Response surface method-based comprehensive pipe gallery jet fan position and angle optimization arrangement method

Abstract

The invention relates to a comprehensive pipe gallery jet fan position and angle optimization arrangement method based on a response surface method, and the method is characterized in that the method sequentially comprises the following steps: response surface experiment design, CFD sample simulation, response surface model construction, multi-objective optimization solution and optimal scheme verification. Wherein the response surface experiment design comprises variable definition, design method and sample size determination; the variables defined by the variables comprise the mounting position of the jet fan in the length direction of the comprehensive pipe gallery, the mounting angles of the jet fan on the upper and lower wall surfaces of the comprehensive pipe gallery, the mounting angles of the jet fan on the left and right wall surfaces of the comprehensive pipe gallery and the ventilation frequency; the design method is a Box-Behnken design method, and the Bhnken design method is a Bhnken. The sample size is four variables and three levels, and totally 27 samples are obtained. According to the method, optimization of airflow organization in the comprehensive pipe gallery can be achieved, the uniformity of temperature and humidity distribution is improved, the risk of local high temperature and high humidity is reduced, the ventilation efficiency is improved, and a ventilation strategy dynamically adjusted according to monitoring data or environment changes is achieved in an auxiliary mode.

Description

Technical Field

[0001] This invention relates to the field of design and installation of jet fans in integrated utility tunnels, and in particular to a method for optimizing the position and angle of jet fans in integrated utility tunnels based on response surface methodology. Background Technology

[0002] Integrated utility tunnels are crucial urban infrastructure, housing various municipal pipelines essential for the city's normal operation. They offer advantages such as eliminating the need for repeated road excavation, improving the urban landscape, conserving valuable underground space, protecting pipeline safety, strong integration capabilities, high efficiency, and convenient maintenance. However, as enclosed underground tunnels, integrated utility tunnels suffer from poor ventilation, leading to excessively high temperatures and humidity, which compromises their safety and reliability. Therefore, there is an urgent need for new and effective ventilation methods to improve the ventilation effect of these tunnels.

[0003] Huang Jian et al. invented a long-distance ventilation section and ventilation method for integrated utility tunnels (patent number CN111810212A) and a long-distance section structure for integrated utility tunnels (patent number CN213654889U). The long-distance ventilation section structure for integrated utility tunnels consists of an air inlet, an air outlet, and a ventilation section. A jet fan is arranged at regular intervals along the longitudinal direction of the integrated utility tunnel, which significantly increases the length of the ventilation section, reduces the number of ventilation outlets, and thus significantly reduces the project cost. The disadvantage of the above technology is that it only considers arranging one jet fan at regular intervals in the integrated utility tunnel, without considering the influence of factors such as the installation angle of the jet fan.

[0004] For the study of the flow field characteristics inside integrated utility tunnels, due to factors such as incomplete coverage of on-site measurement data, high cost and time investment, and operation and maintenance interference, high-precision computational fluid dynamics (CFD) is usually used to simulate and optimize a large number of schemes. The disadvantages of this technology are: the simulation and optimization of a large number of schemes using high-precision CFD is extremely time-consuming, and different utility tunnel structures, pipeline layouts, and environmental conditions vary greatly, thus limiting its engineering application and making it impossible to dynamically adjust the optimization strategy based on monitoring data or environmental changes.

[0005] Previous studies on adding jet fans to integrated utility tunnels have often only considered the spacing of the jet fans, with insufficient research on other influencing factors such as installation angle and specific installation location. In terms of optimization, most studies have only used high-precision computational fluid dynamics, which is time-consuming and lacks research on the inability to dynamically adjust optimization strategies based on monitoring data or environmental changes. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method for optimizing the position and angle of jet fans in integrated utility tunnels based on response surface methodology. This method can optimize the airflow organization in integrated utility tunnels, improve the uniformity of temperature and humidity distribution, reduce the risk of local high temperature and high humidity, improve ventilation efficiency, and assist in realizing a ventilation strategy that is dynamically adjusted according to monitoring data or environmental changes.

[0007] The present invention relates to a method for optimizing the location and angle of jet fans in integrated pipe corridors based on response surface methodology. The method is characterized by comprising the following steps in sequence: response surface experimental design, CFD sample simulation, response surface model construction, multi-objective optimization solution, and optimal solution verification.

[0008] The response surface methodology includes: variable definition, design method, and sample size determination. The variables defined include: the installation position of the jet fan along the length of the utility tunnel, the installation angle of the jet fan on the upper and lower walls of the utility tunnel, the installation angle of the jet fan on the left and right walls of the utility tunnel, and the number of air changes. The design method is the Box-Behnken design method. The sample size is 27 samples with 4 variables and 3 levels.

[0009] The CFD sample simulation involves simulating the sample in the response surface methodology to obtain temperature and humidity evaluation indices. These indices include: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature.

[0010] The response surface model construction includes: establishing a second-order polynomial model and model validation; the model validation is an analysis of variance.

[0011] The multi-objective optimization solution includes: objective function, constraints, and optimization algorithm; the objective function includes: maximization, minimization, target value, and range, which are selected as needed; the constraints are the range of design variables and the response target limit; the optimization algorithm is the gradient descent method.

[0012] The optimal solution is verified by performing CFD simulation on the optimization results obtained from the multi-objective optimization solution.

[0013] The method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology includes two typical operating conditions: natural air intake and mechanical exhaust, and mechanical air intake and mechanical exhaust. The specific process is as follows:

[0014] A total of 27 samples with 4 variables and 3 levels were determined, including the installation position of the jet fan along the length of the integrated utility tunnel, the installation angle of the jet fan on the upper and lower walls of the integrated utility tunnel, the installation angle of the jet fan on the left and right walls of the integrated utility tunnel, and the number of air changes. The samples were simulated by CFD, and the evaluation indicators of temperature and humidity non-uniformity coefficient, non-guarantee rate, and average temperature were calculated and processed.

[0015] A response surface model is constructed based on CFD simulation results and the model is validated. The validated response surface model is then subjected to multi-objective optimization to obtain an optimized solution, which is then verified through CFD simulation.

[0016] Technical effects of the present invention:

[0017] The samples used in the CFD simulation of this invention are obtained through response surface methodology, which ensures the effectiveness of the samples with the same number of samples, reduces the number of inefficient samples in CFD simulation, and reduces time costs. The variable design innovatively introduces the installation position of the jet fan in the integrated utility tunnel and two installation angles on the upper and lower walls and left and right walls for subsequent coupled research, providing a reference scheme for the application of jet fans in integrated utility tunnels and similar underground building spaces. The optimal solution to be verified is obtained through multi-objective optimization using the response surface methodology, which better saves time and reduces costs. The verified optimal solution has better temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature evaluation index, which can optimize airflow organization within the integrated utility tunnel, improve the uniformity of temperature and humidity distribution, reduce the risk of local high temperature and high humidity, improve ventilation efficiency, and assist in realizing a ventilation strategy that is dynamically adjusted based on monitoring data or environmental changes. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention;

[0019] Figure 2 This is a schematic diagram of the installation position of the jet fan of the present invention; in the figure: 1 is the air inlet of the integrated pipe gallery, 2 is the air outlet of the integrated pipe gallery, 3 is the normally closed fire door, 4 is the normally open fire door, and 5, 6, and 7 are the three different installation positions of the jet fan in the integrated pipe gallery with a section length L;

[0020] Figure 3 This is a top view schematic diagram of the installation angle of the jet fan relative to the left and right walls of the integrated pipe gallery of the present invention;

[0021] Figure 4 This is a front view structural schematic diagram of the installation angle of the jet fan relative to the upper and lower walls of the integrated pipe gallery according to the present invention.

[0022] In the diagram: 1. Left wall of the utility tunnel; 2. Right wall of the utility tunnel; 3, 4, and 5. Three installation angles of the jet fan on the left and right walls of the utility tunnel; 6. Upper wall of the utility tunnel; 7. Lower wall of the utility tunnel; 8, 9, and 10. Detailed Implementation

[0023] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0024] The method for optimizing the location and angle of jet fans in integrated pipe corridors based on response surface methodology of this invention includes the following steps: response surface experimental design, CFD sample simulation, response surface model construction, multi-objective optimization solution, and optimal solution verification.

[0025] The response surface methodology includes: variable definition, design method, and sample size determination. The variables defined include: the installation position of the jet fan along the length of the utility tunnel, the installation angle of the jet fan on the upper and lower walls of the utility tunnel, the installation angle of the jet fan on the left and right walls of the utility tunnel, and the number of air changes. The design method is the Box-Behnken design method. The sample size is 27 samples with 4 variables and 3 levels (the 4 variables refer to the installation position of the jet fan along the length of the utility tunnel, the installation angle of the jet fan on the upper and lower walls of the utility tunnel, the installation angle of the jet fan on the left and right walls of the utility tunnel, and the number of air changes; the 3 levels refer to the 4 variables taking 3 different values).

[0026] The CFD sample simulation involves simulating the sample in the response surface methodology to obtain temperature and humidity evaluation indices. These indices include: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature.

[0027] The response surface model construction includes: establishing a second-order polynomial model and model validation; the model validation is an analysis of variance.

[0028] The formula for the second-order polynomial model is as follows:

[0029]

[0030] In the formula: Y, the response variable, i.e., the optimization result; β0, the constant term; X i X j Independent variable; k, number of factors; β i The linear effect coefficient represents the linear effect coefficient of factor X. i The degree and direction of the main influence on the response Y; β ij The secondary effect coefficient represents the effect of factor X. i The bending effect; β ij The interaction effect coefficient represents the interaction effect coefficient of factor X. i and X j The interaction between them affects the response Y; ε, the random error term.

[0031] The multi-objective optimization solution includes: objective function, constraints, and optimization algorithm; the objective function includes: maximization, minimization, target value, and range, which are selected as needed; the constraints are the range of design variables and the response target limit; the optimization algorithm is the gradient descent method.

[0032] The optimal solution is verified by performing CFD simulation on the optimization results obtained from the multi-objective optimization solution.

[0033] Example 1:

[0034] See Figure 1 The method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology includes response surface experimental design, CFD sample simulation, response surface model construction, multi-objective optimization solution, and optimal solution verification. For variable definitions in response surface experimental design, please refer to [link to relevant documentation]. Figure 2 , Figure 3 and Figure 4 The jet fans are installed at three locations: 1 / 2L, 2 / 3L, and 5 / 6L (L refers to the distance between the air inlet and outlet 2 of the integrated utility tunnel). The relative angles between the jet fans and the upper and lower walls are 0°, 7.5°, and 15°, respectively. The relative angles between the jet fans and the left and right walls are 0°, 30°, and 60°, respectively. The air exchange rates are 2 times per hour, 4 times per hour, and 6 times per hour, respectively. This air exchange rate refers to the number of times the ventilation system completely replaces the air in the entire space inside the utility tunnel within a unit of time (e.g., 1 hour), which is different from the interval air exchange rate within an hour.

[0035] CFD sample simulation is used to simulate the samples obtained from the response surface experimental design. The integrated pipe gallery in the CFD simulation adopts a mechanical air intake and mechanical exhaust ventilation method. Based on the analysis and calculation of the CFD simulation results, five evaluation indicators are obtained: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature.

[0036] CFD simulations were conducted for scenarios with an air exchange rate of 4 times per hour, a jet fan installation position of 1 / 2 L, a relative angle of 7.5° between the jet fan and the upper and lower walls, and a 60° angle between the jet fan and the left and right walls. The simulation analysis process strictly followed the CFD simulation preprocessing, solution calculation, and post-processing procedures. During the simulation analysis, a comprehensive utility tunnel model was created using CAD software. The model was then imported into the CFD simulation software for mesh generation, model setup, and solution calculation. In the post-processing stage of the CFD simulation software, 100 points were uniformly distributed across the cross-section, with a 1-meter interval between each cross-section. The temperature and humidity data for all data points were exported and imported into Excel. In Excel, the exported temperature and humidity data were averaged, subtracted, and squared to obtain the evaluation index values: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature were 9.6%, 14.5%, 4.5%, 6.3%, and 35.2℃, respectively.

[0037] The response surface model was constructed based on the evaluation index obtained from the CFD simulation; variance analysis was performed on the established second-order polynomial model to complete the model validation of the response surface model.

[0038] Multi-objective optimization was performed based on a validated response surface model. The objective functions for minimizing the temperature unguaranteed rate and humidity non-uniformity coefficient were set (i.e., the objective function was set to the minimum value, and the optimization process actively sought the minimum value). The target values ​​for the number of air changes per hour were successively set to 2, 4, and 6 times per hour. The multi-objective optimization results were obtained, with the specific values ​​for the temperature unguaranteed rate at 4 times per hour being 3.0% and the humidity non-uniformity coefficient being 13.3%. Further verification of the optimal solution is pending.

[0039] The optimal solution verification is based on the optimization results obtained from the multi-objective optimization. The optimal solution to be verified is simulated by CFD to obtain the temperature and humidity evaluation index of the optimal solution to be verified. The specific values ​​of the temperature non-guarantee rate for 4 hours are 3.1% and the humidity non-uniformity coefficient is 13.6%. The multi-objective optimization results of the optimal solution to be verified are compared and analyzed with the results obtained by CFD simulation. The error between the two is less than 5%, and the optimal solution verification is completed.

[0040] Example 2:

[0041] See Figure 1 The method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology includes response surface experimental design, CFD sample simulation, response surface model construction, multi-objective optimization solution, and optimal solution verification. For variable definitions in response surface experimental design, please refer to [link to relevant documentation]. Figure 2 , Figure 3 and Figure 4 The jet fan is installed at three locations: 1 / 2L, 2 / 3L, and 5 / 6L (L refers to the distance between the air inlet and outlet 2 of the integrated pipe gallery); the relative angles between the jet fan and the upper and lower walls are 0°, 7.5°, and 15°, respectively; the installation angles between the jet fan and the left and right walls are 0°, 30°, and 60°, respectively; and the air exchange rates are 2 times per hour, 4 times per hour, and 6 times per hour, respectively.

[0042] CFD sample simulation is used to simulate the samples obtained from the response surface experimental design; the integrated pipe gallery in the CFD simulation adopts a ventilation form of natural air intake and mechanical exhaust; based on the analysis and calculation of the CFD simulation results, five evaluation indicators are obtained: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature.

[0043] The response surface model was constructed based on the evaluation index obtained from the CFD simulation; variance analysis was performed on the established second-order polynomial model to complete the model validation of the response surface model.

[0044] Multi-objective optimization was performed based on a validated response surface model. The objective functions for minimizing the mean temperature and humidity non-guarantee rates were set, and the target values ​​for the number of air changes were set to 2, 4, and 6 times per hour, respectively. The multi-objective optimization results were obtained, and the next step is to verify the optimal solution.

[0045] The optimal solution verification is based on the optimization results obtained from the multi-objective optimization. The optimal solution to be verified is simulated by CFD to obtain the temperature and humidity evaluation index of the optimal solution to be verified. The multi-objective optimization results of the optimal solution to be verified are compared and analyzed with the results obtained by CFD simulation. If the error between the two is less than 5%, the optimal solution verification is completed.

[0046] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the invention application patent should be included in the scope of the present invention.

Claims

1. A method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology, characterized in that, The steps are as follows: Response surface methodology, CFD sample simulation, response surface model construction, multi-objective optimization solution, and optimal solution verification.

2. The method for optimizing the location and angle of integrated utility tunnel jet fans based on response surface methodology according to claim 1, characterized in that, The response surface methodology includes: variable definition, design method, and sample size determination. The variables defined include: the installation position of the jet fan along the length of the utility tunnel, the installation angle of the jet fan on the upper and lower walls of the utility tunnel, the installation angle of the jet fan on the left and right walls of the utility tunnel, and the number of air changes. The design method is the Box-Behnken design method. The sample size is 27 samples with 4 variables and 3 levels.

3. The method for optimizing the location and angle of integrated utility tunnel jet fans based on response surface methodology according to claim 1, characterized in that, The CFD sample simulation specifically involves simulating the sample in the response surface experimental design to obtain temperature and humidity evaluation indicators. The temperature and humidity evaluation indicators include: temperature non-uniformity coefficient, humidity non-uniformity coefficient, temperature non-guarantee rate, humidity non-guarantee rate, and average temperature.

4. The method for optimizing the location and angle of integrated utility tunnel jet fans based on response surface methodology according to claim 1, characterized in that, The response surface model construction includes: establishing a second-order polynomial model and model validation; the model validation is analysis of variance.

5. The method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology according to claim 1, characterized in that, The multi-objective optimization solution includes: objective function, constraints, and optimization algorithm; the objective function includes: maximization, minimization, target value, and range; the constraints are the range of design variables and the response target limit; the optimization algorithm is the gradient descent method.

6. The method for optimizing the location and angle of integrated utility tunnel jet fans based on response surface methodology according to claim 1, characterized in that, The optimal solution is verified by performing CFD simulation on the optimization results obtained from the multi-objective optimization solution.

7. The method for optimizing the location and angle of integrated utility tunnel jet fans based on response surface methodology according to claim 1, characterized in that, The method for optimizing the location and angle of jet fans in integrated utility tunnels based on response surface methodology includes two typical operating conditions: natural air intake and mechanical exhaust, and mechanical air intake and mechanical exhaust. The specific process is as follows: A total of 27 samples with 4 variables and 3 levels were determined, including the installation position of the jet fan along the length of the integrated utility tunnel, the installation angle of the jet fan on the upper and lower walls of the integrated utility tunnel, the installation angle of the jet fan on the left and right walls of the integrated utility tunnel, and the number of air changes. The samples were simulated by CFD, and the evaluation indexes of temperature and humidity non-uniformity coefficient, non-guarantee rate, and average temperature were calculated and processed. A response surface model is constructed based on CFD simulation results and the model is validated. The validated response surface model is then subjected to multi-objective optimization to obtain an optimized solution, which is then verified through CFD simulation.

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