Air conditioner air port layout design method

Abstract

The invention discloses an air conditioner air port layout design method. The method comprises the steps that firstly, space and load analysis is conducted, surveying and mapping are conducted on a target area, functions are determined, a digital twin space is set up, static parameters are integrated, data are collected through a distributed optical fiber sensor, and a thermal load database is built in combination with historical data; 2, preliminary layout planning is conducted, air conditioner control subareas are divided according to surveying and mapping results and a database, the number and density of air outlets are adjusted, and customer dense areas are avoided; thirdly, airflow simulation and optimization are conducted, calculation fluid dynamics software is used for simulation, digital twinborn feedback data are used for establishing a deviation correction model, and an air outlet is adjusted; 4, dynamic adjustment design is carried out, intelligent air ports are arranged for the partitions, and a connection system is automatically adjusted; 5, verification and adjustment are conducted, simulation precision is optimized through field testing, and layout is adjusted. According to the method, the number and arrangement density of the air outlets are reasonably planned, the energy efficiency of the air conditioning system is improved, and energy waste is reduced.

Description

Technical Field

[0001] This invention relates to the field of air conditioning installation technology, specifically to a method for designing the layout of air conditioning vents. Background Technology

[0002] In large public buildings such as shopping malls, the performance of air conditioning systems directly affects customer comfort and the mall's energy consumption. However, existing air conditioning vent layout design methods have many technical problems. The problem of direct cold air blowing or excessively strong local airflow is quite prominent. In order to cool down quickly, some shopping malls place the air outlets in the customer activity area or adopt high-speed air outlet design, which causes the cold air to blow directly on the human body, making customers feel uncomfortable, especially in summer, which can easily cause customers to catch a cold in some areas. Poor airflow circulation and the existence of "dead zones" are also common problems. Shopping mall spaces include complex areas such as open halls, narrow corridors, and high-ceilinged atriums. If the layout of the air outlets is not designed in accordance with the spatial form, "heat islands" or "cold islands" are easily formed in corners, behind columns, and in areas with dense shelves. High-ceilinged spaces such as atriums will also experience "temperature stratification". The mismatch between regional load and air supply capacity is common. The heat load varies significantly in different areas of the shopping mall. For example, the catering area has a high heat load due to the heat dissipation of the stoves, while the clothing area has a high heat dissipation due to the high density of people. However, most designs adopt the concept of "uniform air distribution" and do not adjust the number, location or air volume of air outlets according to the load differences, resulting in some areas having higher temperatures and some areas wasting energy. The dynamic load adaptability is weak. The flow of people in the mall changes drastically with time and season, and the heat load fluctuates greatly. However, the air outlet layout is a fixed design, which cannot adjust the air outlet direction or intensity according to the real-time load. During peak and off-peak hours, there are problems of insufficient cooling capacity and excessive cooling, respectively. Therefore, we propose a method for designing the layout of air conditioning vents. Summary of the Invention

[0003] The purpose of this invention is to provide a method for designing the layout of air conditioning vents, which solves the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an air conditioning vent layout design method, comprising the following steps: Step 1: Spatial and Load Analysis: Spatial mapping of the target area is carried out to determine the functions of different areas. A dynamic collaborative control system driven by digital twins is introduced. A digital twin space is built based on the building information model, integrating static parameters. Physical quantity data of the entire area is collected through distributed fiber optic sensors, and a heat load database is established by combining historical data. Step 2: Preliminary layout planning: Divide the air conditioning control zones according to the spatial mapping results and heat load database, adjust the number and density of air outlets according to the heat load of each zone, and avoid air outlets in areas with high customer activity. Step 3: Airflow simulation and optimization: Use computational fluid dynamics software to simulate the airflow of the initial layout. The digital twin feeds back the real-time collected data to the simulation engine to build a deviation correction model. Adjust the position, angle and air volume of the air outlets according to the simulation results. Step 4: Dynamic Adjustment Design: Equip each air conditioning control zone with intelligent air outlets. The intelligent air outlets are connected to the central control system and the dynamic collaborative control system driven by digital twins, and automatically adjust the air outlet direction and air volume according to real-time monitoring data. Step 5: Verification and Adjustment: Conduct field tests, optimize simulation accuracy using a digital twin-driven dynamic collaborative control system, and adjust the vent layout based on the test results.

[0005] In a preferred embodiment of the present invention, in the space and load analysis step, the distributed optical fiber sensor is embedded in the ceiling, wall or ground, and the collected data includes temperature, humidity, airflow speed and personnel movement trajectory.

[0006] In a preferred embodiment of the present invention, in the preliminary layout planning step, the number and arrangement density of air outlets are increased for areas with high heat load, and the number of air outlets is reduced for areas with low heat load.

[0007] In a preferred embodiment of the present invention, in the airflow simulation and optimization step, side air outlets or increased air volume of the air outlets are provided for corners and positions behind columns that are prone to forming dead zones.

[0008] In a preferred embodiment of the present invention, in the airflow simulation and optimization step, for the high-ceilinged atrium area, a layered air supply method is adopted, with air outlets set at different heights.

[0009] In a preferred embodiment of the present invention, in the dynamic adjustment design step, the digital twin processes fiber optic sensor data in real time through edge computing nodes and generates a dynamic thermal load model by combining machine learning algorithms. When the regional function is adjusted, the system automatically identifies and calls the load characteristics of similar historical scenarios to correct the parameters.

[0010] In a preferred embodiment of the present invention, during the verification and adjustment steps, the computational grid for airflow simulation is optimized in real time using a particle swarm optimization algorithm to address dynamic interference factors such as customer flow.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention effectively solves the problem of mismatch between regional load and air supply capacity by rationally planning the number and density of air outlets, thereby improving the energy efficiency of the air conditioning system and reducing energy waste; When using CFD software to simulate airflow, a "measured-simulated" deviation correction model is established in conjunction with the system's real-time calibration mechanism. This makes the simulation results deviate less from the actual scene, allowing for the early detection and resolution of issues such as poor airflow circulation and "dead zones." Combined with the design of avoiding air outlets in areas with high customer activity, direct cold air blowing or excessively strong local airflow is avoided, thus improving customer comfort. The connection between the intelligent air outlet and the central control system and digital twin system enables dynamic adjustment of air supply according to real-time load, enhancing the dynamic load adaptability and maintaining good air conditioning effect during peak and off-peak hours. The dynamic collaborative control system driven by digital twins achieves real-time acquisition of physical quantities across the entire domain through distributed fiber optic sensors. Combined with machine learning algorithms, it generates a dynamic heat load model, which not only makes heat load data acquisition more comprehensive and efficient, reduces costs and eliminates the need for regular maintenance, but also solves the problems of high maintenance costs and slow response of traditional databases. The dynamic heat load model update response time is shortened. Attached Figure Description

[0012] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of an air conditioning vent layout design method according to the present invention. Detailed Implementation

[0013] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0014] For large shopping malls, the aim is to solve problems such as direct cold air blowing, poor air circulation, mismatch between regional loads, and weak dynamic load adaptability in existing air conditioning systems through scientific air conditioning vent layout design, thereby improving customer comfort and reducing energy consumption.

[0015] like Figure 1 As shown, an air conditioning vent layout design method is implemented as follows: Space and Load Analysis Spatial mapping: A comprehensive mapping of the shopping center is conducted using laser rangefinders and 3D scanners to generate a high-precision 3D spatial model; Functional zoning: Based on the layout of the shopping center, functional areas such as dining area, retail area, rest area, and atrium are divided; Sensor deployment: Distributed fiber optic sensors are embedded in ceilings, walls, and floors to collect real-time data on temperature, humidity, airflow speed, and personnel movement trajectories; Heat load database establishment: Combining historical operational data, a building information model (BIM) is built using digital twin technology, integrating static parameters with real-time collected data to form a heat load database.

[0016] Preliminary layout planning Air conditioning control zoning: Based on spatial mapping results and heat load database, the shopping center is divided into multiple air conditioning control zones, each with similar heat load characteristics; Determine the number and density of air vents: Increase the number and density of air vents in areas with high heat loads, such as dining areas; reduce the number of air vents in areas with low heat loads, such as rest areas. Ensure that air vents avoid main customer activity paths and rest areas.

[0017] Airflow simulation and optimization CFD software simulation: Computational fluid dynamics (CFD) software is used to simulate airflow in the preliminary layout and analyze the airflow distribution in each area; Deviation correction model establishment: The digital twin feeds back the real-time collected data to the CFD simulation engine to establish a "measured-simulated" deviation correction model, thereby improving simulation accuracy; Air outlet adjustment: Based on the simulation results, side air outlets are added or the air volume of the air outlets is increased in corners and behind columns where airflow dead zones are likely to form; for high-ceilinged atrium areas, a layered air supply method is adopted, and air outlets are set at different heights.

[0018] Dynamic adjustment design Smart air vent installation: Equip each air conditioning control zone with smart air vents, which have the function of automatically adjusting the air outlet direction and air volume; System Connection and Integration: Connect the intelligent air outlets with the central control system and the dynamic collaborative control system driven by digital twins to achieve real-time data interaction and command issuance; Dynamic heat load model generation: The digital twin processes fiber optic sensor data in real time through edge computing nodes and combines it with machine learning algorithms to generate a dynamic heat load model. When regional functions are adjusted, the system automatically identifies and calls upon load characteristics from similar historical scenarios to correct parameters.

[0019] Verification and Adjustment Field testing: A month-long field test was conducted in the shopping mall to record temperature, humidity, and customer comfort feedback in each area; Simulation accuracy optimization: By leveraging a digital twin-driven dynamic collaborative control system, the computational grid for airflow simulation is optimized in real time using a particle swarm optimization algorithm, thereby improving simulation accuracy; Air vent layout adjustment: Based on test results and customer feedback, the air vent layout is fine-tuned to ensure that the air conditioning effect in each area reaches the best.

[0020] In summary, this invention utilizes a digital twin-driven dynamic collaborative control system. Through distributed fiber optic sensors, it achieves real-time acquisition of physical quantities across the entire domain. Combined with machine learning algorithms, it generates a dynamic heat load model. This not only makes heat load data acquisition more comprehensive and efficient, reducing costs and eliminating the need for regular maintenance, but also solves the problems of high maintenance costs and slow response times associated with traditional databases. The dynamic heat load model update response time is shortened. By rationally planning the number and density of air outlets, it effectively solves the problem of mismatch between regional load and air supply capacity, improving the energy efficiency of the air conditioning system and reducing energy waste. When using CFD software for airflow simulation, the system's real-time calibration mechanism establishes a "measured-simulated" deviation correction model, minimizing the deviation between simulation results and actual scenarios. This allows for the early detection and resolution of airflow stagnation and "dead zones." Combined with the design of avoiding air outlets in areas with high customer activity, it prevents direct cold airflow or excessively strong local airflow, improving customer comfort. The connection between the intelligent air outlets and the central control system and digital twin system enables dynamic adjustment of air supply based on real-time load, enhancing dynamic load adaptability and maintaining good air conditioning performance during both peak and off-peak hours.

[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0022] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for designing the layout of air conditioning vents, characterized in that: The methods and steps include the following: Step 1: Spatial and Load Analysis: Spatial mapping of the target area is carried out to determine the functions of different areas. A dynamic collaborative control system driven by digital twins is introduced. A digital twin space is built based on the building information model, integrating static parameters. Physical quantity data of the entire area is collected through distributed fiber optic sensors, and a heat load database is established by combining historical data. Step 2: Preliminary layout planning: Divide the air conditioning control zones according to the spatial mapping results and heat load database, adjust the number and density of air outlets according to the heat load of each zone, and avoid air outlets in areas with high customer activity. Step 3: Airflow simulation and optimization: Use computational fluid dynamics software to simulate the airflow of the initial layout. The digital twin feeds back the real-time collected data to the simulation engine to build a deviation correction model. Adjust the position, angle and air volume of the air outlets according to the simulation results. Step 4: Dynamic Adjustment Design: Equip each air conditioning control zone with intelligent air outlets. The intelligent air outlets are connected to the central control system and the dynamic collaborative control system driven by digital twins, and automatically adjust the air outlet direction and air volume according to real-time monitoring data. Step 5: Verification and Adjustment: Conduct field tests, optimize simulation accuracy using a digital twin-driven dynamic collaborative control system, and adjust the vent layout based on the test results.

2. The air conditioning vent layout design method according to claim 1, characterized in that: In the space and load analysis step, the distributed fiber optic sensors are embedded in the ceiling, wall or ground, and the data collected includes temperature, humidity, airflow speed and personnel movement trajectory.

3. The air conditioning vent layout design method according to claim 1, characterized in that: In the initial layout planning stage, the number and density of air outlets are increased in areas with high heat load, while the number of air outlets is reduced in areas with low heat load.

4. The air conditioning vent layout design method according to claim 1, characterized in that: In the airflow simulation and optimization process, side air outlets or increased air volume at the outlets can be installed in corners or behind columns where dead zones are likely to form.

5. The air conditioning vent layout design method according to claim 1, characterized in that: In the airflow simulation and optimization process, for high-ceilinged atrium areas, a layered air supply method is adopted, with air outlets set at different heights.

6. The air conditioning vent layout design method according to claim 5, characterized in that: In the dynamic adjustment design step, the digital twin processes fiber optic sensor data in real time through edge computing nodes and generates a dynamic thermal load model by combining machine learning algorithms. When the regional function is adjusted, the system automatically identifies and calls the load characteristics of similar historical scenarios to correct the parameters.

7. The air conditioning vent layout design method according to claim 1, characterized in that: In the verification and adjustment steps, the computational grid for airflow simulation is optimized in real time using the particle swarm optimization algorithm to address dynamic interference factors such as customer flow.

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