Method for predicting the waveform of waves passing through branch shafts, a waveform prediction device therefor, and a waveform prediction program for waves passing through branch shafts.

Abstract

This invention provides a method for predicting the waveform of a wave passing through a branch tunnel, a waveform prediction device, and a waveform prediction program for a wave passing through a branch tunnel, which can predict the waveform of the wave passing through the branch tunnel quickly and accurately. [Solution] When a train 1 moving through the main tunnel 3A passes through the branch section 3C where the branch tunnel 3B branches off from the main tunnel 3A, a branch tunnel passage wave W is generated. 31 ,W 32 A method for predicting the waveform of a branch tunnel-passing wave, wherein the waveform of the branch tunnel-passing wave W is predicted by a compact Green's function based on the cross-sectional area change rate distribution of the front of train 1. 31 ,W 32 The process includes a waveform prediction step to predict the waveform of the compressed wave passing through the main tunnel 3A and the waveform of the wave passing through the branch tunnel W. 31 The process includes predicting the waveform of the expansion wave passing through branch shaft 3B as the branch shaft passing wave W. 32 This process includes predicting the waveform.

Description

[Technical Field]

[0001] This invention relates to a method for predicting the waveform of a branch tunnel passage wave, a waveform prediction device, and a branch tunnel passage wave waveform prediction program, which predict the waveform of a branch tunnel passage wave generated when a moving object moving through the main tunnel passes through a branch section where a branch tunnel branches off from the main tunnel. [Background technology]

[0002] Pressure waves generated when trains pass through branch tunnels within a tunnel have been analyzed using model experiments or numerical simulations. In generalized flow prediction methods for transient flows generated by trains in single-track tunnels, friction, stepwise area changes, heat transfer, heat dissipation from the train, and the effects of gravity are modeled, and experiments are conducted in full-scale railway tunnels (see, for example, Non-Patent Document 1). In model experiments and analyses of pressure waves emitted from the entrance of tunnels with branches, model experiments are conducted to investigate pressure waves generated by trains passing through branch tunnels and pulse waves radiated from the entrances of the main tunnel and branch tunnels (see, for example, Non-Patent Document 2). In high-speed railway tunnels with large branch tunnels, experimental results have confirmed that as the ratio of the branch cross-sectional area to the tunnel cross-sectional area increases, the pressure rise in the main tunnel due to the train's leading end passing through the branch junction increases (see, for example, Non-Patent Document 3).

[0003] Furthermore, the fundamental theory of compression waves when entering a tunnel has been developed by Howe. It has been described that in the compression waves generated when a high-speed train enters a tunnel, the train is modeled by a continuous distribution of monopole sound sources whose intensity is determined by the train's leading profile (see, for example, Non-Patent Document 4). [Prior art documents] [Patent Documents]

[0004] [Non-Patent Document 1] WAWoods, “A generalized flow prediction method for the unsteady flow generated by a train in a single-track tunnel”, Journal of Wind Engineering and Industrial Aerodynamics, 7, 1981, p. 331-360

[0005] [Non-Patent Document 2] Miyachi.T, Fukuda.T, Saito.S, “Model experiment and analysis of pressure waves emitted from portals of at tunnel with a branch”, J.Sound Vib, 333, 2014, p.6156-6169.

[0006] [Non-Patent Document 3] H.Okubo, T.Miyachi, K.Sugiyama, “Pressure fluctuation and a micro-pressure wave in a high-speed railway tunnel with large branch shaft”, Journal of Wind Engineering and Industrial Aerodynamics, Volume 217,2021,104751

[0007] [Non-Patent Document 4] MSHowe, “The compression wave produced by a high-speed train entering a tunnel”, Proc.R.Soc.Lond.A, 1998, 454, p. 1523-1534 [Overview of the project] [Problems that the invention aims to solve]

[0008] Until now, there has been no method to analytically predict the waveform of pressure waves generated when trains pass through branch tunnels within a tunnel system. Analysis has been conducted using model experiments and numerical simulations. However, this method has presented challenges, as it requires significant resources such as experimental equipment, time, and cost.

[0009] The object of this invention is to provide a method for predicting the waveform of a wave passing through a branch tunnel, a waveform prediction device, and a waveform prediction program for a wave passing through a branch tunnel that can predict the waveform of the wave passing through the branch tunnel quickly and accurately. [Means for solving the problem]

[0010] This invention solves the aforementioned problem by the following means of solution. The embodiments of this invention will be described using corresponding reference numerals, but the invention is not limited to these embodiments. The invention of claim 1 is a method for predicting the waveform of a branch tunnel passage wave generated when a moving body (1) moving in the main tunnel passes through a branch section (3C) where a branch tunnel (3B) branches off from the main tunnel (3A), as shown in Figures 3, 6, and 8, wherein the cross-sectional area change rate distribution (∂A) of the front part of the moving body is * Based on / ∂Y1), the branch tunnel-passed wave (W) is determined by the compact Green's function (G). 31 ,W 32 This is a method (#100) for predicting the waveform of a branch tunnel-passing wave, characterized by including a waveform prediction step (#120) for predicting the waveform of the branch tunnel-passing wave.

[0011] The invention of claim 2 is a waveform prediction method for branch tunnel passage waves as described in claim 1, wherein, as shown in Figures 3 and 8, the waveform prediction step is to predict the branch tunnel passage waves (W) generated in the main tunnel. 31 This method for predicting the waveform of a branch tunnel-passed wave is characterized by including a step of predicting the waveform of the branch tunnel-passed wave.

[0012] The invention according to claim 3 is the method for predicting the waveform of the branch tunnel passing wave according to claim 2, wherein the waveform prediction step includes a step of predicting the waveform of the branch tunnel passing wave generated in the main tunnel of the tunnel using a compact Green's function G represented by the following mathematical formula. A method for predicting the waveform of a branch tunnel passing wave, characterized by TIFF2026060520000002.tif180154.

[0013] The invention according to claim 4 is the method for predicting the waveform of the branch tunnel passing wave according to claim 1. As shown in FIGS. 3 and 8, the waveform prediction step includes a step of predicting the waveform of the branch tunnel passing wave (W 32 ) generated in the branch tunnel.

[0014] The invention according to claim 5 is the method for predicting the waveform of the branch tunnel passing wave according to claim 4. The waveform prediction step includes a step of predicting the waveform of the branch tunnel passing wave generated in the branch tunnel using a compact Green's function G represented by the following mathematical formula. A method for predicting the waveform of a branch tunnel passing wave, characterized by TIFF2026060520000003.tif147154.

[0015] The invention according to claim 6 is the method for predicting the waveform of the branch tunnel passing wave according to claim 1. As shown in FIG. 8, the waveform prediction step includes a step of predicting the waveform obtained by adding the multiple reflection components (W 32 ) of the branch tunnel passing wave (W 51 ) at the end (3c) of this branch tunnel and the branch portion (3C) to the branch tunnel passing wave (W 31 ) generated in the main tunnel of the tunnel.

[0016] The invention according to claim 7 is the method for predicting the waveform of the branch tunnel passing wave according to claim 1. As shown in FIG. 8, the waveform prediction step includes a step of predicting the waveform obtained by adding the multiple reflection components (W 32 ) of the branch tunnel passing wave (W 52 ) at the end (3c) of this branch tunnel and the branch portion (3C) to the branch tunnel passing wave (W 32This method for predicting the waveform of a wave passing through a branch tunnel is characterized by including a step of predicting the waveform added to the above.

[0017] The invention of claim 8 is a waveform prediction device for branch tunnel passage waves that predicts the waveform of branch tunnel passage waves generated when a moving body (1) moving in the main tunnel passes through a branch section (3C) where a branch tunnel (3B) branches off from the main tunnel (3A), as shown in Figures 1, 3 and 8, wherein the cross-sectional area change rate distribution (∂A) of the front part of the moving body * Based on / ∂Y1), the branch tunnel-passed wave (W) is determined by the compact Green's function (G). 31 ,W 32 The waveform prediction device (4) for branch tunnel passing waves is characterized by comprising a waveform prediction unit (6) that predicts the waveform of ).

[0018] The invention of claim 9 is a waveform prediction program for branch tunnel passage waves, which predicts the waveform of branch tunnel passage waves generated when a moving body (1) moving in the main tunnel passes through a branch section (3C) where a branch tunnel (3B) branches off from the main tunnel (3A), as shown in Figures 1, 3, 5, and 8, wherein the cross-sectional area change rate distribution (∂A) of the front part of the moving body * Based on / ∂Y1), the branch tunnel-passed wave (W) is determined by the compact Green's function (G). 31 ,W 32 This is a waveform prediction program for branch shaft passing waves, characterized by causing a computer to execute a waveform prediction procedure that predicts the waveform (S110). [Effects of the Invention]

[0019] According to this invention, the performance of tunnel buffer structures can be easily evaluated in a short time and at low cost. [Brief explanation of the drawing]

[0020] [Figure 1] This is a configuration diagram of a waveform prediction device for branch tunnel passage waves according to the first embodiment of this invention. [Figure 2] This is a conceptual diagram illustrating the micro-pressure waves generated when a train enters the main tunnel. [Figure 3]This is a conceptual diagram illustrating the branch tunnel passage waves generated when a train passes through a branch tunnel where a branch tunnel branches off from the main tunnel. [Figure 4] This is a schematic diagram showing the coordinates of the axis of travel of a train that has entered the main tunnel. [Figure 5] This is a flowchart illustrating the operation of the waveform prediction device for branch tunnel passes-through waves according to the first embodiment of this invention. [Figure 6] This is a process diagram of a waveform prediction method for branch tunnel-passed waves according to the first embodiment of this invention. [Figure 7] This graph shows the results of an analysis using a compact Green's function according to the waveform prediction method for branch tunnel passage waves according to the first embodiment of this invention, compared with the results of an analysis using a numerical simulation. (A) is a graph showing a comparison of branch tunnel passage wave waveforms, and (B) is a graph showing a comparison of pressure gradient waveforms. [Figure 8] This is a conceptual diagram schematically illustrating the multiple reflection components generated in a branch tunnel when a train passes through the branch point where a branch tunnel branches off from the main tunnel. [Modes for carrying out the invention]

[0021] (First Embodiment) A first embodiment of this invention will be described in detail below with reference to the drawings. The train 1 shown in Figures 2 and 3 is a moving object that moves along the track 2. The train 1 is moving through the main tunnel 3A. The train 1 is, for example, a railway vehicle that travels on the Shinkansen (registered trademark) at a high speed of 320 km / h or more, or a magnetic levitation railway vehicle that levitates by magnetic attraction and magnetic repulsion and travels at a high speed of 550 km / h or more.

[0022] Track 2 is the passage on which train 1 travels. If train 1 is a railway vehicle, track 2 is the track on which this railway vehicle travels, and if train 1 is a magnetic levitation railway vehicle, track 2 is the guideway on which this magnetic levitation railway vehicle travels. Track 2 is a double track consisting of two main lines, an up main line and a down main line, and is laid inside the main tunnel 3A.

[0023] Tunnel 3 is a fixed structure (civil engineering structure) that penetrates the ground to allow train 1 to pass through. Tunnel 3, shown in Figures 2 and 3, is a double-track railway tunnel (double-track tunnel) that accommodates two main lines within a single fixed structure. Tunnel 3 is, for example, an urban tunnel constructed in an urban area with unconsolidated or poorly consolidated ground, or a mountain tunnel constructed in bedrock using mountain tunneling methods. Tunnel 3 comprises a main tunnel shaft (main shaft) 3A, a branch shaft 3B, and a branch section 3C.

[0024] Tunnel main shaft 3A is the main tunnel at the center of tunnel 3. Tunnel main shaft 3A is constructed using the shield tunneling method, in which segments are assembled within a steel shell (shield) by a tunnel boring machine that excavates tunnel main shaft 3A. Tunnel main shaft 3A has portals 3a and 3b, which serve as entrances and exits for train 1. Tunnel main shaft 3A is constructed with an inner diameter of approximately 10 to 13 meters.

[0025] Branch tunnel 3B is a tunnel that branches off from the main tunnel 3A. As shown in Figures 2 and 3, branch tunnel 3B is a vertical shaft dug perpendicular to the main tunnel 3A. Branch tunnel 3B is used, for example, during the construction of tunnel 3 as a construction route for transporting in and out tunnel boring machines, equipment, workers, or materials necessary for the construction of the main tunnel 3A. After the completion of tunnel 3, branch tunnel 3B is used, for example, as an evacuation route to move people from inside tunnel 3 to a safe place in the event of a disaster such as a fire, or for ventilation purposes inside tunnel 3. Branch tunnel 3B has a portal 3c that serves as an entrance and exit between branch tunnel 3B and the surface. Branch tunnel 3B is constructed, for example, with an inner diameter of about 10 to 30 m and is constructed at intervals of about 5 to 10 km along the length of the main tunnel 3A.

[0026] Branch section 3C is the point where branch tunnel 3B branches off from the main tunnel 3A. Branch section 3C functions as a connection point between the main tunnel 3A and branch tunnel 3B, and is used for launching or reaching the shield tunneling method during the construction of tunnel 3.

[0027] The tunnel compression wave W1 shown in Figure 2 is a compression wave generated inside the main tunnel 3A in front of train 1 when train 1 enters the tunnel entrance 3a of the main tunnel 3A. Micro-pressure wave (main tunnel micro-pressure wave) W 21 This is a pressure wave radiated from the opposite portal 3b when train 1 enters portal 3a of the main tunnel 3A. Micro-pressure wave W 21 The tunnel compression wave W1 propagates within the main tunnel shaft 3A, becoming a pulsed pressure wave (tunnel micro-pressure wave) which is then radiated to the outside from the tunnel entrance 3b, opposite to the entrance entrance 3a. Micro-pressure wave (branch tunnel micro-pressure wave) W 22 This is a pressure wave radiated from the entrance 3c of the branch tunnel 3B when train 1 enters the entrance 3a of the main tunnel 3A. Micro-pressure wave W 22 The tunnel compression wave W1 propagates from the main tunnel 3A into the branch tunnel 3B, becoming a pulsed pressure wave (tunnel micro-pressure wave) which is then radiated to the outside from the tunnel entrance 3c of the branch tunnel 3B.

[0028] Micro-pressure wave W 21 ,W 22 This can cause environmental problems by generating impact noises near mine entrances 3b and 3c, and by shaking the fixtures and fittings of houses near mine entrances 3b and 3c. Micro-pressure wave W 21 ,W 22 The magnitude of the micro-pressure wave W is roughly proportional to the rise of the compression wave W1 inside the tunnel, and is determined by the maximum value of the time derivative (pressure gradient waveform) of the compression wave W1 inside the tunnel, becoming larger as the compression wave W1 rises more rapidly. 21 ,W 22 This is reduced by, for example, ground-side measures such as installing a tunnel buffer structure covering the tunnel entrance 3a into which train 1 enters, with a length corresponding to the speed of train 1, or vehicle-side measures such as lengthening the shape of the front of train 1 or making the cross-sectional area distribution of the front of train 1 appropriate, so that the compression wave W1 inside the tunnel rises as slowly as possible (reducing the maximum value of the pressure gradient of the compression wave W1 inside the tunnel).

[0029] The branch tunnel passing wave W shown in Figure 3 31 ,W 32This is a pressure wave generated when train 1 passes through branch section 3C. Branch tunnel passage wave (branch tunnel passage compression wave) W 31 This is a compression wave that passes through the main tunnel 3A when train 1 passes through the branch section 3C, and it is generated in the main tunnel 3A ahead of train 1, having a positive pressure gradient. Branch tunnel passing wave (branch tunnel passing expansion wave) W 32 This is an expansion wave that passes through branch shaft 3B when train 1 passes through branch section 3C, and it is generated within branch shaft 3B and has a negative pressure gradient.

[0030] Radiation W passing through the main tunnel 41 This is the pressure wave radiated from the tunnel entrance 3b of the main tunnel 3A when train 1 passes through the branching section 3C. Main tunnel radiated wave W 41 This refers to the branch tunnel passage wave W that is generated in the main tunnel 3A ahead of train 1 when train 1 passes through the branch section 3C. 31 The pressure propagates within the main tunnel shaft 3A, becoming a pulsed pressure wave that is radiated to the outside from the tunnel entrance 3b, which is opposite to the entrance 3a.

[0031] Branch tunnel-passing radiation W 42 This is the pressure wave radiated from the entrance 3c of the branch tunnel 3B when train 1 passes through the branch section 3C. Branch tunnel radiated wave W 42 This is the branch tunnel pass-through wave W that is generated in the branch tunnel 3B when train 1 passes through the junction 3C. 32 The pressure propagates within branch tunnel 3B, becoming a pulsed pressure wave that is radiated to the outside from the tunnel entrance 3c of branch tunnel 3B.

[0032] Here, after the tunnel compression wave W1 is generated, the main tunnel radiation wave W is generated. 41 and radiation waves W passing through branch tunnels 42 This explains the process leading up to the occurrence of [the event]. As shown in Figure 2, when train 1 enters the tunnel entrance 3a of the main tunnel 3A, a tunnel compression wave W1 is generated inside the main tunnel 3A ahead of train 1 and propagates within the main tunnel 3A. When the tunnel compression wave W1 propagating within the main tunnel 3A reaches the tunnel entrance 3b on the opposite side from the entrance 3a, a micro-pressure wave W 21This is radiated to the outside from the tunnel entrance 3b. On the other hand, when the tunnel compression wave W1 propagating from the main tunnel 3A into the branch tunnel 3B reaches the tunnel entrance 3c of the branch tunnel 3B, a micro-pressure wave W 22 It is emitted to the outside from the mine entrance 3c.

[0033] As shown in Figure 3, when train 1 enters the portal 3a of the main tunnel 3A and proceeds through the main tunnel 3A, passing through the branch section 3C, a branch tunnel wave W enters the main tunnel 3A. 31 As this occurs, branch tunnel-transmitted wave W enters branch tunnel 3B. 32 A wave W is generated in the main tunnel 3A, passing through a branch tunnel. 31 When it propagates within the main tunnel 3A and reaches the tunnel entrance 3b, the main tunnel passing radiation wave W 41 This is radiated to the outside from the mine entrance 3b. Meanwhile, branch tunnel transit wave W is generated inside branch tunnel 3B. 32 When it propagates within branch tunnel 3B and reaches tunnel entrance 3c, branch tunnel radiated wave W 42 It is emitted to the outside from the mine entrance 3c.

[0034] The waveform prediction device 4 shown in Figure 1 predicts the branch tunnel passage wave W that is generated when train 1 passes through branch section 3C. 31 ,W 32 This device predicts the waveform of the branch tunnel passing wave W that occurs in the main tunnel 3A. 31 and branch tunnel passing wave W generated within branch tunnel 3B 32 The compact Green's function used to find the branch tunnel pass-through wave W 31 ,W 32 The waveform is predicted. The waveform prediction device 4 comprises an input unit 5, a waveform prediction unit 6, a data storage unit 7, a waveform prediction program storage unit 8, a display unit 9, and a control unit 10. The waveform prediction device 4 is configured, for example, by a personal computer and executes predetermined processing according to the waveform prediction program.

[0035] The input unit 5 is a means for inputting various information to the waveform prediction device 4. For example, the input unit 5 can input branch tunnel pass-through wave W 31 ,W 32These include input devices such as a keyboard for inputting various parameters necessary for predicting the waveform, and auxiliary input devices such as a mouse used to select the operation of the waveform prediction device 4.

[0036] The waveform prediction unit 6 calculates the cross-sectional area change rate distribution ∂A at the front of train 1. * Based on / ∂Y1, branch-through wave W is determined by the compact Green's function G. 31 ,W 32 waveform p C ([T]),p B This is a means of predicting ([T]). The waveform prediction unit 6 predicts the waveform by convolution integral of the cross-sectional area distribution of the front of the train 1, which is represented as a moving sound source, and the compact Green's function. The waveform prediction unit 6 predicts the branch tunnel passing wave W that occurs in the main tunnel 3A as shown in Figure 3. 31 waveform p C The waveform prediction unit 6 predicts ([T]) using the compact Green's function G shown in Equation 1 below, and predicts the branch tunnel passage wave W that occurs in the main tunnel 3A. 31 waveform p C Predict ([T]).

[0037]

number

[0038] The waveform prediction unit 6 predicts the branch tunnel pass-through wave W that occurs in the branch tunnel 3B as shown in Figure 3. 32 waveform p B The waveform prediction unit 6 predicts ([T]) using the compact Green's function G shown in Equation 2 below, and predicts the branch tunnel pass-through wave W generated in branch tunnel 3B. 32 waveform p B Predict ([T]).

[0039]

number

[0040] The data storage unit 7 is a means for storing various data related to the waveform prediction device 4. For example, the data storage unit 7 stores the branch tunnel pass-through wave W input from the input unit 5.31 ,W 32 waveform p of C ([T]), p B various input data necessary for predicting ([T]), p and the waveform prediction unit 6 predicts the branch tunnel passing wave W 31 ,W 32 waveform p of C ([T]), p B formula data regarding numbers 1 and 2 used for predicting ([T]) and the waveform prediction unit 6 estimates the branch tunnel passing wave W 31 ,W 32 waveform p of C ([T]), p B It is a storage device that stores waveform data regarding ([T]), p, etc.

[0041] The waveform prediction program storage unit 8 stores the waveform prediction program for predicting the branch tunnel passing wave W generated when the train 1 passes through the branch unit 3C 31 ,W 32 waveform p of C ([T]), p B ([T]). It is a means for storing the waveform prediction program. The waveform prediction program storage unit 8 is a storage device that stores this waveform prediction program read from, for example, an information recording medium that records the waveform prediction program or a telecommunication line that transmits the waveform prediction program. [[ID=Z36]]

[0042] The display unit 9 is a part that displays various data regarding the waveform prediction device 4. The display unit 9, for example, displays the branch tunnel passing wave W 31 ,W 32 waveform p of C ([T]), p B various input data necessary for predicting ([T]), p, the branch tunnel passing wave W 31 ,W 32 waveform p of C ([T]), p B formula data regarding numbers 1 and 2 used for predicting ([T]) and the branch tunnel passing wave W 31 ,W 32 waveform p of C ([T]), p B ([T]). It is a display device that displays waveform data on the screen.

[0043] The control unit 10 is a central processing unit (CPU) that controls various operations related to the waveform prediction device 4. The control unit 10 reads out the waveform prediction program from the waveform prediction program storage unit 8 and executes a series of waveform prediction processes. The control unit 10, for example, stores the input data input from the input unit 5 in the data storage unit 7, reads out the input data from the data storage unit 7 and outputs it to the waveform prediction unit 6, and the branch tunnel passing wave W 31 ,W 32 of the waveform p C ([T]), p B ([T]) to the waveform prediction unit 6, or commands the display unit 9 to display various data. The control unit 10 is communicably connected to the input unit 5, the waveform prediction unit 6, the data storage unit 7, the waveform prediction program storage unit 8, and the display unit 9.

[0044] Next, the operation of the waveform prediction device for the branch tunnel passing wave according to the first embodiment of this invention will be described. Hereinafter, the operation of the control unit 10 shown in FIG. 1 will be mainly described. In step (hereinafter referred to as S) 100, the control unit 10 reads the waveform prediction program. The control unit 10 reads the waveform prediction program from the waveform prediction program storage unit 8 and executes a series of waveform prediction processes according to the waveform prediction program.

[0045] In S200, the control unit 10 commands the waveform prediction unit 6 to predict the branch tunnel passing wave W 31 ,W 32 . Based on the cross-sectional area change rate distribution ∂A * / ∂Y1 of the front part of the train 1, the waveform p 31 ,W 32 of the branch tunnel passing wave W C ([T]), p B ([T]) is predicted by the waveform prediction unit 6. The waveform p 31 of the branch tunnel passing wave W C ([T]) in the main tunnel 3A of the tunnel is predicted by the waveform prediction unit 6 according to Equation 1, and the waveform p 32 of the branch tunnel passing wave W B ([T]) in the branch tunnel 3B is predicted by the waveform prediction unit 6 according to Equation 2.

[0046] Next, a method for predicting the waveform of a branch tunnel-passed wave according to the first embodiment of this invention will be described. The waveform prediction method #100 shown in Figure 6 is the branch tunnel passage wave W generated when train 1 passes through branch section 3C. 31 ,W 32 waveform p C ([T]),p B This is a method for predicting ([T]). Waveform prediction method #100 includes a data input step #110 and a waveform prediction step #120.

[0047] Data input process #110 is branch tunnel pass-through wave W 31 ,W 32 waveform p C ([T]),p B This is the process of inputting the data necessary for predicting ([T]). In data input process #110, the cross-sectional area distribution A of train 1 shown in equations 1 and 2, the speed U of train 1, and branch tunnel passing wave W such as the tunnel cross-sectional area A0 are entered. 31 ,W 32 waveform p C ([T]),p B The parameters necessary for predicting ([T]) are input as input data.

[0048] Waveform prediction step #120 is the distribution of the rate of change of the cross-sectional area at the front of train 1 ∂A * Based on / ∂Y1, the branch-through wave W is determined by the compact Green's function. 31 ,W 32 waveform p C ([T]),p B This is the process of predicting ([T]). In waveform prediction process #120, the branch tunnel passing wave W that occurs in the main tunnel 3A shown in Figure 3 is predicted. 31 waveform p C ([T]) is predicted by equation 1. In waveform prediction step #120, branch tunnel pass-through wave W generated in branch tunnel 3B shown in Figure 3 is predicted. 32 waveform p B Predict ([T]) using the number 2.

[0049] In Figure 7(A), the vertical axis represents the pressure (normalized pressure) of the wave passing through the branch tunnel, and the horizontal axis represents time. In Figure 7(B), the vertical axis represents the pressure gradient (normalized pressure gradient), and the horizontal axis represents time. In Figure 7, the solid line represents the analysis results using the compact Green's function (CG), and the dotted line represents the analysis results using computational fluid dynamics (CFD), with the branch tunnel-to-main tunnel cross-sectional area ratio R b =0.5 and R b The results of the CG analysis and CFD calculation for the case where =0.25 are shown. As shown in Figure 7, the analysis results using the compact Green's function according to the first embodiment and the analysis results using CFD agree well, confirming that the compact Green's function according to the first embodiment can make predictions equivalent to those of CFD.

[0050] The waveform prediction method for branch tunnel-passed waves, the waveform prediction device, and the waveform prediction program for branch tunnel-passed waves according to the embodiment of this invention have the following effects. (1) In this embodiment, the distribution of the rate of change of the cross-sectional area of ​​the front of the train 1 is ∂A * Based on / ∂Y1, the branch-through wave W is determined by the compact Green's function. 31 ,W 32 waveform p C ([T]),p B ([T]) is predicted. Therefore, the compact Green's function, which was previously only given for limited cases such as tunnel portals, external flow, and cavities, can now be newly provided as a compact Green's function for the main tunnel 3A and branch tunnel 3B. As a result, the branch tunnel passing wave W 31 ,W 32 waveform p C ([T]),p BThis enables accurate and rapid prediction of ([T]), reducing resources such as time and cost. For example, it eliminates the need to prepare models for model experiments or create numerical simulation programs, allowing for low-cost and rapid prediction. Furthermore, it can predict branch tunnel waves W generated in urban tunnels and deep tunnels where large branch tunnels 3B with a larger diameter than the main tunnel 3A are laid. 31 ,W 32 Radiation wave W passing through the main tunnel 41 and radiation waves W passing through branch tunnels 42 The waveform can be easily predicted.

[0051] (2) In this embodiment, branch tunnel passing wave W generated in the main tunnel 3A 31 waveform p C ([T]) is predicted. Therefore, branch tunnel passing wave W that occurs in the main tunnel 3A is predicted. 31 Using the compact Green's function to find the wave W passing through the branch tunnel in the main tunnel 3A, 31 waveform p C Distribution of the rate of change of cross-sectional area of ​​([T]) and train 1 ∂A * To clarify the relationship with / ∂Y1, and to clarify the wave W passing through the branch tunnel in the main tunnel 3A. 31 waveform p C ([T]) can be predicted quickly and accurately.

[0052] (3) In this embodiment, branch tunnel pass-through wave W generated in branch tunnel 3B 32 waveform p B ([T]) is predicted. Therefore, branch tunnel pass-through wave W generated in branch tunnel 3B. 32 Using the compact Green's function to find the branch tunnel pass-through wave W in branch tunnel 3B, 32 waveform p B Distribution of the rate of change of cross-sectional area of ​​([T]) and train 1 ∂A * Clarify the relationship with / ∂Y1, and the branch tunnel passing wave W in branch tunnel 3B. 32 waveform p B ([T]) can be predicted quickly and accurately.

[0053] (Second Embodiment) In the following, parts identical to those shown in Figures 1 to 4 are denoted by the same reference numerals, and detailed explanations are omitted. This second embodiment involves the branch tunnel pass-through wave W generated in the branch tunnel 3B shown in Figure 8. 32 Multiple reflection components W at the entrance 3c and branch section 3C of branch tunnel 3B. 51 ,W 52 However, the branch tunnel passing wave W is generated in the main tunnel 3A. 31 When superimposed on the above, and when branch tunnel pass-through wave W is generated in branch tunnel 3B 32 This embodiment takes into account the case where it is superimposed on the other.

[0054] The multiple reflection component W shown in Figure 8 51 This is the branch tunnel pass-through wave W that occurred in branch tunnel 3B. 32 This is a multiple-reflected wave that undergoes multiple reflections at the mine entrance (end of branch shaft) 3c and the branch section 3C and propagates within the branch shaft 3B. Multiple reflection component W 52 This is the branch tunnel pass-through wave W that occurred in branch tunnel 3B. 32 This is a multiple reflected wave that undergoes multiple reflections at the tunnel entrance 3c and the branch section 3C, and propagates from within the branch tunnel 3B into the main tunnel 3A.

[0055] The waveform prediction unit 6 shown in Figure 1 predicts the branch tunnel pass-through wave W generated in the branch tunnel 3B. 32 Multiple reflection components W at the tunnel entrance 3c and branch section 3C 51 This is the branch tunnel passing wave W that occurs in the main tunnel 3A. 31 The waveform prediction unit 6 predicts the waveform that will be added to the main tunnel 3A, as shown in Figure 8, and the branch tunnel passage wave shown in Equation 1 that will occur in the main tunnel 3A. 31 waveform p C ([T]) shows branch tunnel pass-through wave W, as shown in number 2, occurring in branch tunnel 3B. 32 waveform p B Multiple reflection component W of ([T]) 51 Predict the waveform with added information.

[0056] The waveform prediction unit 6 shown in Figure 1 predicts the branch tunnel pass-through wave W generated in the branch tunnel 3B. 32 Multiple reflection components W at the tunnel entrance 3c and branch section 3C 51 The branch tunnel passing wave W generated in branch tunnel 3B. 32The waveform prediction unit 6 predicts the applied waveform W shown in number 2 that occurs in branch tunnel 3B, as shown in Figure 8. 32 waveform p B ([T]) shows branch tunnel pass-through wave W, as shown in number 2, occurring in branch tunnel 3B. 32 waveform p B Multiple reflection component W of ([T]) 52 Predict the waveform with added information.

[0057] The waveform prediction method for branch shaft waves, the waveform prediction device, and the waveform prediction program for branch shaft waves according to the second embodiment of this invention have the following effects in addition to the effects of the first embodiment. (1) In this second embodiment, branch tunnel pass-through wave W generated in branch tunnel 3B 31 Multiple reflection components W at the tunnel entrance 3c and branch section 3C 51 This is the branch tunnel passing wave W that occurs in the main tunnel 3A. 31 The waveform added to this is predicted. Therefore, multiple reflection components W generated in branch tunnel 3B are predicted. 52 Considering this, the wave passing through the branch tunnel within the main tunnel 3A is W 31 waveform p C ([T]) can be predicted.

[0058] (2) In this second embodiment, branch tunnel pass-through wave W generated in branch tunnel 3B 31 Multiple reflection components W at the tunnel entrance 3c and branch section 3C 52 The branch tunnel passing wave W generated in branch tunnel 3B. 31 The waveform added to this is predicted. Therefore, multiple reflection components W generated in branch tunnel 3B are predicted. 52 Taking this into consideration, branch tunnel pass-through wave W in branch tunnel 3B 32 waveform p B ([T]) can be predicted.

[0059] This invention is not limited to the embodiments described above, and various modifications or changes are possible as described below, and these also fall within the scope of this invention. In this embodiment, the case where track 2 is double-tracked was used as an example, but this invention can also be applied to cases where track 2 is single-tracked or quadruple-tracked. Furthermore, in this embodiment, the case where branch shaft 3B is a vertical shaft was used as an example, but this invention is not limited to vertical shafts. For example, this invention can also be applied to horizontal shafts dug horizontally to the main tunnel shaft 3A or inclined shafts dug diagonally to the main tunnel shaft 3A, which are used for transporting workers or materials during the construction of tunnel 3. [Explanation of Symbols]

[0060] 1 Train (mobile) 2 tracks 3 Tunnel 3A Main Tunnel (Main Pit) 3B Branch pit 3C branching point 3a,3b,3c well mouth 4. Waveform prediction device 5 Input section 6. Waveform prediction section 7. Data Storage Unit 8. Waveform prediction program storage unit 9 Display section 10 Control Unit W1 tunnel compression wave W 21 Micro-pressure waves (micro-pressure waves in the main mine) W 22 Micro-pressure waves (branch pit micro-pressure waves) W 31 Branch tunnel-passed wave (branch tunnel-passed compressed wave) W 32 Branch tunnel passing wave (branch tunnel passing expansion wave) W 41 Radiation waves passing through the main mine W 42 Radiation wave passing through branch shaft W 51 ,W 52 Multiple reflection components (multiple reflection waves) ∂A * / ∂Y1 Cross-sectional area change rate distribution p C ([T]) Branch tunnel passing wave W generated in the main tunnel 3A 31 waveform p B ([T]) Branch tunnel pass-through wave W generated in branch tunnel 3B 32 waveform

Claims

1. A method for predicting the waveform of a branch tunnel passage wave, which is generated when a moving object moves through the main tunnel, at a branch point where a branch tunnel branches off from the main tunnel, The process includes a waveform prediction step that predicts the waveform of the branch tunnel-passing wave using a compact Green's function based on the distribution of the rate of change of the cross-sectional area of ​​the front part of the moving body. A waveform prediction method for branch tunnel-passed waves characterized by the following.

2. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 1, The waveform prediction step includes a step of predicting the waveform of the wave passing through the branch tunnel that occurs in the main tunnel. A waveform prediction method for branch tunnel-passed waves characterized by the following.

3. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 2, The waveform prediction step includes a step of predicting the waveform of branch tunnel passage waves generated in the main tunnel using the compact Green's function G shown in the following formula. A waveform prediction method for branch tunnel-passed waves characterized by the following.

4. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 1, The waveform prediction step includes a step of predicting the waveform of the branch tunnel passing wave generated in the branch tunnel as the waveform of the branch tunnel passing wave. A waveform prediction method for branch tunnel-passed waves characterized by the following.

5. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 4, The waveform prediction step includes a step of predicting the waveform of the branch shaft passing wave generated in the branch shaft using the compact Green's function G shown in the following formula. A waveform prediction method for branch tunnel-passed waves characterized by the following.

6. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 1, The waveform prediction step includes a step of predicting the waveform of the branch tunnel by adding the multiple reflection components of the branch tunnel passage wave generated in the branch tunnel at the end of the branch tunnel and the branch section to the branch tunnel passage wave generated in the main tunnel, A waveform prediction method for branch tunnel-passed waves characterized by the following.

7. In the method for predicting the waveform of a branch tunnel-passed wave according to claim 1, The waveform prediction step includes a step of predicting the waveform of the branch tunnel by adding the multiple reflection components of the branch tunnel passage wave generated in the branch tunnel at the end of the branch tunnel and the branch section to the branch tunnel passage wave generated in the branch tunnel, A waveform prediction method for branch tunnel-passed waves characterized by the following.

8. A waveform prediction device for branch tunnel passage waves that predicts the waveform of branch tunnel passage waves generated when a moving object moves through the main tunnel of a tunnel at a branch point where a branch tunnel branches off from the main tunnel, The system includes a waveform prediction unit that predicts the waveform of the branch tunnel-passing wave using a compact Green's function based on the distribution of the rate of change of the cross-sectional area of ​​the front part of the moving body. A waveform prediction device for branch tunnel-passed waves, characterized by the following:

9. A waveform prediction program for branch tunnel passage waves, which predicts the waveform of branch tunnel passage waves generated when a moving object moves through the main tunnel of a tunnel at a branch point where a branch tunnel branches off from the main tunnel, The computer is instructed to perform a waveform prediction procedure that predicts the waveform of the branch tunnel-passing wave using a compact Green's function based on the distribution of the rate of change of the cross-sectional area of ​​the front part of the moving body. A waveform prediction program for branch tunnel-passed waves characterized by the following:

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