CN103049612B - Building indoor environment optimization method based on model order reduction technology - Google Patents

Abstract

Translated fromChinese

本发明公开了一种基于模型降阶技术的建筑室内环境优化方法，包括三个主要步骤：1）利用CFD软件对室内环境作稳态仿真，并构造各类环境参数的变化空间；2）通过POD技术重构低阶的参数变化子空间；3）运用遗传算法搜索最优的空调送风温度和速度。本发明运用POD技术构造室内环境参数的变化子空间，从而在优化策略中能充分考虑空间分布对环境参数的影响，提高了优化的精确性。本征正交分解（POD）的模型降阶方法，将原空间内的控制方程映射到某个正交子空间内，且保证能量意义上映射误差最小。相对目前的环境优化策略，本发明具有优化精度高，速度快等优点。

The invention discloses a building indoor environment optimization method based on model reduction technology, which includes three main steps: 1) use CFD software to simulate the indoor environment in a steady state, and construct the change space of various environmental parameters; 2) through POD technology reconstructs the low-order parameter change subspace; 3) Use genetic algorithm to search for the optimal air-conditioning air supply temperature and speed. The invention utilizes the POD technology to construct the changing subspace of the indoor environment parameters, so that the influence of the space distribution on the environment parameters can be fully considered in the optimization strategy, and the accuracy of the optimization is improved. The model reduction method of intrinsic orthogonal decomposition (POD) maps the governing equations in the original space to an orthogonal subspace, and ensures the minimum mapping error in the energy sense. Compared with the current environment optimization strategy, the present invention has the advantages of high optimization precision, fast speed and the like.

Description

Translated fromChinese

一种基于模型降阶技术的建筑室内环境优化方法A Method for Building Indoor Environment Optimization Based on Model Reduction Technology

技术领域technical field

本发明涉及一种基于模型降阶技术的建筑室内环境优化方法，属于建筑环境与建筑节能领域。 The invention relates to a method for optimizing the indoor environment of a building based on model reduction technology, which belongs to the field of building environment and building energy saving. the

背景技术Background technique

随着人们对居住品质和建筑节能要求的不断提高，如何协调和优化建筑室内环境舒适度与空调能耗越来越受到关注。目前众多学者已提出多种系统级的多目标优化控制策略。优化算法从早期的依赖梯度的寻优方法发展到目前广泛运用的各类智能优化算法（如进化规划、遗传算法等），优化参数则涵盖了室内环境的各方面，包括热舒适度、空气质量以及空调能耗等。 With the continuous improvement of people's living quality and building energy-saving requirements, how to coordinate and optimize the indoor environment comfort of buildings and air-conditioning energy consumption has attracted more and more attention. At present, many scholars have proposed a variety of system-level multi-objective optimal control strategies. The optimization algorithm has developed from the early gradient-dependent optimization method to various intelligent optimization algorithms (such as evolutionary programming, genetic algorithm, etc.) widely used at present, and the optimization parameters cover all aspects of the indoor environment, including thermal comfort, air quality and air conditioning energy consumption. the

在环境优化控制策略中，如何针对候选控制变量快速准确地解算环境响应是一个核心问题。由于现成的建筑室内环境模型很难同时满足优化的实时性和精确度要求，目前通常的做法是假设室内空气完全混合，即忽略空间分布对环境参数的影响，采用经验模型或半机理模型的方法求解环境响应。而实际上，对于大多数空调系统，特别像置换通风系统来说，室内的环境参数在空间上有较大差异。忽略这种差异会导致优化效果与室内各区域人员的实际感受不符，引起各种舒适度抱怨。目前国际上这方面研究很有限。原因是室内多参数环境建模复杂，须借助 CFD 工具，且很难直接与在线的优化控制算法整合。 In the environmental optimization control strategy, how to quickly and accurately solve the environmental response to the candidate control variables is a core issue. Since it is difficult for the ready-made building indoor environment model to meet the real-time and accuracy requirements of optimization at the same time, the current common practice is to assume that the indoor air is completely mixed, that is, ignoring the influence of spatial distribution on environmental parameters, and adopting empirical models or semi-mechanistic models. Solve for the environmental response. In fact, for most air-conditioning systems, especially displacement ventilation systems, the indoor environmental parameters vary greatly in space. Neglecting this difference will lead to a discrepancy between the optimization effect and the actual feelings of people in various areas of the room, causing various comfort complaints. At present, international research in this area is very limited. The reason is that the modeling of the indoor multi-parameter environment is complex, and CFD tools must be used, and it is difficult to directly integrate with the online optimization control algorithm. the

2009年，有文献提出通过神经网络训练的方法得到CFD模型的简化模型，用于环境优化策略中参数指标的快速求解。此方法通过CFD仿真得到足够多的输入/输出数据。通过这些数据对的训练和测试建立基于神经网络的环境指标替代模型。在优化算法的每次迭代中利用替代模型快速求解目标函数，减少优化算法的复杂度，提高实时性。此方法考虑了环境参数的空间分布影响，但是神经网络模型本质上是经验模型，只能对指定的环境指标作建模。当性能指标有变化，或关注的室内用户区域改变，则须重新建模，应变能力较差。 In 2009, some literature proposed to obtain a simplified model of the CFD model through the method of neural network training, which is used to quickly solve the parameter indicators in the environment optimization strategy. This method obtains enough input/output data through CFD simulation. Through the training and testing of these data pairs, a neural network-based environmental indicator surrogate model is established. In each iteration of the optimization algorithm, the surrogate model is used to quickly solve the objective function, reducing the complexity of the optimization algorithm and improving real-time performance. This method takes into account the influence of the spatial distribution of environmental parameters, but the neural network model is essentially an empirical model and can only model specified environmental indicators. When the performance index changes, or the indoor user area of concern changes, it must be remodeled, and the adaptability is poor. the

发明内容Contents of the invention

针对现有建筑室内环境优化方法所存在的上述缺陷，本发明提供一种基于模型降阶技术的建筑室内环境优化方法。其特点在于通过构造低阶的环境参数变化子空间，将相关的室内环境参数模型直接嵌入寻优过程中，实现环境响应的精确、快速解算。 Aiming at the above-mentioned defects in existing building indoor environment optimization methods, the present invention provides a building indoor environment optimization method based on model reduction technology. Its feature is that by constructing a low-order environmental parameter change subspace, the relevant indoor environmental parameter models are directly embedded in the optimization process to achieve accurate and fast calculation of environmental responses. the

基于本征正交分解（POD）的模型降阶方法本质上是一种映射方法。它将原空间内的控制方程映射到某个正交子空间内，且保证能量意义上映射误差最小。典型的CFD 模型通过POD 降阶方法可转换为关于POD模式系数的低阶模型，能同时满足建模精度与实时性要求，适合优化策略中环境响应的快速解算任务。 The model reduction method based on intrinsic orthogonal decomposition (POD) is essentially a mapping method. It maps the governing equations in the original space to an orthogonal subspace, and ensures the minimum mapping error in the energy sense. A typical CFD model can be converted into a low-order model about POD model coefficients through the POD order reduction method, which can meet the modeling accuracy and real-time requirements at the same time, and is suitable for the fast solution task of the environmental response in the optimization strategy. the

本发明的技术方案是： Technical scheme of the present invention is:

一种基于模型降阶技术的建筑室内环境优化方法，包括如下步骤：A method for optimizing the indoor environment of a building based on model reduction technology, comprising the following steps:

（1）建立基于CFD的室内环境模型；(1) Establish an indoor environment model based on CFD;

（2）根据控制变量可能的变化范围，等间距选择控制变量数据点，作相应的CFD稳态仿真；从CFD仿真结果中提取环境参数分布，构造参数变化空间；提取的参数类型包括室内温度、风速、污染物浓度及热舒适度指标；(2) According to the possible variation range of the control variable, select the data points of the control variable at equal intervals, and perform the corresponding CFD steady-state simulation; extract the distribution of environmental parameters from the CFD simulation results, and construct the parameter variation space; the extracted parameter types include indoor temperature, Wind speed, pollutant concentration and thermal comfort index;

（3）利用POD模型降阶技术重构出步骤（2）所得参数变化空间的低阶子空间；(3) Reconstruct the low-order subspace of the parameter variation space obtained in step (2) by using the POD model reduction technology;

（4）选择室内环境指标及能耗指标，用于评估室内环境及空调能耗；(4) Select indoor environment indicators and energy consumption indicators for evaluating indoor environment and air-conditioning energy consumption;

（5）设置目标函数，利用优化算法对控制变量进行迭代优化。在每次优化迭代过程中，通过在参数子空间内的多维插值快速得到系统响应，进而快速求解目标函数。(5) Set the objective function, and use the optimization algorithm to iteratively optimize the control variables. In each optimization iteration process, the system response is quickly obtained through multi-dimensional interpolation in the parameter subspace, and then the objective function is quickly solved.

所述步骤（1）中，CFD仿真使用Airpak计算流体力学软件；室内环境模型为三维模型；室内环境模型的建立步骤如下： In the step (1), the CFD simulation uses Airpak computational fluid dynamics software; the indoor environment model is a three-dimensional model; the steps for establishing the indoor environment model are as follows:

A、利用Airpak软件建立房间围护的几何模型；确定空调送风口和回风口的位置与尺寸；确定房间内主要陈设的位置与尺寸；A. Use Airpak software to establish the geometric model of the room enclosure; determine the position and size of the air supply outlet and return air outlet of the air conditioner; determine the position and size of the main furnishings in the room;

B、对建立的房间模型划分网格；B. Mesh the established room model;

C、利用Fluent求解器耦合求取质量、动量、能量及污染物浓度方程的稳态解。C. Use the Fluent solver coupling to obtain the steady-state solutions of mass, momentum, energy and pollutant concentration equations.

所述步骤（2）中，控制变量包括空调送风口温度和风速；控制变量的选择间隔为：送风口温度0.1摄氏度，送风口风速0.1米/秒；对每一组控制变量利用步骤（1）所述的室内环境模型进行稳态仿真；通过Airpak软件的导出功能提取每一组控制变量对应的稳态参数分布，组成各类参数的变化空间。 In the step (2), the control variables include the temperature and wind speed of the air outlet of the air conditioner; the selection interval of the control variables is: the temperature of the air outlet is 0.1 degrees Celsius, and the wind speed of the air outlet is 0.1 m/s; for each group of control variables, use the step (1) The indoor environment model is used for steady-state simulation; the steady-state parameter distribution corresponding to each group of control variables is extracted through the export function of the Airpak software to form the variation space of various parameters. the

所述步骤（3）中，POD模型降阶的基本思想为：在n维向量空间H中有一组数据集，找到其一组m维子集构成S子空间（m<n），使原数据集映射到子集的误差在能量意义上最小。POD模型降阶的基本步骤如下： In the step (3), the basic idea of POD model order reduction is: there is a set of data sets in the n-dimensional vector space H, and a set of m-dimensional subsets are found to form an S subspace (m<n), so that the original data The set-to-subset mapping error is minimal in an energy sense. The basic steps of POD model reduction are as follows:

A、利用各参数变化空间组成矩阵：A. Use the variable space of each parameter to form a matrix:

                         

                         

这里

维的矩阵表示参数变化空间，

是房间内网格点总数，是控制变量组数；

为

的转置；here

dimensional The matrix represents the parameter variation space,

is the total number of grid points in the room, is the number of control variable groups;

for

the transposition of

B、求解矩阵

的特征值和特征向量

；选择合适的截断值

，使得前

个特征值包含的系统动能占比

大于99%，这里

表示为：B. Solve the matrix

The eigenvalues of and eigenvectors

; Choose an appropriate cutoff value

, so that before

The proportion of system kinetic energy contained in each eigenvalue

Greater than 99%, here

Expressed as:

                        

                        

C、低阶参数变化子空间可描述为

个特征向量及其系数的线性组合：C. The low-order parameter variation subspace can be described as

A linear combination of eigenvectors and their coefficients:

                      

                      

这里

表示第

组控制变量对应的稳态参数分布，

表示POD模式系数。here

Indicates the first

The steady-state parameter distribution corresponding to the group control variables,

Indicates the POD mode coefficient.

所述步骤（4）中，室内环境指标包括热舒适度指标及室内空气质量指标。 In the step (4), the indoor environment index includes a thermal comfort index and an indoor air quality index. the

热舒适度指标采用预测平均投票指标（PMV）。PMV 指标将人体冷热感觉量化为以下七级：冷（-3）、凉（-2）、稍凉（-1）、舒适（0）、稍暖（1）、暖（2）、热（3），并将其与空气温度、太阳辐射、空气流速、空气湿度、人体新陈代谢率、及人体着衣等六个因素用函数联系起来，是目前国际上最为通用的热舒适度定量指标。 The thermal comfort index adopts the predictive mean voting index (PMV). The PMV index quantifies the human body's hot and cold sensations into the following seven levels: cold (-3), cool (-2), slightly cool (-1), comfortable (0), slightly warm (1), warm (2), hot ( 3), and linking it with six factors such as air temperature, solar radiation, air velocity, air humidity, human metabolic rate, and human clothing, is currently the most commonly used quantitative index of thermal comfort in the world. the

室内空气质量指标采用通风效力指标（

）： Indoor air quality index adopts ventilation efficiency index (

):

                       

                       

这里，

和

分别为空调回风口和送风口的污染物浓度，

为室内人员头部高度的污染物平均浓度；here,

and

are the pollutant concentrations at the return air outlet and air supply outlet of the air conditioner, respectively,

is the average concentration of pollutants at the head height of indoor personnel;

本发明将空调系统能耗分解为两个部分：风机能耗和制冷能耗，并作适当简化，得到能耗指标为：The present invention decomposes the energy consumption of the air-conditioning system into two parts: fan energy consumption and refrigeration energy consumption, and appropriately simplifies it to obtain the energy consumption index as follows:

                     

                     

这里，

为风机能耗，

为风机压升，为总送风量，

为制冷能耗，

为用于除显热负载的制冷能耗，为对新风进行除湿降温的能耗；here,

is the fan energy consumption,

is the fan pressure rise, is the total air volume,

For cooling energy consumption,

is the cooling energy used to remove sensible heat loads, Energy consumption for dehumidification and cooling of fresh air;

所述步骤（5）中，目标函数设置为：In the step (5), the objective function is set as:

这里，

为各指标的加权系数，下标

指对应性能指标的最大值，用于各个指标的归一化；

为惩罚项，用于反映室内人员头脚温差和周边风速过大对热舒适度的影响。here,

is the weighting coefficient of each indicator, subscript

Refers to the maximum value of the corresponding performance index, which is used for the normalization of each index;

As a penalty item, it is used to reflect the influence of the temperature difference between the head and feet of the indoor occupants and the excessive wind speed around them on the thermal comfort.

优化算法采用遗传算法，参数子空间内的多维插值算法采用样条插值。 The optimization algorithm adopts genetic algorithm, and the multi-dimensional interpolation algorithm in parameter subspace adopts spline interpolation. the

本发明提出一种基于模型降阶技术的建筑室内环境优化方法，充分考虑空间分布对环境指标的影响，将“室内环境”降阶之后直接嵌入环境优化算法，以满足优化的精确性和实时性要求。 The present invention proposes a building indoor environment optimization method based on model reduction technology, which fully considers the influence of spatial distribution on environmental indicators, and directly embeds the "indoor environment" into the environment optimization algorithm after the reduction of the order to meet the accuracy and real-time optimization Require. the

相对目前的环境优化方法，本发明的优点表现在： Compared with the current environment optimization method, the advantages of the present invention are as follows:

1、优化的精确性。1. The accuracy of optimization.

本发明不再假设室内空气“充分混合”，而是利用CFD工具对建筑室内环境做精确建模，并通过模型降阶的方法将得到的低阶“室内环境”嵌入环境优化策略中，使得优化结果更精确。在体育馆，宾馆大厅，医院，学校等大空间场合，本发明提出的方法尤其具备明显的精度优势。 The present invention no longer assumes that the indoor air is "fully mixed", but uses CFD tools to accurately model the indoor environment of the building, and embeds the obtained low-order "indoor environment" into the environment optimization strategy through the method of model reduction, so that the optimization The result is more precise. In large space occasions such as gymnasiums, hotel halls, hospitals, schools, etc., the method proposed by the present invention has obvious advantages in precision. the

2、优化的快速性。 2. The speed of optimization. the

本发明通过对参数子空间作多维插值可快速求解目标函数，使得优化算法在提高精度的同时满足实时性要求，可实际运用于在线优化场合。 The invention can quickly solve the objective function by performing multi-dimensional interpolation on the parameter subspace, so that the optimization algorithm can meet the real-time requirement while improving the precision, and can be actually applied to on-line optimization occasions. the

附图说明Description of drawings

图1是一个3D办公室模型示意图； Figure 1 is a schematic diagram of a 3D office model;

图2 是优化前的室内污染物浓度分布情况图；Figure 2 is a diagram of the indoor pollutant concentration distribution before optimization;

图3是优化后的室内污染物浓度分布情况图。Figure 3 is a diagram of the optimized indoor pollutant concentration distribution.

具体实施方式Detailed ways

为了更为具体地描述本发明，下面结合附图和具体实施例对本发明进行详细说明。 In order to describe the present invention more specifically, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. the

图1为一个3D办公室模型示意图。该办公室的长宽高分别为5.16m×3.65m×2.44m。室内设计有两个坐姿的办公人员（c,d）、两张办公桌（g,h）、两台电脑（d,e）、两个文件柜（j,k）及六盏日光灯（l - q）。房间左侧墙有一面3.65m×1.04m的窗户（i），置换通风系统的送风口（a）设置在窗的对面墙上，回风口（b）布置在天花板中心位置。空气污染物的挥发口设计于人员的头部位置。 Figure 1 is a schematic diagram of a 3D office model. The length, width and height of the office are 5.16m×3.65m×2.44m respectively. The interior design has two sitting office workers (c, d), two desks (g, h), two computers (d, e), two filing cabinets (j, k) and six fluorescent lamps (l - q). There is a 3.65m×1.04m window (i) on the left wall of the room, the air supply outlet (a) of the displacement ventilation system is set on the wall opposite to the window, and the return air outlet (b) is arranged in the center of the ceiling. The volatilization port of air pollutants is designed at the head position of the personnel. the

下面详细描述本发明方法的实施步骤： Describe the implementation steps of the inventive method in detail below:

步骤0. 建立基于CFD的室内环境模型。建模方法为：Step 0. Establish a CFD-based indoor environment model. The modeling method is:

步骤0.1 确定包括房间的围护（墙、地板及天花板等）、空调送风口/回风口以及室内陈设（包括人员）的位置与尺寸，利用Airpak软件搭建如图1所示的办公室几何模型；Step 0.1 Determine the location and size of the enclosure including the room (wall, floor and ceiling, etc.), air-conditioning air supply/return vents, and interior furnishings (including personnel), and use Airpak software to build the office geometric model as shown in Figure 1;

步骤0.2 对建立的几何模型划分网格，本例中共划分72282个不规则网格；Step 0.2 Mesh the established geometric model. In this example, a total of 72282 irregular meshes are divided;

步骤0.3 利用Fluent求解器耦合求取质量、动量、能量及污染物浓度方程的稳态解；求解之前，相关的边界条件设置如下：空调送风口设置为速度入口边界；回风口设置为自然流出边界；墙、地面及天花板设置为温度边界。相关的模型定义和求解策略设置如下：室内气体假设为低速流动的不可压缩粘性牛顿流体，湍流模型选用

标准模型，近壁处理采用标准壁面函数，浮力效应采用 Boussinesq 近似方式，不考虑粘性发热，压力速度耦合计算采用 SIMPLE 算法。Step 0.3 Use the Fluent solver coupling to obtain the steady-state solution of the mass, momentum, energy, and pollutant concentration equations; before solving, the relevant boundary conditions are set as follows: the air-conditioning air supply port is set as the velocity inlet boundary; the air return port is set as the natural outflow boundary ; Walls, floors, and ceilings are set as temperature boundaries. The relevant model definition and solution strategy settings are as follows: the indoor gas is assumed to be an incompressible viscous Newtonian fluid flowing at a low speed, and the turbulence model is selected as

In the standard model, the standard wall function is used for the near-wall treatment, the Boussinesq approximation method is used for the buoyancy effect, viscous heating is not considered, and the SIMPLE algorithm is used for the pressure-velocity coupling calculation.

步骤1. 控制变量在本例中的变化范围为：空调送风口温度：17—21摄氏度，空调送风口速度：0.1—0.5米/秒。等间距（0.1度以及0.1米/秒）地选择共25个控制变量数据点，利用步骤0.3作相应的CFD稳态仿真；从CFD仿真结果中提取环境参数分布。提取的参数类型包括室内温度、风速、污染物浓度及热舒适度指标。对每类参数构造

维的参数变化空间，其中n为网格点总数（本例为72282），m为控制变量数据点总数（本例为25）； Step 1. The variation range of the control variables in this example is: air-conditioning outlet temperature: 17-21 degrees Celsius, air-conditioning outlet speed: 0.1-0.5 m/s. A total of 25 control variable data points are selected at equal intervals (0.1 degree and 0.1 m/s), and the corresponding CFD steady-state simulation is performed using step 0.3; the environmental parameter distribution is extracted from the CFD simulation results. The extracted parameter types include indoor temperature, wind speed, pollutant concentration and thermal comfort index. For each type of parameter construction

Dimensional parameter change space, where n is the total number of grid points (72282 in this example), and m is the total number of control variable data points (25 in this example);

步骤2.利用POD模型降阶技术重构出步骤1所得参数变化空间的低阶子空间。模型降阶步骤为：Step 2. Use the POD model reduction technology to reconstruct the low-order subspace of the parameter variation space obtained instep 1. The model reduction steps are:

步骤2.1 利用各参数变化空间组成矩阵：Step 2.1 Use each parameter change space to form a matrix:

这里

维的

矩阵表示参数变化空间，

为

的转置；here

dimensional

The matrix represents the parameter variation space,

for

the transposition of

步骤2.2 求解矩阵的特征值

和特征向量；选择合适的截断值

，使得前

个特征值包含的系统动能占比

大于99%，其中表示为：Step 2.2 Solve the matrix The eigenvalues of

and eigenvectors ; Choose an appropriate cutoff value

, so that before

The proportion of system kinetic energy contained in each eigenvalue

greater than 99%, of which Expressed as:

本例中，当

时，系统动能占比

即大于99%。In this example, when

When , the kinetic energy of the system accounts for

That is greater than 99%.

步骤2.3 低阶参数变化子空间可描述为个特征向量及其系数的线性组合： Step 2.3 The low-order parameter change subspace can be described as A linear combination of eigenvectors and their coefficients:

这里

表示第

组控制变量对应的稳态参数分布，

表示特征向量的系数或称为POD模式系数；here

Indicates the first

The steady-state parameter distribution corresponding to the group control variables,

Represents the coefficient of the eigenvector or called the POD mode coefficient;

步骤3. 选择室内环境指标及能耗指标，用于评估室内环境及空调能耗；Step 3. Select indoor environment indicators and energy consumption indicators for evaluating indoor environment and air-conditioning energy consumption;

室内环境指标包括热舒适度指标及室内空气质量指标；Indoor environmental indicators include thermal comfort indicators and indoor air quality indicators;

热舒适度指标采用预测平均投票指标（PMV）。PMV 指标将人体冷热感觉量化为以下七级：冷（-3）、凉（-2）、稍凉（-1）、舒适（0）、稍暖（1）、暖（2）、热（3），并将其与空气温度、太阳辐射、空气流速、空气湿度、人体新陈代谢率、及人体着衣等六个因素用函数联系起来，是目前国际上最为通用的热舒适度定量指标。The thermal comfort index adopts the predictive mean voting index (PMV). The PMV index quantifies the human body's hot and cold sensations into the following seven levels: cold (-3), cool (-2), slightly cool (-1), comfortable (0), slightly warm (1), warm (2), hot ( 3), and linking it with six factors such as air temperature, solar radiation, air velocity, air humidity, human metabolic rate, and human clothing, is currently the most commonly used quantitative index of thermal comfort in the world.

室内空气质量指标采用通风效力指标（

）： Indoor air quality index adopts ventilation efficiency index (

):

                       

                       

这里，

和分别为空调回风口和送风口的污染物浓度，

为室内人员头部高度的污染物平均浓度；here,

and are the pollutant concentrations at the return air outlet and air supply outlet of the air conditioner, respectively,

is the average concentration of pollutants at the head height of indoor personnel;

本发明将空调系统能耗分解为两个部分：风机能耗和制冷能耗，并作适当简化，得到能耗指标为：The present invention decomposes the energy consumption of the air-conditioning system into two parts: fan energy consumption and refrigeration energy consumption, and appropriately simplifies it to obtain the energy consumption index as follows:

这里，

为风机能耗，

为风机压升，

为总送风量，

为制冷能耗，

为用于除显热负载的制冷能耗，

为对新风进行除湿降温的能耗；here,

is the fan energy consumption,

is the fan pressure rise,

is the total air volume,

For cooling energy consumption,

is the cooling energy used to remove sensible heat loads,

Energy consumption for dehumidification and cooling of fresh air;

步骤4. 利用优化算法对控制变量进行迭代优化。本例中的优化算法为遗传算法，设置目标函数为环境及能耗指标的加权多项式，Step 4. Use the optimization algorithm to iteratively optimize the control variables. The optimization algorithm in this example is a genetic algorithm, and the objective function is set as a weighted polynomial of environmental and energy consumption indicators.

这里，

为各指标的加权系数，下标

指对应性能指标的最大值，用于各个指标的归一化；为惩罚项，用于反映室内人员头脚温差和周边风速过大对热舒适度的影响；here,

is the weighting coefficient of each indicator, subscript

Refers to the maximum value of the corresponding performance index, which is used for the normalization of each index; As a penalty item, it is used to reflect the influence of the temperature difference between the head and feet of the indoor personnel and the excessive wind speed around them on the thermal comfort;

在遗传算法的每次迭代过程中，通过在参数子空间内的多维插值快速得到系统响应，进而求解目标函数。这里的多维插值算法采用样条插值算法。In each iteration of the genetic algorithm, the system response is quickly obtained through multi-dimensional interpolation in the parameter subspace, and then the objective function is solved. The multidimensional interpolation algorithm here adopts the spline interpolation algorithm.

为了对比优化效果，选择空调送风口温度17摄氏度，速度0.1米/秒作为优化前的室内环境基础案例。应用本优化方法，空调能耗最多可降低41.2%，热舒适度最多可提高48.6%，室内空气质量最多可改善38%。 In order to compare the optimization effect, the temperature of the air outlet of the air conditioner is 17 degrees Celsius and the speed is 0.1 m/s as the basic case of the indoor environment before optimization. Applying this optimization method, the air-conditioning energy consumption can be reduced by up to 41.2%, the thermal comfort can be increased by up to 48.6%, and the indoor air quality can be improved by up to 38%. the

图2和图3 为优化前后室内空气污染物浓度的对比图。图2描述了优化前的室内污染物浓度分布情况，图3描述了优化后的室内污染物浓度分布情况。由图可见，本发明通过在优化策略中充分考虑环境参数的空间分布情况，能够明显改善通风效果，提高室内空气质量。 Figure 2 and Figure 3 are the comparison charts of indoor air pollutant concentrations before and after optimization. Figure 2 describes the distribution of indoor pollutant concentration before optimization, and Figure 3 describes the distribution of indoor pollutant concentration after optimization. It can be seen from the figure that the present invention can significantly improve the ventilation effect and improve the indoor air quality by fully considering the spatial distribution of environmental parameters in the optimization strategy. the

上面已经结合具体实施步骤说明了本发明，然而对于本领域的技术人员来说，可以在不背离本发明的精神和范围的前提下，对本发明做出不同的改进和变型。因而落入本发明的权利要求范围内的各种改进和变型，都应属于本发明的保护范围之内。 The present invention has been described above in conjunction with specific implementation steps. However, for those skilled in the art, various improvements and modifications can be made to the present invention without departing from the spirit and scope of the present invention. Therefore, various improvements and modifications falling within the scope of the claims of the present invention shall fall within the protection scope of the present invention. the

Claims (6)

1. A building indoor environment optimization method based on a model order reduction technology specifically comprises the following steps:

(1) establishing an indoor environment model based on CFD;

(2) selecting control variable data points at equal intervals according to the possible variation range of the control variable, and performing corresponding CFD steady-state simulation; extracting environmental parameter distribution from a CFD simulation result, and constructing a parameter change space;

(3) reconstructing a low-order subspace of the parameter change space obtained in the step (2) by using a POD model order reduction technology;

(4) selecting an indoor environment index and an energy consumption index for evaluating the indoor environment and the energy consumption of the air conditioner;

(5) and setting an objective function, and performing iterative optimization on the control variable by using an optimization algorithm.

2. The method for optimizing the indoor environment of the building based on the model order reduction technology as claimed in claim 1, wherein: in the step (1), Airpak computational fluid dynamics software is used for CFD simulation; the indoor environment model is a three-dimensional model; the indoor environment model is established by the following steps:

(1) establishing a geometric model of the room enclosure by using Airpak software; determining the positions and the sizes of an air supply outlet and an air return inlet of the air conditioner; determining the position and size of a main display in a room;

(2) dividing grids for the established room model;

(3) and (4) solving the steady state solution of the mass, momentum, energy and pollutant concentration equation by using the Fluent solver coupling.

3. The method for optimizing the indoor environment of the building based on the model order reduction technology as claimed in claim 1, wherein: in the step (2), the control variables comprise the temperature and the wind speed of an air conditioner air supply outlet; the selected intervals of the control variables are: the temperature of the air supply outlet is 0.1 ℃, and the air speed of the air supply outlet is 0.1 m/s; performing steady-state simulation on each set of control variables by using the indoor environment model in the step (1); extracting the steady-state parameter distribution corresponding to each set of control variable through the derivation function of the Airpak software to form a variation space of various parameters; the extracted environmental parameter types comprise indoor temperature, wind speed, pollutant concentration and thermal comfort level indexes.

4. The method for optimizing the indoor environment of the building based on the model order reduction technology as claimed in claim 1, wherein: in the step (3), the basic steps of POD model order reduction are as follows:

(1) forming a matrix by using the variation space of each parameter:

here, the

Of dimension

The matrix represents the space of variation of the parameters,

is the total number of grid points in the room,

is the number of control variable groups;

is composed of

Transposing;

(2) solving the matrix

Characteristic value of

And feature vectors

(ii) a Selecting a suitable cutoff value

So that it is at the front

The ratio of kinetic energy of system contained in each characteristic value

Greater than 99%, here

Expressed as:

(3) the low-order parametric variations subspace can be described as

Linear combination of individual feature vectors and their coefficients:

here, the

Is shown as

The steady state parameter distribution corresponding to the group control variable,

representing POD mode coefficients.

5. The method for optimizing the indoor environment of the building based on the model order reduction technology as claimed in claim 1, wherein: in the step (4), the indoor environment index comprises a thermal comfort index and an indoor air quality index, and the energy consumption index refers to an air conditioner energy consumption index, wherein:

the thermal comfort index adopts a prediction average voting index PMV;

the indoor air quality index adopts ventilation efficiency index：

Here, ,and

respectively the pollutant concentrations of the air return inlet and the air supply outlet of the air conditioner,

average concentration of contaminants at head height of indoor personnel;

the energy consumption index of the air conditioner is decomposed into two parts, namely a fan energy consumption index and a refrigeration energy consumption index:

here, ,the energy consumption of the fan is reduced,

in order to increase the pressure of the fan,the total air supply quantity is the total air supply quantity,

in order to reduce the energy consumption for refrigeration,

for the purpose of removing the refrigeration energy consumption of the sensible heat load,

energy consumption for dehumidifying and cooling the fresh air.

6. The method for optimizing the indoor environment of the building based on the model order reduction technology as claimed in claim 1, wherein: in the step (5), the objective function is set as:

wherein,

for weighting coefficients, subscripts, of the indices

The maximum value of the corresponding performance index is used for normalization of each index;

the punishment item is used for reflecting the influence of the head-foot temperature difference of indoor personnel and overlarge ambient wind speed on the thermal comfort; the optimization algorithm adopts a genetic algorithm, and the multi-dimensional interpolation algorithm in the parameter subspace adopts spline interpolation; in each optimization iteration process, system response is quickly obtained through multi-dimensional interpolation in the parameter subspace, and an objective function is quickly solved.

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