CN115186555B - Digital twinning-based drying equipment live simulation method and related equipment - Google Patents
Digital twinning-based drying equipment live simulation method and related equipment Download PDFInfo
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Abstract
The application discloses a drying equipment live simulation method based on digital twinning and related equipment, wherein the method comprises the following steps: establishing a full-size structure simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model; determining boundary conditions of the numerical model; determining a control equation of the numerical model and initial values of various parameters of the control equation; optimizing initial values of all parameters to obtain optimal parameters; and carrying the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data. The technical effect that this application had is: the running state of the drying equipment is detected in real time, so that the production quality of products is improved.
Description
Technical Field
The application relates to the technical field of lithium battery drying equipment, in particular to a digital twinning-based live simulation method for drying equipment and related equipment.
Background
The lithium battery industry is an important component of new energy technology rapidly developed in the current era, and demands on technological, automatic and intelligent production lines are increasing, so that the industrial upgrading of related processes of lithium battery production by combining digital twin technology is a current hot problem.
Among the many process flows in the production of lithium batteries, drying of lithium batteries is a critical step in determining battery quality, and control of drying quality is the direction of research by lithium battery manufacturers. The evaluation of the drying quality is usually to detect the dried pole piece sample, in the drying process, the detection is performed through limited sensors arranged in the drying equipment, but the detection is limited by technology and the number and the positions of the arranged sensors, and the real-time detection of the drying process is difficult, so that the production strategy cannot be adjusted in time, the quality of the product is easily reduced, even unqualified products are produced, and the waste of cost is wasted.
Disclosure of Invention
In order to improve the problem that real-time detection of a drying process is difficult, the application provides a digital twin-based drying equipment live simulation method and related equipment.
In a first aspect, the present application provides a digital twinning-based live simulation method for a drying device, which adopts the following technical scheme:
the digital twinning-based live simulation method for the drying equipment is suitable for computer equipment and comprises the following steps:
establishing a full-size structure simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model;
determining boundary conditions of the numerical model;
determining a control equation of the numerical model and initial values of various parameters of the control equation;
optimizing initial values of all parameters to obtain optimal parameters;
and carrying the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data.
Through the technical scheme, the numerical model of the drying equipment is established, the connection between the product drying production line and the numerical model is established based on the digital twin technology, the digital twin of the battery drying equipment is realized, and then the parameters of the numerical model are optimized, so that the calculation result and the measurement result of the numerical model are kept consistent, and the calculation accuracy and the calculation authenticity of the numerical model are ensured. The data of the drying equipment are obtained in real time, the data are transmitted to the numerical model for real-time calculation, and real-time detection data are output, so that the whole process simulation of the product drying production is completed, the production process is visualized, and the real-time detection of the drying process is facilitated.
Preferably, after the obtaining the numerical model, the method further includes:
simplifying the numerical model;
and carrying out grid division and contact setting on the numerical model.
Through the technical scheme, the structure model and the flow field model are simplified, the chamfering is removed, the components with small influence on thermal analysis are removed, then the structure model and the flow field model are subjected to grid division and contact arrangement, the grid division instruction can be improved, and the calculated amount can be reduced.
Preferably, in determining the boundary condition of the numerical model, the method includes:
transmitting the data information to the numerical model by serial communication;
taking the data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition;
and taking temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition.
According to the technical scheme, the real drying equipment is communicated with the numerical model through the serial port, so that measured data can be smoothly transmitted to the numerical model; aiming at the problem of thermal analysis of drying equipment, the temperature data of the air inlet and the temperature information of the outer wall are used as boundary conditions of the numerical model, so that the accuracy and the authenticity of calculation of the numerical model are effectively improved.
Preferably, the control equation includes a turbulence equation, the turbulence equation being:
wherein G is b G is the turbulence energy caused by buoyancy k For the turbulence energy term caused by the average velocity gradient, Y M Is the contribution of pulsation expansion in turbulence, k is the turbulence pulsation kinetic energy, epsilon is the specific turbulence energy dissipation, ρ is the average density, μ t U is the turbulent viscosity i And u j X is the air velocity vector component in the battery pack i 、x j For the orthogonal coordinate components (i, j=1, 2,3; x 1 ＝x,x 2 ＝y,x 3 ＝z)，C 1ε ，C 2ε ，C 3ε ，C μ ，σ k ，σ ε Is the turbulence equation parameter.
According to the technical scheme, for the thermal analysis problem, the turbulence model is the most important component, the standard k-epsilon turbulence numerical model is selected to describe the flow rule of hot air in the box body according to the flow state of hot air in the drying equipment chamber, and a wind field river basin model in the drying equipment is established, wherein C 1ε ，C 2ε ，C 3ε ，C μ ，σ k ，σ ε For model constants, more experience is accumulated in engineering practice and process specifications for the selection of the model constants, corresponding model constants can be selected as initial values according to experience, parameters of turbulence simulation are mainly optimized, and accordingly workload of parameter optimization can be effectively reduced.
Preferably, the optimizing the initial value of each parameter to obtain the optimal parameter includes;
the initial value is brought into the numerical model to be calculated, and a calculation output result is obtained;
comparing the calculated output result with the acquisition result of the drying equipment;
and optimizing the control equation flow parameters based on the comparison result.
According to the technical scheme, the parameters are brought into the numerical model for real-time calculation, the obtained calculation result is compared with the actual measurement result, if the calculation result and the actual measurement result have larger deviation, the parameters of the control equation are optimized, and the accuracy of the simulation system data result can be improved.
Preferably, the optimizing the turbulence equation parameter based on the comparison result includes:
judging whether the difference value between the calculated output result and the acquired result meets a preset condition or not;
and if not, optimizing the control equation parameters by using an optimization algorithm.
Preferably, the optimizing the control equation parameters by using an optimization algorithm includes:
performing multiple iterations on the control equation parameters, and obtaining iteration results and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the turbulence equation parameter after iteration as an optimal parameter.
According to the technical scheme, when a large deviation occurs between the current calculation result and the actual measurement result, parameter iterative optimization is performed, the current parameters are transmitted to the simulation parameter optimization model and serve as initial values, a group of optimization parameters are generated through an algorithm according to the initial parameters and errors of the calculation result, and the optimization parameters are transmitted back to the numerical model. And after the numerical model is received and optimized, the model is calculated again, and is judged again, if the requirements are not met, the steps are repeated until the error is within a preset threshold, iterative optimization is stopped, the parameters are optimized by utilizing a genetic algorithm, and only a small amount of experimental data is required to be measured in the early stage, so that the workload can be reduced.
In a second aspect, the present application provides a live simulation device based on digital twin drying equipment, which adopts the following technical scheme:
a digital twin drying equipment based live simulation device, comprising:
the modeling module is used for establishing a full-size structure simulation model of the drying equipment and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model;
the boundary determining module is used for determining the boundary condition of the numerical model;
a parameter determining module, configured to determine a control equation of the numerical model, and initial values of respective parameters of the control equation; the parameter optimization module is used for optimizing initial values of all the parameters to obtain optimal parameters;
and the acquisition and calculation module is used for bringing the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data.
Through the technical scheme, the numerical model of the drying equipment is established, the connection between the product drying production line and the numerical model is established based on the digital twin technology, the digital twin of the battery drying equipment is realized, and then the parameters of the numerical model are optimized, so that the calculation result and the measurement result of the numerical model are kept consistent, and the accuracy and the authenticity of numerical name calculation are ensured. The data of the drying equipment are obtained in real time, the data are transmitted to the numerical model for real-time calculation, and real-time detection data are output, so that the whole process simulation of the product drying production is completed, the production process is visualized, and the real-time detection of the drying process is facilitated.
In a third aspect, the present application provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, the present application provides a computer device, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the digital twin technology is used for establishing the connection between the product drying production line and the numerical model, realizing the digital twin of the battery drying equipment, and providing a dynamic parameter identification method for adjusting parameters in the numerical model in real time, so that the calculation result of the parameters in the key position is consistent with the measurement result of the sensor, and the calculation accuracy and the calculation authenticity of the numerical model are ensured.
2. The working condition change of the production line is monitored in real time through the sensor and is transmitted to the numerical model in real time for adjustment, the whole process simulation of the product drying production is completed, the deep integration of digital twin and industrial technology is effectively realized, the development process of digitization, networking and intellectualization of the product drying process is forcefully promoted, the production process is transparent, and the management efficiency of a manager and the manufacturing quality of the product are improved.
Drawings
FIG. 1 is a flow chart of a digital twinning-based live simulation method for a drying appliance in an embodiment of the present application;
FIG. 2 is a flow chart of a digital twinning-based drying apparatus live simulation method in another embodiment of the present application;
FIG. 3 is a flow chart of S40 in an embodiment of the present application;
FIG. 4 is a block diagram of a digital twinning-based drying apparatus live simulation system in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below in conjunction with figures 1-5.
The embodiment of the application discloses a digital twinning-based drying equipment live simulation method, which is based on a digital twinning-based drying equipment live simulation device, establishes a full-size structure simulation model of drying equipment, and establishes a flow field simulation model on the basis of the structure simulation model to obtain a numerical model; determining boundary conditions of the numerical model; determining a control equation of the numerical model and initial values of various parameters of the control equation; optimizing initial values of all parameters to obtain optimal parameters; and carrying the optimal parameters into the numerical model for real-time calculation to obtain real-time detection data.
The embodiment of the application discloses a digital twinning-based drying equipment live simulation method, which comprises the following steps as shown in fig. 1:
s10: and establishing a full-size structure simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model.
Specifically, the digital twin is to fully utilize data such as a physical model, sensor update, motion history and the like, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and complete mapping in a virtual space, so as to reflect the full life cycle process of corresponding entity equipment.
For example, a structural simulation model, i.e. a solid model of a drying device, is established by a finite element method (Finite Element Method, FEM) and is used for processing a heat conduction process of a structure and fluid, and then a flow field simulation model, i.e. a wind field space in the drying device, is established by computational fluid dynamics (Computational Fluid Dynamics, CFD) and is used for processing hot air flow, temperature distribution and heat convection processes in a box body, and the structural simulation model and the flow field simulation model are mutually coupled to complete a fluid-solid coupling process of thermal analysis of the drying device.
S20: boundary conditions of the numerical model are determined.
Specifically, the actual drying equipment is related to the numerical model by using a sensor, the commonly used sensors are a flow velocity sensor and a temperature sensor, the installation positions are generally the air inlet and the air outlet of the drying equipment, the battery pole piece and the position with larger flow velocity and temperature change, the data obtained by the sensor are transmitted to the numerical model, particularly, part of temperature data are designed to be boundary conditions of the numerical model aiming at the thermal analysis problem of the battery drying equipment, the boundary conditions of the position of the air inlet are generally set to be fixed temperature boundary conditions and flow velocity boundary conditions, the outer wall of the structural model is set to be room temperature boundary conditions, the other part of temperature data can be used as model verification data, and generally, the flow velocity and the temperature of the air outlet are set to be verification data.
S30: a control equation for the numerical model is determined, along with initial values for various parameters of the control equation.
Specifically, according to the thermal analysis characteristics of the battery drying equipment, thermal convection and thermal conduction analysis should be included, and the basic control equation can be determined to include in the fluid domain: continuity equation (mass conservation equation), momentum conservation equation, energy conservation equation, turbulence equation describing flow field, and heat conduction equation describing heat conduction should be included in the solid domain.
For the selection of the model constants, more experience is accumulated in engineering practice and process specifications, corresponding model constants can be selected as initial values according to experience to perform pre-calculation, the control equation is well embedded into various commercial numerical software, such as numerical software of ANSYS Fluent, ABAQUS, COMSOL and the like, and modeling and calculation can be conveniently performed by directly using the commercial numerical software.
S4: and optimizing the initial values of all the parameters to obtain the optimal parameters.
Specifically, a simulation parameter optimization model is established, a relation between model key parameters and calculation output results is established through the optimization model, and optimal parameters under the current working condition are obtained through iterative calculation, wherein the optimization technology is used as a generic term and comprises a genetic algorithm, an ant colony algorithm, simulated degradation and other optimization technologies. The simulation parameter optimization model automatically calculates new parameters through errors and parameter values in the last calculation of the numerical model, then carries the new parameters into the numerical model to calculate, obtains output results and sensor measurement results to compare, judges errors again, further establishes the relation between the model key parameters and the calculated output results, and takes the currently obtained parameters as optimal parameters under the current working condition through multiple times of falling-off calculation until the errors are not converged or meet the precision requirement.
Taking a genetic algorithm as an example, displaying a workflow of a simulation parameter optimization model, uniformly taking values in the corresponding value ranges of all parameters to obtain an initial parameter set, converting all parameters into binary numbers to form an initial population of the genetic algorithm, wherein each binary number is a chromosome gene in each string.
The method comprises three operations of selecting, crossing and mutating an initial population, wherein the selection refers to selecting individuals adapting to the environment from the population, the individuals are used for reproducing the next generation, the crossing refers to exchanging genes of two different individuals in the selected individuals so as to generate some new individuals, the mutating refers to mutating the genes in the selected individuals, namely, carrying out 0 and 1 transformation in binary so that the solution is not limited to a local optimal solution, and carrying out iterative computation for many times until the new population meets the iteration termination judgment condition, so that the optimal parameters can be obtained, wherein the optimal parameters can be a group of parameters or a plurality of groups of parameters and are determined according to the accuracy requirement.
S50: and carrying the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data.
Specifically, the numerical model performs real-time calculation, and simultaneously the sensor continuously collects the current state data of the equipment, so that real-time parameter identification and whole process simulation are realized.
Optionally, referring to fig. 2, after S10, further includes:
s11: and simplifying the numerical model.
S12: and performing meshing and contact setting on the numerical model.
Specifically, under the condition of limited calculation resources, the construction of the virtual numerical simulation model needs to simplify the structural model and the flow field simulation model of the drying equipment, remove operations affecting the grid division quality such as chamfering, and the like, and remove components with smaller influence on thermal analysis through trial calculation, wherein after one component is removed, if the final calculation result is not changed greatly, the component is considered to be a non-critical component and can be deleted. Meanwhile, corresponding contact setting is required to be deleted from the numerical model, grid division and contact setting are performed on the structure simulation model and the flow field simulation model, the grid quality and the contact type are required to be paid attention to, the grid independence is ensured, and then the preliminary structure simulation model and the flow field simulation model are established.
Optionally, in S20, the method further includes:
s21: and collecting data information of different positions of the drying equipment in actual operation, wherein the data information comprises temperature information and flow rate information.
S22: and transmitting the data information to the numerical model by using serial communication.
Specifically, the simulation model mainly reads temperature information and flow rate information, and in a control computer, a Python programming is used as a core module for information interaction, and the module functions comprise reading serial port data; modifying an input file of numerical calculation software and driving calculation; calling a parameter optimization module to obtain new optimization parameters; parameters in the input file of the numerical calculation software are calculated again.
The sensor is connected to the switch or the serial port of the control computer through a data line, for example, a serial module of Python is utilized, so that the serial port can be conveniently and directly checked and read, the serial port can be opened through an open () function, then the read () function can be used for reading serial port data, or a data file generated by sensor client software can be directly read.
And modifying input files of ANSYS or ABAQUS numerical calculation software, such as a command stream file of ANSYS and an inp file of ABAQUS, by using Python according to temperature and wind speed information in the read data, and finding out a command of a boundary condition in the files by a keyword searching mode to modify temperature and wind speed data in the corresponding boundary condition.
And directly driving ANSYS or ABAQUS numerical software to calculate by utilizing shell commands of a Python embedded Linux system or dos commands of windows to obtain a global temperature and wind speed calculation result, and reading calculation results of an air outlet or other setting positions.
When the calculation result does not meet the precision requirement, the optimization module is called by using the Python, and the optimization module can be conveniently realized by using a pandas library of the Python or independently writing a corresponding optimization algorithm by using the Python to obtain new numerical model parameters.
And changing an input file of numerical calculation software by using the Python again, replacing the original model parameters with numerical model parameters given by the optimization model, and driving the numerical software to calculate again until the accuracy of a calculation result meets the requirement.
S23: and taking the data information belonging to the air inlet position of the drying equipment as a first boundary condition.
S24: and taking temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition.
In particular, for the thermal analysis problem of the battery drying equipment, part of the temperature data is designed as a boundary condition of a numerical model, the boundary condition of the position of the air inlet is generally set as a fixed temperature boundary condition and a flow velocity boundary condition, the outer wall of the structural model is set as a temperature boundary condition of room temperature, the other part of the temperature data can be used as model verification data, and generally, the flow velocity and the temperature of the air outlet are set as verification data.
For the thermal analysis problem, a turbulence model is the most important component, a standard k-epsilon turbulence numerical model is selected to describe the flow rule of hot air in a box body according to the flow state of hot air in a drying oven air chamber, a wind field river basin model in the drying oven is built, and a key control equation of fluid in motion is as follows:
wherein G is b Is caused by buoyancyTurbulence energy term, G k For the turbulence energy term caused by the average velocity gradient, Y M Is the contribution of pulsation expansion in turbulence, k is the turbulence pulsation kinetic energy, epsilon is the specific turbulence energy dissipation, ρ is the average density, μ t U is the turbulent viscosity i And u j X is the air velocity vector component in the battery pack i 、x j For the orthogonal coordinate components (i, j=1, 2,3; x 1 ＝x,x 2 ＝y,x 3 ＝z)，C 1ε ，C 2ε ，C 3ε ，C μ ，σ k ，σ ε Is a turbulence equation parameter.
For example, according to simulation experience, C 1ε ＝1.44，C 2ε When the main flow direction of the wind field is consistent with the gravity direction, =1.92, C 3ε =1, when vertical, C 3ε When the angle is equal to or smaller than 0, the value between 0 and 1 is set according to the angle. In addition, for other empirical constants, C is generally preferred μ ＝0.09，σ k ＝1.0，σ ε =1.3. In addition, it is also necessary to set the thermal conductivity of the case material, as determined by the specific equipment material.
For some specific optimization models, an initial parameter set is generated, typically by setting a parameter range and uniformly taking values within the value range, C 1ε ，C 2ε The value range is [1,3 ]]，C 3ε The value range is [0,1 ]]，C με The value range is [0,1 ]]，σ k The value range is [0,2 ]]，σ ε The value range is [0,2 ]]。
In addition to the turbulence numerical model, the flow of hot air in the drying apparatus should satisfy basic conservation laws: the law of conservation of mass, the law of conservation of momentum, the law of conservation of energy, and in the control equations of the three laws, the physical properties of the basic materials are referred to and can be determined by referring to a standard table. The mass conservation equation comprises the air density in the battery box, the momentum conservation equation comprises the aerodynamic viscosity in the box, the capacity conservation equation comprises the heat transfer coefficient and the air specific heat capacity of the air in the box, when the air type is determined, the air type can be directly obtained by looking up a table, for example, nitrogen is used, and the density is 1 when the room temperature is 25 ℃ by looking up the table.13kg/m 3 The dynamic viscosity was 17.805. Mu. Pa.s.
Optionally, referring to fig. 3, the following sub-steps are included in S40:
s41: and (5) taking the initial value into a numerical model for calculation to obtain a calculation output result.
S42: and comparing the calculated output result with the acquisition result of the drying equipment.
S43: and optimizing the control equation flow parameters based on the comparison result.
S44: judging whether the difference between the calculated output result and the acquired result meets a preset condition.
S45: if not, optimizing the control equation parameters by using an optimization algorithm.
S46: and carrying out multiple iterations on the control equation parameters, and obtaining an iteration result and iteration parameters.
S47: and judging whether the iteration stop condition is met.
S48: and if the iteration stopping condition is met, stopping iteration, and taking the control equation parameter after iteration as an optimal parameter.
Specifically, the numerical model performs real-time calculation, meanwhile, the sensor continuously collects the current state data of the equipment, the data is transmitted to a judging system, the judgment of errors is composed of two steps, whether the measurement result is changed greatly is judged, meanwhile, the system compares whether the calculation result of the numerical model and the measurement result meet the set requirement, namely, whether the error between the simulation calculation result and the measurement result of the sensor is larger than a certain threshold k, generally in engineering, when the threshold k is smaller than or equal to 5%, the engineering requirement is met, when k is larger than or equal to 5%, a dynamic parameter identification system is required to be called for identifying and optimizing the model parameters, the simulation parameter optimization model is required to receive the current model parameters and is used as an initial value, a group of optimization parameters are generated through an algorithm according to the errors of the initial parameters and the calculation result, the optimization parameters are transmitted back to the numerical model, the model is calculated again after the numerical model is received and judged again in the judging unit, if the requirements are not met, the steps are repeated, and if the requirements are met, the parameters under the current working condition are calculated.
And the current optimization parameters are transmitted to the numerical model to perform real-time calculation under the current working condition until the working condition changes or the current calculation result and the actual measurement result deviate greatly, and the parameter optimization system is called again to realize real-time parameter identification and whole process simulation.
However, because the convergence of the model is difficult to predict, even if the selected basic model cannot well simulate the operation of the equipment, even if the parameters are quite accurate, a large error exists, at this time, a diversified iteration termination mode needs to be set to avoid falling into infinite loop calculation, whether iteration is continued is generally determined according to factors such as overall error, error convergence condition, parameter convergence condition and the like, wherein the overall error is the error of a direct calculation result and a measurement result of the index value model, the error convergence refers to whether the error obtained by calculating by using a new parameter is obviously reduced relative to the error calculated by using the last old parameter, the parameter convergence condition refers to whether the new parameter is obviously changed relative to the old parameter, if the overall error cannot always converge to the allowable precision, the error convergence condition and the parameter convergence condition are judged, if the convergence requirement is met, the unit outputs a signal, the iteration of the optimization model is stopped, and the current parameter is output as the optimal parameter through the optimization parameter generating unit.
The embodiment of the application also discloses a drying equipment live simulation device based on digital twinning, and referring to fig. 4, the device comprises the following modules:
the modeling module is used for establishing a full-size structure simulation model of the drying equipment and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model.
And the boundary determining module is used for determining the boundary condition of the numerical model.
And the parameter determining module is used for determining a control equation of the numerical model and initial values of various parameters of the control equation.
And the parameter optimization module is used for optimizing the initial values of all the parameters to obtain the optimal parameters.
And the acquisition and calculation module is used for bringing the optimal parameters into the numerical model, acquiring the data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data.
The embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are adapted to be loaded by a processor and execute the digital twin-based live simulation method for a drying apparatus according to the embodiment shown in fig. 1 to fig. 4, and the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to fig. 4, which is not repeated herein.
Referring to fig. 5, a schematic structural diagram of a computer device is provided in an embodiment of the present application. As shown in fig. 5, the computer device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire server 1000 using various interfaces and lines, and performs various functions of the server 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 5, an operating system, a network communication module, a user interface module, and a digital twinning-based dry equipment live simulation application may be included in a memory 1005 as one type of computer storage medium.
In the computer device 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and obtains data input by the user; while the processor 1001 may be used to invoke the digital twinning-based drying apparatus live simulation application stored in the memory 1005 and specifically:
establishing a full-size structure simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model;
determining boundary conditions of the numerical model;
determining a control equation of the numerical model and initial values of various parameters of the control equation;
optimizing initial values of all parameters to obtain optimal parameters;
and carrying the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data.
In one embodiment, after executing the derived numerical model, the processor 1001 further performs the following operations:
simplifying the numerical model;
and carrying out grid division and contact setting on the numerical model.
In one embodiment, the processor 1001, in performing the determining the boundary conditions of the numerical model, further performs the following operations:
collecting data information of different positions of the drying equipment in actual operation, wherein the data information comprises temperature information and flow rate information; transmitting the data information to the numerical model by serial communication;
taking the data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition;
and taking temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition.
In one embodiment, the processor 1001 in executing the control equation includes a turbulence equation, the turbulence equation being:
wherein G is b G is the turbulence energy caused by buoyancy k For the turbulence energy term caused by the average velocity gradient, Y M Is the contribution of pulsation expansion in turbulence, k is the turbulence pulsation kinetic energy, epsilon is the specific turbulence energy dissipation, ρ is the average density, μ t U is the turbulent viscosity i And u j X is the air velocity vector component in the battery pack i 、x j For the orthogonal coordinate components (i, j=1, 2,3; x 1 ＝x,x 2 ＝y,x 3 ＝z)，C 1ε ，C 2ε ，C 3ε ，C μ ，σ k ，σ ε Is the turbulence equation parameter.
In one embodiment, the processor 1001 performs the optimization on the initial values of the parameters to obtain the optimal parameters, and further performs the following operations:
the initial value is brought into the numerical model to be calculated, and a calculation output result is obtained;
comparing the calculated output result with the acquisition result of the drying equipment;
and optimizing the control equation flow parameters based on the comparison result.
In one embodiment, the processor 1001, when performing the optimization of the turbulence equation parameters based on the comparison, further performs the following operations:
judging whether the difference value between the calculated output result and the acquired result meets a preset condition or not;
and if not, optimizing the control equation parameters by using an optimization algorithm.
In one embodiment, the processor 1001, when executing the optimizing the turbulence equation parameters using an optimization algorithm, further performs the following operations:
performing multiple iterations on the control equation parameters, and obtaining iteration results and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the turbulence equation parameter after iteration as an optimal parameter.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains.
Claims (8)
1. A digital twinning-based live simulation method for a drying device, which is applicable to a computer device, and is characterized by comprising the following steps:
establishing a full-size structure simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model;
determining boundary conditions of the numerical model;
determining a control equation of the numerical model and initial values of various parameters of the control equation;
optimizing initial values of all parameters to obtain optimal parameters;
the optimal parameters are brought into the numerical model, data information of the drying equipment is obtained in real time, and the data information is transmitted to the numerical model for real-time calculation, so that real-time detection data are obtained;
wherein said determining boundary conditions of said numerical model comprises:
collecting data information of different positions of the drying equipment in actual operation, wherein the data information comprises temperature information and flow rate information;
transmitting the data information to the numerical model by serial communication;
taking the data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition;
taking temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition;
wherein, optimize the initial value of each parameter, get the optimal parameter, including:
the initial value is brought into the numerical model to be calculated, and a calculation output result is obtained;
comparing the calculated output result with the acquisition result of the drying equipment;
and optimizing the control equation flow parameters based on the comparison result.
2. The digital twinning-based drying apparatus live simulation method of claim 1, further comprising, after the deriving the numerical model:
simplifying the numerical model;
and carrying out grid division and contact setting on the numerical model.
3. The digital twinning-based drying apparatus live simulation method of claim 1, wherein the control equation comprises a turbulence equation, the turbulence equation being:
in the method, in the process of the invention,G b as an energy of the turbulence caused by the buoyancy,G k for the turbulence energy term caused by the average velocity gradient,Y M is the contribution of pulsation expansion in turbulence, k is the turbulence pulsation kinetic energy, epsilon is the specific turbulence energy dissipation, ρ is the average density, μ t U is the turbulent viscosity i And u j X is the air velocity vector component in the battery pack i、 x j For the orthogonal coordinate components (i, j=1, 2,3; x 1 =x，x 2 =y，x 3 =z），C 1ε ，C 2ε ，C 3ε ， C μ ，σ k ，σ ε Is the turbulence equation parameter.
4. A digital twinning-based drying apparatus live simulation method according to claim 3, wherein optimizing the turbulence equation parameters based on the comparison results comprises:
judging whether the difference value between the calculated output result and the acquired result meets a preset condition or not;
and if not, optimizing the control equation parameters by using an optimization algorithm.
5. The digital twinning-based drying apparatus live simulation method of claim 4, wherein optimizing the control equation parameters using an optimization algorithm comprises:
performing multiple iterations on the control equation parameters, and obtaining iteration results and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the turbulence equation parameter after iteration as an optimal parameter.
6. A live simulation device based on digital twin drying equipment, comprising:
the modeling module is used for establishing a full-size structure simulation model of the drying equipment and establishing a flow field simulation model on the basis of the structure simulation model to obtain a numerical model;
the boundary determining module is used for determining the boundary condition of the numerical model;
a parameter determining module, configured to determine a control equation of the numerical model, and initial values of respective parameters of the control equation;
the parameter optimization module is used for optimizing initial values of all the parameters to obtain optimal parameters;
the acquisition and calculation module is used for bringing the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information into the numerical model for real-time calculation to obtain real-time detection data;
the boundary determining module is also used for collecting data information of different positions of the drying equipment in actual operation, wherein the data information comprises temperature information and flow rate information; transmitting the data information to the numerical model by serial communication; taking the data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition; taking temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition;
the parameter optimization module is also used for bringing the initial value into the numerical model for calculation to obtain a calculation output result;
comparing the calculated output result with the acquisition result of the drying equipment; and optimizing the control equation flow parameters based on the comparison result.
7. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps of any one of claims 1 to 5.
8. A computer device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-5.
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