivity is established using an intelligent nonlinear fitting method using a neural network. Considering the cavity effect, a volumetric dynamics model of the temperature at the silicon resistor end is established to obtain the dynamic variation patterns of temperature. Using the series principle, a temperature correction model for the pressure measurement signal is established based on the temperature volumetric dynamics model, a dynamic correction model, and a steady-state correction model. Specifically, the dynamic correction model is used to calculate the model output value, which is then used as the input value for the steady-state correction model. The steady-state correction model is then used to calculate the corrected value for the silicon resistor. The measured silicon resistor value and the corrected silicon resistor value are superimposed to obtain the corrected silicon resistor output value. Based on the resistance-pressure characteristics of the silicon resistor, the pressure value exerted on the silicon resistor is calculated from the output value of the silicon resistor, that is, the total pressure of the gas at the silicon resistor end of the short measuring tube. At this point, the temperature correction model of the dynamic total pressure is completed, that is, the mathematical modeling of the temperature-dynamic total pressure coupling process.

[0049] The design of the intelligent pressure sensor prototype includes the design of a short measuring tube with a single bend of uniform diameter, an intelligent pressure signal detection unit, and intelligent pressure sensing software based on the compressor outlet pressure intelligent sensing model. The short measuring tube consists of a bend and a silicon piezoresistor. The structure of the short measuring tube will be designed using UG / CAD. The bend is constructed by connecting two perpendicular straight sections. The inner and outer diameters of the bend are both equal. The length of the bend is determined by the thickness of the aircraft engine compressor outlet casing and the boundary layer thickness of the airflow. Theoretically, the length should be greater than the sum of the casing thickness and the boundary layer thickness. The silicon piezoresistor is installed at one end of the short measuring tube, which is sealed to prevent leakage of the measured gas.

[0050] The silicon piezoresistor is connected to the pressure signal detection intelligent unit through a wire. The design of the pressure signal detection intelligent unit is mainly based on the central processing unit (CPU) of ARM and FPGA, and also includes a measurement bridge, shaping and filtering circuit, signal acquisition circuit, power supply circuit, reset circuit and crystal oscillator circuit. The hardware solution of the intelligent unit is as follows: Figure 5 As shown in the figure. In the design of the intelligent pressure sensing software, Verilog and C languages ​​are primarily used to write the embedded dynamic total pressure recovery model and dynamic total pressure temperature correction model software, which are then downloaded to the intelligent unit CPU. The design process is as follows: when there is no signal trigger, the CPU is in an idle state; when the analog-to-digital conversion chip completes the conversion, the conversion completion enable signal triggers the CPU state transition, causing the CPU to enter the calculation state; after entering the calculation state, the CPU reads the reg-type pressure and temperature signals, and then calculates the dynamic pressure according to the dynamic total pressure recovery model, the dynamic total pressure temperature correction model, and the short-tube enhanced heat exchange model calculation algorithm, and stores it in a reg-type variable; after the calculation is complete, the CPU jumps to the sending state, which converts the dynamic pressure value into a binary value and outputs it as a wire-type signal, and then the CPU state jumps back to the idle state.

[0051] Based on the total pressure recovery model and the temperature correction model for the pressure measurement signal, an intelligent sensing model for dynamic total pressure at the compressor outlet is established based on the measured data. Using a "series" approach, the output of the temperature correction model—the corrected pressure value—is transmitted to the dynamic total pressure recovery model as its input. This model then calculates the model output, which is the total pressure at the short measuring tube inlet.

[0052] Relying on a low-speed compressor test platform, the compressor outlet flow field was simulated. Pressure data from this flow field was measured using a prototype of a short-tube piezoresistive intelligent pressure sensor, and temperature data from this flow field was measured using a temperature sensor. Based on the measured pressure and temperature data, a nonlinear fitting method was used to modify the total pressure decay model and the temperature correction model for the pressure measurement signal, namely the dynamic total pressure recovery model and the dynamic total pressure temperature correction model. This resulted in an intelligent soft-sensing method for high-temperature gas dynamic total pressure based on micro-cavity multi-dynamics.

[0053] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent soft-measurement method for the dynamic total pressure of high-temperature airflow based on micro-cavity multi-dynamics, characterized by: The following steps are involved: Step 1: Mathematical modeling of total pressure decay in a small cavity in the flow field: Establish a nonlinear mapping between the dynamic pressure at the measuring end and the pressure at the inlet of the measuring tube in the flow field, and construct a dynamic total pressure recovery model; Step 2: Mathematical modeling of the coupling of temperature and dynamic total pressure in the flow field: Using the pressure and temperature in the tiny cavity calculated by the 3D flow field model as boundary constraints, a short measuring tube enhanced heat transfer model is constructed. The silicon resistor measurement values, including temperature and pressure, and their correction values ​​after correction by the steady-state correction model are superimposed to obtain the corrected silicon resistor output value. The total gas pressure at the silicon resistor end is calculated, and a temperature correction model for the dynamic total pressure is constructed. Step 3: Design of the prototype of the short-tube piezoresistive intelligent pressure sensor: Based on the dynamic total pressure recovery model, the dynamic total pressure temperature correction model, and the micro-cavity enhanced heat exchange model, an intelligent sensing model for the dynamic total pressure at the compressor outlet based on the measured data is established; Step 4: Design of intelligent soft-sensing method for high-temperature gas dynamic total pressure: Nonlinear fitting is performed on the measured pressure data and temperature data to correct the dynamic total pressure recovery model and the dynamic total pressure temperature correction model.

2. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 is characterized by: Step 1: The method for constructing a dynamic total pressure recovery model is to establish a simplified model of the compressor outlet flow field, based on the numerical simulation results of the 3D flow field calculation model of a short measuring tube micro-cavity with one end open and the other end closed, and the closed end pressure dynamic data calculated by the volumetric dynamics model of the gas in the cavity, establish a nonlinear mapping between the dynamic pressure at the measuring end and the measuring tube inlet pressure in the flow field, which is the dynamic total pressure recovery model.

3. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 or 2, characterized in that: The specific steps in step 1 are as follows: Step 1.1: Based on the engine component-level aerodynamic thermodynamic model, a simplified compressor outlet flow field model is established; In step 1.2, the airflow in the microcavity is considered as a steady compressible flow. A 3D flow field computational model of a microcavity with a short measuring tube that is open at one end and closed at the other is established. Based on this model, a numerical simulation of the stagnation process of the steady compressible flow in the cavity is performed to obtain the pressure distribution along the cavity flow path during the stagnation process, and then the variation of the total pressure and static pressure in the microcavity with the flow path is determined. Step 1.3: Considering the compressibility of the airflow in the tiny cavity, a volumetric dynamics model of the gas in the cavity is established to obtain the dynamic law of the pressure at the closed end of the cavity; In step 1.4, based on the numerical simulation results of the 3D flow field calculation model and the closed end pressure dynamic data obtained by volumetric dynamics calculation, the extreme learning machine algorithm is used to establish a nonlinear mapping between the dynamic pressure at the measuring end, i.e., the closed end of the micro-cavity, and the pressure at the measuring tube inlet, i.e., the open end of the micro-cavity in the flow field. This is the dynamic total pressure recovery model, which realizes soft measurement of the dynamic pressure at a certain point in the flow field without considering the influence of temperature.

4. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 is characterized by: Step 2: The method for constructing a short measuring tube enhanced heat transfer model and a dynamic total pressure temperature correction model is as follows: using the pressure and temperature in the tiny cavity calculated by the 3D flow field model as boundary constraints, a short measuring tube enhanced heat transfer model is established; using the series principle, a temperature correction model of the pressure measurement signal is established based on the temperature volume dynamics model, dynamic correction model and steady-state correction model of the silicon resistor, that is, the output of the temperature volume dynamics model of the silicon piezoresistive temperature is used as the input of the dynamic correction model, and the output of the dynamic pressure correction model - the corrected dynamic pressure is used as the input of the steady-state correction model, and the pressure corrected by the dynamic temperature and steady-state temperature is obtained through the steady-state correction model; the silicon resistor measurement value and the silicon resistor correction value are superimposed to obtain the corrected silicon resistor output value, the total gas pressure at the silicon resistor end is calculated, and the temperature correction model modeling of the dynamic total pressure is completed.

5. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 or 4, characterized in that: The specific steps in step 2 are as follows: Step 2.1: Using the pressure and temperature in the microcavity calculated using the 3D flow field model within the tube as boundary constraints, an enhanced heat transfer model for the short tube is established based on the polyvariable gas thermodynamic process, including the environment, the short tube, and the gas within the tube. Based on this heat transfer model, the variation patterns of gas parameters along the tube, including temperature and gas, are calculated under enhanced heat transfer. Step 2.2, using the intelligent nonlinear fitting method of neural network, a steady-state correction model of temperature on silicon resistor bias and sensitivity is established; Step 2.3, using the transfer function method, establish a temperature dynamic correction model for the output of the silicon resistor; Step 2.4: Considering the cavity effect, establish a temperature volume dynamics model for the silicon resistor end, and calculate the dynamic temperature change law based on the differential equation of the model; Step 2.5: Using the series connection principle, establish a temperature correction model for the pressure measurement signal based on the temperature-volume dynamics model, the dynamic correction model, and the steady-state correction model. Specifically, use the dynamic correction model to calculate the model output value, which is used as the input value for the steady-state correction model, which then calculates the correction value for the silicon resistor. Step 2.6, superimposing the silicon resistance measurement value and the silicon resistance correction value to obtain a corrected silicon resistance output value; Based on the resistance-pressure characteristics of the silicon resistor, the pressure value exerted on the silicon resistor is calculated from the output value of the silicon resistor, that is, the total pressure of the gas at the silicon resistor end of the short measuring tube. At this point, the temperature correction model of the dynamic total pressure is completed, that is, the mathematical modeling of the temperature-dynamic total pressure coupling process.

6. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 is characterized by: Step 3: The method for constructing the intelligent sensing model of the dynamic total pressure at the compressor outlet is as follows: first, the measured pressure measurement signal and the temperature signal of the micro-cavity enhanced heat exchange model material are used as the input of the temperature correction model of the dynamic total pressure to obtain the output of the model, that is, the dynamic total pressure signal after temperature correction; secondly, the dynamic total pressure signal after temperature correction and the temperature output of the micro-cavity enhanced heat exchange model are used as the input of the dynamic total pressure recovery model, and the dynamic total pressure of the airflow at the inlet of the short measuring tube in the flow field is obtained through the dynamic total pressure recovery model to complete the construction of the intelligent sensing model of the dynamic total pressure at the compressor outlet.

7. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 or 6, characterized in that: The specific steps in step 3 are as follows: Step 3.1: Design a short measuring tube with a single bend of uniform diameter. The short measuring tube consists of a bend and a silicon piezoresistor. The bend is formed by connecting two perpendicular straight tube sections, with the inner and outer diameters of the bend being equal. The bend length is greater than the sum of the compressor outlet casing thickness and the boundary layer thickness. The end of the short measuring tube where the silicon piezoresistor is located is sealed. The silicon piezoresistor is connected to the pressure signal detection intelligent unit via a wire. Step 3.2: Design the pressure signal detection intelligent unit. The intelligent unit includes an ARM and FPGA-based central processing unit, a measurement bridge, a shaping and filtering circuit, a signal acquisition circuit, a power supply circuit, a reset circuit, and a crystal oscillator circuit. Step 3.3, design the pressure intelligent sensing software based on the compressor outlet pressure intelligent sensing model, and use Verilog and C languages ​​to write the embedded dynamic total pressure recovery model and dynamic total pressure temperature correction model.

8. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 7 is characterized by: The step 3.3 is based on the pressure intelligent sensing software of the compressor outlet pressure intelligent sensing model, and the design process of the embedded dynamic total pressure recovery model and the dynamic total pressure temperature correction model is written in the hardware description language Verilog: when there is no signal trigger, the design CPU is in an idle state; when the analog-to-digital conversion chip completes the conversion, the conversion completion enable signal triggers the CPU state transfer, so that the CPU enters the calculation state; after entering the calculation state, the CPU reads the reg type pressure signal and temperature signal, and then calculates the size of the dynamic pressure according to the dynamic total pressure recovery model, the dynamic total pressure temperature correction model and the short measuring tube enhanced heat exchange model calculation algorithm, and stores it in the reg type variable; after the calculation is completed, the CPU jumps to the sending state, which converts the dynamic pressure value into a binary quantity and outputs it as a wire type signal, and then the CPU state jumps back to the idle state.

9. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 is characterized by: The method for step 4 to correct the total pressure attenuation model and the temperature correction model of the pressure measurement signal is as follows: simulate the compressor outlet flow field, measure pressure data with a short-tube piezoresistive intelligent pressure sensor principle sample, and measure temperature data with a temperature sensor, perform nonlinear fitting, and correct the total pressure attenuation model and the temperature correction model of the pressure measurement signal.

10. The intelligent soft measurement method for high-temperature airflow dynamic total pressure based on micro-cavity multi-dynamics according to claim 1 or 9, characterized in that: The specific steps in step 4 are as follows: Step 4.1: Based on the total pressure recovery model and the temperature correction model for the pressure measurement signal, establish an intelligent sensing model for the dynamic total pressure at the compressor outlet based on the measured data. Using a "series" approach, the output of the temperature correction model, i.e., the corrected pressure value, is passed to the dynamic total pressure recovery model as the input. The model output is then calculated based on the model, which is the total pressure at the short measuring pipe inlet. Step 4.2: Using a low-speed compressor test platform, simulate the compressor outlet flow field, measure the flow field pressure data using a short-tube piezoresistive intelligent pressure sensor prototype, and measure the flow field temperature data using a temperature sensor. In step 4.3, based on the measured pressure and temperature data, a nonlinear fitting method is used to correct the total pressure attenuation model and the temperature correction model of the pressure measurement signal. This forms an intelligent soft measurement method for the dynamic total pressure of high-temperature gas based on micro-cavity multi-dynamics.

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