CN112859789B - CFD-based method and system for constructing digital twin body of data center - Google Patents
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Abstract
The invention discloses a method and a system for constructing a digital twin body of a data center based on CFD, which construct a 3D digital twin body corresponding to the actual data center by utilizing physical parameters of the actual data center, simulate the thermal process of the data center through CFD technology, realize high-precision digital cloning of physical properties and processes of the data center, replace the actual data center to provide data and environment basis for intelligent decision and intelligent operation and maintenance, and the like, thereby ensuring that an optimal strategy (such as an energy-saving strategy, a task scheduling strategy, and the like) obtained in the digital twin body has practicability and deployment performance, can be rapidly and conveniently deployed in the actual physical data center, simultaneously automatically calibrate the 3D digital twin body according to an automatic calibration method based on a generation countermeasure network (cGAN), avoid time-consuming and labor-consuming manual calibration, on one hand, improve the simulation precision of the 3D digital twin body, and on the other hand, enable the digital twin body to be rapidly applied to other data centers or machine rooms.
Description
Technical Field
The invention relates to the field of data centers, in particular to a method and a system for constructing a digital twin body of a data center based on CFD.
Background
The data center has strong data processing capability, can provide basic information guarantee such as storage, calculation, network and the like for technologies and applications such as artificial intelligence, big data, intelligent manufacturing and the like, and is a foundation for intelligent decision making and intelligent operation and maintenance of the data center facing high-precision digital modeling of the industrial data center. As a production environment, the data center needs to ensure operation safety and user service quality, and some configurations of the data center such as task loads or cooling facilities cannot be adjusted at will, so that the data center cannot directly provide data and environment basis for working contents such as intelligent decision making and intelligent operation and maintenance (such as training solution of energy-saving optimization algorithm, policy verification, hypothesis verification or task scheduling). Therefore, the contents such as training solution or strategy verification of the existing data center optimization algorithm can only depend on a mathematical model or a pure simulation environment, namely, the design is carried out without an actual data center, the practicability is not achieved, and the method is difficult to be truly used for the data center. In addition, the existing simulation software (including the method of simulating by using an offline data set) does not interact with the actual data center, and cannot reflect the actual operation condition of the actual data center in real time, and the existing computational fluid dynamics (CFD, computational Fluid Dynamics) software can only simulate the thermal and airflow processes of the data center by configuring the data offline, so that even if a data center CFD model constructed by using very mature commercial CFD software is still difficult to reach the industrial level, the main reason is that the physical (calibration) parameters for constructing the data center CFD model and the actual parameters after long-term operation of the equipment have a certain difference. Therefore, the constructed CFD model of the data center needs to be calibrated manually (such as ACU wind speed, server fan air volume calibration and the like). However, manual calibration can make it difficult to quickly apply the CFD-based simulation model to other data centers or rooms, and any physical equipment/facility change requires manual recalibration of the CFD model.
Disclosure of Invention
The invention aims to provide a method and a system for constructing a digital twin of a data center based on CFD, and a 3D digital twin corresponding to the actual data center is constructed, so that the data and environment foundation can be provided for intelligent decision and intelligent operation and maintenance and the like instead of the actual data center.
The invention is realized by the following technical scheme:
a method of constructing a digital twin mass for a data center based on CFD, comprising the steps of:
s1, collecting physical parameters of an actual data center;
s2, constructing a 3D digital twin body corresponding to the actual data center according to physical parameters of the actual data center;
s3, importing real-time configuration parameters of an actual data center into a 3D digital twin body;
s4, according to the CFD technology, the running state of the actual data center is simulated in real time by using the 3D digital twin body with the real-time configuration parameters imported in the step S3;
s5, constructing a cGAN calibration model, and calibrating model parameters of the 3D digital twin body in the operation of the step S4 in real time according to the constructed cGAN calibration model to obtain a calibrated 3D digital twin body;
s6, simulating various working conditions of an actual data center by using the calibrated 3D digital twin body to obtain an optimal solution; the actual data center is deployed according to an optimal solution, wherein various working conditions comprise training solution of an energy-saving optimization algorithm, strategy verification, hypothesis verification or task scheduling by a task center.
In the prior art, as the data center is used as a production environment, data and environment foundations cannot be directly provided for intelligent decision making, intelligent operation and maintenance (such as training solution, strategy verification, hypothesis verification or task scheduling) and the like of an energy-saving optimization algorithm, in addition, the existing simulation software usually adopts manual calibration for calibrating the simulation precision, the calibration precision is low and the operation is complex, so that a data center model simulated by the simulation software lacks universality, and therefore, the scheme utilizes physical parameters of an actual data center, such as a layout structure, IT, refrigeration equipment and corresponding parameters, sensor real-time data, operation history data and the like, constructs a 3D digital twin corresponding to the actual data center, simulates the thermal process of the data center through a CFD technology, and realizes high-precision digital cloning of physical properties and processes of the data center. The 3D digital twin body not only can display the dynamic state of the data center in real time, but also can simulate the IT load, the thermal process and the like of the data center with high precision, so that the constructed 3D digital twin body corresponding to the actual data center can be used as a working platform to provide data and environment foundation for the working content of the actual data center, the working content comprises training solution, strategy verification, hypothesis verification, task scheduling and the like of an energy-saving optimization algorithm, the practicability and the deployment of intelligent decision are ensured, meanwhile, according to an automatic calibration method based on a generated countermeasure network (cGAN), the 3D digital twin body corresponding to the actual data center can be automatically calibrated, the time-consuming and labor-consuming manual calibration is avoided, the simulation precision of the 3D digital twin body can be improved, and the simulation model can be rapidly applied to other data centers or machine rooms.
Further, the physical parameters collected in step S1 include physical parameters of the machine room, the refrigeration equipment, and the IT equipment.
Further, the physical parameters of the machine room include the area, the height, the shape and whether to use the raised floor as a cold air channel; the physical parameters of the refrigeration equipment comprise the position, the size, the rated power consumption, the maximum and minimum wind speed and the maximum and minimum set temperature of the refrigeration equipment; the physical parameters of the IT equipment include the cabinet size, the leg size, the placement position, the rated power consumption, the number of required servers, and the size of the servers of the IT equipment.
Further, the 3D digital twin body includes a machine room 3D model, a refrigeration equipment 3D model, and an IT equipment 3D model of the actual data center constructed according to CFD software.
Further, the specific process of constructing the cGAN calibration model in step S5 is as follows:
step S511, inputting random noise z and a condition parameter y into a generator G to obtain generated data, wherein the condition parameter y comprises configuration parameters of a 3D digital twin body;
step S512, inputting the condition parameter y into the 3D digital twin body to obtain simulation data;
step S513, respectively inputting the generated data, the condition parameters y and the simulation data into a discriminator D to judge authenticity;
step S514, continuously iterating the training generator G and the discriminator D according to the judging result of the discriminator D;
step S515, until the generator G and the arbiter D converge, a cGAN calibration model is obtained.
Further, the calibration process in the step S5 is as follows:
step S521, inputting real-time configuration parameters of the 3D digital twin body as target configuration parameters into the cGAN calibration model to obtain an output result of the target configuration parameters;
step S522, judging whether the error between the output result of the target configuration parameter and the measured value of the actual data center is smaller than a set value;
if the judgment result is smaller than the set value, the target configuration parameter is used as the calibrated configuration parameter;
and if the judgment result is greater than or equal to the set value, adjusting the target configuration parameters, and repeating the steps S521-S522.
In addition, the invention provides a system for constructing a digital twin body of a data center based on CFD, which comprises a model construction module, an analog module, a calibration module and an application module, wherein,
the model construction module is used for constructing a cGAN calibration model and a 3D digital twin body corresponding to the data center;
the simulation module simulates the running state of the actual data center according to the 3D digital twin body which is constructed by the model construction module and corresponds to the data center;
the calibration module calibrates the configuration parameters of the 3D digital twin body in operation according to the operation state of the 3D digital twin body corresponding to the data center in the analog module;
the application module obtains an optimal solution according to various working conditions of the 3D digital twin body simulation actual data center after the calibration of the calibration module; the actual data center is deployed according to an optimal solution.
Further, the 3D digital twin body includes a machine room 3D model, a refrigeration equipment 3D model, and an IT equipment 3D model corresponding to the actual data center.
Further, the running states in the simulation module comprise task scheduling of an actual data center, IT equipment load variation and refrigeration equipment temperature variation.
Further, the configuration parameters calibrated in the calibration module include the wind speed of the refrigeration equipment, the server fan air volume, the IT load distribution, and the set temperature and wind speed of the ACU.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the method and the system for constructing the digital twin of the data center based on the CFD, the 3D digital twin corresponding to the actual data center is constructed according to the actual data center, the 3D digital twin corresponding to the actual data center is generated, the high-precision digital mapping of physical properties and operation processes of the actual data center is realized, the data and environment foundation can be provided for intelligent decision and intelligent operation and maintenance of the data center instead of the actual data center, the working contents comprise training solution, strategy verification, hypothesis verification, task scheduling and the like of an energy-saving optimization algorithm, meanwhile, according to an automatic calibration method based on a generated countermeasure network (cGAN), the 3D digital twin of the data center can be automatically calibrated, time and labor consumption are avoided, the simulation precision of the digital twin can be improved, and the constructed digital twin can be rapidly applied to other data centers or machine rooms.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a diagram of the construction and operation of a 3D digital twin mass for a data center;
fig. 3 is a flow chart for creating a cGAN calibration model.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known structures, circuits, materials, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an example," or "in an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, it should be understood that the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the scope of the present invention.
Example 1
As shown in fig. 1, the method for constructing a digital twin body of a data center based on CFD according to this embodiment includes the following steps:
s1, collecting physical parameters of an actual data center;
s2, constructing a 3D digital twin body corresponding to the actual data center according to physical parameters of the actual data center;
s3, importing real-time configuration parameters of an actual data center into a 3D digital twin body;
s4, according to the CFD technology, the running state of the actual data center is simulated in real time by using the 3D digital twin body with the real-time configuration parameters imported in the step S3;
s5, constructing a cGAN calibration model, and calibrating real-time configuration parameters of the 3D digital twin body in the operation of the step S4 in real time according to the constructed cGAN calibration model to obtain a calibrated 3D digital twin body;
s6, simulating various working conditions of an actual data center by using the calibrated 3D digital twin body to obtain an optimal solution; the actual data center is deployed according to an optimal solution, wherein various working conditions comprise training solution of an energy-saving optimization algorithm, strategy verification, hypothesis verification or task scheduling by a task center.
In one embodiment, the physical parameters collected in step S1 include the area, the height, and the raised floor of the machine room as a cold air channel; the position, the size, the rated power consumption, the maximum and minimum wind speed and the maximum and minimum set temperature of the refrigeration equipment; the cabinet size, the frame leg size and the placement position, and the number, the size and the rated power consumption of the servers are required to be related parameters; the size of a cabinet, the size of frame legs, the placement position, rated power consumption, the number of required servers and the size of the servers of the IT equipment, and meanwhile, the position, the size, the power consumption and other parameters of other equipment (such as PDU, UPS and the like) of a machine room also need to be acquired.
Specifically, the 3D digital twin body constructed by using CFD software according to the physical parameters of the actual data center in step S2 includes a machine room 3D model, a refrigeration equipment 3D model, and an IT equipment 3D model, and the CFD software may use OpenFoam,6Sigma,EnergyPlus,ANSYS or the like.
Specifically, as shown in fig. 2, a construction and operation diagram of a 3D digital twin body of a data center is shown in fig. 2, where (a) is a 3D structure diagram of an actual data center, and as shown in (b) in fig. 2, CFD software is used to construct the 3D digital twin body of the data center corresponding to the actual data center according to physical parameters such as a machine room structure (area shape, wall, ceiling, pipeline, etc.), IT equipment (cabinet, machine location, server parameters, etc.), and the number and deployment of refrigeration equipment (ACU, sensor deployment, etc.) of the actual data center; then, setting configuration parameters (such as IT load distribution, ACU set temperature, wind speed and the like) for the 3D digital twin body of the data center or importing real-time configuration parameters of the actual data center; finally, the operational status of the actual data center (such as task scheduling, load variation, temperature variation, etc.) is simulated using CFD technique as shown in fig. 2 (c).
In another embodiment, as shown in fig. 3, fig. 3 is a schematic diagram of training a cGAN model by using random noise z and a condition parameter y, and the specific process includes steps S511-S515:
step S511, inputting random noise z and a condition parameter y into a generator G to obtain generated data, wherein the condition parameter y comprises configuration parameters of a 3D digital twin body;
step S512, inputting the condition parameter y into the 3D digital twin body to obtain simulation data;
step S513, respectively inputting the generated data, the condition parameters y and the simulation data into a discriminator D to judge authenticity;
step S514, continuously iterating the training generator G and the discriminator D according to the judging result of the discriminator D;
step S515, until the generator G and the arbiter D converge, a cGAN calibration model is obtained.
In one embodiment, the calibration process in step S5 includes steps S521-S522:
step S521, inputting real-time configuration parameters of the 3D digital twin body as target configuration parameters into the cGAN calibration model to obtain an output result of the target configuration parameters;
step S522, judging whether the error between the output result of the target configuration parameter and the measured value of the actual data center is smaller than a set value;
if the judgment result is smaller than the set value, the target configuration parameter is used as the calibrated configuration parameter;
and if the judgment result is greater than or equal to the set value, adjusting the target configuration parameters, and repeating the steps S521-S522.
In the prior art, the data center cannot be directly used as a working platform to provide data and environment basis for training solving, strategy checking, hypothesis verification, task scheduling and the like of an energy-saving optimization algorithm, so that the energy-saving optimization algorithm cannot be used for intelligent energy saving of the data center, or solving of an optimal strategy is carried out in an actual data center, and the existing simulation software usually adopts manual calibration for calibrating the simulation precision, the calibration precision is low and the operation is complex, so that a data center model simulated by the simulation software lacks universality. The 3D digital twin body of the data center can display the dynamic state of the data center in real time, and can simulate the IT load, the thermal process and the like of the data center with high precision, so that the 3D digital twin body of the data center can be used as a working platform to provide data and environment foundation for the working content which is required to be operated by the actual data center instead of the actual data center, the working content comprises training solution, strategy verification, hypothesis verification or task scheduling of an energy-saving optimization algorithm, so that the practicability and the deployment of the working content are ensured, meanwhile, according to an automatic calibration method based on a generated countermeasure network (cGAN), the 3D digital twin body of the data center can be calibrated automatically, the time and labor consumption is avoided, the simulation precision of the 3D digital twin body of the data center can be improved, and the simulation model can be applied to other data centers or machine rooms rapidly.
Example 2
As shown in fig. 2, the difference between the present embodiment and embodiment 1 is that, based on the method of embodiment 1, a system for constructing a digital twin body of a data center based on CFD is provided, which includes a model construction module, a simulation module, a calibration module, and an application module, wherein,
the model construction module is used for constructing a cGAN calibration model and a 3D digital twin body corresponding to the data center;
the simulation module simulates the running state of the actual data center according to the 3D digital twin body which is constructed by the model construction module and corresponds to the data center;
the calibration module calibrates the configuration parameters of the 3D digital twin body in operation according to the operation state of the 3D digital twin body corresponding to the data center in the analog module;
the application module obtains an optimal solution according to various working conditions of the 3D digital twin body simulation actual data center after the calibration of the calibration module; the actual data center is deployed according to an optimal solution.
The data center 3D digital twin body comprises a machine room 3D model, a refrigeration equipment 3D model and an IT equipment 3D model which correspond to the actual data center.
Specifically, the above-described operating conditions include task scheduling of the actual data center, IT equipment load variation, and refrigeration equipment temperature variation.
Specifically, the configuration parameters calibrated in the calibration module include the wind speed of the refrigeration equipment, the server fan air volume, the IT load distribution, and the set temperature and wind speed of the ACU.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. A method for constructing a digital twin body of a data center based on CFD, comprising the steps of:
s1, collecting physical parameters of an actual data center;
s2, constructing a 3D digital twin body corresponding to the actual data center according to physical parameters of the actual data center;
s3, importing real-time configuration parameters of an actual data center into a 3D digital twin body;
s4, according to the CFD technology, the running state of the actual data center is simulated in real time by using the 3D digital twin body with the real-time configuration parameters imported in the step S3;
s5, constructing a cGAN calibration model, and calibrating model parameters of the 3D digital twin body in the operation of the step S4 in real time according to the constructed cGAN calibration model to obtain a calibrated 3D digital twin body;
s6, simulating various working conditions of an actual data center by using the calibrated 3D digital twin body to obtain an optimal solution; deploying an actual data center according to the optimal solution;
the specific process of constructing the cGAN calibration model in step S5 is as follows:
step S511, inputting random noise z and a condition parameter y into a generator G to obtain generated data, wherein the condition parameter y comprises configuration parameters of a 3D digital twin body;
step S512, inputting the condition parameter y into the 3D digital twin body to obtain simulation data;
step S513, respectively inputting the generated data, the condition parameters y and the simulation data into a discriminator D to judge authenticity;
step S514, continuously iterating the training generator G and the discriminator D according to the judging result of the discriminator D;
step S515, until the generator G and the discriminator D converge, a cGAN calibration model is obtained;
the calibration process in the step S5 is as follows:
step S521, inputting real-time configuration parameters of the 3D digital twin body as target configuration parameters into the cGAN calibration model to obtain an output result of the target configuration parameters;
step S522, judging whether the error between the output result of the target configuration parameter and the measured value of the actual data center is smaller than a set value;
if the judgment result is smaller than the set value, the target configuration parameter is used as the calibrated configuration parameter;
and if the judgment result is greater than or equal to the set value, adjusting the target configuration parameters, and repeating the steps S521-S522.
2. The method of constructing a digital twin mass of a data center based on CFD according to claim 1, wherein the physical parameters collected in step S1 include physical parameters of a machine room, refrigeration equipment and IT equipment.
3. The method for constructing a digital twin body of a data center based on CFD according to claim 2, wherein the physical parameters of the machine room of the data center include the area, height, shape and whether or not a raised floor is used as a cool air passage; the physical parameters of the refrigeration equipment comprise the position, the size, the rated power consumption, the maximum and minimum wind speed and the maximum and minimum set temperature of the refrigeration equipment; the physical parameters of the IT equipment include the cabinet size, the leg size, the placement position, the rated power consumption, the number of required servers, and the size of the servers of the IT equipment.
4. The method for constructing a digital twin body of a data center based on CFD according to claim 1, wherein the 3D digital twin body includes a machine room 3D model, a refrigeration equipment 3D model, and an IT equipment 3D model of an actual data center constructed according to CFD software.
5. A CFD-based system for constructing a digital twin body of a data center, which is characterized in that the method of any of claims 1-4 is adopted, and the system comprises a model construction module, a simulation module, a calibration module and an application module, wherein,
the model construction module is used for constructing a cGAN calibration model and a 3D digital twin body corresponding to the data center;
the simulation module simulates the running state of the actual data center according to the 3D digital twin body which is constructed by the model construction module and corresponds to the data center;
the calibration module calibrates the configuration parameters of the 3D digital twin body in operation according to the operation state of the 3D digital twin body corresponding to the data center in the analog module;
the application module obtains an optimal solution according to various working conditions of the 3D digital twin body simulation actual data center after the calibration of the calibration module; the actual data center is deployed according to an optimal solution.
6. The CFD-based system for constructing a digital twin body of a data center according to claim 5, wherein the 3D digital twin body comprises a machine room 3D model, a refrigeration equipment 3D model and an IT equipment 3D model corresponding to an actual data center.
7. The CFD-based system for constructing a digital twin mass for a data center of claim 5, wherein the operational states in the simulation module include actual data center mission scheduling, IT equipment load variation, and refrigeration equipment temperature variation.
8. The CFD-based system for constructing a digital twin mass in a data center of claim 5, wherein the configuration parameters calibrated in the calibration module include the wind speed of the refrigeration appliance, the server fan air volume, the IT load profile, and the set temperature and wind speed of the ACU.
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