A simulation method and system for train-tunnel coupled transient pressure fluctuations

Abstract

The application discloses a train-tunnel coupling transient pressure fluctuation simulation method and system, and the method comprises the following steps: obtaining to-be-simulated pressure waveform data, detecting the turning point of the to-be-simulated pressure waveform data, dividing the continuous time period according to the turning point and calculating the average pressure change rate of each section; judging the wave band type according to the average change rate and the preset threshold value; extracting the waveform feature parameters of each time period and calculating the target pressure change rate; based on the target change rate and the wave band type, filtering the air port combination meeting the pressure requirement from the preset air port set to form a feasible solution set; and performing priority sorting on the feasible solution set through a multi-level optimization algorithm to generate an optimal air port control scheme. Through the turning point detection algorithm and the pressure wave band mode judgment, the section is divided according to the physical characteristics of the waveform, so that the invalid calculation interval is reduced, and the calculation load of the full-tunnel high-precision simulation is significantly reduced.

Description

Technical Field

[0001] This invention relates to the field of pressure control technology, and more specifically, to a method and system for simulating transient pressure fluctuations in a train-tunnel coupling system. Background Technology

[0002] As my country's railway network extends into mountainous regions, the proportion of tunnels on main railway lines has significantly increased. When running inside tunnels, the confined space between the train body and the tunnel walls triggers complex aerodynamic effects. Specifically, the train's entry into the tunnel causes a violent compression of the air ahead, creating a compression wave that propagates at the speed of sound towards the exit. This compression wave reflects off at the exit, forming a returning expansion wave. The resulting transient pressure fluctuations are high in amplitude and drastic in change. Therefore, when trains pass through long tunnels or tunnel complexes, their air conditioning systems often need to activate forced ventilation to maintain air quality in the passenger compartment. However, this ventilation mechanism directly transmits the intense transient pressure fluctuations outside the train to the passenger compartment, causing a sudden increase in the pressure amplitude inside the train and severely affecting passenger comfort.

[0003] Existing train-tunnel coupled pressure fluctuation simulations mainly include fluid dynamics numerical simulation, real-vehicle experiments, and dynamic model experiments. While numerical simulation can construct a three-dimensional flow field, accurately capturing transient phenomena such as turbulent dissipation and vortex breakup requires millisecond-level time steps and computational resources on the order of tens of millions of grids, resulting in over 100 hours of computation time per single test case and incurring high computational costs. Real-vehicle experiments are limited by spatiotemporal irreproducibility and safety constraints under extreme conditions, making it difficult to systematically obtain multi-condition samples. Dynamic model experiments, due to geometric scaling leading to distortion of compressibility effects and Reynolds number mismatch, cannot accurately reproduce the complex multi-order transmission characteristics of pressure waves formed by multiple reflections within the tunnel, and are particularly difficult to accurately simulate the complex pressure fluctuations formed by multiple reflections in long tunnels.

[0004] In summary, the existing technology system lacks a simulation method that can both efficiently reproduce the pressure waveform transmission process in long tunnels and maintain the necessary accuracy to truly reflect the pressure fluctuation characteristics of train-tunnel coupling. This technical bottleneck seriously restricts the in-depth study of train-tunnel coupling aerodynamic effects. Therefore, it is urgent to develop innovative simulation technology to overcome the contradiction between computational efficiency and simulation accuracy in existing methods. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the present invention provides a simulation method and system for train-tunnel coupled transient pressure fluctuations, in order to solve the problem of difficulty in balancing the efficiency and accuracy of pressure fluctuation reproduction in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for simulating transient pressure fluctuations in train-tunnel coupling, used in a train-tunnel coupling transient pressure waveform simulation device. The simulation device includes a housing, a control device, and several pneumatic devices. The housing is provided with several air ports, each equipped with a solenoid valve. The air ports are respectively connected to the pneumatic devices via the solenoid valves. The pneumatic devices and the solenoid valves are electrically connected to the control device. The method includes:

[0008] S1. Obtain the pressure waveform data to be simulated, and identify the inflection points in the pressure waveform data using an inflection point detection algorithm;

[0009] S2. Based on the inflection point, the pressure waveform is divided into multiple continuous time periods, the average pressure change rate of each time period is calculated, and the average pressure change rate of each time period is compared with a preset change rate threshold to determine the wave type of each time period.

[0010] The band types include rising band, falling band, and stationary band;

[0011] S3. Extract features from the pressure waveform data of each time period to obtain waveform feature parameters for each time period, and calculate the target pressure change rate based on the band type and waveform feature parameters in each time period.

[0012] S4. Based on the target pressure change rate and band type, select the combination of air ports that meets the pressure change requirements from the preset set of air ports to form a feasible solution set.

[0013] S5. Prioritize the feasible solution set using a multi-level optimization algorithm to generate the optimal air inlet control scheme.

[0014] In an optional implementation, it further includes:

[0015] S6. According to the optimal air inlet control scheme, control the pneumatic equipment and solenoid valve to achieve the reproduction of the target pressure waveform.

[0016] In an optional implementation, step S1 specifically includes the following steps:

[0017] Calculate the rate of change of pressure and the acceleration of pressure change at different time points in the pressure waveform data;

[0018] Based on a predefined collaborative function, the fluctuation state of pressure waveform data is obtained;

[0019] Based on predefined inflection point determination rules, the inflection points in the pressure waveform data are determined according to the fluctuation state of the pressure waveform data.

[0020] The expression for the cooperative function is:

[0021] ;

[0022] In the formula, sgn(.) is the cooperative function; v(t) is the rate of change of pressure at time t; The pressure waveform data is in an upward trend; The pressure waveform data is in a stable state; The pressure waveform data is in a decreasing state;

[0023] The functional expression for the turning point determination rule is:

[0024] ;

[0025] In the formula, This marks a turning point; is the change over time; a(t) is the acceleration due to pressure change; This is the acceleration threshold.

[0026] In an optional implementation, step S2 specifically includes the following steps:

[0027] The pressure waveform data is divided into multiple continuous time periods using any two adjacent inflection points as boundaries;

[0028] Calculate the average pressure change rate of the pressure waveform data for each time period;

[0029] The average pressure change rate for each time period is compared with a preset change rate threshold to determine the wave type for each time period.

[0030] The functional expression for the rate of change of average pressure is:

[0031] ;

[0032] In the formula, Let be the average pressure change rate over the k-th time period. and These are the start and end times of the k-th time interval, respectively. For time points The rate of change of pressure; The differential symbol;

[0033] The rule for determining the band type is as follows:

[0034] ;

[0035] In the formula, The band type for the k-th time period; Preset category change rate; This represents the rising segment of the pressure waveform. This represents the descending band of the pressure waveform. This represents the stable band of the pressure waveform.

[0036] In an optional implementation, the waveform characteristic parameters in step S3 include band duration and band pressure change.

[0037] The functional expression for the waveform characteristic parameters is:

[0038] ;

[0039] ;

[0040] In the formula, T is the duration of the band; This is the k-th time boundary point; Let k+1 be the (k+1)th time boundary point; C is the band pressure change. The pressure value at the k-th time boundary point; The pressure value at the (k+1)th time boundary point;

[0041] The functional expression for the target pressure change rate in step S3 is:

[0042] ;

[0043] In the formula, The target pressure change rate.

[0044] In an optional implementation, step S4 specifically includes the following steps:

[0045] Based on the band type of each time period, determine the air outlet control mode for the corresponding time period.

[0046] The air inlet includes an exhaust port and an air inlet; the air inlet control mode includes an air inlet mode and an exhaust mode.

[0047] When the air inlet control mode is in the air intake mode, the air inlet combination that meets the pressure rise requirement is selected from the predefined air inlet set according to the target pressure change rate, so as to form the feasible solution set of the air intake mode.

[0048] When the vent control mode is in the exhaust mode, the combination of vents that meets the pressure drop requirement is selected from the predefined set of vents according to the target pressure change rate, so as to form a feasible solution set for the exhaust mode.

[0049] The functional expression for the feasible solution set of the intake mode is:

[0050] ;

[0051] ;

[0052] In the formula, S represents the feasible solution set for the intake mode; S is the subset of air inlet selection. For a predefined set of air intakes; i This is a unique number for the air intake. is the pressure rise rate; p is the index number; m is the total number of air inlets; The target pressure change rate;

[0053] The functional expression for the feasible solution set of the exhaust mode is:

[0054] ;

[0055] ;

[0056] In the formula, This represents the feasible solution set for the exhaust mode. A predefined set of exhaust ports; A unique number for the exhaust port; is the pressure drop rate; n is the total number of exhaust ports.

[0057] In an optional implementation, the multi-level optimization algorithm in step S5 includes a first priority objective, a second priority objective, and a third priority objective executed sequentially.

[0058] The first priority objective is to minimize the oversupply optimization function;

[0059] The second priority objective is to minimize the number of air inlets function;

[0060] The third priority objective is to minimize the air inlet numbering function; the function expression of the multi-level optimization algorithm is:

[0061] ;

[0062] In the formula, Select a subset of the optimal air inlets, i.e., the optimal air inlet control scheme; F is the feasible solution set; This represents the actual total rate of change of the selected air inlet combination; The function is optimized for oversupply. This is a function of the number of air inlets; This is a function for numbering air inlets.

[0063] Secondly, the present invention provides a simulation system for transient pressure fluctuations in train-tunnel coupling, used in a train-tunnel coupling transient pressure waveform simulation device. The simulation device includes a housing, a control device, and several pneumatic devices. The housing is provided with several air ports, each equipped with a solenoid valve. The air ports are respectively connected to the pneumatic devices via solenoid valves. The pneumatic devices and the solenoid valves are electrically connected to the control device. The system includes:

[0064] The data acquisition module is used to acquire the pressure waveform data to be simulated and to identify the inflection points in the pressure waveform data through an inflection point detection algorithm.

[0065] The waveform classification module is used to divide the pressure waveform into multiple continuous time periods based on the inflection point, calculate the average pressure change rate of each time period, and compare the average pressure change rate of each time period with a preset change rate threshold to determine the wave type of each time period.

[0066] The band types include rising band, falling band, and stationary band;

[0067] The parameter calculation module is used to extract features from the pressure waveform data of each time period, obtain the waveform feature parameters of each time period, and calculate the target pressure change rate based on the band type and waveform feature parameters of each time period.

[0068] The set filtering module is used to filter the gas port combinations that meet the pressure change requirements from the preset gas port set according to the target pressure change rate and wave type, so as to form a feasible solution set.

[0069] The multi-level optimization module is used to prioritize the feasible solution set through a multi-level optimization algorithm to generate the optimal air inlet control scheme.

[0070] In an optional implementation, it further includes:

[0071] The execution control module is used to control the pneumatic equipment and solenoid valve to operate according to the optimal air inlet control scheme, so as to reproduce the target pressure waveform.

[0072] The beneficial effects of the embodiments provided by the present invention include:

[0073] This invention utilizes a turning point detection algorithm and pressure band pattern judgment to divide the waveform into bands based on its physical characteristics, thereby reducing invalid calculation intervals and significantly lowering the computational load for high-precision simulation of the entire tunnel. Simultaneously, it calculates the target pressure change rate based on the waveform feature parameters extracted from each band, and then dynamically selects the optimal air inlet combination by combining the band type. Furthermore, it optimizes the oversupply, number, and number of air inlets through a multi-level optimization algorithm to generate the optimal air inlet control scheme. This effectively overcomes the distortion problem in the simulation of waveforms with multiple reflections and superpositions in long tunnels using traditional methods, and achieves high-precision reproduction of transient pressure fluctuation patterns. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other embodiments can be obtained based on these drawings.

[0075] Figure 1 A schematic flowchart of the train-tunnel coupled transient pressure fluctuation simulation method in the embodiments of this specification is shown;

[0076] Figure 2 This document illustrates another flowchart of the train-tunnel coupled transient pressure fluctuation simulation method in the embodiments of this specification.

[0077] Figure 3 The simulation results of pressure fluctuations in the example of this specification are shown in Figure 1;

[0078] Figure 4 The simulation results of pressure fluctuations in the example in this specification are shown in Figure 2;

[0079] Figure 5 A schematic diagram of the train-tunnel coupled transient pressure fluctuation simulation system in the embodiments of this specification is shown. Detailed Implementation

[0080] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention. Furthermore, the terms “first,” “second,” and “third” are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0081] Research methods for the aerodynamic effects of high-speed train-tunnel coupling are constantly evolving. Traditional calculation methods based on actual track measurements or simplified models have limitations in accurately simulating complex transient air pressure waveforms. Aerodynamic experimental devices, as an important technical means in this field (such as the Chinese patent application—a ground experimental device and method for simulating train-tunnel coupling transient pressure), have the core function of accurately reproducing the pressure fluctuation environment encountered by the train when running in the tunnel. The control module of this device undertakes the key task of receiving pressure simulation commands and accurately adjusting the pressure inside the outer cabin model by controlling the opening and closing of solenoid valves and the operation of pneumatic equipment to generate the target pressure waveform.

[0082] However, the core challenge in using such devices for high-fidelity simulations lies in how to precisely control the response of the hardware system (such as pneumatic equipment) to generate the complex target pressure waveform with multiple reflections and superpositions in real time. Existing control strategies face numerous problems. For instance, current strategies often rely on fixed thresholds to match a single device or a fixed combination, failing to intelligently adapt to large fluctuations in the target waveform's rate of change. When simulating pressure waves with varied shapes and high-order harmonics caused by high-speed passage through long tunnels, simple control easily leads to equipment response lag or overshoot, failing to reproduce the complex characteristics of the waveform and causing non-optimal start-stop or continuous extreme operation of the equipment, resulting in excessive energy consumption and accelerated component wear.

[0083] The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0084] Example 1

[0085] This embodiment discloses a method for simulating transient pressure fluctuations in train-tunnel coupling, which is used in a train-tunnel coupling transient pressure waveform simulation device;

[0086] The train-tunnel coupled transient pressure waveform simulation device mentioned in this embodiment will be described in detail below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0087] The train-tunnel coupled transient pressure waveform simulation device in this embodiment includes a housing, control equipment, and several pneumatic devices. The housing is provided with several air ports. The pneumatic devices include a vacuum pump and an air compressor. The air ports include an exhaust port and an inlet port.

[0088] Specifically, the vacuum pump is connected to the exhaust port via a first connecting pipe, and a first solenoid valve is installed on the first connecting pipe; the air compressor is connected to the air inlet via a second connecting pipe, and a second solenoid valve is installed on the second connecting pipe; the vacuum pump, the first solenoid valve, the air compressor, and the second solenoid valve are electrically connected to the control equipment.

[0089] The control device is electrically connected to the first solenoid valve to control the opening and closing of the exhaust port; the control device is electrically connected to the vacuum pump control system to provide negative pressure to the simulation device through the exhaust port after startup; the control device is electrically connected to the second solenoid valve to control the opening and closing of the air inlet; and the control device is electrically connected to the air compressor to provide positive pressure to the experimental device through the air inlet after startup.

[0090] like Figure 1 As shown in this embodiment, a simulation method for train-tunnel coupled transient pressure fluctuations includes:

[0091] S1. Obtain the pressure waveform data to be simulated, and identify the inflection points in the pressure waveform data using an inflection point detection algorithm;

[0092] For example, the specific steps of step S1 include:

[0093] Calculate the rate of change of pressure and the acceleration of pressure change at different time points in the pressure waveform data;

[0094] Based on a predefined collaborative function, the fluctuation state of pressure waveform data is obtained;

[0095] Based on predefined inflection point determination rules, the inflection points in the pressure waveform data are determined according to the fluctuation state of the pressure waveform data.

[0096] In this embodiment, let the pressure measurement value at time point t be p(t). Then, the pressure change rate and pressure change acceleration at that point are respectively:

[0097] ;

[0098] ;

[0099] In the formula, v(t) is the rate of change of pressure at time t; a(t) is the acceleration of pressure change. When a(t) > 0, it indicates that the pressure fluctuation is accelerating; when a(t) < 0, it indicates that the pressure fluctuation is decelerating; when a(t) = 0, it indicates that the pressure fluctuation remains unchanged.

[0100] Specifically, the expression for the cooperative function is:

[0101] ;

[0102] In the formula, sgn(.) is a cooperative function; The pressure waveform data is in an upward trend; The pressure waveform data is in a stable state; The pressure waveform data is in a decreasing state;

[0103] Specifically, the functional expression for the turning point determination rule is:

[0104] ;

[0105] In the formula, This marks a turning point; This reflects the pressure fluctuation status of the previous time period; This indicates the pressure fluctuation status for the next time period. is the change over time; a(t) is the acceleration due to pressure change; The acceleration threshold;

[0106] In this embodiment, considering factors such as noise, there will be a certain amount of tiny waveforms in the pressure fluctuation data. These tiny fluctuations will affect the segmented coordination algorithm's judgment of the turning point. Therefore, an acceleration threshold is set to filter out the tiny fluctuations.

[0107] Specifically, the detailed classification information for turning points is shown in Table 1:

[0108] ;

[0109] Table 1 shows the specific classification information for turning points.

[0110] S2. Based on the inflection point, the pressure waveform is divided into multiple continuous time periods, the average pressure change rate of each time period is calculated, and the average pressure change rate of each time period is compared with a preset change rate threshold to determine the wave type of each time period.

[0111] Among them, the band types include rising band, falling band, and stationary band;

[0112] For example, the specific steps of step S2 include:

[0113] The pressure waveform data is divided into multiple continuous time periods using any two adjacent inflection points as boundaries;

[0114] Calculate the average pressure change rate of the pressure waveform data for each time period;

[0115] The average pressure change rate for each time period is compared with a preset change rate threshold to determine the wave type for each time period.

[0116] Specifically, the functional expression for the rate of change of mean pressure is:

[0117] ;

[0118] In the formula, Let be the average pressure change rate over the k-th time period. and These are the start and end times of the k-th time interval, respectively. For time points The rate of change of pressure; The differential symbol;

[0119] Specifically, the rules for determining the band type are as follows:

[0120] ;

[0121] In the formula, The band type for the k-th time period; Preset category change rate; This represents the rising segment of the pressure waveform. This represents the descending band of the pressure waveform. This represents the stable band of the pressure waveform.

[0122] S3. Extract features from the pressure waveform data of each time period to obtain waveform feature parameters for each time period, and calculate the target pressure change rate based on the band type and waveform feature parameters in each time period.

[0123] For example, the waveform characteristic parameters in step S3 include the band duration and the band pressure change.

[0124] Specifically, the functional expression for the waveform characteristic parameters is:

[0125] ;

[0126] ;

[0127] In the formula, T is the duration of the band; This is the k-th time boundary point; Let k+1 be the (k+1)th time boundary point; C is the band pressure change. The pressure value at the k-th time boundary point; The pressure value at the (k+1)th time boundary point;

[0128] Specifically, the functional expression for the target pressure change rate in step S3 is:

[0129] ;

[0130] In the formula, The target pressure change rate.

[0131] S4. Based on the target pressure change rate and band type, select the combination of air ports that meets the pressure change requirements from the preset set of air ports to form a feasible solution set.

[0132] For example, the specific steps of step S4 include:

[0133] Based on the band type of each time period, determine the air outlet control mode for the corresponding time period.

[0134] Among them, the air intake control mode includes intake mode and exhaust mode;

[0135] When the air inlet control mode is in the air intake mode, the air inlet combination that meets the pressure rise requirement is selected from the preset air inlet set according to the target pressure change rate, so as to form the feasible solution set of the air intake mode.

[0136] When the air port control mode is in the exhaust mode, based on the target pressure change rate, air port combinations that meet the pressure reduction requirements are selected from the preset air port set to form a feasible solution set for the exhaust mode.

[0137] Specifically, the functional expression for the feasible solution set of the intake mode is:

[0138] ;

[0139] ;

[0140] In the formula, S represents the feasible solution set for the intake mode; S is the subset of air inlet selection. For a predefined set of air intakes; i This is a unique number for the air intake. is the pressure rise rate; p is the index number; m is the total number of air inlets;

[0141] Specifically, the functional expression for the feasible solution set of the exhaust mode is:

[0142] ;

[0143] ;

[0144] In the formula, This represents the feasible solution set for the exhaust mode. A predefined set of exhaust ports; A unique number for the exhaust port; is the pressure drop rate; n is the total number of exhaust ports.

[0145] In this embodiment, the pressure change rate of each air port in the preset air port set was obtained through multiple experimental calibrations. This set can be reused in multiple rounds of simulation processes, avoiding repeated calibration. In addition, each exhaust port and air inlet port is assigned a unique identifier number, and the identifier number has a monotonically increasing relationship with the pressure change rate, that is, the larger the number, the higher the pressure change rate.

[0146] For example, inlet (2,100) means that inlet number 2 can increase the pressure at a rate of 100 Pa / s; inlet (6,400) means that inlet number 6 can increase the pressure at a rate of 400 Pa / s.

[0147] S5. Prioritize the feasible solution set using a multi-level optimization algorithm to generate the optimal air inlet control scheme.

[0148] For example, the multi-level optimization algorithm in step S5 includes a first priority objective, a second priority objective, and a third priority objective that are executed sequentially;

[0149] The first priority objective is to minimize the oversupply optimization function; the second priority objective is to minimize the number of gas inlets function; and the third priority objective is to minimize the gas inlet numbering function.

[0150] Among them, the oversupply refers to the difference between the actual total change rate of the selected gas port combination and the target pressure change rate, which is used to quantify the extent to which the output capacity of the gas port combination exceeds the target demand.

[0151] Specifically, the function expression for the multi-level optimization algorithm is:

[0152] ;

[0153] In the formula, Select a subset of the optimal air inlets, i.e., the optimal air inlet control scheme; F is the feasible solution set; This represents the actual total rate of change of the selected air inlet combination; The function is optimized for oversupply. This is a function of the number of air inlets; This is a function for numbering air inlets.

[0154] In this embodiment, first priority optimization is performed by minimizing the oversupply optimization function to select the air port combination whose actual capacity is closest to the target demand, thereby improving system stability. Second, if the first priority is the same, second priority optimization is performed by minimizing the air port number function to select the scheme with the fewest open air ports, avoiding energy waste. Finally, if the first and second priorities are the same, third priority optimization is performed by minimizing the numbering function to select the air port combination with the smallest number, avoiding frequent use of air ports with high pressure change rates, reducing air port wear, and simplifying the control logic to a certain extent.

[0155] In some embodiments, it also includes:

[0156] S6. According to the optimal air inlet control scheme, control the pneumatic equipment and solenoid valve to achieve the reproduction of the target pressure waveform.

[0157] In this embodiment, when targeting the exhaust mode, the control device determines the number and timing of opening exhaust ports according to the optimal exhaust port control scheme, so as to control the operation of the vacuum pump and the first solenoid valve to achieve a pressure drop.

[0158] When targeting the intake mode, the control equipment determines the number and timing of opening the intake ports according to the optimal intake port control scheme, thereby controlling the operation of the air compressor and the second solenoid valve to achieve a pressure increase.

[0159] To facilitate understanding by those skilled in the art, two sets of preferred embodiments are provided below:

[0160] like Figure 3 As shown, under the simulated condition of a train traveling at 300 km / h through a 5 km long tunnel:

[0161] Experimental results show that the coefficients of determination of the simulated waveforms and the original measured waveforms in the three independent experiments are all greater than 0.9, and the fitting degree between the simulated data and the original measured data is excellent. This proves that the waveform reproduction accuracy meets the engineering standards for high-speed train aerodynamic simulation, that is, this method can capture the dynamic characteristics of pressure fluctuations on the surface of the car body in a long tunnel environment with high precision.

[0162] Specifically, the simulation results are within the preset engineering allowable error range in the pressure amplitude drastic change range (such as the 4-5 second range) and the pressure continuous change range (such as the 40-49 second range), and the calculated pressure change rate meets the actual engineering requirements, which fully verifies that the simulation method can effectively reproduce the transient pressure fluctuations in long tunnels.

[0163] Furthermore, in the repeatability experiment, no significant differences were observed among the three sets of independent simulation data, and the relative deviation met the requirements, effectively eliminating random factors and further confirming that the simulation algorithm has excellent stability and repeatability.

[0164] like Figure 4 As shown, this is under the condition that the train continuously passes through a tunnel group with a total length of 4 km at a speed of 200 km / h:

[0165] Experimental data show that the coefficients of determination between the simulated waveforms and the original measured waveforms in the three independent experiments are all greater than 0.9, indicating excellent fit between the simulated data and the original measured data. This proves that the waveform reproduction accuracy meets the engineering standards for high-speed train aerodynamic simulation, meaning that this method can accurately capture the dynamic changes in pressure fluctuations on the outer surface of a vehicle in a continuous tunnel environment.

[0166] Specifically, in the section where pressure changes continuously and rapidly (such as the 1-20 second section), the simulation results are all within the preset engineering allowable error range, and the calculated pressure change rate meets the actual engineering requirements, which fully verifies that the simulation method can effectively reproduce the transient pressure fluctuations in continuous tunnels.

[0167] Furthermore, in the repeatability experiment, no significant differences were observed among the three sets of independent simulation data, and the relative deviation met the requirements, effectively eliminating random factors and further confirming that the simulation algorithm has excellent stability and repeatability.

[0168] Example 2

[0169] like Figure 5 As shown, this embodiment provides a simulation system 100 for train-tunnel coupled transient pressure fluctuations, used for simulating train-tunnel coupled transient pressure waveforms, including:

[0170] The data acquisition module 101 is used to acquire the pressure waveform data to be simulated and to identify the inflection points in the pressure waveform data through an inflection point detection algorithm.

[0171] The waveform classification module 102 is used to divide the pressure waveform into multiple continuous time periods based on the inflection point, calculate the average pressure change rate of each time period, and compare the average pressure change rate of each time period with a preset change rate threshold to determine the wave type of each time period.

[0172] Among them, the band types include rising band, falling band, and stationary band;

[0173] The parameter calculation module 103 is used to extract features from the pressure waveform data of each time period, obtain waveform feature parameters of each time period, and calculate the target pressure change rate based on the band type and waveform feature parameters of each time period.

[0174] The set filtering module 104 is used to filter the gas port combinations that meet the pressure change requirements from the preset gas port set according to the target pressure change rate and band type, so as to form a feasible solution set.

[0175] The multi-level optimization module 105 is used to prioritize the feasible solution set through a multi-level optimization algorithm to generate the optimal air inlet control scheme.

[0176] In some embodiments, the device further includes an execution control module 106, which controls the execution module of the corresponding air port in the simulation device to operate according to the optimal air port control scheme, so as to reproduce the target pressure waveform.

[0177] Example 3

[0178] This embodiment provides a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor;

[0179] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0180] The non-transient software program and instructions required to implement the train-tunnel coupled transient pressure fluctuation simulation method of the above embodiments are stored in memory. When executed by the processor, the train-tunnel coupled transient pressure fluctuation simulation method of the above embodiments is executed, for example, the method described above is executed. Figure 1 The method steps S1 to S6 are described in the text.

[0181] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0182] Example 4

[0183] This embodiment provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute a simulation method for train-tunnel coupled transient pressure fluctuations as described in Embodiment 1.

[0184] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0185] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0186] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages ​​such as Python, Java, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0187] In summary, this invention first uses a turning point detection algorithm and pressure band pattern judgment to accurately divide the pressure waveform into several segments based on the physical characteristics of the waveform, thereby significantly reducing invalid calculation intervals and substantially lowering the computational load for high-precision simulation of the entire tunnel. Subsequently, the method calculates the target pressure change rate based on the waveform feature parameters extracted from each segment and dynamically selects the optimal air inlet combination from the air inlet set that meets the requirements, combined with the band type. Finally, a multi-level optimization algorithm generates the optimal air inlet control scheme. This scheme effectively eliminates the distortion problem that occurs in traditional methods when simulating waveforms with multiple reflections and superpositions in long tunnels, achieving high-precision reproduction of transient pressure fluctuation patterns.

[0188] This embodiment tightly integrates a physics-driven segmented collaborative mechanism with a goal-oriented multi-level optimization algorithm. Through the specific control flow disclosed in Embodiment 1, it filters air vents based on feasible solution sets, optimizes their opening quantity and timing, and coordinates the control of the air compressor / vacuum pump and its associated solenoid valves to achieve efficient generation of pressure waveforms that closely approximate real-world operating conditions. This scheme effectively overcomes the problems of low efficiency in numerical simulation, distortion due to scaled-down dynamic models, and difficulty in obtaining system samples from actual vehicle experiments. It provides a quantifiable, repeatable, and high-fidelity simulation technology foundation for the systematic study of the physical mechanisms of the generation, propagation, and evolution of transient pressure fluctuations in train-tunnel coupling.

[0189] The above description is merely a preferred embodiment of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the present invention is defined by the appended claims rather than the foregoing description, and thus all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention.

Claims

1. A method for simulating transient pressure fluctuations in train-tunnel coupling, used in a train-tunnel coupling transient pressure waveform simulation device, the simulation device comprising a housing, a control device, and several pneumatic devices; the housing is provided with several air ports, each air port being equipped with a solenoid valve, the air ports being respectively connected to the pneumatic devices via solenoid valves, and the pneumatic devices and solenoid valves being electrically connected to the control device, characterized in that, The method includes: S1. Obtain the pressure waveform data to be simulated, and identify the inflection points in the pressure waveform data using an inflection point detection algorithm; S2. Based on the inflection point, the pressure waveform is divided into multiple continuous time periods, the average pressure change rate of each time period is calculated, and the average pressure change rate of each time period is compared with a preset change rate threshold to determine the wave type of each time period. The band types include rising band, falling band, and stationary band; S3. Extract features from the pressure waveform data for each time period to obtain waveform feature parameters for each time period, and calculate the target pressure change rate based on the waveform feature parameters. S4. Based on the target pressure change rate and band type, select the combination of air ports that meets the pressure change requirements from the preset set of air ports to form a feasible solution set. S5. Prioritize the feasible solution set using a multi-level optimization algorithm to generate the optimal air inlet control scheme.

2. The simulation method according to claim 1, characterized in that, Also includes: S6. According to the optimal air inlet control scheme, control the pneumatic equipment and solenoid valve to achieve the reproduction of the target pressure waveform.

3. The simulation method according to claim 1, characterized in that, The specific steps of step S1 include: Calculate the rate of change of pressure and the acceleration of pressure change at different time points in the pressure waveform data; Based on a predefined collaborative function, the fluctuation state of pressure waveform data is obtained; Based on predefined inflection point determination rules, the inflection points in the pressure waveform data are determined according to the fluctuation state of the pressure waveform data. The expression for the cooperative function is: ； In the formula, sgn(.) is the cooperative function; v(t) is the rate of change of pressure at time t; The pressure waveform data is in an upward trend; The pressure waveform data is in a stable state; The pressure waveform data is in a decreasing state; The functional expression for the turning point determination rule is: ； In the formula, This marks a turning point; is the change over time; a(t) is the acceleration due to pressure change; This is the acceleration threshold.

4. The simulation method according to claim 1, characterized in that, The specific steps of step S2 include: The pressure waveform data is divided into multiple continuous time periods using any two adjacent inflection points as boundaries; Calculate the average pressure change rate of the pressure waveform data for each time period; The average pressure change rate for each time period is compared with a preset change rate threshold to determine the wave type for each time period. The functional expression for the rate of change of average pressure is: ； In the formula, Let be the average pressure change rate over the k-th time period. This is the k-th time boundary point; This is the (k+1)th time boundary point; For time points The rate of change of pressure; The differential symbol; The rule for determining the band type is as follows: ； In the formula, The band type for the k-th time period; Preset category change rate; This represents the rising segment of the pressure waveform. This represents the descending band of the pressure waveform. This represents the stable band of the pressure waveform.

5. The simulation method according to claim 1, characterized in that, The waveform characteristic parameters in step S3 include the band duration and the band pressure change. The functional expression for the waveform characteristic parameters is: ； ； In the formula, T is the duration of the band; This is the k-th time boundary point; Let k+1 be the (k+1)th time boundary point; C is the band pressure change. The pressure value at the k-th time boundary point; The pressure value at the (k+1)th time boundary point; The functional expression for the target pressure change rate in step S3 is: ； In the formula, The target pressure change rate.

6. The simulation method according to claim 1, characterized in that, The specific steps of step S4 include: Based on the band type of each time period, determine the air outlet control mode for the corresponding time period. The air inlet includes an exhaust port and an air inlet; the air inlet control mode includes an air inlet mode and an exhaust mode. When the air inlet control mode is in the air intake mode, the air inlet combination that meets the pressure rise requirement is selected from the predefined air inlet set according to the target pressure change rate, so as to form the feasible solution set of the air intake mode. When the vent control mode is in the exhaust mode, the combination of vents that meets the pressure drop requirement is selected from the predefined set of vents according to the target pressure change rate, so as to form a feasible solution set for the exhaust mode. The functional expression for the feasible solution set of the intake mode is: ； ； In the formula, S represents the feasible solution set for the intake mode; S is the subset of air inlet selection. For a predefined set of air intakes; i This is a unique number for the air intake. is the pressure rise rate; p is the index number; m is the total number of air inlets; The target pressure change rate; The functional expression for the feasible solution set of the exhaust mode is: ； ； In the formula, This represents the feasible solution set for the exhaust mode. For a predefined set of exhaust ports; i A unique number for the exhaust port; denoted as , where is the pressure drop rate; and 'n' is the total number of exhaust ports.

7. The simulation method according to claim 1, characterized in that, The multi-level optimization algorithm in step S5 includes a first priority objective, a second priority objective, and a third priority objective that are executed sequentially. The first priority objective is to minimize the oversupply optimization function; The second priority objective is to minimize the number of air inlets function; The third priority objective is to minimize the air inlet numbering function; the function expression of the multi-level optimization algorithm is: ； In the formula, Select a subset of the optimal air inlets, i.e., the optimal air inlet control scheme; F is the feasible solution set; This represents the actual total rate of change of the selected air inlet combination; The function is optimized for oversupply. This is a function of the number of air inlets; This is a function for numbering air inlets.

8. A simulation system for train-tunnel coupled transient pressure fluctuations, used in a train-tunnel coupled transient pressure waveform simulation device, the simulation device comprising a housing, a control device, and several pneumatic devices; the housing is provided with several air ports, each air port being equipped with a solenoid valve, the air ports being respectively connected to the pneumatic devices via solenoid valves, and the pneumatic devices and solenoid valves being electrically connected to the control device, characterized in that, The system includes: The data acquisition module is used to acquire the pressure waveform data to be simulated and to identify the inflection points in the pressure waveform data through an inflection point detection algorithm. The waveform classification module is used to divide the pressure waveform into multiple continuous time periods based on the inflection point, calculate the average pressure change rate of each time period, and compare the average pressure change rate of each time period with a preset change rate threshold to determine the wave type of each time period. The band types include rising band, falling band, and stationary band; The parameter calculation module is used to extract features from the pressure waveform data of each time period, obtain the waveform feature parameters of each time period, and calculate the target pressure change rate based on the band type and waveform feature parameters of each time period. The set filtering module is used to filter the gas port combinations that meet the pressure change requirements from the preset gas port set according to the target pressure change rate and wave type, so as to form a feasible solution set. The multi-level optimization module is used to prioritize the feasible solution set through a multi-level optimization algorithm to generate the optimal air inlet control scheme.

9. The simulation system according to claim 8, characterized in that, Also includes: The execution control module is used to control the pneumatic equipment and solenoid valve to operate according to the optimal air inlet control scheme, so as to reproduce the target pressure waveform.

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