CN115186555A - Drying equipment live simulation method based on digital twin and related equipment - Google Patents
Drying equipment live simulation method based on digital twin and related equipment Download PDFInfo
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Abstract
The application discloses a drying equipment live simulation method based on digital twins 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 the initial value of each parameter to obtain an optimal parameter; and substituting the optimal parameters into the numerical model, acquiring data information of drying equipment in real time, and transmitting the data information to the numerical model for real-time calculation to obtain real-time detection data. The application has the technical effects that: the production quality of the product is improved by detecting the running state of the drying equipment in real time.
Description
Technical Field
The application relates to the technical field of lithium battery drying equipment, in particular to a drying equipment live simulation method based on digital twinning and related equipment.
Background
The lithium battery industry is an important component of new energy technology which is rapidly developed in the modern times, and increasingly demands for the technological, automatic and intelligent production lines are increased, so that the industrial upgrading of the related technology of lithium battery production by combining the digital twin technology is a current hot problem.
In many process flows in lithium battery production, drying of lithium batteries is a crucial step for determining battery quality, and control of drying quality is the direction of research of lithium battery manufacturers. The evaluation of the drying quality usually comprises the detection of a dried pole piece sample, and in the drying process, the detection is carried out through limited sensors arranged in drying equipment, but the detection is limited by the technology and the number and positions of the arranged sensors, and the real-time detection is difficult to carry out in the drying process, so that the production strategy cannot be adjusted in time, the product quality 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 the drying process is difficult to detect in real time, the application provides a drying equipment live simulation method based on digital twins and related equipment.
In a first aspect, the application provides a drying equipment live simulation method based on digital twins, which adopts the following technical scheme:
a drying equipment live simulation method based on digital twinning 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 the initial value of each parameter to obtain an optimal parameter;
and bringing the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information to 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 twinning technology, the digital twinning of the battery drying equipment is realized, then the parameters of the numerical model are optimized, the calculation result of the numerical model is consistent with the measurement result, and the accuracy and the authenticity of the numerical model calculation are guaranteed. The data of the drying equipment are acquired in real time, the data are conveyed to the numerical model to be calculated in real time, and real-time detection data are output, so that the whole process simulation of the drying production of the product is completed, the production process is visual, and the real-time detection of the drying process is facilitated.
Preferably, after the obtaining of the numerical model, the method further includes:
simplifying the numerical model;
and carrying out meshing and contact setting on the numerical model.
Through the technical scheme, the structural model and the flow field model are simplified, members with small influence on thermal analysis are removed through chamfering, and then meshing and contact setting are carried out on the structural model and the flow field model, so that the instruction of meshing can be improved, and the calculation amount can be reduced.
Preferably, the determining the boundary condition of the numerical model includes:
transmitting the data information to the numerical model by using serial port communication;
taking data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition;
and taking the temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition.
Through 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 position are used as boundary conditions of the numerical model, so that the accuracy and the authenticity of numerical model calculation are effectively improved.
Preferably, the governing equation comprises a turbulence equation, the turbulence equation being:
in the formula, G b Turbulent energy term due to buoyancy, G k The term of turbulent kinetic energy due to the mean velocity gradient, Y M For the contribution of pulsating expansion in turbulence, k is the turbulent pulsating kinetic energy, epsilon is the specific turbulent kinetic energy dissipation, rho is the average density, mu t To turbulent viscosity, u i And u j Is the vector component of the air velocity in the battery pack, x i 、x j Is a function of 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 flowing rule of hot air in the box body according to the flowing state of the hot air in the drying equipment chamber, and the wind field flow domain model in the drying equipment is established, wherein C 1ε ，C 2ε ，C 3ε ，C μ ，σ k ，σ ε For the model constant, for the modelThe selection of the model constant accumulates more experience in engineering practice and process specifications, the corresponding model constant can be selected as an initial value according to the experience, and the optimization is mainly carried out on parameters of turbulence simulation, so that the workload of parameter optimization can be effectively reduced.
Preferably, the optimizing the initial values of the parameters to obtain the optimal parameters includes;
substituting the initial value into the numerical model for calculation to obtain a calculation output result;
comparing the calculation output result with the acquisition result of the drying equipment;
and optimizing the control equation flow parameters based on the comparison result.
By 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, and if the calculation result and the actual measurement result have larger deviation, the parameters of the control equation are optimized, so that the accuracy of the data result of the simulation system can be improved.
Preferably, the optimizing the turbulence equation parameter based on the comparison result includes:
judging whether the difference value between the calculation output result and the acquisition result meets a preset condition or not;
if not, optimizing the control equation parameters by using an optimization algorithm.
Preferably, the optimizing the parameters of the control equation by using an optimization algorithm includes:
carrying out multiple iterations on the control equation parameters, and obtaining an iteration result and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the iterated turbulence equation parameters as optimal parameters.
According to the technical scheme, if the current calculation result has a large deviation from the actual measurement result, parameter iterative optimization is carried out, the current parameters are transmitted to the simulation parameter optimization model and are used as initial values, a group of optimization parameters are generated through an algorithm according to the errors of the initial parameters and the calculation result of the initial parameters, and the optimization parameters are transmitted back to the numerical model. And the numerical model receives the optimized model, calculates the model again, judges the model again, repeats the steps if the model does not meet the requirements, stops iterative optimization until the error is within a preset threshold value, optimizes the parameters by using a genetic algorithm, only needs to measure a small amount of experimental data in the early stage, and can reduce the workload.
In a second aspect, the present application provides a live simulation device based on a digital twin drying device, which adopts the following technical scheme:
a digital twin drying apparatus based live simulation apparatus comprising:
the modeling module is used for establishing a full-size structural simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structural simulation model to obtain a numerical model;
a boundary determination module for determining boundary conditions of the numerical model;
the parameter determination module is used for determining a control equation of the numerical model and initial values of all parameters of the control equation; the parameter optimization module is used for optimizing the initial value of each parameter to obtain the optimal parameter;
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 to 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 twinning technology, the digital twinning of the battery drying equipment is realized, then the parameters of the numerical model are optimized, the calculation result of the numerical model is consistent with the measurement result, and the accuracy and the authenticity of numerical name calculation are guaranteed. The data of the drying equipment are acquired in real time, the data are conveyed to the numerical model to be calculated in real time, and real-time detection data are output, so that the whole process simulation of the drying production of the product is completed, the production process is visual, and the real-time detection of the drying process is facilitated.
In a third aspect, the present application provides a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to carry out the above-mentioned 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 relation between a product drying production line and a numerical model is established based on a digital twinning technology, the digital twinning of battery drying equipment is realized, a dynamic parameter identification method is provided for adjusting parameters in the numerical model in real time, the calculation result of the numerical model at the key position is consistent with the measurement result of a sensor, and the accuracy and the authenticity of the numerical model calculation are guaranteed.
2. The working condition change of the production line is monitored in real time through the sensor, and the working condition change is transmitted to the numerical model in real time to be adjusted, so that the whole process simulation of product drying production is completed, the deep integration of digital twin and industrial technology is effectively realized, the development processes of digitization, networking and intelligence of the product drying process are powerfully promoted, the production process is transparent, and the management efficiency of a manager and the manufacturing quality of products are improved.
Drawings
FIG. 1 is a flow chart of a method for live simulation of a digital twin-based drying appliance in an embodiment of the present application;
FIG. 2 is a flow chart of a digital twin based drying apparatus live simulation method in another embodiment of the present application;
FIG. 3 is a flowchart of S40 in the embodiment of the present application;
FIG. 4 is a block diagram of a live simulation system of a drying device based on digital twinning in the embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-5.
The embodiment of the application discloses a drying equipment live simulation method based on a digital twin, which is characterized in that a drying equipment full-size structure simulation model is established based on a drying equipment live simulation device of the digital twin, and a flow field simulation model is established 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 all parameters of the control equation; optimizing the initial value of each parameter to obtain an optimal parameter; and substituting the optimal parameters into the numerical model for real-time calculation to obtain real-time detection data.
The embodiment of the application discloses a drying equipment live simulation method based on digital twins, which comprises the following steps as shown in figure 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 a simulation process integrating multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities by fully utilizing data such as a physical model, sensor updating and motion history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected.
For example, a structure simulation model, that is, a solid model of the drying device, is established by a Finite Element Method (FEM) to process a heat conduction process between the structure and the Fluid, and a flow field simulation model, that is, an air field space in the drying device is established by Computational Fluid Dynamics (CFD) to process a hot air flow, a temperature distribution and a heat convection process in the box, and the two models are coupled with each other to complete a Fluid-solid coupling process for thermal analysis of the drying device.
S20: the boundary conditions of the numerical model are determined.
Specifically, the real drying equipment is connected with the numerical model by using a sensor, the commonly used sensor is a flow velocity sensor and a temperature sensor, the installation positions are generally an air inlet, an air outlet, a battery pole piece and positions with large changes of flow velocity and temperature of the drying equipment, data obtained by the sensor are transmitted to the numerical model, particularly, for the problem of thermal analysis of the battery drying equipment, part of temperature data is designed to be boundary conditions of the numerical model, the boundary conditions of the air inlet position are generally set to be fixed temperature boundary conditions and flow velocity boundary conditions, the temperature boundary conditions of room temperature are set to be the outer wall of the structural model, the other part of temperature data can be used as model verification data, and the flow velocity and the temperature of the air outlet are generally set to be verification data.
S30: a control equation of the numerical model is determined, and initial values of respective parameters of the control equation are determined.
Specifically, based on the thermal analysis characteristics of the battery drying apparatus, which should include thermal convection and thermal conduction analysis, it can be determined that the basic control equations 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 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 the experience to be pre-calculated, the control equations are well embedded into various commercial numerical software, such as ANSYS Fluent, ABAQUS, COMSOL and other numerical software, and the modeling and calculation can be conveniently carried out by directly using the commercial numerical software.
S4: and optimizing the initial value of each parameter to obtain the optimal parameter.
Specifically, a simulation parameter optimization model is established, a relation between model key parameters and a calculation output result is established through the optimization model, optimal parameters under the current working condition are obtained through iterative calculation, and the optimization technology is used as a general term and comprises various optimization technologies such as a genetic algorithm, an ant colony algorithm, simulated degradation and the like. The simulation parameter optimization model automatically calculates new parameters through errors and parameter values of the numerical model in the last calculation, then brings the new parameters into the numerical model for calculation, obtains an output result, compares the output result with a sensor measurement result, judges the errors again, further establishes the relationship between the key parameters of the model and the calculation output result, and takes the currently obtained parameters as the optimal parameters under the current working condition through repeated successive generations of calculation until the errors are not converged or meet the precision requirement.
Taking a genetic algorithm as an example, the workflow of the simulation parameter optimization model is displayed, uniform values are taken from the value ranges corresponding to the parameters to obtain an initial parameter group, the parameters are converted into binary systems to form an initial population of the genetic algorithm, and each binary bit is a gene of a chromosome in each string.
The method comprises the following steps of performing three operations of selection, crossing and variation on an initial population, wherein the selection refers to selecting an individual adaptive to the environment from the population, the individuals are used for breeding the next generation, the crossing refers to interchanging genes of two different individuals in the selected individual so as to generate a plurality of new individuals, the variation refers to mutating the genes in the selected individual, namely, performing 0 and 1 transformation in a binary system, so that the solution is not limited to the local optimal solution, and the optimal parameters can be obtained through repeated iterative calculation until the new population meets the iterative termination judgment condition, wherein the optimal parameters can be one group of parameters or a plurality of groups of parameters and are determined according to the precision requirement.
S50: and bringing the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information to the numerical model for real-time calculation to obtain real-time detection data.
Specifically, the numerical model performs real-time calculation, and the sensor continuously acquires current state data of the equipment, so that real-time parameter identification and overall process simulation are realized.
Optionally, referring to fig. 2, after S10, the method further includes:
s11: and simplifying the numerical model.
S12: and carrying out meshing and contact setting on the numerical model.
Specifically, under the condition of limited computing resources, the establishment of the virtual numerical simulation model needs to simplify the structure model and the flow field simulation model of the drying equipment, remove operations such as chamfers and the like which affect the grid division quality, and components which have small influence on thermal analysis can be removed through trial calculation, wherein after one component is removed, if the final calculation result is not changed greatly, the component is considered as a non-critical component and can be deleted. Meanwhile, corresponding contact setting needs to be deleted from the numerical model, grid division and contact setting are carried out on the structure simulation model and the flow field simulation model, grid quality and contact types need to be paid extra attention, grid independence is guaranteed, and then preliminary establishment of the structure simulation model and the flow field simulation model is completed.
Optionally, in S20, the method further includes:
s21: data information of different positions of the drying equipment in actual operation is collected, and the data information comprises temperature information and flow rate information.
S22: and transmitting the data information to the numerical model by using serial port communication.
Specifically, the simulation model mainly reads temperature information and flow rate information, and takes a Python compiling program as an information interaction core module in a control computer, wherein the module has the function of 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; the parameters in the input file of the software are calculated numerically and calculated again.
The sensor is connected to a serial port of a switch or a control computer through a data line, for example, a serial port can be checked and read directly conveniently by using a serial module of Python, the serial port can be opened through an open () function, and then the read () function can be used for reading serial port data or directly reading a data file generated by sensor client software.
And modifying an input file of ANSYS or ABAQUS numerical calculation software, such as an ANSYS command stream file and an ABAQUS inp file, by using Python according to the read temperature and wind speed information in the data, finding a command of the boundary condition in the file by a keyword search mode, and modifying the temperature and wind speed data in the corresponding boundary condition.
And directly driving ANSYS or ABAQUS numerical software to calculate by using a shell command of a Python embedded Linux system or a dos command of windows to obtain a global temperature and wind speed calculation result, and reading a calculation result of an air outlet or other set positions.
When the calculation result does not meet the precision requirement, the Python is used for calling the optimization module, and the optimization module can usually conveniently realize the optimization algorithm by using the pandas library of the Python or independently write the corresponding optimization algorithm by using the Python to obtain a new numerical model parameter.
And changing the input file of the numerical calculation software by using Python again, replacing the original model parameters with the numerical model parameters given by the optimization model, and driving the numerical software to calculate again until the accuracy of the calculation result meets the requirement.
S23: and taking the data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition.
S24: and taking the 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 temperature data is designed as the boundary condition of a numerical model, the boundary condition of the position of an air inlet is generally set as a fixed temperature boundary condition and a flow velocity boundary condition, the temperature boundary condition of room temperature is set for the outer wall of a structural model, the other part of temperature data can be used as model verification data, and the flow velocity and the temperature of the air outlet are generally 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 flowing rule of hot air in a box body according to the flowing state of hot air in an air chamber of the drying oven, an air field flow domain model in the drying oven is established, and a key control equation of the fluid in the motion is as follows:
in the formula, G b The term of buoyancy-induced turbulence energy, G k The term of turbulent kinetic energy due to the mean velocity gradient, Y M For the contribution of pulsating expansion in turbulence, k is the turbulent pulsating kinetic energy, epsilon is the specific turbulent kinetic energy dissipation, rho is the average density, mu t Is a turbulent viscosity of u i And u j Is the vector component of the air velocity in the battery pack, x i 、x j Is a function of 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 Are turbulence equation parameters.
For example, according to simulation experience, C 1ε ＝1.44，C 2ε =1.92, when wind field main flow direction is consistent with gravity direction, C 3ε =1, when vertical, C 3ε And =0, and values are taken between 0 and 1 according to different angles when the angle is at other angles. In addition to other empirical constants, C may be taken in general μ ＝0.09，σ k ＝1.0，σ ε =1.3. In addition, the thermal conductivity of the tank material needs to be set, depending on the specific equipment material.
For some specific optimization models, an initial parameter set needs to be generated, generally, the initial parameter set can be obtained by setting a parameter range and uniformly taking values in the value range, and 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 ]]。
Besides the turbulence numerical model, the hot air flow in the drying equipment should satisfy the basic conservation law: the law of conservation of mass, the law of conservation of momentum, the law of conservation of energy, in the governing equations of these three laws, the fundamental physical properties of a material are involved, which can be determined by looking up a standard table. The conservation of mass equation comprises air density in the battery box, the conservation of momentum equation comprises aerodynamic viscosity in the box, and the conservation of capacity equation comprises heat transfer coefficient and air specific heat capacity of air in the box, and when determining air type, the density can be obtained by directly looking up a table, such as using nitrogen, and looking up the table at room temperature of 25 ℃ to obtain the density of 1.13kg/m 3 The dynamic viscosity was 17.805. Mu. Pa · s.
Optionally, referring to fig. 3, in S40, the following sub-steps are included:
s41: and substituting the initial value into a numerical model for calculation to obtain a calculation output result.
S42: and comparing the calculation output result with the acquisition result of the drying equipment.
S43: and optimizing the control equation flow parameters based on the comparison result.
S44: and judging whether the difference value between the calculation output result and the acquisition result meets a preset condition or not.
S45: if not, optimizing the parameters of the control equation 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 iterated control equation parameters as optimal parameters.
Specifically, a numerical model is calculated in real time, meanwhile, a sensor continuously collects current state data of equipment and transmits the data to a judgment system, the judgment of errors is composed of two steps, firstly, 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 set requirements, 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 less than or equal to 5%, the engineering requirements are met, when the k is greater than 5%, a dynamic parameter identification system is required to be called to identify and optimize model parameters, a simulation parameter optimization model receives current model parameters and uses the current model parameters as initial values, a group of optimization parameters are generated through an algorithm according to the errors of the initial parameters and the calculation results, the optimization parameters are transmitted back to the numerical model, the model is calculated again after the model receives the optimization, the judgment is carried out again in a judgment 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 transmitting the current optimized parameters to the numerical model to perform real-time calculation under the current working condition, and calling the parameter optimization system again until the working condition changes or the current calculation result has larger deviation with the actual measurement result, so as to realize real-time parameter identification and whole-process simulation.
However, since the convergence of the model is difficult to predict, even if the selected basic model cannot simulate the operation of the equipment well, even if the parameters are very accurate, a large error exists, and at this time, a diversified iteration termination mode needs to be set to avoid trapping in infinite loop calculation, and whether to continue iteration is generally determined according to factors such as a total error, an error convergence condition, and a parameter convergence condition, where the total error refers to an error between a direct calculation result and a measurement result of the numerical model, the error convergence refers to whether an error obtained by calculating using a new parameter is significantly reduced relative to an error obtained by calculating a previous old parameter, the parameter convergence condition refers to whether a new parameter is significantly changed relative to an old parameter, if the total error cannot be converged to an allowable precision all the time, the error convergence condition and the parameter convergence condition are determined, and if the convergence requirement is met, the unit outputs a signal, stops optimizing the iteration of the model, and outputs a current parameter as an optimal parameter through the optimized parameter generation unit.
The embodiment of the application also discloses a drying equipment live simulation device based on digital twins, and with reference to fig. 4, the device comprises the following modules:
and the modeling module is used for establishing a full-size structural simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structural 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 all parameters of the control equation.
And the parameter optimization module is used for optimizing the initial values of 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 to the numerical model for real-time calculation to obtain real-time detection data.
An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method for simulating a drying device live based on a digital twin according to the embodiment shown in fig. 1 to 4, and a specific execution process may refer to specific descriptions of the embodiment shown in fig. 1 to 4, which is not described herein again.
Referring to fig. 5, a schematic structural diagram of a computer device is provided for an embodiment of the present application. As shown in fig. 5, the computer apparatus 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
The communication bus 1002 is used to implement connection communication among these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also 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.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set 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 various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 5, a memory 1005, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a digital twin based drying appliance live simulation application.
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 acquiring data input by the user; and the processor 1001 may be configured to invoke the digital twin based drying apparatus live simulation application stored in the memory 1005 and in particular to perform the following operations:
establishing a full-size structure simulation model of 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 all parameters of the control equation;
optimizing the initial value of each parameter to obtain an optimal parameter;
and substituting the optimal parameters into the numerical model, acquiring data information of drying equipment in real time, and transmitting the data information to the numerical model for real-time calculation to obtain real-time detection data.
In one embodiment, the processor 1001, after executing the derived numerical model, further performs the following operations:
simplifying the numerical model;
and carrying out meshing and contact setting on the numerical model.
In one embodiment, the processor 1001, in performing the determining the boundary condition of the numerical model, further performs the following operations:
collecting data information of different positions of 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 using serial port communication;
taking data information belonging to the air inlet position of the drying equipment in the data information as a first boundary condition;
and taking the 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:
in the formula, G b Turbulent energy term due to buoyancy, G k For turbulent kinetic energy term, Y, due to mean velocity gradient M For the contribution of pulsating expansion in turbulence, k is the turbulent pulsating kinetic energy, epsilon is the specific turbulent kinetic energy dissipation, rho is the average density, mu t To turbulent viscosity, u i And u j Is the vector component of the air velocity in the battery pack, x i 、x j Is a function of 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 an 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:
substituting the initial value into the numerical model for calculation to obtain a calculation output result;
comparing the calculation 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, in executing the optimization of the turbulence equation parameters based on the comparison result, further executes the following operations:
judging whether the difference value between the calculation output result and the acquisition result meets a preset condition or not;
if not, optimizing the control equation parameters by using an optimization algorithm.
In one embodiment, the processor 1001, in executing the optimization algorithm to optimize the parameters of the turbulence equation, further performs the following operations:
carrying out multiple iterations on the control equation parameters to obtain an iteration result and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the iterated turbulence equation parameters as optimal parameters.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. It is intended that all equivalent variations and modifications made in accordance with the teachings of the present disclosure be covered thereby. 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 variations, 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 (10)
1. A drying device live simulation method based on digital twinning is suitable for computer equipment 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 the initial value of each parameter to obtain an optimal parameter;
and bringing the optimal parameters into the numerical model, acquiring data information of the drying equipment in real time, and transmitting the data information to the numerical model for real-time calculation to obtain real-time detection data.
2. The live simulation method of a digital twin-based drying apparatus according to claim 1, wherein after the obtaining of the numerical model, further comprising:
simplifying the numerical model;
and carrying out meshing and contact setting on the numerical model.
3. The method of claim 1, wherein the determining boundary conditions of the numerical model comprises:
collecting data information of different positions of 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 using serial port 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 the temperature information of the outer wall position belonging to the structural model in the data information as a second boundary condition.
4. The digital twin based drying apparatus live simulation method of claim 1, wherein the governing equation comprises a turbulence equation, the turbulence equation being:
in the formula, G b The term of buoyancy-induced turbulence energy, G k The term of turbulent kinetic energy due to the mean velocity gradient, Y M For the contribution of pulsating expansion in turbulence, k is the turbulent pulsating kinetic energy, epsilon is the specific turbulent kinetic energy dissipation, rho is the average density, mu t To turbulent viscosity, u i And u j Is the vector component of the air velocity in the battery pack, x i 、x j Is a function of 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.
5. The live simulation method of a digital twin-based drying device according to claim 1, wherein the optimizing initial values of the respective parameters to obtain optimal parameters comprises:
substituting the initial value into the numerical model for calculation to obtain a calculation output result;
comparing the calculation output result with an acquisition result of a drying device;
and optimizing the control equation flow parameters based on the comparison result.
6. The live simulation method of a digital twin-based drying apparatus according to claim 5, wherein the optimizing the turbulence equation parameters based on the comparison result comprises:
judging whether the difference value between the calculation output result and the acquisition result meets a preset condition or not;
if not, optimizing the control equation parameters by using an optimization algorithm.
7. The digital twin based drying plant live simulation method of claim 1, wherein the optimizing the governing equation parameters using an optimization algorithm comprises:
carrying out multiple iterations on the control equation parameters to obtain an iteration result and iteration parameters;
judging whether an iteration stop condition is met;
and if the iteration stopping condition is met, stopping iteration, and taking the iterated turbulence equation parameters as optimal parameters.
8. A live simulation device based on a digital twin drying device is characterized by comprising:
the modeling module is used for establishing a full-size structural simulation model of the drying equipment, and establishing a flow field simulation model on the basis of the structural simulation model to obtain a numerical model;
a boundary determining module for determining boundary conditions of the numerical model;
the parameter determination module is used for determining a control equation of the numerical model and initial values of all parameters of the control equation;
the parameter optimization module is used for optimizing the initial value of each parameter to obtain the optimal parameter;
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 to the numerical model for real-time calculation to obtain real-time detection data.
9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1 to 7.
10. 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 to 7.
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