A method and system for simulating and evaluating the flow field of a sampling pipeline for respiratory gas analysis

Abstract

Method and system for simulating and evaluating the flow field of a respiratory gas analysis sampling pipeline of the present invention: performing pre-processing on the geometric model of the oxygen concentration detection host; extracting the internal flow field of the geometric model of the oxygen concentration detection host to establish an internal flow field sampling pipeline model; performing mesh division; setting working conditions; selecting a physical model; setting gas physical property parameters; setting boundary conditions and simulation conditions; setting up and initializing the solution; taking the cross-section formed at the vertical middle position of the mesh model as the monitoring surface, and taking the position corresponding to the gas sensor on the monitoring surface as the monitoring point; setting up iterative calculations; simulating the breathing process within a preset time under the calmest and most intense working conditions, respectively performing transient simulation of the internal flow field of the sampling pipeline on the mesh model, solving the selected physical model using a coupling algorithm, sampling the monitoring surface and monitoring points under the two working conditions during the solution process; obtaining and displaying the simulation results, and analyzing the simulation results to evaluate the advantages and disadvantages of the designed oxygen concentration detection host.

Description

Technical Field

[0001] The present invention relates to the technical field of respirator modeling and simulation, and particularly to a method and system for simulating and evaluating the flow field of a gas analysis sampling pipeline of a respirator. Background Art

[0002] After the inventors of the present invention searched the prior art, there is no existing technology for simulating the flow field of a gas analysis sampling pipeline of a respirator, and specifically, there is no technology for simulating the flow field of an oxygen concentration detection host to evaluate the advantages and disadvantages of the oxygen concentration detection host. For a designed oxygen concentration detection host, it is necessary to evaluate its working performance in a frogman respirator, evaluate the differences in different structures, and find the optimal structure. Based on this, the present invention designs a method and system for simulating and evaluating the flow field of a gas analysis sampling pipeline of a respirator. Summary of the Invention

[0003] The present invention aims at the problems and deficiencies existing in the prior art, and provides a method and system for simulating and evaluating the flow field of a gas analysis sampling pipeline of a respirator.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] The present invention provides a method for simulating and evaluating the flow field of a gas analysis sampling pipeline of a respirator, which is characterized in that it includes the following steps:

[0006] S1. Geometric preprocessing: Perform geometric preprocessing on the geometric model of the oxygen concentration detection host in the gas analysis sampling pipeline of the respirator;

[0007] S2. Modeling: Extract the internal flow field of the geometric model of the oxygen concentration detection host after geometric preprocessing, and establish an internal flow field sampling pipeline model of the oxygen concentration detection host;

[0008] S3. Mesh generation: Generate a mesh for the internal flow field sampling pipeline model to obtain an internal flow field sampling pipeline mesh model;

[0009] S4. Set working conditions: Take the set diving depth as the simulated working condition environment, and set that when the user breathes, the change in their breathing flow rate conforms to a sine curve form and has two extreme states, namely the calmest state and the most intense state;

[0010] S5. Selection of physical models: According to the working principle and process of the oxygen concentration detection host, select a turbulence equation to describe the movement of the airflow and the generation and dissipation of turbulence, select an energy equation to calculate the change in temperature, and select a component transport equation to describe the multi-component diffusion process in the gas, so as to simulate the changes in the oxygen and carbon dioxide content in the oxygen concentration detection host;

[0011] S6. Gas Physical Property Parameter Setting: Set the physical property parameters of the gas, where the physical property parameters include oxygen, nitrogen, carbon dioxide, and water vapor;

[0012] S7. Boundary Condition and Simulation Condition Setting: Set the inlet boundary condition and outlet boundary condition of the internal flow field sampling pipeline grid model, and set the simulation conditions including the calmest condition and the most severe condition;

[0013] S8. Solution Setting and Initialization: Set to use a coupling algorithm to solve the selected physical model, and use standard initialization to assign values to the oxygen content, carbon dioxide content, water vapor content, and temperature of the initial field to simulate the initial state of the oxygen concentration detection host;

[0014] S9. Monitoring Surface and Monitoring Point Construction: Take the cross-section formed at the vertical middle position of the internal flow field sampling pipeline grid model as the constructed monitoring surface for monitoring the change of physical quantities during the breathing process, and take the positions on the monitoring surface corresponding to the gas sensors as the constructed monitoring points;

[0015] S10. Iterative Calculation Setting: Set the preset time and time step for each simulation condition;

[0016] S11. Simulation: Simulate the breathing process within the preset time under the calmest condition and the most severe condition, respectively perform transient simulation of the internal flow field of the sampling pipeline on the internal flow field sampling pipeline grid model, use a coupling algorithm to solve the selected physical model, and sample the pressure, velocity, temperature, and oxygen concentration of the monitoring surface and monitoring points under the two conditions during the solution process;

[0017] S12. Simulation Results: Obtain and display the simulation results, including the velocity field cloud diagram, velocity vector distribution diagram during inhalation and exhalation, pressure cloud diagram, temperature cloud diagram, and oxygen content distribution diagram of the monitoring surface within the preset time under the calmest condition and the most severe condition, and the velocity change curve, pressure change curve, temperature change curve, and oxygen mass fraction change curve of the monitoring points within the preset time under the calmest condition and the most severe condition, and analyze this simulation result to evaluate the advantages and disadvantages of the designed oxygen concentration detection host.

[0018] The present invention also provides a respirator gas analysis sampling pipeline flow field simulation evaluation system, which is characterized in that it includes a pre-processing module, a modeling module, a mesh generation module, a working condition setting module, a physical model selection module, a physical property parameter setting module, a condition working condition setting module, a solution initialization setting module, a monitoring construction module, an iterative calculation setting module, a simulation module, and a simulation result analysis module;

[0019] The pre-processing module is used for geometric pre-processing of the geometric model of the oxygen concentration detection host in the respirator gas analysis sampling pipeline;

[0020] The modeling module is used to extract the internal flow field of the geometric model of the oxygen concentration detection host after geometric preprocessing, and establish the internal flow field sampling pipeline model of the oxygen concentration detection host;

[0021] The mesh generation module is used to generate a mesh for the internal flow field sampling pipeline model to obtain the internal flow field sampling pipeline mesh model;

[0022] The working condition setting module is used to set the diving depth as the simulated working environment, and set that when the user breathes, the change of their breathing flow rate conforms to the form of a sine curve and has two limit states, namely the calmest state and the most intense state;

[0023] The physical model selection module is used to select a turbulence equation to describe the movement of the air flow and the generation and dissipation of turbulence, select an energy equation to calculate the temperature change, and select a component transport equation to describe the multi-component diffusion process in the gas according to the working principle and process of the oxygen concentration detection host, so as to simulate the changes in the contents of oxygen and carbon dioxide in the oxygen concentration detection host;

[0024] The physical property parameter setting module is used to set the physical property parameters of the gas, and the physical property parameters include oxygen, nitrogen, carbon dioxide and water vapor;

[0025] The condition working condition setting module is used to set the inlet boundary condition and outlet boundary condition of the internal flow field sampling pipeline mesh model, and set the simulation working conditions including the calmest working condition and the most intense working condition;

[0026] The solution initialization setting module is used to set to solve the selected physical model using a coupling algorithm, adopt standard initialization, and assign values to the oxygen content, carbon dioxide content, water vapor content and temperature of the initial field to simulate the initial state of the oxygen concentration detection host;

[0027] The monitoring construction module is used to form a cross-section at the vertical middle position of the internal flow field sampling pipeline mesh model as the constructed monitoring surface for monitoring the change of physical quantities during the breathing process, and use the position corresponding to the gas sensor on the monitoring surface as the constructed monitoring point;

[0028] The iterative calculation setting module is used to set the preset time and time step of each simulation working condition;

[0029] The simulation module is used to simulate the breathing process within the preset time under the calmest working condition and the most intense working condition, respectively perform transient simulation of the internal flow field of the sampling pipeline on the internal flow field sampling pipeline mesh model, solve the selected physical model using a coupling algorithm, and sample the pressure, velocity, temperature and oxygen concentration of the monitoring surface and monitoring points under the two working conditions respectively during the solution process;

[0030] The simulation result analysis module is used to obtain and display simulation results, including the velocity field contour maps, velocity vector distribution maps during inhalation and exhalation, pressure contour maps, temperature contour maps, and oxygen content distribution maps of the monitoring surface within a preset time under the calmest condition and the most severe condition, as well as the velocity change curves, pressure change curves, temperature change curves, and oxygen mass fraction change curves of the monitoring points within a preset time under the calmest condition and the most severe condition, and analyze the simulation results to evaluate the advantages and disadvantages of the designed oxygen concentration detection main unit.

[0031] The positive and progressive effects of the present invention are as follows:

[0032] The present invention models the oxygen concentration detection main unit, establishes an internal flow field sampling pipeline model of the oxygen concentration detection main unit, obtains an internal flow field sampling pipeline grid model through grid division, and based on the established internal flow field sampling pipeline grid model, uses ANSYS simulation software to perform three-dimensional flow field simulations on it under the calm condition and the severe condition respectively, obtains the distributions of velocity, pressure, oxygen concentration, and temperature in the pipeline flow field and their changes over time under the calm condition and the severe condition, analyzes the advantages and disadvantages of the oxygen concentration detection main unit through the simulation results, and can obtain the optimal structural scheme of the oxygen concentration detection main unit through comparison. Description of the Drawings

[0033] Figure 1 It is a flow chart of the method for simulating and evaluating the flow field of the breathing apparatus gas analysis sampling pipeline.

[0034] Figure 2 They are geometric model diagrams of the first, second, and third oxygen concentration detection main units.

[0035] Figure 3 It is a structural schematic diagram of the third oxygen concentration detection main unit.

[0036] Figure 4 It is a diagram of the internal flow field sampling pipeline model of the first oxygen concentration detection main unit.

[0037] Figure 5 It is a diagram of the internal flow field sampling pipeline model of the second oxygen concentration detection main unit.

[0038] Figure 6 It is a diagram of the internal flow field sampling pipeline model of the third oxygen concentration detection main unit.

[0039] Figure 7 It is a diagram of the internal flow field sampling pipeline grid model of the first oxygen concentration detection main unit.

[0040] Figure 8 It is a diagram of the internal flow field sampling pipeline grid model of the second oxygen concentration detection main unit.

[0041] Figure 9 It is a diagram of the internal flow field sampling pipeline grid model of the third oxygen concentration detection main unit.

[0042] Figure 10 It is the inlet and outlet boundary condition diagram of Oxygen Concentration Detection Host 1.

[0043] Figure 11 It is the inlet and outlet boundary condition diagram of Oxygen Concentration Detection Host 2.

[0044] Figure 12 It is the inlet and outlet boundary condition diagram of Oxygen Concentration Detection Host 3.

[0045] Figure 13 It is the velocity change diagram at the inlet of the Oxygen Concentration Detection Host during the breathing process (the calmest state).

[0046] Figure 14 It is the velocity change diagram at the inlet of the Oxygen Concentration Detection Host during the breathing process (the most intense state).

[0047] Figure 15 It is the monitoring surface and monitoring point position diagram corresponding to Oxygen Concentration Detection Host 1.

[0048] Figure 16 It is the monitoring surface and monitoring point position diagram corresponding to Oxygen Concentration Detection Host 2.

[0049] Figure 17 It is the monitoring surface and monitoring point position diagram corresponding to Oxygen Concentration Detection Host 3.

[0050] Figure 18 It is the velocity field cloud diagram of Oxygen Concentration Detection Host 1 under calm and intense working conditions at 30 seconds.

[0051] Figure 19 It is the velocity field cloud diagram of Oxygen Concentration Detection Host 2 under calm and intense working conditions at 30 seconds.

[0052] Figure 20 It is the velocity field cloud diagram of Oxygen Concentration Detection Host 3 under calm and intense working conditions at 30 seconds.

[0053] Figure 21 It is the velocity vector diagram of Oxygen Concentration Detection Host 1 under calm and intense working conditions at 30 seconds.

[0054] Figure 22 It is the velocity vector diagram of Oxygen Concentration Detection Host 2 under calm and intense working conditions at 30 seconds.

[0055] Figure 23 It is the velocity vector diagram of Oxygen Concentration Detection Host 3 under calm and intense working conditions at 30 seconds.

[0056] Figure 24 It is the velocity change curve diagram of the sensor monitoring points under calm working conditions from 0 to 30 seconds.

[0057] Figure 25It is a curve graph showing the speed change of the sensor monitoring point under the severe working condition from 0 to 30 seconds.

[0058] Figure 26 It is a curve graph showing the pressure change of the sensor monitoring point under the calm working condition from 0 to 30 seconds.

[0059] Figure 27 It is a curve graph showing the pressure change of the sensor monitoring point under the severe working condition from 0 to 30 seconds. Detailed implementation manners

[0060] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] As Figure 1 shown, this embodiment provides a method for simulating and evaluating the flow field of a breathing gas analysis sampling pipeline of a respirator, which includes the following steps:

[0062] Step 101, geometric preprocessing: For three oxygen concentration detection hosts with different structural designs, namely oxygen concentration detection host one, oxygen concentration detection host two, and oxygen concentration detection host three, successively seen Figure 2 in the left-to-right direction, geometric preprocessing is performed on the three structures respectively through ANSYS SpaceClaim software, removing redundant lines, bolts, useless fillets, etc. that are useless for the internal flow field of each structure, and repairing interference structures such as sealing rubber rings and assembly gaps.

[0063] This embodiment includes the structure of the oxygen detection device in the invention patent with the application number 2024116337235 and the name of a dual-chamber underwater wireless data monitoring system for a diving respirator, which constitutes the structure of oxygen concentration detection host one.

[0064] This embodiment includes the structure of the oxygen detection host in the invention patent with the application number 202410559489X and the name of a breathing gas oxygen concentration monitoring system for a closed-circuit respirator, which constitutes the structure of oxygen concentration detection host two.

[0065] The structure of oxygen concentration detection host three in this embodiment is shown in Figure 3 , Figure 3 wherein, label 1 is a differential pressure sensor, label 2 is an oxygen sensor, label 3 is a control circuit board, label 4 is a battery, and label 5 is a communication module.

[0066] Step 102, Modeling: Extract the internal flow field of the oxygen concentration detection host structure after various geometric pre-processings, and establish the internal flow field sampling pipeline models of each oxygen concentration detection host.

[0067] Among them, Figure 4 is the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 1, Figure 5 is the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 2, Figure 6 is the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 3.

[0068] Step 103, Mesh Generation: Use the Ansys Fluent Meshing grid tool to perform mesh generation on each internal flow field sampling pipeline model respectively to obtain the corresponding internal flow field sampling pipeline grid models. The mesh generation in this step realizes the spatial discretization of the fluid domain and prepares for the subsequent fluid flow solution.

[0069] Among them, for the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 1, generate quadrilateral surface meshes, generate boundary layers in a smooth-transition manner, and select the polyhedron mesh with a hexahedron core (Poly-Hexcore mesh) as the volume mesh to fill the internal mesh of the flow field. The number of meshes is 277,000, and the minimum orthogonal quality of the mesh is 0.28, thus obtaining the internal flow field sampling pipeline grid model of Oxygen Concentration Detection Host 1 (see Figure 7 ). The judgment criterion for the minimum orthogonal quality of the mesh is usually greater than 0.1, so this mesh can be used for flow field simulation analysis.

[0070] For the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 2, generate quadrilateral surface meshes, generate boundary layers in a smooth-transition manner, and select the polyhedral mesh as the volume mesh to fill the internal mesh of the flow field. The number of meshes is 180,000, and the minimum orthogonal quality of the mesh is 0.32, thus obtaining the internal flow field sampling pipeline grid model of Oxygen Concentration Detection Host 2 (see Figure 8 ). The judgment criterion for the minimum orthogonal quality of the mesh is usually greater than 0.1, so this mesh can be used for flow field simulation analysis.

[0071] For the internal flow field sampling pipeline model of Oxygen Concentration Detection Host 3, generate quadrilateral surface meshes, generate boundary layers in a smooth-transition manner, and select the polyhedral mesh as the volume mesh to fill the internal mesh of the flow field. The number of meshes is 417,700, and the minimum orthogonal quality of the mesh is 0.25, thus obtaining the internal flow field sampling pipeline grid model of Oxygen Concentration Detection Host 3 (see Figure 9 ). The judgment criterion for the minimum orthogonal quality of the mesh is usually greater than 0.1, so this mesh can be used for flow field simulation analysis.

[0072] Step 104, Calculation of operating conditions: Taking the diving depth of 0 - 40m as the simulated operating environment, according to the design requirements, when the user breathes, the change in their breathing flow rate conforms to a sine curve. In actual use, the user has two extreme states, namely the calmest state and the most intense state. The peak inhalation volume in the calmest state is 600ml, and the breathing frequency is 13 times per minute. The peak inhalation volume in the most intense state is 5000ml, and the frequency is 21 times per minute. The following analyzes the two extreme operating conditions.

[0073] Calmest operating condition: The peak inhalation volume in the calmest state is 600ml, the breathing frequency is 13 times per minute, the period is 60 / 13 seconds, and the function of the breathing flow curve is where t is time in seconds.

[0074] Most intense operating condition: The peak inhalation volume in the most intense state is 5000ml, the breathing frequency is 21 times per minute, the period is 60 / 21 seconds, and the function of the breathing flow curve is where t is time in seconds.

[0075] Step 105, Selection of physical model: According to the working principle and process of the oxygen concentration detection host, a mathematical model is used to describe its physical process. During the working process of the oxygen concentration detection host, when the human body inhales, the gas in the inhalation bag of the respirator will flow through the internal flow field area of the oxygen concentration detection host and then enter the human body from the outlet through the connecting pipe and the one-way breathing valve. The turbulent equation (SST k-omega equation) is used to describe the movement of the airflow and the generation and dissipation of turbulence; in order to calculate the temperature change, the energy equation is used to describe it; in order to simulate the changes in the oxygen and carbon dioxide content in the oxygen concentration detection host, the Species transport component transport equation is used to describe the multi-component diffusion process in the gas; the simulation equation settings of the three internal flow field sampling pipeline grid models are the same.

[0076] Among them, the SST k-omega turbulence model is a two-equation model, and k and omega are solved through the following equations respectively:

[0077]

[0078] where, G k represents the generation of turbulent kinetic energy due to the average velocity gradient, G w represents the generation of w, τ k and τ w respectively represent the effective diffusion of k and w, Y k and Y w respectively represent the dissipation of k and w due to turbulence.

[0079] The component transport equations solved by Ansys Fluent are as follows:

[0080]

[0081] ρ is the density of the mixture; i is each component; v is the velocity of the diffusing component; Ri is the net production rate of component i; Si is the production rate of component i due to the source term. By solving this equation, the local mass fraction of each component is predicted to obtain the concentration distribution.

[0082] The energy equation solved by Ansys Fluent is as follows:

[0083]

[0084] where k eff is the effective thermal conductivity (k + k t ), k t is the turbulent heat transfer coefficient, which is used according to the turbulence model; Jj is the diffusion flux of component j. The three terms in the parentheses on the right side of the equation represent conduction, component diffusion, and turbulent thermal diffusion from left to right; S h is the volume heat source term; h is the enthalpy in the system.

[0085] Step 106, setting of material property parameters: Select the property parameters in the built-in model material database of the fluid. The mixed gas parameters corresponding to the three oxygen concentration detection hosts are the same.

[0086] Table 1

[0087]

[0088] Step 107, setting of boundary conditions and simulation conditions: The conditions are that when the human body is in the calmest state and the most intense state, during inhalation, the gas in the inhalation bag flows into the oxygen concentration detection host from the inlet and flows out from its outlet, and the exhaled gas does not flow into the oxygen concentration detection host. A transient numerical simulation of this process is carried out for half a minute. The initial boundary conditions are shown in Table 2 below.

[0089] Table 2

[0090]

[0091] The inlet of the oxygen concentration detection host is connected to the inhalation bag. Therefore, the gas entering is the gas composition in the bag, that is, composed of 32% oxygen and 68% nitrogen.

[0092] The outlet of the oxygen concentration detection main unit is connected to the suction pipe and the one-way breathing valve. The initial state is the state when the product is installed and filled with air. The mixed gas components are the atmospheric gas components, with oxygen O2 accounting for 20.946%, carbon dioxide CO2 accounting for 0.037%, water vapor H2O accounting for 0.25%, and the remaining gas being nitrogen N2.

[0093] The inlet and outlet boundary conditions of Oxygen Concentration Detection Main Unit 1 are shown in Figure 10 , and the inlet and outlet boundary conditions of Oxygen Concentration Detection Main Unit 2 are shown in Figure 11 , and the inlet and outlet boundary conditions of Oxygen Concentration Detection Main Unit 3 are shown in Figure 12 .

[0094] During the exhalation cycle of the human body, the exhaled gas enters the exhalation bag through the one-way breathing valve and then enters the CO2 absorption tank or is discharged through the exhaust pipe. During the inhalation cycle, the gas in the inhalation bag flows into the oxygen concentration detection main unit from the inlet on one side of the inhalation bag. The functional form of the gas velocity at the inlet of the oxygen concentration detection main unit within half a minute under different human body states is as Figure 13 and Figure 14 .

[0095] Step 108, Solution settings and initialization: The selected physical model is solved using the Coupled coupling algorithm. In the coupling algorithm, the gradient discretization format is the least squares method, and the pressure, momentum, turbulence, component, and energy discretizations all use the second-order upwind format to improve the solution accuracy. Standard initialization is used, and the initial field is assigned values of 20.946% for the oxygen O2 content, 0.037% for the carbon dioxide CO2 content, 0.25% for the water vapor H2O content, and a temperature of 20 °C to simulate the initial state of the oxygen concentration detection main unit. The solution settings and initialization of the three internal flow field sampling pipeline grid models are the same.

[0096] Step 109, Construction of monitoring surfaces and monitoring points: The cross-section formed at the vertical middle position of the internal flow field sampling pipeline grid model is used as the constructed monitoring surface for monitoring the changes in physical quantities during the breathing process. The positions on the monitoring surface corresponding to the gas sensors are used as the constructed monitoring points. During the calculation process, physical quantity data sampling is performed on the monitoring surface and monitoring points, including pressure, velocity, O2 concentration, and temperature.

[0097] Among them, the positions of the monitoring surface and monitoring points corresponding to Oxygen Concentration Detection Main Unit 1 are shown in Figure 15 , and the positions of the monitoring surface and monitoring points corresponding to Oxygen Concentration Detection Main Unit 2 are shown in Figure 16 , and the positions of the monitoring surface and monitoring points corresponding to Oxygen Concentration Detection Main Unit 3 are shown in Figure 17 .

[0098] Step 110, Iterative calculation setting: The preset time for each model and working condition simulation is 30 seconds, the simulation time step is 0.02 seconds, and 20 iterations are performed within each time step to ensure convergence within each time step and ensure simulation accuracy. With a preset time of 30 seconds, the number of time steps is 30 / 0.02 = 1500 steps.

[0099] Step 111, Simulation: Simulate the breathing process within the preset time (30 seconds) under the calmest working condition and the most severe working condition. Perform transient simulation of the internal flow field of the sampling pipeline for each internal flow field sampling pipeline grid model under the two working conditions. Use the coupling algorithm to solve the selected physical model, and sample the pressure, velocity, temperature, and oxygen concentration at the monitoring surfaces and monitoring points under the two working conditions during the solution process. The simulation processes of the three internal flow field sampling pipeline grid models are the same, and each model and working condition simulate the breathing process for an actual time of 30 seconds.

[0100] Step 112, Simulation results: Obtain and display the simulation results of each internal flow field sampling pipeline grid model, including the velocity field contour map, velocity vector distribution map during breathing, pressure contour map, temperature contour map, and oxygen content distribution map of the monitoring surface within the preset time under the calmest working condition and the most severe working condition, the velocity change curve, pressure change curve, temperature change curve, and oxygen mass fraction change curve of the monitoring points within the preset time under the calmest working condition and the most severe working condition. Compare the velocity field contour maps of the last second under the two working conditions of the three internal flow field sampling pipeline grid models, compare the velocity vector distribution maps during breathing of the last second under the two working conditions of the three grid models, compare the pressure contour maps of the last second under the two working conditions of the three grid models, compare the temperature contour maps of the last second under the two working conditions of the three grid models, compare the oxygen content distribution maps of the last second under the two working conditions of the three grid models, compare the velocity change curves of the monitoring points within the preset time under the two working conditions of the three grid models, compare the pressure change curves of the monitoring points within the preset time under the two working conditions of the three grid models, compare the temperature change curves of the monitoring points within the preset time under the two working conditions of the three grid models, compare the oxygen mass fraction change curves of the monitoring points within the preset time under the two working conditions of the three grid models, and compare and evaluate the advantages and disadvantages of the three oxygen concentration detection hosts.

[0101] For example: 1. Velocity field comparison

[0102] (1) Velocity contour map

[0103] Compare the velocity field contour maps at 30 seconds under different working conditions of various internal flow field sampling pipeline grid models (see Figures 18 - 20)。It can be seen from the comparison that the maximum speed of the model corresponding to Oxygen Concentration Detection Host 1 is 0.23 m / s in the calmest state and 1.25 m / s in the most intense state; the maximum speed of the model corresponding to Oxygen Concentration Detection Host 2 is 0.18 m / s in the calmest state and 0.88 m / s in the most intense state. The minimum speed of the models corresponding to these two oxygen concentration detection hosts is 0 m / s. This indicates that the gas flow in the model corresponding to Oxygen Concentration Detection Host 1 is faster, and the gas replacement in the main channel is faster.

[0104] In addition, there is a small bent gap channel between the bottom space of the model corresponding to Oxygen Concentration Detection Host 1 and the main channel. Some gas will flow into this area, and the gas in this area cannot be updated or is very difficult to update, which belongs to the dead zone position; the airflow of the model corresponding to Oxygen Concentration Detection Host 2 will basically fill all areas and update the gas at all times, and there is basically no large-area dead zone.

[0105] It can be seen from the flow field simulation results of the model corresponding to Oxygen Concentration Detection Host 3 that the maximum speed is 0.21 m / s in the calmest state and 1.48 m / s in the most intense state, both of which are greater than the previous two models. This indicates that the gas flow rate inside the structure of Oxygen Concentration Detection Host 3 is fast, and when a person inhales, the oxygen in the airbag is more easily delivered to the human body.

[0106] (2) Comparison of velocity vectors during breathing

[0107] Compare the velocity vector diagrams of various internal flow field sampling pipeline grid models at 30 seconds under different working conditions (see Figures 21 - 23 ). It can be seen from the comparison of the velocity vector diagrams that there are certain recirculation regions in each model. The recirculation region of the model corresponding to Oxygen Concentration Detection Host 1 is mainly located above the main channel, between the main channel pressure difference sensor and the oxygen sensor, and in the large space below the pressure difference sensor (at the rectangular frame in the figure). The recirculation region of the model corresponding to Oxygen Concentration Detection Host 2 is mainly located in the pressure difference sensor and each circumferential corner region (at the rectangular frame in the figure). The recirculation region of the model corresponding to Oxygen Concentration Detection Host 3 is mainly located in the pressure difference sensor and the bottom structure region (at the rectangular frame in the figure).

[0108] 2. Pressure field comparison

[0109] Compare the pressure contour maps of the grid models of various internal flow field sampling pipelines at 30 seconds under different working conditions. From the comparison, it can be seen that for the model corresponding to Oxygen Concentration Detection Host 1, the maximum pressure is 0.38 Pa at the calmest state and 2.75 Pa at the most intense state, both located at the position of the one-way breathing valve connection port. The minimum pressure is -0.09 Pa at the calmest state and -0.54 Pa at the most intense state. The pressure difference is 0.47 Pa at the calmest state and 3.29 Pa at the most intense state. For the model corresponding to Oxygen Concentration Detection Host 2, the maximum pressure is 0.17 Pa at the calmest state and 0.998 Pa at the most intense state, and the position is the same as that of the model corresponding to Oxygen Concentration Detection Host 1. The minimum pressure is -0.157 Pa at the calmest state and -1.11 Pa at the most intense state. The pressure difference is 0.327 Pa at the calmest state and 2.11 Pa at the most intense state. Through comparison, it can be seen that the overall pressure fluctuation in the sampling pipeline of the model corresponding to Oxygen Concentration Detection Host 1 is greater.

[0110] Perform a flow field analysis on the model corresponding to Oxygen Concentration Detection Host 3. It can be seen that the maximum pressure is 0.196 Pa at the calmest state and 0.951 Pa at the most intense state, the minimum pressure is -0.111 Pa at the calmest state and -0.882 Pa at the most intense state. The pressure differences at the calmest state and the most intense state are 0.307 Pa and 1.833 Pa respectively. The overall pressure fluctuation is smaller than that of the models corresponding to Oxygen Concentration Detection Host 1 and Oxygen Concentration Detection Host 2. Therefore, the overall structure has the smallest flow resistance to gas and the fastest flow rate, which is conducive to obtaining oxygen more quickly during inhalation.

[0111] In addition, the relatively large pressures of the model corresponding to Oxygen Concentration Detection Host 3 are all near the pressure difference sensor, so it is more sensitive to the pressure fluctuation of the flow field and can more easily and clearly detect the pressure fluctuation of the gas.

[0112] 3. Temperature Comparison

[0113] Compare the temperature contour maps of the grid models of various internal flow field sampling pipelines at 30 seconds under different working conditions. From the comparison, it can be seen that since only the gas at 20 °C in the airbag enters each oxygen concentration monitoring host, the internal temperature is 20 °C at 30 seconds.

[0114] 4. Oxygen O2 Mass Concentration Comparison

[0115] Compare the oxygen content distribution maps of various internal flow field sampling pipeline grid models at 30 seconds under different working conditions. Through comparison, it can be seen that the oxygen mass fraction in the main channel of the model corresponding to Oxygen Concentration Detection Host 1 is 32%. In the large space below the pressure difference sensor, the oxygen mass fraction is around 24%, indicating that the gas is not updated in time and remains the residual oxygen in the initial state air composition. The main reason is that the curved channel between the main channel and this space hinders the exchange of old and new air. The overall oxygen mass fraction of the model corresponding to Oxygen Concentration Detection Host 2 is 32%, indicating that the oxygen is updated relatively promptly.

[0116] Perform a flow field analysis on the model corresponding to Oxygen Concentration Detection Host 3. At 30 seconds, whether in a calm state or a violent state, the oxygen concentration is 32%, which also indirectly proves that the air flow enters Oxygen Concentration Detection Host 3 from the air bag and has carried away all the initial air.

[0117] 5. Comparison of Sensor Monitoring Point Data

[0118] (1) Sensor Monitoring Point Velocity Curve during 30s Breathing

[0119] Compare the velocity change curves of the sensor monitoring points during 30s breathing of various internal flow field sampling pipeline grid models under different working conditions (see Figures 24 - 25 ). From the curve comparison, it can be seen that the maximum velocity of the model corresponding to Oxygen Concentration Detection Host 1 is significantly greater than that of the models corresponding to Oxygen Concentration Detection Host 2 and Oxygen Concentration Detection Host 3, indicating that the velocity fluctuation at the sensor position of the model corresponding to Oxygen Concentration Detection Host 1 is greater than that of the model corresponding to Oxygen Concentration Detection Host 2. This is because the internal air flow of Oxygen Concentration Detection Host 1 is a direct-through structure, and when inhaling, the gas can quickly pass through the internal space after entering from the inhalation bag. While the air flows of Oxygen Concentration Detection Host 2 and Oxygen Concentration Detection Host 3 are bypass structures, resulting in smaller air flow velocity fluctuations at the sensor position.

[0120] (2) Sensor Monitoring Point Pressure Curve during 30s Breathing

[0121] Compare the pressure change curves of the sensor monitoring points during 30s breathing of various internal flow field sampling pipeline grid models under different working conditions (see Figures 26 - 27 ). From the curve comparison, it can be seen that the pressure fluctuation of the model corresponding to Oxygen Concentration Detection Host 1 is significantly smaller than that of the models corresponding to Oxygen Concentration Detection Host 2 and Oxygen Concentration Detection Host 3. The model corresponding to Oxygen Concentration Detection Host 3 has the largest pressure fluctuation, indicating that under the structure of the model corresponding to Oxygen Concentration Detection Host 3, the pressure difference sensor is most sensitive to the change of pressure fluctuation and can best detect the pressure change.

[0122] The oxygen concentration detection host one has an internal direct-through structure with a fast air flow velocity, resulting in a small air pressure on the sensor. In contrast, the internal air flow of the oxygen concentration detection host two and the oxygen concentration detection host three is in a swirling flow pattern, especially gathering in the narrow space near the sensor, causing a large pressure on the sensor and thus a large pressure fluctuation. Additionally, the differential pressure sensor of the oxygen concentration detection host three is perpendicular to the air flow direction, resulting in the largest pressure fluctuation.

[0123] (3) Temperature curve of the sensor monitoring point during a 30s breathing process

[0124] The temperature change curves of the sensor monitoring points during a 30s breathing process under different working conditions of various internal flow field sampling pipeline grid models are compared. It can be seen from the curves that since the exhaled gas does not enter the oxygen concentration detection host, the temperature always remains the same as that in the inhalation bag, which is 20°C.

[0125] (4) Oxygen O2 mass fraction curve of the sensor monitoring point during a 30s breathing process

[0126] The oxygen mass fraction change curves of the sensor monitoring points during a 30s breathing process under different working conditions of various internal flow field sampling pipeline grid models are compared. Through the curve comparison, it can be seen that the time for the models corresponding to the oxygen concentration detection host one and the oxygen concentration detection host two to reach oxygen concentration equilibrium (32%) is significantly longer than that of the model corresponding to the oxygen concentration detection host three. This is because the oxygen concentration detection host three is located on the side connected to the inhalation bag, and as soon as the gas enters the oxygen concentration detection host, it will be immediately detected. In contrast, the oxygen concentration detection host one and the oxygen concentration detection host two are located inside the sampling pipeline, and it takes a certain amount of time to update the internal gas, resulting in a certain lag.

[0127] In summary, the structural design of the oxygen concentration detection host three is the most reasonable and is the optimal solution. The oxygen concentration detection host three can detect pressure fluctuations most quickly, capture changes in oxygen concentration, and has the fastest flow velocity inside, which is beneficial for the human body to inhale the required oxygen.

[0128] This embodiment also provides a respirator gas analysis sampling pipeline flow field simulation evaluation system, which includes a preprocessing module, a modeling module, a mesh generation module, a working condition setting module, a physical model selection module, a physical property parameter setting module, a condition working condition setting module, a solution initialization setting module, a monitoring construction module, an iterative calculation setting module, a simulation module, and a simulation result analysis module.

[0129] The preprocessing module is used to perform geometric preprocessing on the geometric model of the oxygen concentration detection host in the respirator gas analysis sampling pipeline.

[0130] The modeling module is used to extract the internal flow field of the geometric model of the oxygen concentration detection host after geometric preprocessing and establish an internal flow field sampling pipeline model of the oxygen concentration detection host.

[0131] The mesh generation module is used to generate a mesh for the internal flow field sampling pipeline model to obtain an internal flow field sampling pipeline mesh model.

[0132] The working condition setting module is used to set the simulated working environment with the set diving depth. When setting the user's breathing, the change in their breathing flow rate conforms to a sine curve form and has two limit states, namely the calmest state and the most intense state.

[0133] The physical model selection module is used to select a turbulence equation to describe the movement of the air flow, the generation and dissipation of turbulence, select an energy equation to calculate the change in temperature, and select a component transport equation to describe the multi-component diffusion process in the gas according to the working principle and process of the oxygen concentration detection host, in order to simulate the changes in the oxygen and carbon dioxide contents in the oxygen concentration detection host.

[0134] The physical property parameter setting module is used to set the physical property parameters of the gas. The physical property parameters include oxygen, nitrogen, carbon dioxide, and water vapor.

[0135] The condition working condition setting module is used to set the inlet boundary condition and outlet boundary condition of the internal flow field sampling pipeline mesh model, and set the simulated working conditions including the calmest working condition and the most intense working condition.

[0136] The solution initialization setting module is used to set the selected physical model to be solved using a coupling algorithm, and use standard initialization to assign values to the oxygen content, carbon dioxide content, water vapor content, and temperature of the initial field to simulate the initial state of the oxygen concentration detection host.

[0137] The monitoring construction module is used to form a cross-section at the vertical middle position of the internal flow field sampling pipeline mesh model as the constructed monitoring surface for monitoring the change in physical quantities during the breathing process, and use the position on the monitoring surface corresponding to the gas sensor as the constructed monitoring point.

[0138] The iterative calculation setting module is used to set the preset time and time step for each simulated working condition.

[0139] The simulation module is used to simulate the breathing process within the preset time under the calmest working condition and the most intense working condition, respectively perform transient simulation of the internal flow field of the sampling pipeline on the internal flow field sampling pipeline mesh model, use a coupling algorithm to solve the selected physical model, and sample the pressure, velocity, temperature, and oxygen concentration of the monitoring surface and monitoring points under the two working conditions during the solution process.

[0140] The simulation result analysis module is used to obtain and display simulation results, including velocity field contour maps, velocity vector distribution maps during inhalation and exhalation, pressure contour maps, temperature contour maps, and oxygen content distribution maps of the monitoring surface within a preset time under the calmest condition and the most severe condition, as well as velocity change curves, pressure change curves, temperature change curves, and oxygen mass fraction change curves of the monitoring points within a preset time under the calmest condition and the most severe condition, and analyze the simulation results to evaluate the advantages and disadvantages of the designed oxygen concentration detection host.

[0141] Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that these are only examples, and the protection scope of the present invention is defined by the appended claims. Without departing from the principles and essence of the present invention, those skilled in the art can make various changes or modifications to these embodiments, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for simulating and evaluating the flow field of a breathing apparatus gas analysis sampling pipeline, characterized in that, It includes the following steps: S1. Geometric preprocessing: Perform geometric preprocessing on the geometric model of the oxygen concentration detection host in the breathing apparatus gas analysis sampling pipeline; S2. Modeling: Extract the internal flow field of the geometric model of the oxygen concentration detection host after geometric preprocessing, and establish the internal flow field sampling pipeline model of the oxygen concentration detection host; S3. Mesh generation: Generate a mesh for the internal flow field sampling pipeline model to obtain the internal flow field sampling pipeline mesh model; S4. Set working conditions: Take the set diving depth as the simulated working environment. When setting the user's breathing, the change in their breathing flow rate conforms to a sine curve form and has two limit states, namely the calmest state and the most intense state; S5. Selection of physical model: According to the working principle and process of the oxygen concentration detection host, select a turbulence equation to describe the movement of the air flow and the generation and dissipation of turbulence, select an energy equation to calculate the temperature change, and select a component transport equation to describe the multi-component diffusion process in the gas, so as to simulate the changes in the oxygen and carbon dioxide contents in the oxygen concentration detection host; S6. Setting of gas physical property parameters: Set the physical property parameters of the gas, and the physical property parameters include oxygen, nitrogen, carbon dioxide, and water vapor; S7. Setting of boundary conditions and simulation conditions: Set the inlet boundary conditions and outlet boundary conditions of the internal flow field sampling pipeline mesh model, and set the simulation conditions including the calmest condition and the most intense condition; S8. Solving settings and initialization: Set to use a coupling algorithm to solve the selected physical model, and use standard initialization to assign values to the oxygen content, carbon dioxide content, water vapor content, and temperature of the initial field to simulate the initial state of the oxygen concentration detection host; S9. Construction of monitoring surface and monitoring points: Take the cross-section formed at the vertical middle position of the internal flow field sampling pipeline mesh model as the constructed monitoring surface for monitoring the change of physical quantities during the breathing process, and take the position corresponding to the gas sensor on the monitoring surface as the constructed monitoring point; S10. Iterative calculation settings: Set the preset time and time step size for each simulation condition; S11. Simulation: Simulate the breathing process within the preset time under the calmest condition and the most intense condition, respectively perform transient simulation of the internal flow field of the sampling pipeline on the internal flow field sampling pipeline mesh model, use a coupling algorithm to solve the selected physical model, and sample the pressure, velocity, temperature, and oxygen concentration of the monitoring surface and monitoring points under the two conditions during the solving process; S12. Simulation results: Obtain and display the simulation results, including the velocity field cloud diagram, velocity vector distribution diagram during breathing, pressure cloud diagram, temperature cloud diagram, and oxygen content distribution diagram of the monitoring surface within the preset time under the calmest condition and the most intense condition, the velocity change curve, pressure change curve, temperature change curve, and oxygen mass fraction change curve of the monitoring points within the preset time under the calmest condition and the most intense condition, and analyze this simulation result to evaluate the advantages and disadvantages of the designed oxygen concentration detection host.

2. The method for simulating and evaluating the flow field of the breathing apparatus gas analysis sampling pipeline according to claim 1, characterized in that In step S1, for the oxygen concentration detection hosts designed with three different structures, namely oxygen concentration detection host one, oxygen concentration detection host two, and oxygen concentration detection host three, geometric preprocessing is performed on the geometric models of the three structures respectively, removing the redundant lines, bolts, and useless fillets that are useless for the internal flow fields of each structure, and repairing the interference structures and assembly clearances; In step S2, the internal flow fields of the geometric models of the oxygen concentration detection hosts after each geometric preprocessing are extracted to establish the internal flow field sampling pipeline models of each oxygen concentration detection host; In step S3, grid division is performed on each internal flow field sampling pipeline model respectively to obtain the corresponding internal flow field sampling pipeline grid models; In step S4, the operating conditions of the three internal flow field sampling pipeline grid models are set to be the same; In step S5, the physical model selections of the three internal flow field sampling pipeline grid models are set to be the same; In step S6, the gas physical property parameter selections of the three internal flow field sampling pipeline grid models are set to be the same; In step S7, the boundary conditions and simulation operating conditions of the three internal flow field sampling pipeline grid models are set to be the same. Among them, the type of the inlet boundary condition is velocity inlet, including velocity, relative pressure, turbulent intensity, hydraulic diameter, nitrogen volume fraction, water vapor volume fraction, oxygen volume fraction, and carbon dioxide volume fraction, and the type of the outlet boundary condition is free outflow boundary; In step S8, the solution settings and initializations of the three internal flow field sampling pipeline grid models are set to be the same; In step S9, the monitoring surfaces and monitoring points of the three internal flow field sampling pipeline grid models are constructed to be the same; In step S10, the iterative calculation settings of the three internal flow field sampling pipeline grid models are set to be the same; In step S11, the simulation processes of the three internal flow field sampling pipeline grid models are set to be the same; In step S12, comparison of simulation results: Obtain and display the simulation results of each internal flow field sampling pipeline grid model, including the velocity field cloud diagram, velocity vector distribution diagram during inhalation and exhalation, pressure cloud diagram, temperature cloud diagram, and oxygen content distribution diagram of the monitoring surface within a preset time under the calmest condition and the most intense condition, as well as the velocity change curve, pressure change curve, temperature change curve, and oxygen mass fraction change curve of the monitoring points within a preset time under the calmest condition and the most intense condition. Compare the velocity field cloud diagrams of the last second of the two conditions of the three internal flow field sampling pipeline grid models, compare the velocity vector distribution diagrams during inhalation and exhalation of the last second of the two conditions of the three grid models, compare the pressure cloud diagrams of the last second of the two conditions of the three grid models, compare the temperature cloud diagrams of the last second of the two conditions of the three grid models, compare the oxygen content distribution diagrams of the last second of the two conditions of the three grid models, compare the velocity change curves of the monitoring points within a preset time of the two conditions of the three grid models, compare the pressure change curves of the monitoring points within a preset time of the two conditions of the three grid models, compare the temperature change curves of the monitoring points within a preset time of the two conditions of the three grid models, and compare the oxygen mass fraction change curves of the monitoring points within a preset time of the two conditions of the three grid models. Compare and evaluate the advantages and disadvantages of the three oxygen concentration detection hosts.

3. The method for simulating and evaluating the flow field of the breathing apparatus gas analysis sampling pipeline according to claim 2, characterized in that, In step S3, for the internal flow field sampling pipeline model of oxygen concentration detection host 1, generate quadrilateral surface grids, generate boundary layers using a smooth transition method, and select polyhedral grids with hexahedral cores as volume grids to fill the internal grids of the flow field, thereby obtaining the internal flow field sampling pipeline grid model of oxygen concentration detection host 1; For the internal flow field sampling pipeline model of oxygen concentration detection host 2, generate quadrilateral surface grids, generate boundary layers using a smooth transition method, and select polyhedral grids as volume grids to fill the internal grids of the flow field, thereby obtaining the internal flow field sampling pipeline grid model of oxygen concentration detection host 2; For the internal flow field sampling pipeline model of oxygen concentration detection host 3, generate quadrilateral surface grids, generate boundary layers using a smooth transition method, and select polyhedral grids as volume grids to fill the internal grids of the flow field, thereby obtaining the internal flow field sampling pipeline grid model of oxygen concentration detection host 3.

4. The method for simulating and evaluating the flow field of the breathing apparatus gas analysis sampling pipeline according to claim 1 or 2, characterized in that, In step S4, set the peak inhalation volume at the calmest state to 600 ml and the breathing frequency to 13 times per minute, and set the peak inhalation volume at the most intense state to 5000 ml and the frequency to 21 times per minute; Most calm condition: The function of the respiratory flow curve is Most severe condition: The function of the breathing flow rate curve is where t is time, with the unit of second.

5. The method for simulating and evaluating the flow field of the breathing apparatus gas analysis sampling pipeline according to claim 1 or 2, characterized in that In step S8, in the coupling algorithm, the gradient discretization format is the least squares method, and second-order upwind schemes are used for the discretization of pressure, momentum, turbulence, components, and energy to improve the solution accuracy.

6. A respirator gas analysis sampling pipeline flow field simulation evaluation system, characterized in that, It includes a preprocessing module, a modeling module, a grid generation module, a working condition setting module, a physical model selection module, a physical property parameter setting module, a conditional working condition setting module, a solution initialization setting module, a monitoring construction module, an iterative calculation setting module, a simulation module, and a simulation result analysis module; The preprocessing module is used to perform geometric preprocessing on the geometric model of the oxygen concentration detection host in the breathing apparatus gas analysis sampling pipeline; The modeling module is used to extract the internal flow field of the geometric model of the oxygen concentration detection host after geometric preprocessing, and establish an internal flow field sampling pipeline model of the oxygen concentration detection host; The mesh generation module is used to generate meshes for the internal flow field sampling pipeline model to obtain an internal flow field sampling pipeline mesh model; The working condition setting module is used to set the simulated working condition environment with the set diving depth, and set that when the user breathes, the change of their breathing flow rate conforms to the form of a sine curve, and there are two extreme states, namely the calmest state and the most intense state; The physical model selection module is used to select a turbulence equation to describe the movement of the air flow and the generation and dissipation of turbulence, select an energy equation to calculate the change of temperature, and select a component transport equation to describe the multi-component diffusion process in the gas according to the working principle and process of the oxygen concentration detection host, so as to simulate the change of the oxygen and carbon dioxide content in the oxygen concentration detection host; The physical property parameter setting module is used to set the physical property parameters of the gas, and the physical property parameters include oxygen, nitrogen, carbon dioxide and water vapor; The condition working condition setting module is used to set the inlet boundary condition and outlet boundary condition of the internal flow field sampling pipeline mesh model, and the simulated working conditions include the calmest working condition and the most intense working condition; The solution initialization setting module is used to set to solve the selected physical model using a coupling algorithm, adopt standard initialization, and assign values to the oxygen content, carbon dioxide content, water vapor content and temperature of the initial field to simulate the initial state of the oxygen concentration detection host; The monitoring construction module is used to use the cross-section formed at the vertical middle position of the internal flow field sampling pipeline mesh model as the constructed monitoring surface to monitor the change of physical quantities during the breathing process, and use the position corresponding to the gas sensor on the monitoring surface as the constructed monitoring point; The iterative calculation setting module is used to set the preset time and time step length for each simulated working condition; The simulation module is used to simulate the breathing process within the preset time under the calmest working condition and the most intense working condition, respectively perform transient simulation of the internal flow field of the sampling pipeline on the internal flow field sampling pipeline mesh model, and solve the selected physical model using a coupling algorithm. During the solution process, sample the pressure, velocity, temperature and oxygen concentration of the monitoring surface and monitoring points under the two working conditions respectively; The simulation result analysis module is used to obtain and display the simulation results, including the velocity field cloud diagram, breathing gas velocity vector distribution diagram, pressure cloud diagram, temperature cloud diagram and oxygen content distribution diagram of the monitoring surface within the preset time under the calmest working condition and the most intense working condition, the velocity change curve, pressure change curve, temperature change curve and oxygen mass fraction change curve of the monitoring points within the preset time under the calmest working condition and the most intense working condition, and analyze this simulation result to evaluate the advantages and disadvantages of the designed oxygen concentration detection host.

7. The respirator gas analysis sampling pipeline flow field simulation evaluation system according to claim 6, characterized in that The pre - processing module is used to perform geometric pre - processing on the geometric models of three oxygen concentration detection hosts with different structural designs, namely oxygen concentration detection host one, oxygen concentration detection host two, and oxygen concentration detection host three, remove redundant lines, bolts, and useless fillets that are not useful for the internal flow field of each structure, and repair interference structures and assembly clearances; The modeling module is used to extract the internal flow field of the geometric models of the oxygen concentration detection hosts after each geometric pre - processing and establish the internal flow field sampling pipeline models of each oxygen concentration detection host; The mesh generation module is used to perform mesh generation on each internal flow field sampling pipeline model respectively to obtain the corresponding internal flow field sampling pipeline mesh model; The operating condition setting module is used to set the same operating conditions for the three internal flow field sampling pipeline mesh models; The physical model selection module is used to set the same physical model selection for the three internal flow field sampling pipeline mesh models; The physical property parameter setting module is used to set the same selection of gas physical property parameters for the three internal flow field sampling pipeline mesh models; The conditional operating condition setting module is used to set the same boundary conditions and simulation operating conditions for the three internal flow field sampling pipeline mesh models. Among them, the type of the inlet boundary condition is velocity inlet, including velocity, relative pressure, turbulence intensity, hydraulic diameter, nitrogen volume fraction, water vapor volume fraction, oxygen volume fraction, and carbon dioxide volume fraction, and the type of the outlet boundary condition is free outflow boundary; The solution initialization setting module is used to set the same solution settings and initialization for the three internal flow field sampling pipeline mesh models; The monitoring construction module is used to set the same construction of monitoring surfaces and monitoring points for the three internal flow field sampling pipeline mesh models; The iterative calculation setting module is used to set the same iterative calculation settings for the three internal flow field sampling pipeline mesh models; The simulation module is used to set the same simulation processes for the three internal flow field sampling pipeline mesh models; The simulation result analysis module is used to obtain and display the simulation results of each internal flow field sampling pipeline grid model, including the velocity field contour maps, velocity vector distribution maps during inhalation and exhalation, pressure contour maps, temperature contour maps, and oxygen content distribution maps of the monitoring surface within a preset time under the calmest condition and the most intense condition, the velocity change curves, pressure change curves, temperature change curves, and oxygen mass fraction change curves of the monitoring points within a preset time under the calmest condition and the most intense condition. Compare the velocity field contour maps of the last second under the two conditions of the three internal flow field sampling pipeline grid models, compare the velocity vector distribution maps during inhalation and exhalation of the last second under the two conditions of the three grid models, compare the pressure contour maps of the last second under the two conditions of the three grid models, compare the temperature contour maps of the last second under the two conditions of the three grid models, compare the oxygen content distribution maps of the last second under the two conditions of the three grid models, compare the velocity change curves of the monitoring points within a preset time under the two conditions of the three grid models, compare the pressure change curves of the monitoring points within a preset time under the two conditions of the three grid models, compare the temperature change curves of the monitoring points within a preset time under the two conditions of the three grid models, compare the oxygen mass fraction change curves of the monitoring points within a preset time under the two conditions of the three grid models, and compare and evaluate the advantages and disadvantages of the three oxygen concentration detection hosts.

8. The respirator gas analysis sampling pipeline flow field simulation evaluation system according to claim 7, wherein The grid generation module is used to generate quadrilateral surface grids for the internal flow field sampling pipeline model of the first oxygen concentration detection host, generate boundary layers using a smooth transition method, and select polyhedral grids with hexahedral cores as volume grids to fill the internal grids of the flow field, so as to obtain the internal flow field sampling pipeline grid model of the first oxygen concentration detection host; For the internal flow field sampling pipeline model of the second oxygen concentration detection host, generate quadrilateral surface grids, generate boundary layers using a smooth transition method, and select polyhedral grids as volume grids to fill the internal grids of the flow field, so as to obtain the internal flow field sampling pipeline grid model of the second oxygen concentration detection host; For the internal flow field sampling pipeline model of the third oxygen concentration detection host, generate quadrilateral surface grids, generate boundary layers using a smooth transition method, and select polyhedral grids as volume grids to fill the internal grids of the flow field, so as to obtain the internal flow field sampling pipeline grid model of the third oxygen concentration detection host.

9. The respiratory gas analysis sampling pipeline flow field simulation evaluation system according to claim 6 or 7, characterized in that The working condition setting module is used to set the peak inhalation volume at the calmest state to 600 ml and the breathing frequency to 13 times per minute, and the peak inhalation volume at the most intense state to 5000 ml and the frequency to 21 times per minute; The calmest condition: the function of the breathing flow curve is Most severe condition: The function of the respiratory flow curve is Where t is the time, in seconds.

10. The respirator gas analysis sampling pipeline flow field simulation evaluation system according to claim 6 or 7, characterized in that The solution initialization setting module is used to set the gradient discretization format in the coupling algorithm to the least squares method, and adopt the second-order upwind format for the discretization of pressure, momentum, turbulence, components, and energy to improve the solution accuracy.

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