Multi-shaft ultra-long high-ground-temperature railway tunnel fan optimization configuration method

Abstract

The invention discloses a multi-shaft ultra-long high-ground-temperature railway tunnel fan optimization configuration method which comprises the following steps: acquiring basic calculation parameters of a multi-shaft ultra-long railway tunnel, and further establishing a physical model; based on the physical model, establishing a multi-shaft super-long railway tunnel surrounding rock-lining-airflow-ventilation network heat, humidity and flow coupling mathematical model, and performing rapid solving; on the basis of a mathematical equation solving result of the mathematical model, the feasibility of temperature control and humidity control under the working condition of only natural ventilation is judged, for the working condition that only natural ventilation is not feasible, a ventilation fan decision-making variable is selected, and optimization design is conducted on fan configuration with ventilation energy consumption as the target and the maximum temperature and humidity along the way as the constraint; according to the method, optimization of the fan configuration scheme under the multi-factor working condition is achieved.

Description

Technical Field

[0001] This invention relates to the field of railway tunnel technology, and in particular to a method for optimizing the configuration of fans in multi-shaft ultra-long high-temperature railway tunnels. Background Technology

[0002] Long, deep-buried tunnels are mostly characterized by high ground temperatures, long distances, and multiple working faces. Multiple working faces can be achieved through construction shafts, which can be directly sealed or used as ventilation channels after construction. Long-distance tunnels often employ segmented ventilation, so some shafts can also serve as operational ventilation channels. High ground temperatures and heat hazards are urgent problems to be solved in existing long, deep-buried tunnels. Current tunnel temperature and humidity control technologies mainly focus on the working faces during construction, but some tunnels still experience high temperatures and humidity after completion. The temperature and humidity environment of completed railway tunnels is related to the safety of train operation and maintenance personnel. In engineering, it is generally believed that natural ventilation is enhanced after tunnel completion, and temperature and humidity can be controlled using natural ventilation. For conditions where natural ventilation cannot meet the requirements, mechanical ventilation should be used to enhance temperature and humidity control. However, the optimized design of mechanical ventilation fan configuration presents two main technical challenges:

[0003] 1. Optimized design of fan configuration scheme:

[0004] Existing mechanical ventilation fan optimization configuration technologies fall into two categories: obtaining fan configuration schemes by utilizing the minimum required air volume, and obtaining fan configuration schemes through iteration with a certain control standard as the target.

[0005] (1) Obtaining the fan configuration scheme using minimum required air volume: The existing method for configuring mechanical ventilation fans in tunnels is mainly aimed at pollutant control, that is, after knowing the minimum air volume of the tunnel, the configuration of jet fans and axial fans is solved according to the main tunnel wind pressure balance law. However, it is difficult to accurately calculate the minimum required air volume for mechanical ventilation design based on temperature and humidity control. The existing methods for calculating the minimum required air volume of high ground temperature tunnels based on temperature and humidity control are divided into two categories: (I) Using steady-state calculation method to obtain the residual heat / humidity in the tunnel, and then using the ratio of residual heat / humidity to the temperature / humidity difference at the tunnel inlet and outlet to characterize the minimum required air volume. However, the temperature and humidity in the tunnel and the surrounding rock heat regulation zone are all changing hourly. Therefore, the residual heat in the tunnel is also dynamically changing. In addition, the temperature at the tunnel inlet and outlet is difficult to determine and is also dynamically changing. It is difficult to accurately determine the minimum required air volume directly through this formula. (II) A three-dimensional explicit / implicit difference method is used to solve for the temperature of the surrounding rock wall and the airflow temperature after the preset ventilation time, and then to solve for the heat dissipation of the wall. The ratio of the heat dissipation in the tunnel to the difference between the airflow temperature and the standard temperature is defined as the minimum required air volume. However, this technology sets all temperature standards along the tunnel to 28℃, but the temperature along the tunnel varies significantly due to heat exchange with the surrounding rock. The maximum temperature should be controlled to be less than 28℃. Therefore, this technical solution will underestimate the minimum required air volume. In addition, this technology only focuses on the thermal environment control of the tunnel and does not consider the humid environment of the tunnel.

[0006] (2) Obtaining fan configuration scheme through iteration with a certain control standard as the target: In addition to obtaining fan configuration by using minimum air volume, some technical solutions use control standards as the target and obtain fan configuration through iterative methods. The existing technology proposes a fan configuration method for ventilation system in power tunnels. This technology obtains the fan start and configuration scheme through iteration with insulating gas concentration control as the target. However, this technology uses the calculation results of three-dimensional CFD model for determination and iteration. For long-distance, multi-shaft tunnels, the model size is large and the number of grids is large, which requires a lot of computing resources and computing time. This technology only gives the minimum fan configuration amount under a specific fan location scheme, and cannot realize the horizontal comparison and optimization of the configuration amount under multiple shaft conditions and multiple fan locations. In addition, this technology focuses on pollutants and does not focus on temperature and humidity control.

[0007] In summary, the above optimization methods do not propose a natural ventilation optimization method based on temperature and humidity control requirements, nor do they propose a horizontal comparison and optimization method for fan configurations involving multiple factors (multiple fan locations in complex ventilation networks) based on the effective utilization of natural wind. 2. Temperature and humidity calculation method for multi-shaft ultra-long railway high-ground-temperature tunnels:

[0008] To optimize the configuration of mechanical fans, the calculation of temperature and humidity along the route is required. Currently, there is no analytical calculation method that couples the heat and humidity transfer of the surrounding rock with the heat and humidity transfer of the airflow. The characteristics of long-distance tunnels with multiple ventilation shafts in high-temperature tunnels pose two major requirements for numerical calculation methods: (1) Improve the numerical calculation model to reduce the computational resources and time requirements of the model for long distances; (2) The model needs to be able to calculate various segmented longitudinal ventilation under the condition of multiple ventilation shafts. Existing relevant calculation methods include the following:

[0009] (1) CFD Computation Model: Existing tunnel flow field analysis methods mostly adopt CFD modeling. Its main process includes geometric parameter modeling, determining boundary conditions, calculating the number of model grids, selecting a turbulence model, solving equations, and analyzing results. However, for multi-shaft ultra-long railway tunnels, the computational resources and time required to establish a three-dimensional CFD model are enormous.

[0010] (2) Ventilation Network Model: The ventilation network model calculates the multi-channel ventilation distribution law using lumped parameters based on the flow balance law and the pressure balance law. This model can take into account a series of source terms such as natural wind, piston wind from train movement, and mechanical ventilation. The temperature and humidity distribution in the cross-section of railway tunnels is relatively uniform. Most existing studies have simplified the temperature and humidity of the cross-section to a single point. Therefore, the heat and humidity coupling transfer of humid air can be modeled in one dimension. Most studies only consider radial heat and humidity transfer from the surrounding rock. Therefore, the heat and humidity coupling transfer from the surrounding rock can also be simplified to a one-dimensional model. However, existing ventilation network technologies do not consider the impact of the heat and humidity accumulation of the surrounding rock on thermal pressure natural ventilation, nor do they consider the impact of non-uniform temperature distribution along the route on thermal pressure.

[0011] In summary, existing fan optimization configuration technologies mainly focus on pollutant control and lack fan configuration optimization design technologies based on temperature and humidity control. Existing fan optimization configuration processes do not consider the impact of natural ventilation; on the one hand, they do not propose the feasibility of temperature and humidity control under natural ventilation conditions alone; on the other hand, they do not propose mechanical fan optimization design methods based on the effective utilization of natural wind. Existing fan optimization configuration processes only optimize the number / specification of fans at specific fan locations, without involving horizontal comparison and optimization under multi-factor conditions (multiple fan location settings in complex ventilation networks). The calculation models in existing fan optimization configuration technologies mostly use CFD models, resulting in huge meshes, long calculation times, and extremely high computer requirements. Existing ventilation network models used for tunnel ventilation do not consider dynamic natural wind pressure (including thermal pressure and ultra-clean pressure difference), and they do not consider the impact of the accumulation of heat and moisture in the surrounding rock on thermal pressure natural ventilation. Existing ventilation network models used for tunnel ventilation all consider the temperature inside the tunnel as a constant value and calculate thermal pressure based on its temperature difference with the atmosphere, without considering the impact of non-uniform temperature distribution along the tunnel on thermal pressure. Summary of the Invention

[0012] To address the problems existing in the prior art, the purpose of this invention is to provide an optimized configuration method for ventilation fans in multi-shaft ultra-long high-temperature railway tunnels. This invention achieves optimization of the ventilation fan configuration scheme under multiple operating conditions.

[0013] To achieve the above objectives, the technical solution adopted by this invention is: a method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels, comprising the following steps:

[0014] Step 1: Obtain the basic calculation parameters of multi-shaft ultra-long railway tunnels, and then establish a physical model;

[0015] Step 2: Based on the physical model, establish a mathematical model of the thermal, humidity, and flow coupling of the surrounding rock-lining-airflow-ventilation network of multi-shaft ultra-long railway tunnels, and solve it quickly;

[0016] Step 3: Based on the solution results of the mathematical equations of the mathematical model, determine the feasibility of temperature and humidity control under natural ventilation only. For conditions where natural ventilation is not feasible, select the decision variable of the ventilation fan, take ventilation energy consumption as the target, and take the highest temperature and humidity along the path as constraints to optimize the fan configuration.

[0017] As a further improvement to the present invention, step 1 is specifically as follows:

[0018] The tunnel structure cross-sectional dimensions, length, elevation, meteorological parameters at the tunnel entrance and exit, thermal and moisture properties of the surrounding rock, and initial temperature and humidity distribution are obtained. The configuration of the adit tunnels and the ventilation network are investigated, and then a physical model is established.

[0019] As a further improvement of the present invention, the meteorological parameters at the tunnel entrance and exit include hourly temperature, hourly humidity, atmospheric pressure, hourly wind speed, and wind direction.

[0020] As a further improvement of the present invention, in step 2, the mathematical model of the hot and humid environment of multi-shaft ultra-long railway tunnel includes a ventilation network model, a one-dimensional humid air hot and humid coupling model, and a one-dimensional surrounding rock hot and humid coupling model coupled together and solved in an integrated and rapid manner.

[0021] As a further improvement of the present invention, the coupled calculation method of the ventilation network model, the one-dimensional humid air thermal-humidity coupling model, and the one-dimensional surrounding rock thermal-humidity coupling model is as follows: the wind speed obtained from the ventilation network model provides wind speed conditions for the humid air thermal-humidity coupling model, and provides calculation conditions for the convective heat transfer coefficient and convective mass transfer coefficient for the surrounding rock thermal-humidity coupling model; the one-dimensional humid air thermal-humidity coupling model provides boundary conditions for the surrounding rock thermal-humidity coupling model, and provides calculation conditions for the thermal pressure in the ventilation network model; the one-dimensional surrounding rock thermal-humidity coupling model provides boundary conditions for the humid air thermal-humidity coupling model; the coupled calculation method solves the ventilation network model under the dynamic natural wind pressure and the cumulative effect of surrounding rock thermal-humidity, wherein the thermal pressure calculation no longer considers the temperature inside the tunnel as a constant value, but calculates the influence of the non-uniform temperature distribution along the tunnel on the thermal pressure;

[0022] The ventilation network model includes node airflow balance equations and loop pressure balance equations. The pressure balance equations include natural wind pressure, traffic wind pressure, and mechanical wind pressure. Dynamic natural wind pressure is related to the thermal pressure due to the cumulative effect of heat and moisture accumulation in the surrounding rock. The thermal pressure calculation considers the non-uniform temperature distribution along the path and the dynamic temperature variation throughout the year. Wherein:

[0023] In the nodal airflow balance equation, the algebraic sum of the inflow and outflow airflow at the nodes per unit time is zero:

[0024]

[0025] In the formula, M j For mass flow rate, a ijThe branch air volume symbol is defined as follows:

[0026]

[0027] In the aforementioned loop pressure balance equation, the wind pressure balance law states that the driving force of the airflow in the loop along the assumed direction is equal to the resistance:

[0028]

[0029] In the formula, c zj The symbol for the direction of branched flow is defined as follows:

[0030]

[0031] In the formula, ΔP prower,j For dynamic wind pressure, Pa; ΔP resistance,j The drag pressure is expressed in Pa.

[0032] The natural wind pressure includes thermal pressure and excess static pressure difference. The calculation method for thermal pressure is as follows:

[0033] P r =∫(ρ w -ρ)g dz

[0034] In the formula, ρ is the air density along the path, ρ w Let dz be the density of the outside air, g be the acceleration due to gravity, and dz be the infinitesimal increment of the elevation element.

[0035] The hot pressing calculation process is as follows: the process is discretized into n elements, the air density at the temperature of each element is substituted into the formula and the product is calculated over the n elements to obtain the hot pressing.

[0036] The method for calculating excess static pressure is as follows:

[0037]

[0038] In the formula, N + For the frequencies of each wind direction consistent with the assumed wind direction, N - For the frequencies of wind directions opposite to the assumed direction, K is the correction factor for the angle between the wind direction and the axis of the opening;

[0039] Using Bernoulli's equation of airflow relative to the train, the pressure difference between the front and rear of the train in a tunnel with side passages such as shafts is obtained, and the calculation method for traffic wind pressure is as follows:

[0040]

[0041] In the formula, P t Where K is the piston wind pressure, K is the piston wind action coefficient, V0 is the train speed, and V t Piston air velocity;

[0042] The specific method for establishing a one-dimensional humid air thermal-humidity coupling model is as follows:

[0043] The flow heat transfer equation is as follows:

[0044]

[0045] In the formula, v is the airflow velocity, λ is the thermal conductivity, and Q0 is the heat source.

[0046] The moisture transfer equation is as follows:

[0047]

[0048] In the formula, c g M represents the water vapor concentration in the airflow. g Let D be the relative molecular mass, D be the diffusion coefficient, and G0 be the moisture source.

[0049] The specific method for establishing a one-dimensional surrounding rock thermal-moisture coupling transfer model is as follows:

[0050] The moisture transfer equation simplifies to the following form:

[0051]

[0052] The governing equations for the thermal balance of the surrounding rock are rearranged as follows:

[0053]

[0054] In the formula, T represents the relative humidity within the pores of the porous medium, and T represents the temperature within the pores of the porous medium. Let D be the mass transfer coefficient caused by the relative humidity gradient. T Let λ be the mass transfer coefficient caused by the temperature gradient. eff The equivalent thermal conductivity is The heat transfer coefficient is caused by the relative humidity gradient.

[0055] As a further improvement of the present invention, step 3 specifically includes the following steps:

[0056] Step 3.1: Analyze the feasibility of temperature and humidity control under natural ventilation only. For working conditions where natural ventilation is feasible only, propose a ventilation tunnel opening scheme.

[0057] Step 3.2: For working conditions where natural ventilation alone is not feasible, the ventilation method, fan location, and fan air volume / pressure are included in the scope of decision variables. Based on the analysis of influencing factors, the fan decision variables for the optimal design are selected.

[0058] Step 3.3: Using the wind turbine decision variables as variables, establish a Box-Behnken test scheme based on the response surface methodology; fit the highest temperature and highest humidity along each section of the tunnel using the calculation results to further improve the calculation speed for subsequent optimization design;

[0059] Step 3.4: Taking ventilation energy consumption as the target, fan decision variables as input values, and the highest temperature and humidity along the main tunnel being lower than the standard requirements as the target, the genetic algorithm is used to optimize the design and obtain the fan configuration result under the optimal ventilation energy consumption. This achieves a comprehensive horizontal comparison and optimization of multiple factors such as ventilation mode, fan location, fan air volume / pressure.

[0060] As a further improvement of the present invention, the Box-Behnken test scheme design method in step 3.3 is as follows:

[0061] Ventilation methods are categorized into full longitudinal ventilation, branch tunnel exhaust longitudinal ventilation, branch tunnel supply longitudinal ventilation, and branch tunnel supply and exhaust longitudinal ventilation. Full longitudinal ventilation has only one influencing factor: the air pressure of the jet fans along the tunnel, therefore no Box-Behnken test is required. Branch tunnel exhaust / supply longitudinal ventilation includes three factors: the supply and exhaust air volume of the branch tunnel and the air pressure of the jet fans in the two sections of the main tunnel, requiring a three-factor Box-Behnken test. Branch tunnel supply and exhaust longitudinal ventilation includes five factors: the supply air volume of the branch tunnel, the exhaust air volume of the branch tunnel, and the air pressure of the jet fans in the three sections of the main tunnel, requiring a five-factor Box-Behnken test.

[0062] As a further improvement to the present invention, the selection principles for the maximum and minimum values ​​in the Box-Behnken test design method are as follows:

[0063] The principle for selecting the minimum supply / exhaust air volume of the branch tunnel is: the natural supply / exhaust air volume of the branch tunnel under natural ventilation conditions only;

[0064] The principle for selecting the maximum supply / exhaust air volume of the branch tunnel is: the supply / exhaust air volume of the branch tunnel that can meet the temperature and humidity control requirements of the tunnel without considering the operating conditions of the jet fan in the main tunnel;

[0065] The principle for selecting the minimum air pressure of the main tunnel jet fan is: no jet fan is installed, that is, the air pressure of the jet fan is 0.

[0066] The principle for selecting the maximum air pressure of the jet fan in the main tunnel is: the air pressure of the jet fan that can meet the temperature and humidity control requirements of the tunnel under the condition of full longitudinal ventilation.

[0067] As a further improvement of the present invention, the expression for the single-objective optimization genetic algorithm in step 3.4 is as follows:

[0068] The optimization target is wind turbine energy consumption:

[0069] E = (Maxial +M jet )*24*365

[0070] In the formula, E represents the energy consumption of the wind turbine, and M represents the energy consumption of the wind turbine. axial For the energy consumption of axial flow fans, M jet Energy consumption of the jet fan.

[0071] The constraints are:

[0072]

[0073] This invention considers the interaction between tunnel temperature and humidity and tunnel ventilation, and proposes an optimized design technology for fan configuration based on temperature and humidity control; it enables feasibility assessment of temperature and humidity control under natural ventilation conditions alone, and proposes an optimized configuration method for mechanical fans based on the effective utilization of natural wind for conditions where natural ventilation alone is not feasible; it enables horizontal comparison and optimization of fan configuration schemes under multi-factor conditions (multiple fan location settings in complex ventilation networks); it reduces the calculation time for ventilation distribution and tunnel thermal and humidity environment in multi-shaft ultra-long railway tunnels, reduces the demand for computing resources, and enables rapid calculation; it enables dynamic calculation of the effect of natural wind on the thermal and humidity environment of tunnels, and the calculation of the effect of the cumulative effect of thermal and humidity of surrounding rock on the thermal and humidity environment of multi-shaft ultra-long railway tunnels in the medium and long term; it further refines the thermal pressure calculation in dynamic natural ventilation, no longer simplifying the temperature inside the tunnel to a single point, but considering the dynamic thermal pressure change law under the non-uniform temperature distribution along the tunnel.

[0074] The beneficial effects of this invention are:

[0075] 1. This invention calculates the temperature and humidity distribution along the tunnel using a model and fits it into a fast calculation equation. With the highest temperature and humidity along the main tunnel as a constraint and the fan energy consumption as the target, the fan configuration is continuously iterated and optimized. Thus, a tunnel fan configuration optimization design process based on temperature and humidity control is proposed, which solves the problem that the fan configuration is difficult to solve under the temperature and humidity control target in the existing technology.

[0076] 2. This invention, through the calculation results of temperature and humidity inside the tunnel under a natural ventilation network, realizes the feasibility determination of temperature and humidity control under natural ventilation only, and proposes an optimal auxiliary air duct opening strategy for natural ventilation only; for conditions where natural ventilation alone is not feasible, it proposes an optimized configuration method for mechanical fans based on the effective utilization of natural wind. This solves the problem that existing technologies do not fully consider the role of natural wind.

[0077] 3. This invention incorporates ventilation method, fan type, and fan model into the scope of investigation, selects decision variables for optimization design based on influencing factor analysis, and performs optimization design through genetic algorithm to achieve horizontal comparison and optimization of fan configuration schemes under multi-factor operating conditions (multiple fan location settings in complex ventilation networks), solving the problem in the prior art that only optimizes the number / specification of fans under specific fan locations;

[0078] 4. This invention employs a method that couples a one-dimensional humid air heat and moisture coupling transfer model, a one-dimensional surrounding rock heat and moisture coupling transfer model, and a ventilation network model for solution. This reduces the dimensionality of the model, thereby reducing the demand for computational resources and achieving rapid computation. It solves the problem of high computational resource and time requirements in existing CFD computing technologies.

[0079] 5. The ventilation network model in this invention is coupled with the air heat and moisture coupling transfer model and the surrounding rock heat and moisture coupling transfer model. Dynamic thermal pressure can be obtained and incorporated into the calculation of natural ventilation. Dynamic heat and moisture migration of the surrounding rock wall can also be obtained and incorporated into the calculation of the tunnel heat and moisture environment. This solves the problem that existing ventilation network technologies cannot consider the dynamic natural wind and the medium- and long-term heat and moisture accumulation effect of the surrounding rock.

[0080] 6. The dynamic thermal pressure calculation in this invention takes into account the influence of non-uniform temperature distribution along the tunnel, and solves the problem of simplifying the tunnel temperature to a single point or a linear distribution in existing ventilation network technologies. Attached Figure Description

[0081] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0082] Figure 2 This is a flowchart illustrating the mathematical model for establishing a multi-shaft ultra-long railway tunnel under hot and humid conditions in an embodiment of the present invention;

[0083] Figure 3 This is a flowchart illustrating the optimized design of the fan configuration in an embodiment of the present invention;

[0084] Figure 4 This is a schematic diagram showing the positional relationship between the main tunnel and the auxiliary tunnel that can be used for ventilation in an embodiment of the present invention;

[0085] Figure 5 This is a schematic diagram of the optimal ventilation network scheme for natural ventilation in an embodiment of the present invention;

[0086] Figure 6 This is a schematic diagram of the optimal fan configuration under different insulation layer thicknesses in an embodiment of the present invention. Detailed Implementation

[0087] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0088] Example 1

[0089] like Figure 1 As shown, a method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels includes:

[0090] Step S1: Obtain the basic calculation parameters of the multi-shaft ultra-long railway tunnel to establish a physical model;

[0091] Step S1.1: Obtain basic information such as the cross-sectional dimensions, length, and elevation of the tunnel structure;

[0092] Step S1.2: Investigate the configuration of the branch tunnels and the ventilation network of the multi-shaft ultra-long tunnels in question;

[0093] Step S1.3: Obtain meteorological parameters at the tunnel entrance and exit locations, including hourly temperature, hourly humidity, atmospheric pressure, hourly wind speed, and wind direction;

[0094] Step S1.4: Obtain the thermal and moisture properties of the surrounding rock of the tunnel and the initial temperature and humidity distribution;

[0095] Step S1.5: Establish a physical model of the ventilation network based on the above information.

[0096] Step S2: As Figure 2 As shown, a mathematical model for calculating the thermal and humid environment of a multi-shaft ultra-long railway tunnel is established and solved. The mathematical model for the thermal and humid environment of a multi-shaft ultra-long railway tunnel includes a ventilation network model, a one-dimensional humid air thermal-humid coupling model, and a one-dimensional surrounding rock thermal-humid coupling model, which are coupled together and solved quickly in an integrated manner. The coupling calculation method of the ventilation network model, the one-dimensional humid air thermal-humid coupling model, and the one-dimensional surrounding rock thermal-humid coupling model is as follows: the wind speed obtained from the ventilation network model provides the wind speed condition for the humid air thermal-humid coupling model, and provides the calculation conditions for the convective heat transfer coefficient and convective mass transfer coefficient for the surrounding rock thermal-humid coupling model; the one-dimensional humid air thermal-humid coupling model provides the boundary conditions for the surrounding rock thermal-humid coupling model, and provides the calculation conditions for the thermal pressure in the ventilation network model; the one-dimensional surrounding rock thermal-humid coupling model provides the boundary conditions for the humid air thermal-humid coupling model.

[0097] This coupled calculation method can solve the ventilation network model under the dynamic natural wind pressure and the cumulative effect of heat and moisture in the surrounding rock. In the thermal pressure calculation, the temperature inside the tunnel is no longer considered as a constant value, but the influence of the non-uniform temperature distribution along the tunnel on the thermal pressure is calculated.

[0098] Step S2.1: Establish a mathematical model of the ventilation network, which includes nodal air volume balance equations and loop pressure balance equations. The pressure balance equations can consider natural wind pressure (thermal pressure + excess static pressure difference), traffic wind pressure, and mechanical wind pressure. Dynamic natural wind pressure is related to the thermal pressure due to the heat and moisture accumulation of the surrounding rock. The thermal pressure calculation takes into account the non-uniform temperature distribution along the route and the dynamic temperature change throughout the year.

[0099] (1) Nodal airflow balance equation:

[0100] The algebraic sum of the inflow and outflow volumes at each node is zero per unit time.

[0101]

[0102] a ij —Branch air volume symbol, defined as follows:

[0103]

[0104] M j —Mass flow rate, kg / s;

[0105] (2) Loop pressure balance equation:

[0106] The wind pressure balance law states that the driving force of airflow in a loop along an assumed direction equals the resistance force.

[0107]

[0108] c zj —The symbol for the direction of branch flow is defined as follows:

[0109]

[0110] Natural wind pressure includes thermal pressure and excess static pressure difference, and its calculation rules are as follows:

[0111] Hot pressing calculation formula:

[0112] P r =∫(ρ w -ρ)g dz

[0113] In the formula, ρ is the air density along the path, in kg / m³. 3 ;ρ w —Ambient air density, kg / m³ 3 g—gravitational acceleration; dz—elevation increment, in meters.

[0114] The hot pressing calculation process is as follows: the process is discretized into n elements, the air density at the temperature of each element is substituted into the formula and the product is calculated over the n elements to obtain the hot pressing.

[0115] Formula for calculating excess static pressure:

[0116]

[0117] In the formula, N + For the frequencies of each wind direction consistent with the assumed wind direction, N -For the frequencies of wind directions opposite to the assumed direction, K is the correction factor for the angle between the wind direction and the axis of the opening;

[0118] Formula for calculating traffic wind pressure:

[0119] Using Bernoulli's equation of airflow relative to the train, the pressure difference between the front and rear of the train in a tunnel with side passages such as shafts can be calculated:

[0120]

[0121] Step S2.2: Establish a one-dimensional heat and moisture coupling transfer model for humid air;

[0122] The flow heat transfer equation is as follows:

[0123]

[0124] In the formula, v is the airflow velocity (m / s); λ is the thermal conductivity (W / (m·K)); and Q0 is the heat source (W / m²). 3 The calculation formula is as follows:

[0125]

[0126] The moisture transfer equation is as follows:

[0127]

[0128] In the formula, c g —Water vapor concentration in the airflow, kg / m³ 3 M g —Relative molecular mass; D —Diffusion coefficient, m 2 / s; G0——Wet source, kg / (m³) 3 ·s), its calculation formula is as follows:

[0129]

[0130] Step S2.3: Establish a one-dimensional thermal and moisture coupling transfer model of the surrounding rock;

[0131] The wet transfer control equations can be simplified to the following form:

[0132]

[0133] The governing equations for the thermal balance of the surrounding rock can be simplified as follows:

[0134]

[0135] Step S2.4: Couple the ventilation network model, the humid air thermal-humid coupling model, and the surrounding rock thermal-humid coupling model for integrated solution.

[0136] The wind speed obtained from the ventilation network model provides wind speed conditions for the humid air thermal-humidity coupling model and calculation conditions for the convective heat transfer coefficient and convective mass transfer coefficient for the surrounding rock thermal-humidity coupling model. The humid air thermal-humidity coupling model provides boundary conditions for the surrounding rock thermal-humidity coupling model and calculation conditions for thermal pressure in the ventilation network model; the surrounding rock thermal-humidity coupling model provides boundary conditions for the humid air thermal-humidity coupling model. Calculations are performed using appropriate software.

[0137] Step S3: As Figure 3 As shown, the wind turbine configuration is optimized.

[0138] Step 3.1: Analyze the feasibility of temperature and humidity control under natural ventilation only. For working conditions where natural ventilation is feasible only, propose a ventilation tunnel opening scheme.

[0139] Step S3.2: For operating conditions where natural ventilation alone is not feasible, the ventilation method, fan location, and fan airflow / pressure are included in the scope of decision variables. Based on the influencing factor analysis, the fan decision variables for optimal design are selected:

[0140] Ventilation method, fan type, and fan model can be included in the scope of decision variables. Ventilation methods include full jet, exhaust, supply, supply and exhaust, semi-lateral, and full lateral. Fan types include jet fans and axial fans. The location, model, and quantity of different types of fans can be further set as influencing factors.

[0141] Step S3.3: Establish a rapid calculation equation for the highest temperature and humidity along the route:

[0142] Using wind turbine decision variables as variables, a Box-Behnken test scheme was established based on the response surface methodology. The calculation results were used to fit the highest temperature and humidity of each section along the tunnel, which further improved the calculation speed for subsequent optimization design.

[0143] The Box-Behnken test design method in step 3.3 is as follows:

[0144] Ventilation methods are categorized into full longitudinal ventilation, branch tunnel exhaust longitudinal ventilation, branch tunnel supply longitudinal ventilation, and branch tunnel supply-exhaust longitudinal ventilation. Full longitudinal ventilation has only one influencing factor: the air pressure of the jet fans along the tunnel; therefore, a Box-Behnken test is not required. Branch tunnel exhaust / supply longitudinal ventilation involves three factors: the supply and exhaust air volume of the branch tunnel, and the air pressure of the jet fans in the two sections of the main tunnel, requiring a three-factor Box-Behnken test. Branch tunnel supply-exhaust longitudinal ventilation involves five factors: the supply air volume of the branch tunnel, the exhaust air volume of the branch tunnel, and the air pressure of the jet fans in the three sections of the main tunnel, requiring a five-factor Box-Behnken test. Specific Box-Behnken test tables are shown in Tables 1 and 2, where Min represents the minimum value of the variable, Max represents the maximum value, and Middle represents the median value.

[0145] Table 1 Box-Behnken Test of Longitudinal Ventilation with Outlet / Inlet Shafts

[0146]

[0147]

[0148] Table 2 Box-Behnken Test of Longitudinal Ventilation with Supply and Exhaust in Branch Tunnels

[0149]

[0150]

[0151] The principles for selecting the maximum and minimum values ​​in the Box-Behnken experimental design method are as follows:

[0152] The principle for selecting the minimum supply / exhaust air volume of the branch tunnel is: the natural supply / exhaust air volume of the branch tunnel under natural ventilation conditions only;

[0153] The principle for selecting the maximum supply / exhaust air volume of the branch tunnel is: the supply / exhaust air volume of the branch tunnel that can meet the temperature and humidity control requirements of the tunnel without considering the operating conditions of the jet fan in the main tunnel;

[0154] The principle for selecting the minimum air pressure of the main tunnel jet fan is: no jet fan is installed, that is, the air pressure of the jet fan is 0.

[0155] The principle for selecting the maximum air pressure of the jet fan in the main tunnel is: the air pressure of the jet fan that can meet the temperature and humidity control requirements of the tunnel under the condition of full longitudinal ventilation.

[0156] Step S3.4: Obtain the optimized design results for the location and number of wind turbines:

[0157] With ventilation energy consumption as the target, fan decision variables as the input values, and the goal of ensuring that the highest temperature and humidity along the main tunnel are lower than the standard requirements, a genetic algorithm is used for optimization design to obtain the fan configuration result under the optimal ventilation energy consumption. This achieves a comprehensive horizontal comparison and optimization of multiple factors such as ventilation mode, fan location, fan air volume / pressure.

[0158] The expression for the single-objective optimization genetic algorithm in step 3.4 is as follows:

[0159] The optimization target is wind turbine energy consumption:

[0160] E = (M axial +M jet )*24*365

[0161] In the formula, E represents the energy consumption of the wind turbine, and M represents the energy consumption of the wind turbine. axial For the energy consumption of axial flow fans, M jet Energy consumption of the jet fan.

[0162] The constraints are:

[0163]

[0164] Example 2

[0165] This embodiment explores an optimized fan configuration scheme for different insulation layer thicknesses in a high-temperature tunnel. The specific steps are as follows:

[0166] Step S1: Obtain the basic calculation parameters of the multi-shaft ultra-long railway tunnel to establish a physical model;

[0167] Step S1.1: The tunnel is 31km long and has a cross-sectional diameter of 9.2m;

[0168] Step S1.2: There are three usable ventilation tunnels (1#, 2#, and 3#) in the high-temperature area, such as... Figure 4 As shown;

[0169] Step S1.3: Input the tunnel meteorological parameters as hourly data;

[0170] Step S1.4: Input the initial temperature and humidity of the tunnel surrounding rock as a non-uniform distribution along the tunnel;

[0171] Step S1.5: Establish a physical model of the ventilation network, such as... Figure 5 As shown.

[0172] Step S2: Establish and solve a mathematical model for calculating the thermal and humid environment of a multi-shaft ultra-long railway tunnel;

[0173] Step S2.1: Establish a mathematical model of the ventilation network, which includes nodal airflow balance equations and loop pressure balance equations, taking into account dynamic natural wind pressure and fan wind pressure;

[0174] Step S2.2: Establish a one-dimensional heat and moisture coupling transfer model for humid air;

[0175] Step S2.3: Establish a one-dimensional thermal and moisture coupling transfer model of the surrounding rock;

[0176] Step S2.4: Couple the ventilation network model, the humid air thermal-humid coupling model, and the surrounding rock thermal-humid coupling model for integrated solution.

[0177] The wind speed obtained from the ventilation network model provides wind speed conditions for the humid air thermal-humidity coupling model and provides calculation conditions for the convective heat transfer coefficient and convective mass transfer coefficient for the surrounding rock thermal-humidity coupling model; the humid air thermal-humidity coupling model provides boundary conditions for the surrounding rock thermal-humidity coupling model and provides calculation conditions for thermal pressure in the ventilation network model; the surrounding rock thermal-humidity coupling model provides boundary conditions for the humid air thermal-humidity coupling model.

[0178] Step S3: Optimization design of fan configuration:

[0179] Step 3.1: Analyze the feasibility of temperature and humidity control under natural ventilation only. For working conditions where natural ventilation is feasible only, propose a ventilation tunnel opening scheme.

[0180] Based on the natural ventilation network model, it was found that opening ventilation tunnels #1 and #2 provides the best effect for temperature and humidity control. When the ventilation time is 1 year, natural ventilation cannot achieve temperature and humidity control. When the ventilation time is 2 years, natural ventilation can achieve temperature and humidity control when the insulation layer thickness is greater than 15cm. When the ventilation time is 3 years, natural ventilation can achieve temperature and humidity control when the insulation layer thickness is greater than 10cm.

[0181] Step S3.2: Select the decision variables for the optimization design based on the influencing factor analysis:

[0182] For the working condition in S3.1 where natural ventilation alone is not feasible, mechanical ventilation design is implemented. Assuming a vertical shaft ventilation system, axial flow fans are installed in the supply and exhaust shafts, and jet fans are installed along the main tunnel. The number of jet fans in the three sections along the main tunnel and the air volume of the axial flow fans in the supply and exhaust shafts are considered as sensitive factors. The results show that the supply air volume, exhaust air volume, jet fan location, and number of jet fans can all significantly affect the temperature and humidity control effect along the tunnel and the fan energy consumption. Therefore, all of these factors must be considered in subsequent optimization designs. It is worth noting that the difference in the location of the jet fans between the inlet and outlet sections is not significant; only the difference between the intermediate section and the outlet section (or inlet section) needs to be considered.

[0183] Step S3.3: Establish a rapid calculation equation for the highest temperature and humidity along the route:

[0184] The surface response method fitting results must simultaneously include the optimization design objective and constraints. The variables to be fitted include: maximum temperature / humidity at the inlet section, maximum temperature / humidity at the middle section, maximum temperature / humidity at the outlet section, air pressure of the supply axial flow fan, and air pressure of the exhaust axial flow fan. The Box-Behnken test scheme uses supply air volume, exhaust air volume, the number of jet fans at the inlet section, the number of jet fans at the middle section, and the number of jet fans at the outlet section as variables, establishing 46 sets of calculation conditions. Based on the results of these 46 sets of calculation conditions, the maximum temperature / humidity at the inlet section, the maximum temperature / humidity at the middle section, the maximum temperature / humidity at the outlet section, the air pressure of the supply axial flow fan, and the air pressure of the exhaust axial flow fan are fitted.

[0185] Step S3.4: Obtain the optimized design results for the location and number of wind turbines;

[0186] Using ventilation energy consumption as the target, and considering air supply volume, exhaust volume, number of jet fans in the inlet section, number of jet fans in the middle section, and number of jet fans in the outlet section as decision variables, and setting the tunnel temperature and humidity standards at 28℃ and 80%, with a ventilation time of one year as an example, the calculation results are as follows: When the insulation layer thickness reaches 12cm, the downward trend of total fan energy consumption slows significantly, indicating that increasing the insulation layer thickness at this point is no longer very meaningful. Figure 6 As shown, the main parameters for this working condition are: 224.29m 3 / s air supply volume, 222.05m³ 3 / s exhaust volume, 2 sets of outlet jet fans, 12cm insulation layer thickness. This operating condition represents the optimal fan configuration for a ventilation period of 1 year.

[0187] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels, characterized in that, Includes the following steps: Step 1: Obtain the basic calculation parameters of multi-shaft ultra-long railway tunnels, and then establish a physical model; Step 2: Based on the physical model, establish a mathematical model of the thermal, humidity, and flow coupling of the surrounding rock-lining-airflow-ventilation network of multi-shaft ultra-long railway tunnels, and solve it quickly; Step 3: Based on the solution results of the mathematical equations of the mathematical model, determine the feasibility of temperature and humidity control under natural ventilation only. For conditions where natural ventilation is not feasible, select the decision variable of the ventilation fan, take ventilation energy consumption as the target, and take the highest temperature and humidity along the path as constraints to optimize the fan configuration.

2. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 1, characterized in that, Step 1 is described in detail as follows: The tunnel structure cross-sectional dimensions, length, elevation, meteorological parameters at the tunnel entrance and exit, thermal and moisture properties of the surrounding rock, and initial temperature and humidity distribution are obtained. The configuration of the adit tunnels and the ventilation network are investigated, and then a physical model is established.

3. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 2, characterized in that, The meteorological parameters at the tunnel entrance and exit include hourly temperature, hourly humidity, atmospheric pressure, hourly wind speed, and wind direction.

4. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 1, characterized in that, In step 2, the mathematical model for the hot and humid environment of multi-shaft ultra-long railway tunnels includes a ventilation network model, a one-dimensional humid air thermal-humid coupling model, and a one-dimensional surrounding rock thermal-humid coupling model, which are coupled together and solved quickly in an integrated manner.

5. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 4, characterized in that, The coupled calculation method for the ventilation network model, the one-dimensional humid air thermal-humidity coupling model, and the one-dimensional surrounding rock thermal-humidity coupling model is as follows: the wind speed obtained from the ventilation network model provides wind speed conditions for the humid air thermal-humidity coupling model and provides calculation conditions for the convective heat transfer coefficient and convective mass transfer coefficient for the surrounding rock thermal-humidity coupling model; the one-dimensional humid air thermal-humidity coupling model provides boundary conditions for the surrounding rock thermal-humidity coupling model and provides calculation conditions for the thermal pressure in the ventilation network model; the one-dimensional surrounding rock thermal-humidity coupling model provides boundary conditions for the humid air thermal-humidity coupling model; the coupled calculation method solves the ventilation network model under the dynamic natural wind pressure and the cumulative effect of surrounding rock thermal-humidity, where the thermal pressure calculation no longer considers the temperature inside the tunnel as a constant value, but instead calculates the influence of the non-uniform temperature distribution along the tunnel on the thermal pressure; The ventilation network model includes node airflow balance equations and loop pressure balance equations. The pressure balance equations include natural wind pressure, traffic wind pressure, and mechanical wind pressure. Dynamic natural wind pressure is related to the thermal pressure due to the cumulative effect of heat and moisture accumulation in the surrounding rock. The thermal pressure calculation considers the non-uniform temperature distribution along the path and the dynamic temperature variation throughout the year. Wherein: In the nodal airflow balance equation, the algebraic sum of the inflow and outflow airflow at the nodes per unit time is zero: In the formula, M j For mass flow rate, a ij The branch air volume symbol is defined as follows: In the aforementioned loop pressure balance equation, the wind pressure balance law states that the driving force of the airflow in the loop along the assumed direction is equal to the resistance: In the formula, c zj The symbol for the direction of branched flow is defined as follows: In the formula, ΔP power,j For dynamic wind pressure, Pa; ΔP resistance,j The drag pressure is expressed in Pa. The natural wind pressure includes thermal pressure and excess static pressure difference. The calculation method for thermal pressure is as follows: P r =∫(ρ w -p)g dz In the formula, ρ is the air density along the path, ρ w Let dz be the density of the outside air, g be the acceleration due to gravity, and dz be the infinitesimal increment of the elevation element. The hot pressing calculation process is as follows: the process is discretized into n elements, the air density at the temperature of each element is substituted into the formula and the product is calculated over the n elements to obtain the hot pressing. The method for calculating excess static pressure is as follows: In the formula, N + For the frequencies of each wind direction consistent with the assumed wind direction, N - For the frequencies of wind directions opposite to the assumed direction, K is the correction factor for the angle between the wind direction and the axis of the opening; Using Bernoulli's equation of airflow relative to the train, the pressure difference between the front and rear of the train in a tunnel with side passages such as shafts is obtained, and the calculation method for traffic wind pressure is as follows: In the formula, P t Where K is the piston wind pressure, K is the piston wind action coefficient, V0 is the train speed, and V t Piston air velocity; The specific method for establishing a one-dimensional humid air thermal-humidity coupling model is as follows: The flow heat transfer equation is as follows: In the formula, v is the airflow velocity, λ is the thermal conductivity, and Q0 is the heat source. The moisture transfer equation is as follows: In the formula, c g M represents the water vapor concentration in the airflow. g Let D be the relative molecular mass, D be the diffusion coefficient, and G0 be the moisture source. The specific method for establishing a one-dimensional surrounding rock thermal-moisture coupling transfer model is as follows: The moisture transfer equation simplifies to the following form: The governing equations for the thermal balance of the surrounding rock are rearranged as follows: In the formula, T represents the relative humidity within the pores of the porous medium, and T represents the temperature within the pores of the porous medium. Let D be the mass transfer coefficient caused by the relative humidity gradient. T Let λ be the mass transfer coefficient caused by the temperature gradient. eff The equivalent thermal conductivity is The heat transfer coefficient is caused by the relative humidity gradient.

6. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step 3.1: Analyze the feasibility of temperature and humidity control under natural ventilation only. For working conditions where natural ventilation is feasible only, propose a ventilation tunnel opening scheme. Step 3.2: For working conditions where natural ventilation alone is not feasible, the ventilation method, fan location, and fan air volume / pressure are included in the scope of decision variables. Based on the analysis of influencing factors, the fan decision variables for the optimal design are selected. Step 3.3: Using the wind turbine decision variables as variables, establish a Box-Behnken test scheme based on the response surface methodology; fit the highest temperature and highest humidity along each section of the tunnel using the calculation results to further improve the calculation speed for subsequent optimization design; Step 3.4: Taking ventilation energy consumption as the target, fan decision variables as input values, and the highest temperature and humidity along the main tunnel being lower than the standard requirements as the target, the genetic algorithm is used to optimize the design and obtain the fan configuration result under the optimal ventilation energy consumption. This achieves a comprehensive horizontal comparison and optimization of multiple factors such as ventilation mode, fan location, fan air volume / pressure.

7. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 6, characterized in that, In step 3.3, the Box-Behnken test design method is as follows: Ventilation methods are categorized into full longitudinal ventilation, branch tunnel exhaust longitudinal ventilation, branch tunnel supply longitudinal ventilation, and branch tunnel supply and exhaust longitudinal ventilation. Full longitudinal ventilation has only one influencing factor: the air pressure of the jet fans along the tunnel, therefore no Box-Behnken test is required. Branch tunnel exhaust / supply longitudinal ventilation includes three factors: the supply and exhaust air volume of the branch tunnel and the air pressure of the jet fans in the two sections of the main tunnel, requiring a three-factor Box-Behnken test. Branch tunnel supply and exhaust longitudinal ventilation includes five factors: the supply air volume of the branch tunnel, the exhaust air volume of the branch tunnel, and the air pressure of the jet fans in the three sections of the main tunnel, requiring a five-factor Box-Behnken test.

8. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 7, characterized in that, The selection principles for the maximum and minimum values ​​in the Box-Behnken experimental design method are as follows: The principle for selecting the minimum supply / exhaust air volume of the branch tunnel is: the natural supply / exhaust air volume of the branch tunnel under natural ventilation conditions only; The principle for selecting the maximum supply / exhaust air volume of the branch tunnel is: the supply / exhaust air volume of the branch tunnel that can meet the temperature and humidity control requirements of the tunnel without considering the operating conditions of the jet fan in the main tunnel; The principle for selecting the minimum air pressure of the main tunnel jet fan is: no jet fan is installed, that is, the air pressure of the jet fan is 0. The principle for selecting the maximum air pressure of the jet fan in the main tunnel is: the air pressure of the jet fan that can meet the temperature and humidity control requirements of the tunnel under full longitudinal ventilation conditions.

9. The method for optimizing the configuration of ventilation fans in multi-shaft ultra-long high-temperature railway tunnels according to claim 6, characterized in that, The expression for the single-objective optimization genetic algorithm in step 3.4 is as follows: The optimization target is wind turbine energy consumption: E=(M axial +N jet )*24*365 In the formula, E represents the energy consumption of the wind turbine, and M represents the energy consumption of the wind turbine. axial For the energy consumption of axial flow fans, M jet Energy consumption of the jet fan. The constraints are:

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