A control method and device for a fan in a high-altitude intelligent center Mini wind wall module

Abstract

The application belongs to the technical field of data center HVAC system design and energy efficiency optimization, and particularly relates to a control method and equipment for a fan in a high-altitude intelligent computing center Mini air wall module. The method comprises selecting an optimal redundancy coefficient with the lowest energy consumption ratio under the condition of meeting the heat dissipation safety boundary, determining an optimal design air volume according to the optimal redundancy coefficient and a theoretical minimum volume air flow, controlling the fan operation according to the optimal redundancy coefficient, and monitoring the outlet liquid temperature of the cooling coil and the return air temperature of the hot aisle in real time to adjust the direct current fan and the electronic speed regulation fan based on the optimal design air volume. The application quantifies the physical problem of "low-density air easy to be disturbed" into "cooling pool temperature standard deviation" constraints for the first time, breaking out of the limitations of the traditional "temperature-only theory". The application optimizes the energy efficiency ratio of the Mini air wall module under the premise of improving the heat carrying capacity of unit mass air and reducing the disturbance of low-density air.

Description

Technical Field

[0001] This application belongs to the field of data center HVAC system design and energy efficiency optimization technology, specifically involving a control method and equipment for a fan in a Mini air wall module of a high-altitude intelligent computing center. Background Technology

[0002] As the infrastructure for training and inference of artificial intelligence models, intelligent computing centers are experiencing a continuous increase in computing power requirements and power density. Intelligent computing centers centered around graphics processing units (GPUs) can have single-rack power exceeding 35 kilowatts, which is 5 to 7 times that of traditional data centers. Mini airwall modules, due to their long air delivery distance and good airflow organization, have become the mainstream cooling solution for high-density intelligent computing centers.

[0003] The national "East-to-West Computing" project encourages the location of high-energy-consuming intelligent computing centers in high-altitude areas of western China to utilize abundant local renewable energy and natural cooling resources, promoting low-carbon, green, and sustainable development of data centers. However, the high-altitude environment severely limits the heat-carrying capacity of the air per unit mass in Mini airflow wall modules. According to thermodynamic formulas, the heat-carrying capacity of air is directly proportional to its density. At an altitude of 3000 meters, the air density is only about 70% of that at sea level. This means that to remove the same amount of heat generated by IT equipment, Mini airflow wall modules need to deliver a larger volumetric flow rate of air. Improving the heat-carrying capacity per unit mass of air becomes crucial for reducing fan power consumption.

[0004] Currently, fan operation is typically controlled by grouping multiple AC fans for start-stop control or by proportional-integral-derivative (PID) control of electronically controlled fans. However, grouping start-stop control of AC fans cannot guarantee continuous airflow regulation, while PID control of electronically controlled fans suffers from oscillation and / or overshoot problems. Summary of the Invention

[0005] This application aims to provide a control method and equipment for the fan in the Mini wind wall module of a high-altitude intelligent computing center. Through quantitative evaluation and optimized control, the energy efficiency ratio is optimized while improving the heat carrying capacity of air per unit mass and reducing low-density air disturbance.

[0006] To address the reduced heat-carrying capacity per unit mass of air and the susceptibility to disturbance caused by lower air density at high altitudes, this application first establishes a dual-constraint heat dissipation safety condition, including the maximum temperature of the hot channel and the standard deviation of the cold pool temperature. The former characterizes the degree of heat-carrying capacity realization, while the latter quantifies the degree of airflow disturbance suppression. Then, it reveals the physical mechanism between increasing the airflow ratio and improving heat-carrying capacity while reducing disturbance, and quantifies this using sensible heat exchange formulas and momentum formulas. Next, using the annual average power utilization efficiency as the objective function, it selects the optimal redundancy coefficient within the heat dissipation safety boundary. Finally, it employs an independent and coordinated strategy of fixed-frequency control of the indoor DC fan and proportional-integral-derivative / percentage control of the outdoor electronically controlled fan to achieve the goals of indoor air field stability and outdoor heat exchange efficiency optimization, respectively.

[0007] As a first aspect of this application, this application provides a control method for the fans in a Mini air wall module of a high-altitude intelligent computing center. The Mini air wall module includes: an electronically controlled speed fan installed in an outdoor integrated cooling station; multiple equipment cabinets arranged side by side and having an air inlet side and an air outlet side; a hot aisle connected to the air outlet side of the multiple equipment cabinets for collecting the hot air discharged from the equipment cabinets; a fan section whose air inlet side is connected to the hot aisle, and a DC fan is installed in the fan section, which operates in a fixed-frequency control mode; a surface cooler whose air inlet side is connected to the air outlet side of the fan section and connected to the outdoor integrated cooling station through a liquid circuit, with chilled water provided by the outdoor integrated cooling station; and a cold pool connected to the air outlet side of the surface cooler and connected to the air inlet side of the multiple equipment cabinets, forming a complete airflow circulation loop.

[0008] The method includes the following steps:

[0009] Determine the heat dissipation safety boundary that satisfies heat dissipation safety. The heat dissipation safety boundary consists of all air volume ratios that meet the heat dissipation safety conditions.

[0010] With the goal of minimizing the annual average power utilization efficiency-to-energy ratio, the optimal redundancy coefficient that minimizes the energy consumption ratio is selected within the heat dissipation safety boundary, and the optimal design air volume is determined based on the optimal redundancy coefficient and the theoretical minimum volumetric air volume.

[0011] The operation of the DC fan is controlled according to the optimal redundancy coefficient so that the actual operating air volume of the DC fan reaches the optimal design air volume.

[0012] The electronic speed-regulating fan is controlled according to the optimal redundancy coefficient to maintain a stable liquid outlet temperature of the surface cooler;

[0013] The outlet liquid temperature of the surface cooler and the return air temperature of the hot channel are monitored in real time, and the DC fan and electronic speed control fan are adjusted based on the optimal design air volume.

[0014] The second aspect of this application provides a mini windbreak module fan control device for a high-altitude intelligent computing center, comprising:

[0015] Memory, used to store computer programs;

[0016] The processor is used to execute a computer program stored in memory to implement the control method of the wind turbine in the Mini wind wall module of the high-altitude intelligent computing center as described in any of the first aspects of this application; and to update the optimal redundancy coefficient and the optimal switching temperature based on actual operating data.

[0017] The input interface is used to receive the altitude of the project location, the server configuration list, the geometric dimensions and equipment layout parameters of the Mini windwall module, and the meteorological data source address;

[0018] The output interface is used to output the optimal redundancy coefficient, optimal design air volume, optimal energy consumption ratio, and energy efficiency rating.

[0019] The communication interface is used to exchange data with the Mini wind wall module controller of the high-altitude intelligent computing center, send the optimal design air volume to the DC fan controller of the fan section, and send the optimal switching temperature to the controller of the outdoor integrated cooling station.

[0020] The display unit is used to graphically display the curves showing the relationship between the redundancy coefficient and the annual average energy consumption ratio, as well as the curves showing the relationship between the natural cooling switching temperature and the energy consumption ratio.

[0021] As can be seen from the above technical solution, this application provides a control method and device for the fan in the Mini air wall module of a high-altitude intelligent computing center. Because this application establishes a computational fluid dynamics model, using the dual constraints of the hot channel's highest temperature not exceeding a first preset threshold and the cold pool temperature standard deviation not exceeding a second preset threshold, it determines the minimum airflow redundancy coefficient to meet heat dissipation safety. Thus, the airflow ratio range greater than or equal to this minimum airflow redundancy coefficient is used as the heat dissipation safety boundary. Therefore, this application, for the first time, quantifies the physical problem of "low-density air being easily disturbed" into a "cold pool temperature standard deviation" constraint, breaking free from the limitations of the traditional "temperature-only" approach. Furthermore, because this application aims to minimize the annual average power utilization efficiency-to-energy ratio, it selects the optimal redundancy coefficient that minimizes the energy consumption ratio within the heat dissipation safety boundary. Therefore, this application achieves a shift from "empirical design" to "quantitative optimization." Furthermore, this application controls the operation of the DC fan and the electronically controlled fan separately based on the optimal redundancy coefficient. The DC fan uses a fixed-frequency control method to maintain stable air pressure in the cold pool and hot aisle, while the electronically controlled fan uses a proportional-integral-derivative / percentage control method to cope with changes in outdoor operating conditions. Therefore, this application achieves independent and coordinated control for both indoor airflow stability and outdoor heat exchange efficiency optimization. Thus, this application optimizes the energy efficiency ratio of the Mini air wall module while improving the heat carrying capacity per unit mass of air and reducing low-density air disturbance. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the Mini air wall module structure provided in one embodiment of this application.

[0023] Figure 2 This is a schematic flowchart illustrating a control method for a fan in a Mini windbreak module of a high-altitude intelligent computing center, provided as an embodiment of this application.

[0024] Figure 3 This is a schematic diagram of step S1 provided in an embodiment of this application.

[0025] Figure 4 This is a schematic diagram of step S2 provided in an embodiment of this application.

[0026] Figure 5 This is a flowchart illustrating step S3 of an embodiment of this application.

[0027] Figure 6 This is a flowchart illustrating step S4 of an embodiment of this application.

[0028] Figure 7 This is a flowchart illustrating step S5 of an embodiment of this application.

[0029] Figure 8 This is a schematic diagram of a mechanism response method provided in an embodiment of the present application for detecting a maximum temperature in a hot channel that is greater than a first preset threshold.

[0030] Figure 9 This is a schematic diagram of a mechanism response method provided in an embodiment of the present application when the standard deviation of the cold pool temperature is detected to be greater than a second preset threshold.

[0031] Figure 10 This is a schematic diagram of a mechanism response method provided in an embodiment of the present application when the highest temperature of the hot channel is detected to be greater than a first preset threshold and the standard deviation of the cold pool temperature is greater than a second preset threshold.

[0032] Figure 11 This is a schematic diagram of a natural cooling switching temperature optimization method provided in an embodiment of this application.

[0033] Figure 12 This is a CFD calculation model diagram of an embodiment of the present application, showing the three-dimensional calculation area of ​​the cabinet and air conditioning layout in the data center.

[0034] Figure 13 This is a graph showing the trend of air conditioner return air temperature changing with the number of grid cells according to an embodiment of this application.

[0035] Figure 14 This is a statistical table of air conditioning return air temperature under different grid numbers according to an embodiment of this application.

[0036] Figure 15 This is a temperature distribution diagram of the air cross section of a smart computing center according to an embodiment of this application.

[0037] Figure 16 This is a diagram showing the air velocity streamline distribution of a computing center according to an embodiment of this application.

[0038] Figure 17 This is a flow chart of air temperature distribution in a smart computing center according to an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Taking a smart computing center in Shannan, Tibet as an example, this center is located in the Yarlung Tsangpo River valley at an altitude of 3,500 meters, where the atmospheric pressure is approximately 65.77 kPa and the air density is approximately 0.771 kg / m³. 3 This is only the air density at sea level (1.225 kg / m³). 3 This represents 58% of the total heat generated by the IT equipment. The equipment room of this intelligent computing center houses 120 server racks and 55 air conditioners, each with a maximum cooling capacity of 70kW. Air enters the server racks for heat exchange; the heated air then flows out of the racks and is drawn back in by the air conditioners for cooling, before being discharged back into the room. Some racks (such as server1_1 to server1_20) each have three servers, each with a heat output of 4.5kW, for a total heat output of 270kW. Other racks each have three servers, each with a heat output of 11kW, for a total heat output of 660kW per module. The total heat output of the IT equipment is 3570kW.

[0042] In this scenario, the Mini air wall module faces two major technical challenges: First, due to the low air density, the heat-carrying capacity per unit mass of air is severely insufficient. Theoretical calculations show that, under the same supply and return air temperature difference, approximately 54% more volumetric air volume is needed than at sea level to remove the same amount of heat. Second, the low-density air has low momentum, significantly shortening the range of airflow from the cold pool to the equipment cabinet. It is also highly susceptible to interference from equipment fans and personnel movement, leading to the mixing of hot and cold airflows and the formation of local hot spots.

[0043] The outdoor integrated chiller station is connected to the surface cooler via a liquid circuit. The outdoor integrated chiller station is equipped with an electronically controlled fan, compressor, and water pump. The electronically controlled fan drives outdoor air to flow through the cooling coils, dissipating heat from the chilled water into the outdoor environment. The electronically controlled fan uses proportional-integral-derivative (PID) or percentage control to adjust its speed to cope with frequent changes in outdoor temperature, humidity, and wind speed. Figure 1 As shown, the Mini air wall module includes the following components:

[0044] Outdoor integrated chiller station: Located outdoors, it contains an electronically controlled fan, compressor, and water pump. The electronically controlled fan drives outdoor air to flow through the cooling coils, dissipating heat from the chilled water into the outdoor environment. The electronically controlled fan uses proportional-integral-derivative (PID) or percentage control to adjust its speed to cope with frequent changes in outdoor temperature, humidity, and wind speed.

[0045] Multiple equipment cabinets: arranged side by side, each with an air inlet and an air outlet. When the equipment is running, cooling airflow enters the equipment cabinet from the air inlet side, carries away the heat generated by the equipment, and then exits from the air outlet side.

[0046] Hot aisle: Connected to the air outlet side of multiple equipment cabinets, it is a closed airflow collection space used to collect the hot airflow discharged from the equipment cabinets.

[0047] Fan Section: Its air inlet side is connected to the hot passage, and a DC fan is installed in the fan section. The DC fan operates in a fixed frequency control mode, that is, each DC fan runs at a constant speed to maintain stable air pressure in the cold pool and hot passage.

[0048] Surface cooler: Its air inlet side is connected to the air outlet side of the fan section, and it is also connected to the outdoor integrated cooling station via a liquid circuit, with chilled water supplied by the outdoor integrated cooling station. The surface cooler contains cooling coils, within which chilled water supplied by the outdoor integrated cooling station flows. When hot air flows through the surface cooler, the heat is carried away by the chilled water, and the air temperature decreases.

[0049] Cold Compartment: Connected to the air outlet side of the surface cooler and the air inlet side of multiple equipment cabinets. The cold compartment is a closed air supply static pressure space used to store the cooled airflow and ensure that the airflow can be evenly distributed to the air inlet side of each equipment cabinet, forming a complete airflow circulation loop.

[0050] The equipment room, housing multiple server racks, contains 120 server racks and 55 air conditioners, each with a maximum cooling capacity of 70kW. Air enters the server racks for heat exchange; the heated air then flows out of the racks and is drawn back in by the air conditioners for cooling, before being discharged back into the room. After necessary and reasonable simplification of the model and extraction of the air region, the following is obtained: Figure 12 The computing area shown, in which the air conditioners and servers are numbered as follows:Figure 12 As shown.

[0051] To overcome the physical limitations of insufficient heat-carrying capacity per unit mass of air and the susceptibility to disturbance of low-density air in high-altitude environments, and to optimize the energy efficiency ratio of the Mini airflow wall module, this application provides a control method for the fan in the Mini airflow wall module of a high-altitude intelligent computing center. The specific process is as follows: Figure 2 As shown, the specific steps include S1 to S5.

[0052] Step S1: Determine the heat dissipation safety boundary that meets the heat dissipation safety conditions. The heat dissipation safety boundary consists of all airflow ratios that meet the heat dissipation safety conditions.

[0053] Specifically, such as Figure 3 As shown, step S1 may include the following steps S11 to S13.

[0054] Step S11: The heat dissipation safety condition is that the highest temperature of the hot channel does not exceed the first preset threshold and the temperature standard deviation of the cold pool does not exceed the second preset threshold.

[0055] The first preset threshold is 38℃, and the second preset threshold is 1.5℃.

[0056] The maximum temperature of the hot aisle is used to characterize the degree of heat carrying capacity per unit mass of air: when the maximum temperature of the hot aisle is less than or equal to the first preset threshold, it is determined that the air mass flow rate passing through the surface cooler and the equipment cabinet surface per unit time is sufficient to remove the heat generated by the equipment; when the maximum temperature of the hot aisle is greater than the first preset threshold, it is determined that the air mass flow rate is insufficient.

[0057] The above-mentioned cold pool temperature standard deviation is used to characterize the degree of suppression of low-density air disturbance: when the cold pool temperature standard deviation is detected to be less than or equal to the second preset threshold, it is determined that the airflow driven by the DC fan operating at fixed frequency has formed a stable piston airflow organization when it delivers air from the cold pool to the air inlet side of the equipment cabinet; when the cold pool temperature standard deviation is detected to be greater than the second preset threshold, it is determined that there is mixing of hot and cold airflow.

[0058] Step S12: Determine the minimum airflow redundancy coefficient that meets the heat dissipation safety conditions through computational fluid dynamics simulation.

[0059] Specifically, such as Figure 3 As shown, step S12 may include the following steps S121 to S124.

[0060] Step S121: Establish a computational fluid dynamics model that includes the Mini wind wall module.

[0061] When establishing the computational fluid dynamics model, the meshing strategy is set based on the actual geometric dimensions of the Mini air wall module: the mesh size of the air inlet and outlet areas of the equipment cabinet is ≤10mm; the mesh size of the air inlet and outlet areas of the fan section is ≤15mm; the mesh size of the air inlet and outlet areas of the surface cooler is ≤15mm; and the mesh size of the hot aisle and cold pool areas is ≤50mm.

[0062] To ensure the reliability and accuracy of the numerical simulation results and eliminate the influence of grid density on the calculation results, a system-wide grid independence verification analysis was conducted. The model grid sizes were 4,502,822, 7,103,536, and 8,808,809, respectively. During the verification process, key parameters such as the physical model, boundary conditions, turbulence model, and solver settings were kept completely consistent. The return air temperatures of air conditioners DWA1_1 to DWA1_5 were selected as the target physical quantity.

[0063] The verification results are as follows Figure 13 As shown, as the grid size gradually increased from 4.5 million to 8.8 million, all target physical quantities exhibited a good convergence trend. Figure 14 As shown, the temperature in the DWA1_1_in operating condition was slightly adjusted from 33.94℃ to 33.81℃, the temperature in the DWA1_2_in operating condition increased slightly from 33.91℃ to 33.98℃, the temperature in the DWA1_3_in operating condition converged from 34.12℃ to 34.21℃, the temperature in the DWA1_4_in operating condition gradually increased from 34.23℃ to 34.30℃, and the temperature in the DWA1_5_in operating condition decreased slightly from 33.92℃ to 33.85℃. The overall difference was controlled within 0.2℃. Considering both computational accuracy and research requirements, 8,808,809 grid cells were selected as the final calculation scheme, and all subsequent numerical simulations were conducted based on this grid size.

[0064] Since the internal structure of the server is unknown, it is simplified as a porous media model. The porous media parameters are approximately set according to the data in the literature "Liu Hongmin, Bian Jiagang. Optimization of thermal performance of server air intake baffle for single-rack data center [J]. Electrical Engineering, 2022(22):95-99." The heat generation power of the rack area is set according to the volume source item.

[0065] Step S122: Set multiple air volume ratios to scale the theoretical minimum volumetric air volume, and perform computational fluid dynamics simulation for each air volume ratio.

[0066] Boundary conditions were set for each airflow ratio, with the wall boundary condition set to a no-slip wall, and heat exchange on the wall not considered. The air inlets of the fan section were set as mass flow inlets, and the mass flow rate of the air inlets of the fan section was equal to the airflow ratio multiplied by the theoretical minimum volumetric airflow multiplied by the local air density, divided by the number of DC fans, and then divided by 3600. The return air inlets of the hot aisle were set as pressure outlets. According to the simulation report, the baseline airflow rate for a single air conditioner was set to 25000 m³ / h. For each airflow ratio α, the mass flow rate of the air inlets of the fan section was calculated as: Mass flow rate = α × 25000 × ρ / 3600 (kg / s), where ρ is the local air density.

[0067] Computational fluid dynamics was performed using a k-ε two-equation turbulence model until convergence. The convergence criterion included: the residuals of each equation being less than 1 × 10⁻⁶. -4 The temperature change rate at the monitoring point is less than 1×10 -5 For 100 consecutive steps, the global energy imbalance is less than 0.1%.

[0068] Step S123: Extract the highest temperature in the hot channel and the standard deviation of the temperature in the cold pool from the simulation results.

[0069] Step S124: The minimum air volume ratio that satisfies the condition that the maximum temperature of the hot aisle does not exceed the first preset threshold and the standard deviation of the cold pool temperature does not exceed the second preset threshold is used as the minimum air volume redundancy coefficient.

[0070] Step S13: Use the range of airflow ratios that are greater than or equal to the minimum airflow redundancy coefficient as the heat dissipation safety boundary.

[0071] Step S2: With the goal of minimizing the annual average power utilization efficiency-to-energy ratio, select the optimal redundancy coefficient that minimizes the energy consumption ratio within the heat dissipation safety boundary, and determine the optimal design air volume based on the optimal redundancy coefficient and the theoretical minimum volumetric air volume.

[0072] Before performing step S2, it is necessary to calculate the theoretical minimum volumetric air volume. For example... Figure 4 As shown, the calculation method for the theoretical minimum volumetric air volume includes the following steps S01 to S04.

[0073] Step S01: Calculate the total heat output Q of the IT equipment based on the server configuration list and simultaneous usage coefficient. IT .

[0074] Step S02: Determine the design supply and return air temperature difference ΔT based on the upper limit of the server's inlet air temperature and the upper limit of the return air temperature.

[0075] Typically, the upper limit for server intake air temperature is 24℃, the upper limit for return air temperature is 38℃, and the designed supply and return air temperature difference ΔT is 14℃.

[0076] Step S03: Calculate the air mass flow rate ṁ according to the sensible heat exchange formula.

[0077] Air mass flow rate (ṁ) equals the total heat output of IT equipment (Q). IT Divide by the local air specific heat capacity c p The product of this and the design supply and return air temperature difference ΔT is ṁ=Q IT / (cp×ΔT).

[0078] Step S04: Calculate the theoretical minimum volumetric air volume Qmin based on the relationship between volumetric flow rate and mass flow rate.

[0079] The theoretical minimum volumetric air volume Qmin is equal to the air mass flow rate ṁ divided by the local air density ρ and then multiplied by 3600, that is, Qmin=ṁ / ρ×3600, the unit is cubic meters per hour.

[0080] Specifically, such as Figure 4 As shown, step S2 may include steps S21 to S25.

[0081] Step S21: Obtain hourly meteorological data for the project location throughout the year.

[0082] The hourly dry-bulb temperature Tdb(t) and wet-bulb temperature Twb(t) for 8760 hours throughout the year were obtained from a typical meteorological year database for the project location.

[0083] Step S22: Adjust the performance parameters of the refrigeration equipment according to altitude.

[0084] Altitude correction includes: adjusting the heat transfer coefficient to be proportional to the 0.8th power of the ratio of local air density to sea-level air density, i.e., h. H =h0×(ρ H / ρ0) 0.8 The wind turbine power is corrected to be proportional to the ratio of local air density to sea-level air density, i.e., W. fan,H =W fan,0 ×(ρ H / ρ0); The compressor's energy efficiency ratio is corrected to be proportional to the 0.3 power of the ratio of local air density to sea-level air density, i.e., COP. H =COP0×(ρ H / ρ0) 0.3 .

[0085] Step S23: Determine whether to use natural cooling mode or mechanical cooling mode based on hourly dry-bulb temperature and water supply temperature.

[0086] When the hourly dry-bulb temperature T db(t) Less than or equal to water supply temperature T supply Subtract the temperature difference T approach When the temperature is high, use natural cooling mode; otherwise, use mechanical cooling mode. Typical parameter: Tsupply =20℃, T approach =2℃, switching temperature is 18℃.

[0087] Step S24: For each airflow rate within the heat dissipation safety boundary, calculate the cooling system energy consumption and IT energy consumption hourly.

[0088] Natural cooling mode: P cooling(t) =P fan,α(t) +P pump(t) In mechanical refrigeration mode: P cooling(t) =P comp(t) +P fan,α(t) +P pump(t) The fan power is directly proportional to the cube of the air volume ratio: P fan,α(t) =P fan,1(t) ×α 3 IT energy consumption: P IT (t)=P IT,rated ×L(t), where L(t) is the hourly load rate.

[0089] Step S25: Calculate the annual average energy consumption ratio corresponding to each air volume ratio, and take the air volume ratio corresponding to the minimum value as the optimal redundancy coefficient α. opt .

[0090] Annual average power utilization efficiency , where P UE,α,avg P represents the annual average energy efficiency. IT (t) represents the power consumption of the IT equipment at time t; P cooling,α (t) represents the power consumption of the cooling system at time t; k loss The power supply and distribution loss coefficient is usually taken as 0.08 to 0.12.

[0091] Step S3: Control the operation of the DC fan according to the optimal redundancy coefficient so that the actual operating air volume of the DC fan reaches the optimal design air volume.

[0092] Specifically, such as Figure 5 As shown, step S3 may include the following steps S31 to S33.

[0093] Step S31: The DC fan adopts a fixed frequency control mode to maintain stable air pressure in the cold pool and hot passage, ensuring that the airflow has sufficient inertia to overcome resistance.

[0094] Step S32: When the optimal redundancy coefficient α opt When the value is less than or equal to the first redundancy threshold, it is determined that simply adjusting the number of DC fans in operation is sufficient to improve the heat carrying capacity of air per unit mass and reduce low-density air disturbance.

[0095] The first redundancy threshold mentioned above is 1.2.

[0096] Step S33: When the optimal redundancy coefficient α opt When the number of DC fans exceeds the first redundancy threshold, it is determined that the number of operating DC fans needs to be increased to jointly achieve the goal of improving the heat carrying capacity of air per unit mass and reducing low-density air disturbance.

[0097] In addition, such as Figure 5 As shown, step S3 may also include initiating a control strategy, specifically including the following steps S34 to S35.

[0098] Step S34: During startup, start each DC fan one by one. After each DC fan starts, it runs at a fixed frequency and rated speed to gradually establish the airflow and air pressure from the cold pool to the air intake side of the equipment cabinet, thereby reducing the initial disturbance of low-density air in the closed-loop airflow organization of the Mini air wall module during the startup phase.

[0099] Step S35: When the DC fan that has been started reaches the rated speed, if the highest temperature of the hot channel still exceeds the first preset threshold or the standard deviation of the cold pool temperature still exceeds the second preset threshold, it is determined that the heat carrying capacity of the unit mass of air is still insufficient or low-density air disturbance still exists, and more DC fans are started in stages.

[0100] Step S4: Control the operation of the electronic speed-regulating fan according to the optimal redundancy coefficient to maintain the stable liquid outlet temperature of the surface cooler.

[0101] Specifically, such as Figure 6 As shown, step S4 may include the following steps S41 to S43.

[0102] Step S41: When the outlet temperature Tout of the surface cooler is higher than the target outlet temperature Ttarget, increase the speed of the electronically controlled fan.

[0103] Step S42: When the outlet temperature Tout of the surface cooler is lower than the target outlet temperature Ttarget, reduce the speed of the electronic speed-regulating fan.

[0104] Step S43: The speed regulation of the electronically controlled fan and the fixed frequency control of the DC fan in the fan section are independent of each other, so as to achieve the purpose of stabilizing the indoor air field and optimizing the outdoor heat exchange efficiency respectively.

[0105] Step S5: Monitor the liquid outlet temperature of the surface cooler and the return air temperature of the hot channel in real time, and adjust the DC fan and electronic speed control fan based on the optimal design air volume.

[0106] Specifically, such as Figure 7 As shown, step S5 may include a heat-carrying insufficiency response strategy, specifically including the following steps S51 to S52.

[0107] Step S51: When the liquid outlet temperature of the surface cooler is higher than the first liquid outlet temperature threshold or the return air temperature of the hot channel is higher than the first return air temperature threshold, it is determined that the heat carrying capacity of the unit mass of air is insufficient.

[0108] The first liquid outlet temperature threshold is 21°C, and the first return air temperature threshold is 38°C.

[0109] Step S52: Perform the following operations: (S521) If the number of DC fans currently in operation is less than the total number of fans, add one DC fan at each stage and operate it at a fixed frequency to increase the mass flow rate of air passing through the surface cooler and equipment cabinet surface per unit time; (S522) If all DC fans have been started and the liquid outlet temperature is still higher than the second liquid outlet temperature threshold, increase the speed of the electronically controlled fan in the outdoor integrated cooling station.

[0110] The second outlet temperature threshold is 22℃.

[0111] like Figure 7 As shown, step S5 may also include a heat-carrying excess response strategy, specifically including the following steps S53 to S54.

[0112] Step S53: When the liquid outlet temperature of the surface cooler is lower than the third liquid outlet temperature threshold and the return air temperature of the hot channel is stable within the predetermined range, it is determined that the heat carrying capacity of the unit mass of air is excessive.

[0113] The third outlet temperature threshold is 19°C, and the predetermined range is 35°C to 38°C.

[0114] Step S54: Perform the following operations: (S541) If the number of DC fans currently in operation is greater than the minimum number of operating fans, shut down one DC fan at a time to reduce the mass flow rate of air passing through the surface cooler and equipment cabinet surface per unit time, but keep at least one DC fan running to ensure that the cold pool and hot aisle have sufficient air pressure to maintain airflow inertia; (S542) If the number of DC fans has been reduced to the minimum number of operating fans and the liquid outlet temperature is still lower than the fourth liquid outlet temperature threshold, reduce the speed of the electronically controlled fans in the outdoor integrated cooling station.

[0115] The fourth outlet temperature threshold mentioned above is 18℃.

[0116] During the adjustment process, the relationship between the actual operating air volume Qactual and the optimal design air volume Qopt is: 0.9×Qopt≤Qactual≤1.1×Qopt.

[0117] When the highest temperature in the hot channel is detected to be greater than a first preset threshold, this application embodiment also provides a mechanism response method, the specific process of which is as follows: Figure 8 As shown, the specific steps include S611 to S614.

[0118] Step S6111: According to the sensible heat exchange formula Calculate the current air mass flow rate ,in ρ is the local air density, and Qv is the current volumetric airflow.

[0119] Step S612: Calculate the target air mass flow rate required to reduce the maximum temperature of the hot aisle below the first preset threshold. Where QIT is the total heat generation of IT equipment, cp is the local specific heat capacity of air, and ΔT is the design supply and return air temperature difference.

[0120] Step S613: Based on the target air mass flow rate ṁ target With current air quality flow rate current The ratio determines the required first air volume ratio Δα1=ṁ target / ṁ current .

[0121] Step S614: Increase the number of DC fans in the fan section or increase their speed to increase the volumetric air volume by the first air volume ratio, thereby increasing the product of the current air mass flow rate and improving the heat carrying capacity of the unit mass of air.

[0122] When the standard deviation of the cold pool temperature is detected to be greater than a second preset threshold, this application embodiment also provides a mechanism response method, the specific process of which is as follows: Figure 9 As shown, the specific steps include S621 to S624.

[0123] Step S621: Calculate the momentum of the current airflow from the cold pool to the air inlet side of the equipment cabinet according to the momentum formula. .

[0124] The above air supply velocity v = Qv / A, where A is the air supply area.

[0125] Step S622: Calculate the target momentum ptarget required to reduce the standard deviation of the cold pool temperature below the second preset threshold.

[0126] The aforementioned target momentum p target The ratio of the current momentum p to the cold pool temperature standard deviation σ is equal to the current momentum p. cold The ratio of the square of to the square of the second preset threshold, i.e., p target / p=(σ cold / 1.5) 2 .

[0127] Step S623: Determine the required second airflow ratio based on the target momentum and the current momentum. .

[0128] Step S624: Increase the number of DC fans operating in the control fan section to increase the volumetric air volume by the second air volume ratio, thereby increasing the air delivery speed v and momentum p, extending the air delivery range L∝ρ×v^2, and reducing the mixing of hot and cold air.

[0129] When the highest temperature in the hot channel is detected to be greater than a first preset threshold and the standard deviation of the cold pool temperature is detected to be greater than a second preset threshold, this application embodiment also provides a joint mechanism response method, the specific process of which is as follows: Figure 10 As shown, the specific steps include S631 to S633.

[0130] Step S631: Calculate the first air volume ratio Δα1 and the second air volume ratio Δα2.

[0131] The first air volume ratio Δα1 is calculated according to the method of Example 4, and the second air volume ratio Δα2 is calculated according to the method of Example 5.

[0132] Step S632: Take the larger of the first air volume ratio and the second air volume ratio as the third air volume ratio Δα3=max(Δα1,Δα2).

[0133] Step S633: Increase the number of DC fans operating in the control fan section to increase the volumetric air volume by the third air volume ratio, thereby improving the heat carrying capacity of the unit mass of air and reducing low-density air disturbance.

[0134] In some embodiments, step S2 further includes a natural cooling switching temperature optimization method, the specific process of which is as follows: Figure 11 As shown, the specific steps include S251 to S253.

[0135] Step S251: Use the natural cooling mode switching temperature Tswitch as an optimization variable and scan within the preset scanning range at a predetermined step size.

[0136] The preset scanning range is 14°C to 22°C, and the preset step size is 1°C.

[0137] Step S252: Repeat the altitude-corrected energy consumption ratio simulation step and the optimal redundancy coefficient selection step for each switching temperature, and select the switching temperature that minimizes the lowest energy consumption ratio as the optimal switching temperature Tswitch,opt.

[0138] Step S253: Switch the cooling source mode of the outdoor integrated cooling station according to the optimal switching temperature.

[0139] When the outdoor dry-bulb temperature is less than or equal to the optimal switching temperature, switch to natural cooling mode, turn off the compressor, and only run the electronically controlled fan and water pump in the outdoor integrated cooling station; when the outdoor dry-bulb temperature is greater than the optimal switching temperature, switch to mechanical cooling mode and turn on the compressor.

[0140] This application also provides an optimal relationship between the optimal redundancy coefficient and the altitude H: α opt =1+0.0001×(H−2000) for H≥2000m, where H is in meters; when H=2000m, α opt =1.2; when H=3500m, α opt =1.4; when H=4000m, α opt =1.5.

[0141] This application also provides a formula for calculating the standard deviation of cold pool temperature: Ti represents the temperature at each monitoring point in the cold pool, Tavg represents the average temperature of the cold pool, and n represents the number of monitoring points.

[0142] In order to implement the above-mentioned control method for the fan in the Mini wind wall module of the high-altitude intelligent computing center, this application embodiment also provides a fan control device for the Mini wind wall module of the high-altitude intelligent computing center, which specifically includes the following components.

[0143] Memory: Used to store computer programs. Memory can be implemented as various storage media such as electrically erasable programmable read-only memory, flash memory, and programmable program read-only memory.

[0144] Processor: Used to execute computer programs stored in memory to implement the control method of the wind turbine in the Mini wind wall module of the high-altitude intelligent computing center as described in any of the above embodiments; and to update the optimal redundancy coefficient and optimal switching temperature based on actual operating data. The processor may be implemented as including one or more central processing units or one or more field-programmable gate arrays.

[0145] Input interface: Used to receive the altitude of the project location, server configuration list, geometric dimensions and equipment layout parameters of the Mini windwall module, and meteorological data source address.

[0146] Output interface: Used to output the optimal redundancy coefficient, optimal design air volume, optimal energy consumption ratio, and energy efficiency rating.

[0147] Communication interface: Used to exchange data with the Mini wind wall module controller of the high-altitude intelligent computing center, send the optimal design air volume to the DC fan controller of the fan section, and send the optimal switching temperature to the controller of the outdoor integrated cooling station.

[0148] Display unit: Used to graphically display the curves showing the relationship between redundancy coefficient and annual average energy consumption ratio, and the curves showing the relationship between natural cooling switching temperature and energy consumption ratio.

[0149] Taking a smart computing center in Shannan, Tibet as an example, the method described in this application is used for control.

[0150] First, based on an altitude of 3548 meters, an air density of ρ=0.709kg / m³, a total heat generation of IT equipment of QIT=3570kW, and a design supply and return air temperature difference of ΔT=14℃, the theoretical minimum volumetric air volume Qmin=1.263×10^6m³ / h is calculated.

[0151] Then, a CFD model is established, and the candidate set of air volume ratio is set as α=[1.0,1.2,1.4,1.6,1.8,2.0].

[0152] Simulation calculations were performed based on the aforementioned CFD model. Figure 15 The figure shows the air cross-sectional temperature distribution of the intelligent computing center. Simulation results show that the temperature on the server exhaust side (hot aisle) of each micro-module is basically maintained between 33 and 38℃, with an average temperature of about 35℃, which meets the design requirement of no more than 38℃. The temperature in the cold pool area is stable at around 24℃, which is consistent with the design supply air temperature.

[0153] The average temperature statistics for the server exhaust surfaces show a maximum of 37.69℃ and a minimum of 33.01℃. Server intake airflow statistics show that the first row of servers has the highest intake airflow at 2315.57 m³ / h and the lowest at 1879.94 m³ / h; the other rows have a maximum intake airflow of 4425 m³ / h and a minimum of 3726.33 m³ / h. Air conditioning intake temperature statistics show a maximum of 36.63℃ and a minimum of 33.81℃.

[0154] Figure 16 The diagram shows the air velocity streamline distribution in the intelligent computing center. The cold airflow propels smoothly like a piston, effectively preventing the backflow and mixing of hot air. The straightness of the velocity streamlines and the clear separation of the cold and hot streamlines represent a relatively ideal airflow distribution pattern. Figure 17 The diagram shows the air temperature streamline distribution in the intelligent computing center. The temperature streamlines in the cold pool region (blue) and the hot aisle region (red) are clearly separated without overlap, representing a relatively ideal pattern. The simulation results above verify the effectiveness of the dual-constraint heat dissipation safety boundary proposed in this application.

[0155] Simulation results show that when α=1.0, T hot,max =42.3℃、σ cold =2.1℃; T when α=1.2 hot,max =39.8℃, σ cold =1.8℃; T when α=1.4 hot,max =37.2℃, σ cold =1.2℃; T when α=1.6 hot,max =35.1℃, σ cold =0.9℃; T when α=1.8 hot,max =33.8℃、σcold =0.7℃; T when α=2.0 hot,max =32.5℃、σ cold =0.5℃. Therefore, the minimum airflow redundancy coefficient αmin=1.4, and the heat dissipation safety boundary is α≥1.4.

[0156] Next, PUE simulations were performed for the scheme with α ≥ 1.4 throughout the year: PUE = 1.12 when α = 1.4, PUE = 1.15 when α = 1.6, PUE = 1.19 when α = 1.8, and PUE = 1.24 when α = 2.0. Therefore, the optimal redundancy coefficient αopt = 1.4, and the optimal design air volume Qopt = 1.768 × 10⁻⁶. 6 m³ / h.

[0157] Finally, according to α opt =1.4 Control the operation of DC fans and electronically controlled fans. The DC fans are controlled by fixed frequency and are all turned on; the electronically controlled fans are controlled by PID to maintain the outlet liquid temperature at 20℃±1℃. After one month of continuous operation, the measured PUE is 1.13, the standard deviation of the cold pool temperature is stable within 1.2℃, and there are no local hot spots in the hot aisle.

[0158] The method and equipment described in this application can be widely applied to high-altitude intelligent computing centers above 2000 meters, especially data centers using Mini airflow modules as a heat dissipation solution. Typical applications include artificial intelligence intelligent computing centers and supercomputing centers in high-altitude areas such as Tibet, Qinghai, western Sichuan, and northern Yunnan. Through quantitative evaluation and optimized control, this application achieves optimal energy efficiency ratio while improving the heat-carrying capacity of air per unit mass and reducing low-density air disturbance, demonstrating significant industrial practicality and economic value.

[0159] The data processing involved in the implementation of this application complies with the requirements of the "Personal Information Protection Law of the People's Republic of China" and related data security laws and regulations. Specifically: the meteorological data used in this method comes from a publicly available database of typical meteorological years and does not involve personal information; the IT equipment configuration data are engineering parameters from the data center design phase and do not involve personal privacy; the geometric model used in the CFD simulation is the building and equipment layout parameters of the data center and does not contain any personal information. The technical solution of this application does not involve biomedical research and is not subject to the "Ethical Review Measures for Biomedical Research Involving Human Subjects".

[0160] The features described above in the disclosed embodiments can be substituted or combined with each other, enabling those skilled in the art to implement or use this application. The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has been disclosed above with reference to preferred embodiments, it is not intended to limit this application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solutions of this application still fall within the protection scope of the technical solutions of this application.

Claims

1. A control method for the fan in a Mini windbreak module of a high-altitude intelligent computing center, characterized in that, The Mini air wall module includes: an electronically controlled speed fan installed in the outdoor integrated cooling station; multiple equipment cabinets arranged side by side with inlet and outlet sides; a hot aisle connected to the outlet sides of the multiple equipment cabinets to collect the hot air discharged from the equipment cabinets; a fan section with its inlet side connected to the hot aisle, containing a DC fan that operates using a fixed frequency control method; a surface cooler with its inlet side connected to the outlet side of the fan section and connected to the outdoor integrated cooling station via a liquid circuit, supplied with chilled water by the outdoor integrated cooling station; and a cold pool connected to the outlet side of the surface cooler and the inlet side of the multiple equipment cabinets, forming a complete airflow circulation loop. The method includes the following steps: Determine the heat dissipation safety boundary that satisfies heat dissipation safety. The heat dissipation safety boundary consists of all air volume ratios that meet the heat dissipation safety conditions. With the goal of minimizing the annual average power utilization efficiency-to-energy ratio, the optimal redundancy coefficient that minimizes the energy consumption ratio is selected within the heat dissipation safety boundary, and the optimal design air volume is determined based on the optimal redundancy coefficient and the theoretical minimum volumetric air volume. The operation of the DC fan is controlled according to the optimal redundancy coefficient so that the actual operating air volume of the DC fan reaches the optimal design air volume. The electronic speed-regulating fan is controlled according to the optimal redundancy coefficient to maintain a stable liquid outlet temperature of the surface cooler; The outlet liquid temperature of the surface cooler and the return air temperature of the hot channel are monitored in real time, and the DC fan and electronic speed control fan are adjusted based on the optimal design air volume.

2. The method according to claim 1, characterized in that, Determine the heat dissipation safety boundary to ensure heat dissipation safety, including: The heat dissipation safety condition is that the highest temperature of the hot channel does not exceed the first preset threshold and the temperature standard deviation of the cold pool does not exceed the second preset threshold. The minimum airflow redundancy coefficient that meets the heat dissipation safety conditions is determined by computational fluid dynamics simulation. The range of airflow ratios that are greater than or equal to the minimum airflow redundancy coefficient is taken as the heat dissipation safety boundary.

3. The method according to claim 2, characterized in that, in, The maximum temperature of the hot aisle is used to characterize the degree of heat carrying capacity per unit mass of air: when the maximum temperature of the hot aisle is less than or equal to the first preset threshold, it is determined that the air mass flow rate passing through the surface cooler and the equipment cabinet surface per unit time is sufficient to remove the heat generated by the equipment; when the maximum temperature of the hot aisle is greater than the first preset threshold, it is determined that the air mass flow rate is insufficient. The cold pool temperature standard deviation is used to characterize the degree of suppression of low-density air disturbances: when the cold pool temperature standard deviation is detected to be less than or equal to the second preset threshold, it is determined that the airflow driven by the DC fan operating at fixed frequency has formed a stable piston airflow organization when it delivers air from the cold pool to the air inlet side of the equipment cabinet; when the cold pool temperature standard deviation is detected to be greater than the second preset threshold, it is determined that there is mixing of hot and cold airflows.

4. The method according to claim 2, characterized in that, The minimum airflow redundancy factor to meet the heat dissipation safety conditions was determined through computational fluid dynamics simulation, including: Establish a computational fluid dynamics model that includes the Mini wind wall module; Multiple air volume ratios are set to scale the theoretical minimum volumetric air volume, and computational fluid dynamics simulations are performed for each air volume ratio. Extract the highest temperature in the hot channel and the standard deviation of the temperature in the cold pool from the simulation results; The minimum airflow ratio that satisfies the condition that the maximum temperature of the hot aisle does not exceed the first preset threshold and the standard deviation of the cold pool temperature does not exceed the second preset threshold is used as the minimum airflow redundancy coefficient.

5. The method according to claim 4, characterized in that, Perform computational fluid dynamics simulations for each airflow ratio, including: Boundary conditions are set for each air volume ratio, where the air outlet of the fan section is set as the mass flow inlet, and the mass flow of the air outlet of the fan section is equal to the air volume ratio multiplied by the theoretical minimum volume air volume multiplied by the local air density divided by the number of DC fans and then divided by 3600. The return air outlet of the hot aisle is set as the pressure outlet. Computational fluid dynamics was performed using a k-ε two-equation turbulence model until convergence. The convergence criterion included: the residuals of each equation being less than 1 × 10⁻⁶. -4 The temperature change rate at the monitoring point is less than 1×10 -5 For 100 consecutive steps, the global energy imbalance is less than 0.1%.

6. The method according to claim 1, characterized in that, With the objective of minimizing the annual average energy efficiency-to-energy ratio, the optimal redundancy coefficient is selected within the heat dissipation safety boundary, including: Obtain hourly meteorological data for the project location throughout the year; Altitude correction for performance parameters of refrigeration equipment; The natural cooling mode and mechanical cooling mode are determined based on the hourly dry-bulb temperature and the water supply temperature. For each airflow ratio within the heat dissipation safety boundary, the energy consumption of the cooling system and IT is calculated hourly, where the power of the DC fan in the fan section is proportional to the cube of the airflow ratio; Calculate the annual average energy consumption ratio corresponding to each air volume ratio, and take the air volume ratio corresponding to the minimum value as the optimal redundancy coefficient.

7. The method according to claim 1, characterized in that, Real-time monitoring of the outlet liquid temperature of the surface cooler and the return air temperature of the hot aisle, and adjustment of the DC fan and electronically controlled speed fan based on the optimal design airflow, also includes: when the highest temperature of the hot aisle is detected to be greater than a first preset threshold, the following steps are executed: Calculate the current air mass flow rate based on the sensible heat exchange formula; Calculate the target air mass flow rate required to reduce the maximum temperature of the hot aisle below a first preset threshold. The required first airflow ratio is determined based on the ratio of the target air mass flow rate to the current air mass flow rate; By increasing the number of DC fans in the control fan section or increasing their speed, the volumetric air volume is increased by the first air volume ratio, thereby increasing the product of the current air mass flow rate and improving the heat carrying capacity of a unit mass of air.

8. The method according to claim 1, characterized in that, Real-time monitoring of the outlet liquid temperature of the surface cooler and the return air temperature of the hot channel, and adjustment of the DC fan and electronically controlled speed fan based on the optimal design air volume, also includes: when the standard deviation of the cold pool temperature is detected to be greater than the second preset threshold, the following steps are executed: Calculate the momentum of the current airflow from the cold pool to the air inlet side of the equipment cabinet using the momentum formula; Calculate the target momentum required to reduce the standard deviation of the cold pool temperature to below the second preset threshold. The ratio of the target momentum to the current momentum is equal to the ratio of the square of the standard deviation of the cold pool temperature to the square of the second preset threshold. Determine the required second air volume ratio based on the target momentum and the current momentum; Increase the number of DC fans operating in the control fan section to increase the volumetric air volume by a second air volume ratio.

9. The method according to claim 1, characterized in that, Real-time monitoring of the outlet liquid temperature of the surface cooler and the return air temperature of the hot channel, and adjustment of the DC fan and electronically controlled speed fan based on the optimal design air volume, also includes: when the highest temperature of the hot channel is simultaneously detected to be greater than a first preset threshold and the standard deviation of the cold pool temperature is greater than a second preset threshold, the following steps are executed: The first air volume ratio is calculated according to the method of claim 7, and the second air volume ratio is calculated according to the method of claim 8; The larger of the first and second air volume ratios is taken as the third air volume ratio; Increase the number of DC fans operating in the control fan section to increase the volumetric air volume by a third air volume ratio.

10. A control device for a Mini windbreak module fan in a high-altitude intelligent computing center for implementing the method of any one of claims 1 to 9, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in memory to implement the method of any one of claims 1 to 9; And update the optimal redundancy coefficient and optimal switching temperature based on actual operating data; The input interface is used to receive the altitude of the project location, the server configuration list, the geometric dimensions and equipment layout parameters of the Mini windwall module, and the meteorological data source address; The output interface is used to output the optimal redundancy coefficient, optimal design air volume, optimal energy consumption ratio, and energy efficiency rating. The communication interface is used to exchange data with the Mini wind wall module controller of the high-altitude intelligent computing center, send the optimal design air volume to the DC fan controller of the fan section, and send the optimal switching temperature to the controller of the outdoor integrated cooling station. The display unit is used to graphically display the curves showing the relationship between the redundancy coefficient and the annual average energy consumption ratio, as well as the curves showing the relationship between the natural cooling switching temperature and the energy consumption ratio.

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