CN111191316A - Response surface-based building natural ventilation performance optimization model and optimization method - Google Patents
Response surface-based building natural ventilation performance optimization model and optimization method Download PDFInfo
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Abstract
The invention relates to the technical field of buildings, and provides a response surface-based building natural ventilation performance optimization model and method. The method comprises the following steps: the system comprises a parametric modeling module and a CFD numerical simulation module, wherein the parametric modeling module is called to perform batch simulation calculation; the sensitivity analysis module determines the sensitivity index of each geometric parameter through correlation analysis and screens the critical geometric parameters; the response surface construction module is used for developing response surface model construction and precision verification on the key geometric parameters by reading the data of the parameter manager and selecting a corresponding experimental design method and a response surface algorithm; and the multi-objective optimization module calls the data of the response surface model through selecting an optimization algorithm to carry out iterative optimization, so as to obtain an optimization candidate scheme. The invention has the beneficial effects that: the parametric modeling technology, the CFD numerical simulation technology, the response surface method and the genetic optimization search technology are integrated, and the rapid optimization of the natural ventilation performance of the building is realized.
Description
Technical Field
The invention relates to the technical field of buildings, in particular to a response surface-based building natural ventilation performance optimization model and method.
Background
The scheme stage is the basis and key link of natural ventilation design, and the design decision related to the space form factor can generate fundamental influence on the natural ventilation performance. In recent years, with the development of a CFD numerical simulation technology and artificial intelligence, existing research starts to couple the CFD numerical simulation technology and artificial intelligence and perform iterative computation at a scheme design stage, so as to realize automatic optimization of a building form under guidance of natural ventilation performance. The natural ventilation performance optimization process follows the following steps: firstly, generating a certain amount of samples in a design space, carrying out batch simulation of natural ventilation performance through a CFD platform, then calling a simulation result by using an optimization algorithm to analyze and compare, taking the comparison result as a guide, then obtaining the next better batch of samples, and repeating the steps until the goal of optimization is achieved. The whole optimization process is driven by an optimization algorithm, manpower participation is not needed, and automatic generation of the space form under the guidance of natural ventilation performance is realized. However, in the optimization process, the basis of evaluation by using an optimization algorithm is batch simulation of natural ventilation performance of a large number of samples, each CFD numerical simulation needs a series of complex processes such as modeling, grid, simulation and post-processing, tens of minutes to hours or even longer are needed frequently, and the rapid decision of the scheme stage is difficult to be effectively supported by huge calculation cost.
Aiming at the problems, the existing research tries to build a visual physical wind tunnel meeting the design requirements of the initial stage of the building, replaces CFD numerical simulation consuming time in calculation, and carries out simulation, data measurement and performance feedback of the wind environment of the building. The flow field data can be rapidly obtained by utilizing a wind tunnel test, and the movement of the mechanical device is driven by an optimization algorithm, so that the automatic optimization and generation of the building form are realized. The optimization method based on the wind tunnel test can solve the problems of calculation period and precision, but is limited by experiment cost and construction difficulty, and is difficult to be widely applied in the scheme design stage.
Disclosure of Invention
The invention aims to provide a response surface-based building natural ventilation performance optimization model and an optimization method, so as to solve the technical problems in the prior art.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a response surface based building natural draft performance optimization model, comprising: the parametric modeling module is used for compiling function expressions among the parameters through a built-in parameter manager to establish a coupling relation among the geometric parameters; the CFD numerical simulation module is used for carrying out batch calculation on the natural ventilation performance indexes by calling the parameterized modeling module, and the calculated data is imported into the parameter manager; the sensitivity analysis module can read the data of the parameter manager, determine the sensitivity index of each geometric parameter through correlation analysis, and screen the critical geometric parameters; the response surface construction module is used for developing response surface model construction and precision verification on the key geometric parameters by reading the data of the parameter manager and selecting a corresponding experimental design method and a response surface algorithm; and the multi-objective optimization module calls the data of the response surface model through selecting an optimization algorithm to carry out iterative optimization, so as to obtain an optimization candidate scheme.
On the other hand, the other technical scheme provided by the invention is as follows: a building natural ventilation performance optimization method based on a response surface comprises the following steps: extracting geometric parameters of a building space model, determining evaluation indexes of natural ventilation performance, and modeling by adopting a parameterized modeling module; step (2), a sensitivity analysis module is adopted to calculate the sensitivity index of each geometric parameter, and key geometric parameters are screened; step (3), sampling the combination of the key geometric parameters through experimental design to form a certain number of uniformly distributed samples, and calling a parameterized model by using a CFD numerical simulation module to perform batch calculation to obtain output data of the uniformly distributed samples; step (4), based on the output data of the experimental design, fitting and interpolating data by adopting a corresponding algorithm to construct a response surface model; and meanwhile, verifying the prediction precision of the response surface model, entering an optimization link if the precision requirement is met, and otherwise, carrying out new experimental design.
In an alternative embodiment, step (5): and calling the response surface model by using an optimization algorithm to perform iterative optimization until an optimization target is achieved.
In an alternative embodiment, the geometric parameters in step (1) are divided into four levels, an air inlet level, an interface level, a cavity level and an air outlet level, based on the order of air flow through the building.
In an optional embodiment, the geometric parameters of the air inlet level include the number of air inlets, the width of the air inlets, the distance from the air inlets to the ground and the height of the air inlets; the geometric parameters of the interface level comprise the number of interface openings, the scaling coefficient of the width of the interface openings, the distance between the interface openings and the ground and the height of the interface openings; the geometric parameters of the cavity layer level comprise the side length of the cavity section and the cavity height; the geometric parameters of the air outlet level comprise air outlet side length scaling coefficients.
In an alternative embodiment, the evaluation indexes of the natural ventilation performance in the step (1) are mainly air age, temperature and wind speed.
In an optional embodiment, the key geometric parameters in step (2) are the number of the air inlets, the width of the air inlets, the distance from the air inlets to the ground, the height of the air inlets, the number of the interface openings, a scaling factor of the width of the interface openings, the distance from the interface openings to the ground, the length of the cross section of the cavity, and a scaling factor of the length of the side of the air outlets.
In an optional embodiment, in the step (3), the data of the uniformly distributed samples is 300 groups, the air age value, the temperature average value and the wind speed average value are extracted, and the response surface model in the step (4) is constructed by using a second-order polynomial, a neural network or a Kriging method.
In an optional embodiment, the response surface model is a multidimensional hypersurface mathematical model, and a mapping relation between the input parameters and the output parameters can be established.
In an optional embodiment, in the step (5), a multi-objective genetic algorithm is adopted to call the data of the response surface model in the step (4) for fast optimization, and the obtained optimization objective includes an optimization candidate scheme and an optimization interval of the geometric parameter.
The invention has the beneficial effects that:
(1) the invention provides a building natural ventilation performance optimization model and an optimization method of a response surface, which comprises the processes of parameter sensitivity analysis, experimental design, response surface model construction, multi-objective optimization and the like, realizes the rapid generation of building forms under the guidance of natural ventilation performance, and provides effective support for the natural ventilation optimization design in a scheme stage.
(2) The invention provides a response surface building natural ventilation performance optimization model and an optimization method, integrates a parametric modeling technology, a CFD numerical simulation technology, a response surface method and a genetic optimization search technology, and realizes the rapid optimization of the building natural ventilation performance.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for optimizing natural ventilation performance of a building based on a response surface according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a response surface-based building natural ventilation performance optimization model platform according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an exemplary model in a response surface-based building natural ventilation performance optimization model according to an embodiment of the present invention.
Fig. 4 is a schematic view of the airflow organization inside a typical model in a response surface-based building natural draft performance optimization model according to an embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating sensitivity analysis of 11 geometric parameters in a response surface-based building natural ventilation performance optimization method according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a response surface model in a response surface-based building natural ventilation performance optimization model according to an embodiment of the present invention.
Fig. 7 is a schematic diagram illustrating verification of accuracy of a response surface model in a response surface-based building natural ventilation performance optimization model according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of the number of iterations in the method for optimizing the natural ventilation performance of the building based on the response surface according to an embodiment of the present invention.
Fig. 9 is a pareto frontier diagram in a response surface-based building natural ventilation performance optimization method according to an embodiment of the present invention.
Fig. 10 is a schematic diagram of an optimization scheme in a non-dominated solution in a response surface-based building natural draft performance optimization method according to an embodiment of the present invention.
Fig. 11 is a schematic parameter interval distribution diagram of an optimization scheme in a response surface-based building natural ventilation performance optimization method according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly or indirectly secured to the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element. The terms "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positions based on the orientations or positions shown in the drawings, and are for convenience of description only and not to be construed as limiting the technical solution. The terms "first", "second" and "first" are used merely for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features. The meaning of "plurality" is two or more unless specifically limited otherwise.
The embodiment aims to provide a response surface-based building natural ventilation performance optimization model. An optimization platform is built by adopting ANSYS17.0, the ANSYS Workbench provides a plurality of functional modules corresponding to an optimization flow and an environment for cooperative work of the modules, and data among the modules can be freely read and called. Corresponding to the optimization process, the optimization platform comprises the following five modules: the system comprises a parametric modeling module, a CFD numerical simulation module, a sensitivity analysis module, a response surface construction module and a multi-objective optimization module, which are shown in the attached figure 1. Design Modeler is a parameterization modeling module, and a built-in parameter manager is used for compiling function expressions among parameters to establish a coupling relation among the parameters. Fluent is a CFD numerical simulation module, and develops batch calculation on natural ventilation performance indexes by calling a parameterized model, and the calculated data is imported into a Parameter Set (Parameter Set). The Parameter Correlation is a sensitivity analysis module, can read data of the Parameter manager, determines sensitivity indexes of various geometric parameters through Correlation analysis, and screens key parameters. The Response Surface is a Response Surface construction module, and a reasonable experiment design method and a Response Surface algorithm are selected to carry out construction and precision verification on a key parameter expansion Response Surface model by reading parameter manager data. The Response Surface Optimization is a multi-objective Optimization module, and an appropriate Optimization algorithm can be selected to call Response Surface model data to perform iterative Optimization to obtain an Optimization candidate scheme.
Specifically, based on the model, another embodiment of the present invention further provides a method for optimizing the natural ventilation performance of a building based on a response surface, as shown in fig. 2, first extracting the geometric parameters of a spatial model, determining the evaluation index of natural ventilation, and modeling by using a parametric modeling technique. And calculating the sensitivity index of each geometric parameter by adopting a sensitivity analysis method, and fitting a continuous response surface by using a limited number of sample points to simulate a real extreme state curved surface so as to construct input variables and screen key geometric parameters. Sampling key parameter combinations through experimental design to form a certain number of uniformly distributed samples, calling a parameterized model by using a CFD numerical simulation program to perform batch calculation, and obtaining output data of the samples. And (3) based on discrete data of an experimental design link, fitting and interpolating the data by adopting a corresponding algorithm, and constructing a response surface model. And meanwhile, the prediction precision is verified, if the precision requirement is met, an optimization link is entered, otherwise, a new round of experimental design is carried out. And finally, calling a response surface model by using an optimization algorithm to perform iterative optimization until an optimization target is achieved. The building form is quickly generated under the guidance of the natural ventilation performance, and effective support is provided for the natural ventilation optimization design in the scheme stage.
TABLE 1 geometric parameters and value ranges thereof
In an alternative embodiment, a typical space model is extracted from 50 typical national fitness centers, as shown in fig. 3, two layers of multifunctional sports halls are vertically stacked, and four layers of auxiliary spaces surround in an L shape. The plane size of each layer of sports hall is 40mX60m, the height is 12m, can hold 1 court, 3 basketball courts, 3 tennis courts, 4 volleyball courts, 12 badminton courts and 24 badminton courts, and can carry out flexible conversion and combination of court according to different sports requirements. In order to form a continuous path of airflow movement, a vertical cavity is required to be implanted into a deep part of a building to construct a natural ventilation system. In various cavities, the ventilation vertical shaft is small in plane size, the integrity of large-space movement is guaranteed in a peripheral arrangement mode, and the ventilation vertical shaft has wide application potential, so that a cavity implantation mode of the peripheral vertical shaft is adopted in the research. The form factors affecting natural ventilation are divided into four levels according to the order of air flow through the building: air inlet, interface, cavity, air outlet, as shown in figure 4. 11 geometric parameters of 4 levels of morphological factors are extracted, and a value range is determined by combining the investigation data, as shown in table 1. The method starts from three aspects of air quality, thermal comfort and process requirements of sports, and adopts three indexes of air age, temperature and wind speed to evaluate the natural ventilation performance. In the CFD numerical simulation, the air age value, the temperature value and the average wind speed value of the reference plane at the height of 1.5m of the first layer and the second layer are respectively extracted, and the average value of the first layer and the second layer of each index is calculated as an output result.
Specifically, the number of samples required to construct a response surface significantly increases with the number of input parameters, thereby significantly increasing computational cost. Therefore, the parameters were first subjected to sensitivity analysis, and key parameters were screened. 200 groups of input parameter data are obtained by adopting a random sampling method, and CFD numerical simulation software fluent17.0 is called by using a parameterized model to calculate the air age, the temperature and the wind speed of the model. And analyzing the calculation result by adopting a Spearman grade Correlation coefficient method in a Parameter Correlation sensitivity analysis module. The sensitivity indexes of geometric input parameters are shown in figure 5, wherein the parameters P7 (the distance between an interface opening and the ground) and P10 (the height of a cavity) are not sensitive to all indexes of natural ventilation performance, so 9 parameters of P1, P2, P3, P4, P5, P6, P8 and P9P11 are selected as input parameters and enter an experimental design link.
In this embodiment, Design of experiments (DOE) refers to sampling a combination of input parameters a limited number of times so that the combination reflects characteristics of the whole Design space as much as possible. The aim is to obtain the mapping relation between the input parameters and the output parameters as much as possible by using the CFD simulation times as few as possible. The DOE is the basis for constructing the response surface model, directly influences the prediction precision of the response surface model, and further influences the accuracy of subsequent optimization. Among various DOE methods, the Latin hypercube design is a widely adopted uniform sampling method, the design space of all input parameters is uniformly divided into partitions with the same number, all levels are randomly combined together, each level of each input parameter is sampled only once, and the whole design space can be uniformly filled with a limited number of sample points. The Latin hypercube sampling has the characteristics of high efficiency and good uniformity, so that higher calculation accuracy can be obtained under the condition of less sampling. The method was chosen to sample 300 times for the 9 input parameter combinations. And (3) performing batch simulation calculation on 300 sampling samples by adopting Fluent17.0, extracting an air age value, an average temperature value and an average wind speed value, and constructing a response surface model.
In this embodiment, the construction method of the response surface model includes a second-order polynomial, a neural network, a Kriging method, and the like, and different types of algorithms are suitable for solving different problems.
A response surface is constructed by using a Kriging algorithm, the Kriging algorithm is proposed by a geologist in DanieKrigie in south Africa, the distribution of mineral reserves is predicted in the early stage, and then the optimization field is introduced. Kriging is a multi-dimensional interpolation technique, which combines the global algorithm of standard second-order polynomial and the local deviation measurement, and the function expression is as follows (formula 1).
y(x)＝PT(x)·a+Z(x) (1)
Where y (x) is an output parameter of the response surface. PT(x) A is a polynomial model resembling a standard response surface; z (x) is a special stochastic process. PT(x) A is the deterministic regression portion of y (x) that provides a global approximation of the approximation model, and z (x) provides an approximation to the model local bias. Due to the characteristic of local and global estimation, the Kriging algorithm can obtain an ideal fitting effect when solving the problem of high nonlinearity. And selecting a Kriging algorithm in a Response Surface module, and performing data fitting and interpolation on 300 samples in the DOE to construct a Response Surface model.
The response surface is a multi-dimensional hyper-curved surface, and for convenient expression, any two input parameters are used as abscissa, and the output parameters are used as ordinate to draw a three-dimensional curved surface, as shown in fig. 6. Taking the input parameters P2 and P4 as examples, three-dimensional response surface maps of three output parameters are respectively drawn. As the response surface is a mathematical model, a complex physical solving process is not needed, corresponding output parameters can be quickly obtained for the combination of a group of input parameters, and an optimization process requiring a large amount of iterations can be effectively supported.
The response surface model is an approximate prediction model, and the mapping relation of input and output parameters is established on the basis of numerical fitting and interpolation, so that the verification of the precision of the response surface model is very important. After the response surface model is constructed, selecting a test sample by using the multiple correlation coefficient R2Correlation analysis was performed on the prediction data and the simulation values (equation 2).
In the formula, yiIn order to test the actual analog value of the sample,
for response surface prediction, NtestIn order to test the number of samples,
is the average of the test samples. The target values of the air age, the temperature and the wind speed are close to the predicted values 0.93736, 0.92105 and 0.95552 through calculation, as shown in the attached figure 7. The response surface model achieves higher prediction precision, and can be used for entering an optimization link instead of CFD numerical simulation.
The input variables in the optimization process are 9 key geometric form parameters after P7 and P10 are removed; the three objective functions are: z1(X) -average air age, Z2(X) -average temperature, Z3(X) -average wind speed, and the lower the values of the three values, the better (formula 3-6):
MinZ1(X)＝Air Age(X) (3)
MinZ2(X)＝Temperature(X) (4)
MinZ3(X)＝Velocity(X) (5)
in the formula, Air Age-average Air Age, Temperature-average Temperature, Velocity-average wind speed;
X＝(P1，P2，P3，P4，P5，P6，P8，P9，P11) (6)
and calling a response surface model by using a Multi-Objective genetic algorithm (MOGA) to perform iterative optimization. To preserve the diversity of the optimization solution set samples and prevent the optimization from converging in advance, the initial sample population is set to 2000, 500 are calculated per iteration, and the maximum iteration is 100 generations. When the pareto percentage reaches 80%, the optimization process converges. Firstly, the geometric parameter information is transmitted to a Response Surface Optimization multi-objective Optimization module, and 2000 initial samples are randomly generated by a genetic algorithm. And then, the input parameters of 2000 samples are transmitted to the response surface model, the output parameters are rapidly calculated, and the calculation result is fed back to the genetic algorithm. And the genetic algorithm performs selection, intersection and variation operations on the output data of 2000 samples according to the objective function to form a new generation of 500 samples, the input parameters are transmitted to the response surface again, the calculation result is judged whether the optimization termination condition is met by the genetic algorithm, if so, the optimization process is terminated, the optimization result is output, and if not, a new iteration is performed until the optimization goal is achieved. Following the above flow, the optimization process converges after 11 times of the optimization iterative computation, as shown in fig. 8, 6141 times of response surface model computation are invoked in total, while the actual CFD simulation computation only needs 300 times, which significantly reduces the computation cost.
Since the variation trends of the three objective functions are contradictory (for example, the increase of the wind speed causes the acceleration of the indoor airflow and inevitably causes the decrease of the temperature and the air age), the increase of one performance index inevitably causes the decrease of other indexes. Thus, the result of multi-objective optimization is not an optimal solution, but a series of sets of "non-dominant solutions" -pareto frontiers, as shown in FIG. 9. The non-dominated solution is an optimization scheme obtained by balancing various indexes, and the difference is that the tilt degree of the non-dominated solution to various performances is different, if the wind speed value is good, other indexes are relatively poor. The non-dominated solution set can be selected according to different design requirements (for example, a certain objective function can be inclined), and in the embodiment, screening is performed around the principle of comprehensively balancing three objective functions and enabling various performances to be balanced.
TABLE 2 geometric parameters and value ranges thereof
In an alternative embodiment, a wind speed limit is first determined, and 0.5m/s is taken as the upper limit of the wind speed in the optimization scheme. As the wind speed decreases, as shown in FIG. 9, the temperature and air age of the non-dominant solution both tend to increase, and therefore should be selected in the region near 0.5m/s to ensure that the temperature and air age values are not too high. The temperature value of the non-dominant solution is in a uniform ascending trend along with the reduction of the wind speed; the air age of the non-dominant solution is kept constant within the range of 0.3-0.5 m/s of wind speed, the wind speed is reduced by less than 0.3m/s, and the air age is rapidly increased, as shown in figure 10. Therefore, the range of the wind speed of 0.3-0.5 m/s is an optimized scheme distribution area, the spatial distribution of the parameters of 50 schemes in the range is shown in figure 11, and the values of each parameter optimization interval are shown in table 2. Within the optimization scheme distribution area, the wind speed values and air age of the ABCDEF six non-dominated solutions are more dominant than the others, and thus serve as the final optimization alternative (table 3). The temperature value and the wind speed value of the non-dominated solution E are very close to those of other non-dominated solutions, but the air age is the lowest, so that the non-dominated solution E is finally selected as an optimization scheme.
TABLE 3 optimization alternatives
It is worth pointing out that, the method for optimizing the building natural ventilation performance of the response surface is provided in this embodiment, and the parameterized modeling technology, the CFD numerical simulation technology, the response surface method, and the genetic optimization search technology are integrated, so that the rapid optimization of the building natural ventilation performance is realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A response surface based building natural draft performance optimization model, comprising:
the parametric modeling module is used for compiling function expressions among the parameters through a built-in parameter manager to establish a coupling relation among the geometric parameters;
the CFD numerical simulation module is used for carrying out batch calculation on the natural ventilation performance indexes by calling the parameterized modeling module, and the calculated data is imported into the parameter manager;
the sensitivity analysis module can read the data of the parameter manager, determine the sensitivity index of each geometric parameter through correlation analysis, and screen the critical geometric parameters;
the response surface construction module is used for developing response surface model construction and precision verification on the key geometric parameters by reading the data of the parameter manager and selecting a corresponding experimental design method and a response surface algorithm;
and the multi-objective optimization module calls the data of the response surface model through selecting an optimization algorithm to carry out iterative optimization, so as to obtain an optimization candidate scheme.
2. A building natural ventilation performance optimization method based on a response surface comprises the following steps:
extracting geometric parameters of a building space model, determining evaluation indexes of natural ventilation performance, and modeling by adopting a parameterized modeling module;
step (2), a sensitivity analysis module is adopted to calculate the sensitivity index of each geometric parameter, and key geometric parameters are screened;
step (3), sampling the combination of the key geometric parameters through experimental design to form a certain number of uniformly distributed samples, and calling a parameterized model by using a CFD numerical simulation module to perform batch calculation to obtain output data of the uniformly distributed samples;
step (4), based on the output data of the experimental design, fitting and interpolating data by adopting a corresponding algorithm to construct a response surface model; and meanwhile, verifying the prediction precision of the response surface model, entering an optimization link if the precision requirement is met, and otherwise, carrying out new experimental design.
3. The method for optimizing natural ventilation performance of a building based on a response surface as claimed in claim 2,
and (5): and calling the response surface model by using an optimization algorithm to perform iterative optimization until an optimization target is achieved.
4. The method for optimizing natural ventilation performance of a building based on a response surface as claimed in claim 2, wherein the geometric parameters in the step (1) are divided into four levels, namely an air inlet level, an interface level, a cavity level and an air outlet level, based on the sequence of air flowing through the building.
5. The method for optimizing the natural ventilation performance of the building based on the response surface as claimed in claim 4, wherein the geometric parameters of the air inlet level comprise the number of air inlets, the width of the air inlets, the distance from the air inlets to the ground and the height of the air inlets; the geometric parameters of the interface level comprise the number of interface openings, the scaling coefficient of the width of the interface openings, the distance between the interface openings and the ground and the height of the interface openings; the geometric parameters of the cavity layer level comprise the side length of the cavity section and the cavity height; the geometric parameters of the air outlet level comprise air outlet side length scaling coefficients.
6. The method for optimizing natural ventilation performance of a building based on a response surface as claimed in claim 2, wherein the evaluation indexes of the natural ventilation performance in the step (1) are mainly air age, temperature and wind speed.
7. The method for optimizing the natural ventilation performance of the building based on the response surface as claimed in claim 5, wherein the key geometric parameters in the step (2) are the number of the air inlets, the width of the air inlets, the distance from the air inlets to the ground, the height of the air inlets, the number of the openings of the interfaces, the scaling coefficient of the widths of the openings of the interfaces, the distance from the ground of the openings of the interfaces, the side length of the cross section of the cavity and the scaling coefficient of the side length of the air outlets.
8. The method for optimizing the natural ventilation performance of the building based on the response surface as claimed in claim 6, wherein in the step (3), the data of the uniformly distributed samples are 300 groups, the air age value, the temperature average value and the wind speed average value are extracted, and the response surface model in the step (4) is constructed by using a second-order polynomial, a neural network or a Kriging method.
9. The method for optimizing the natural ventilation performance of the building based on the response surface as claimed in claim 8, wherein the response surface model is a multidimensional hypersurface mathematical model, and a mapping relation between input parameters and output parameters can be established.
10. The method for optimizing the natural ventilation performance of the building based on the response surface as claimed in claim 3, wherein in the step (5), a multi-objective genetic algorithm is adopted to call the data of the response surface model in the step (4) for fast optimization, and the obtained optimization objective comprises an optimization candidate scheme and an optimization interval of the geometric parameters.
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