CN111898299A - An optimization method of PCR base manufacturing parameters based on numerical simulation of finite element model - Google Patents

Abstract

The invention relates to a method for optimizing the manufacturing parameters of a PCR base based on finite element model numerical simulation. A numerical simulation method is constructed through the commonly used finite element software COMSOL Multiphysics and its with MATLAB interface, and the PCR base is given by a transient analysis method. The finite element model is used to model the actual temperature control process, and the results are used to optimize the design of manufacturing parameters to ensure that the PCR base can achieve optimal dynamic and static performance during the actual thermal cycling process. The optimized manufacturing parameters obtained by the invention, such as the optimal size of the thermal insulation material, can be used to guide the actual pedestal processing under the condition that the temperature performance index of the PCR pedestal is satisfied, so as to ensure that it has good thermal performance under the premise of maintaining a rapid temperature response during the thermal cycle. uniformity. The invention can greatly save time and material cost for the research and development of new PCR bases.

Description

An optimization method of PCR base manufacturing parameters based on numerical simulation of finite element model

technical field

The invention relates to the technical field of PCR base temperature performance optimization design, in particular to a method for optimizing PCR base manufacturing parameters based on finite element model numerical simulation.

Background technique

Heat transfer performance is a central concern in PCR pedestal design. At present, in the manufacture of PCR pedestal, how to optimize the parameters of the PCR pedestal is a difficult point in the design. The method of actual processing and real heat transfer experiment has the shortcomings of long cycle and a lot of waste of manpower and material resources. Most of the existing simulation optimization design methods are aimed at the simulation of the temperature uniformity of the finite element model of the PCR base. Most focus only on solving the steady-state temperature performance of the base. At the same time, these models and methods simply realize the heat transfer simulation between a given temperature as a heat source, and do not consider the influence of the system sensing lag and control link on the temperature performance. These limited models and methods have poor progress in describing the dynamic response of the object, and cannot accurately solve the thermal field distribution of the base and reflect the dynamic temperature changes in the three-temperature cycle of 94℃-55℃-72℃.

The existing design of improving PCR base has the following schemes:

(1) Directly use the base bare for reaction

(2) Wrap 30mm polyethylene foam around the side wall of the PCR base to eliminate the convection heat transfer of the side wall and improve the temperature uniformity. In the 96-well PCR base, when the given temperature is 94 °C, the maximum temperature and the minimum temperature of the base are The temperature difference can be increased from 1.2°C to 0.34°C.

(3) Carrying out the reaction in a vacuum drying oven provides a stable internal environment for the circulation process and eliminates the influence of air convection heat transfer.

Among the above methods (1) although the solution with the bare base has the fastest temperature response, the rapid heat exchange between the base and the ambient temperature leads to poor temperature uniformity of the base; (2) although the introduction of thermal insulation material wrapping can improve the The steady-state thermal uniformity of the base, but if the appropriate design parameters of the insulation material package cannot be selected correctly, the dynamics of the base will be reduced, the temperature response will have a large lag, and the heating and cooling rate will be greatly affected. Whether to use thermal insulation material to wrap and what the thickness of the wrapping is are the problems that need to be solved in the base manufacturing. (3) Since the volume of the vacuum drying box is limited, when the heat source needs an external circuit to maintain the cycle and the corresponding environment has specific requirements, the reaction in the vacuum drying box cannot be realized. The seat has great limitations, and it is difficult to popularize it to the grass-roots health sector. The traditional method of using experiments not only has a long time period, but also it is difficult to find the optimal design, and has the disadvantage of wasting a lot of manpower and material resources.

Terminology Explanation:

Base: Generally, metal materials with good thermal conductivity are selected, such as copper, aluminum and other materials, so that the temperature of the heat source can be better transferred to the surface of the base during the thermal cycle.

Manufacturing parameters: Refers to a series of physical design parameters such as PCR base model material, size, number of holes, whether to coat thermal insulation materials, selection of thermal insulation materials, and thickness.

Insulation material: According to the actual temperature cycle, materials with satisfactory heat resistance (such as foam, EVA, aluminum silicate fiber, polyethylene, etc.) are selected to greatly reduce the influence of the base temperature by the external environment during the cycle.

PCR: The polymerase chain reaction (PCR) is a molecular biology technique used to amplify specific DNA fragments. It can be regarded as a special DNA replication in vitro. The biggest feature of PCR is that it is denatured (94 ℃)-annealing (55℃)-primer extension (72℃) three process cycles can greatly increase the trace amount of DNA.

Finite Element: The Finite Element Method (FEM) is a numerical technique for approximate solutions to boundary value problems of partial differential equations. When solving, the entire problem region is decomposed, and each subregion becomes a simple part.

SUMMARY OF THE INVENTION

In view of this, the object of the present invention is to provide a method for optimizing the manufacturing parameters of the PCR base based on the numerical simulation of the finite element model. The temperature performance requirement has greatly reduced the cost of all aspects during the research and development period, and it is not limited by the environment and has better adaptability.

The present invention adopts the following scheme to realize: a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation, comprising the following steps:

Step S1: Design the 3D model of the base: draw the 3D model of the PCR base according to the number and size of holes required for the actual design;

Step S2: Initialize simulation conditions;

Step S3: setting and simulating the initialization conditions of the finite element model;

Step S4: set the optimization method, and judge whether the constraint conditions are met, if so, continue to execute step S5, otherwise update the variables and return to step S3;

Step S5: save the result O _1k {0<k≤n ₂ ︱k∈Z} to the optimization set O;

Step S6: judging whether the stop condition formula (1) and formula (2) are met, if so, output the optimization set O, and carry out the optimization result processing to complete the optimization of the manufacturing parameters of the PCR base, otherwise update the variables and return to step S3;

Further, the step S2 specifically includes the following steps:

Step S21: initialize the static and dynamic indicators of the PCR base as constraints, the steady-state error is _ess , the overshoot is σ, the heating rate is v _up , the cooling rate is v _down , and the temperature uniformity coefficient is ξ;

Steady-state error _ess = a, overshoot σ = b, heating rate v _up = c, cooling rate v _down = d, temperature uniformity coefficient ξ = f; and the constraints in the finite element simulation are as follows (1 ):

e _ss ≤a, σ≤b, v _up ≥c, v _down ≥d, ζ≤f (1)

Among them, a, b, c, d, and f all represent the design index coefficient; according to the performance of the existing PCR base, the index coefficient is formulated: 0≤a≤0.5, 0≤b≤10%, 2℃/s≤v _up ≤4℃/s, 1.5℃/s≤v _downn ≤3℃/s, 0.4≤ξ≤1;

Step S22: Initialize the manufacturing parameters to be optimized as variables V ₁ , V ₂ , V ₃ . . . V _n , wherein V ₁ refers to the type of base manufacturing material, V ₂ refers to the type of thermal insulation material, and V ₃ refers to the thickness of thermal insulation material, V ₄ ...Vn refers to the parameters including the size of the base, the size of the test tube hole, and whether the surface of the plum blossom hole may have an impact on the PCR base; the step size corresponding to the variable is set according to the actual material type selection;

Step S23: Carry out the realization of sensing and the selection of the control method, by adding the domain point probe ppb1 to the midpoint of any side wall of the PCR base to measure the real-time temperature sensing as the input of the adopted control algorithm, and the control method can include: PID control, fuzzy control, internal model control or Smith predictive control intelligent control algorithm as the system control scheme;

Step S24: formulate the optimized cost-effectiveness coefficient θ according to the actual manufacturing cost, initialize the optimized cost-effectiveness coefficient θ, whose expression is as in formula (2), and define the weight coefficients P of the variables V ₁ , V ₂ , V ₃ , . . . , V _n according to the manufacturing cost ₁ , P ₂ , P ₃ , ..., P _n ;

θ=P ₁ ×V ₁ +P ₂ ×V ₂ +P ₃ ×V ₃ +…+P _n ×V _n (2)

Step S25: Initialize the stop condition and the number of simulation searches; set the stop condition as follows: the number of running iterations reaches the maximum number of iterations: n ₁ >n ₂

The objective function F descends the gradient convergence: that is, F _k -F _k-1 <T

Among them, n ₁ is the number of optimization iterations of the manufacturing parameter optimization data at this time, and n ₂ is the maximum number of iterations of data optimization based on the computing resources in the actual optimization process, taking 500 to 10,000. F _k represents the target value calculated by k iterations, and T is the set threshold constant, which is set to 10 ^-6 .

Further, the specific content of the step S3 is:

Step S31: Set the 3D model:

Import the drawn bare pedestal model into COMSOL Multiphysics as C ₄ , and draw three rectangular parallelepipeds C ₁ , C ₂ , C ₃ in COMSOL Multiphysics; among them C ₁ , C ₂ , C ₃ and the bare pedestal C ₄ imported from outside The length, width and height are respectively (a ₁ , b ₁ , c ₁ ), (a ₂ , b ₂ , c ₂ ), (a ₃ , b ₃ , c ₃ ), (a ₄ , b ₄ , c ₄ ); Let the coordinates of the center point (x ₁ , y ₁ , z ₁ ) and (x ₂ , y ₂ , z ₂ ) of C ₁ and C ₂ agree with the coordinates of the center point of C ₄ (x ₄ , y ₄ , z ₄ ) , and the coordinates of the center point of C ₃ are (x ₄ , y ₄ , z ₄ +0.5c ₃ +0.5c ₄ ), and its relational expression is shown in formula (3):

a ₁ ≥a ₂ =a ₄ , b ₁ ≥b ₂ =b ₄ , c ₁ =c ₂ =c ₄ , (a ₁ -a ₂ )=(b ₁ -b ₂ ) (3)

Among them (a ₃ , b ₃ , c ₃ ) are set according to the size of the selected heat source, so 2*(a ₁ -a ₂ ) is the thickness of the thermal insulation material; let C ₁ and C ₂ form a difference set C ₅ is the base The thermal insulation material covered around it and the combination of C ₃ , C ₄ , and C ₅ are constructed to form a PCR base finite element geometric model with a heat source placed at the bottom of the bare base surrounded by the thermal insulation material; wherein, the C ₃ represents the heat source. , C ₄ represents the bare base, C ₅ represents the thermal insulation material;

Step S32: Analyze the heat transfer model of the PCR base, and select a physical field that can realize the temperature field characteristics of the PCR base in the COMSOL Multiphysics heat transfer module to perform finite element numerical simulation:

Due to the complex shape of the PCR base and no internal heat source, its thermal conductivity problem is described by a differential equation of thermal conductivity in the Cartesian coordinate system under the condition of steady state and no internal heat source, as shown in formula (4):

The boundary conditions for steady-state thermal analysis of the base include: the first type of boundary conditions on the bottom of the base, and the third type of boundary conditions for convection heat transfer on the sidewall of the base; because the base bottom is in contact with the heat source, when the temperature rises/drops to When the value is constant, the contact surface between the bottom surface of the base and the heat source maintains a constant temperature, and there is natural convection heat exchange between the side wall of the base and the air. The boundary conditions are shown in formula (5):

Among them, t _w is the base temperature, t _f is the ambient air temperature, h is the convective heat transfer coefficient, λ ₀ is the thermal conductivity coefficient, and n is the normal direction of the base side wall; The heat source P of the set heat source takes the measurement point ppb1 on the side wall of the base as the input to control the heat power P of the heat source and transfers the heat to the PCR base to complete the temperature change cycle; The solid heat transfer physics in the heat transfer module is used to complete the finite element numerical simulation;

Step S33: Complete the material type and parameter settings for the base, heat source, and thermal insulation material in the solid heat transfer physical field;

Use the built-in material library in COMSOL Multiphysics to search directly or define an empty material by yourself, you need to enter the constant pressure heat capacity C _p [J/(kg·K)], thermal conductivity λ ₀ [W/(m·K) ] and density ρ (kg/m ³ );

Step S34: Set the probe and heat source heat consumption rate settings in the solid heat transfer physics field:

Arrange a temperature sensor on either side wall of the PCR base and add a domain point probe ppb ₁ at the center of the side wall; according to the selected control algorithm, it includes alternative PID control, fuzzy control, internal model control or Smith prediction control The intelligent control algorithm completes the control of the heat source heating power P to change the size of the heat source heat consumption rate Q to realize the three-temperature zone cycle control; the heat source heat consumption rate Q expression is shown in formula (6):

Among them, P is the heat source power, V is the heat source volume;

Step S35: Set the boundary conditions in the solid heat transfer physics field:

Set the natural convection heat exchange between the sidewall of the base and the air. If the base is bare, there is a natural convection heat transfer between the sidewall of the base and the air. There is no need to perform the calculation as in Equation (5). In the COMSOL Multiphysics Heat Flux Module Select external natural convection heat transfer, vertical wall, need to enter wall height L (m), external temperature T _ext (K), absolute pressure P _A (Pa) and select the fluid type as air; if there is thermal insulation material on the side wall of the base , the natural convection heat exchange between the side wall and the air can be ignored, so the temperature of the side wall of the thermal insulation material can be defined as room temperature T ₀ ; the upper surface of the base will be placed with a constant temperature of about 104°C during the actual PCR base reaction process. The cover prevents the volatilization of the reagent, so the upper surface is regarded as adiabatic;

For mesh division: use the free tetrahedron with the best geometric adaptability for mesh division; since the test tube hole in the PCR base is a complex part of the shape, in order to save computing resources, first create a free tetrahedron mesh The grid is drawn globally, and then the hole surface of the test tube and its connections are refined through the refinement function to complete the grid drawing;

Step S36: Configure the solver in the solid heat transfer physics field: select the transient solver, set the relative tolerance to the solver as 0.01; select the time step method as generalized α, select the step size as the intermediate level, and select the algebraic variable In the settings, the backward Euler method is selected for consistent initialization, and the initial step fraction is 0.001. Complete the solver configuration, and save the file in .m format to prepare for subsequent calls to the with Matlab interface;

Step S37: carry out finite element numerical simulation in the solid heat transfer physical field: carry out numerical simulation according to the set finite element model, obtain the dynamic thermal field distribution of the heat exchange process of the PCR base, and calculate the temperature value curve of the domain point probe ppb1 by calculating Steady-state error _ess , overshoot σ rise and fall, temperature rate v _up , v _down , and calculate the optimized cost performance coefficient ξ according to formula (2), and save all the results as output O _1k {V ₁ , V ₂ , V ₃ … V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z}.

Further, the specific content of the step S4 is:

Use COMSOL Multiphysics to generate the .m file of the finite element model set in step S35. Use the with MATLAB interface to perform the .m file on the .m file. Formula (1) is the constraint condition, (1) and (2) in S25 are the stopping conditions, and the number of searches is n ₂ , T is the definition of the set threshold constant; according to the set search strategy, each time the variables V ₁ , V ₂ , V ₃ in (2) are selected, the calculation result O _1k {V ₁ in step S36 is realized , V ₂ , V ₃ ... V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z} output; determine whether the stop iteration condition (1 ), (2), if so, exit the loop and go to step S5; otherwise, according to the constraint of formula (1), update the new simulation parameters and input step S3 to perform the finite element numerical simulation again.

Further, the set search strategy includes grid search, particle swarm algorithm, simulated annealing or ant colony algorithm.

Further, the specific content of the step S6 is: judging whether the stopping conditional expressions (1) and (2) are satisfied, if it is satisfied, stop the calculation, and output the optimization set O{V ₁ , V ₂ , V ₃ . . . V _n , e _ss , σ, v _up , v _down , ξ, θ}, and process the optimization results to complete the optimization of the manufacturing parameters of the PCR base; otherwise, update the variables and return to step S3.

Further, the specific content of the optimization result processing is:

According to the calculation result, all output values O _1k {V ₁ , V ₂ , V ₃ . . . V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0< k≤n ₂ ︱k∈Z} input set O, reorder the set from large to small according to θ _k {0<k≤n ₂ ︱k∈Z}, and submit the output set O to the user; customized according to the user In the set O, according to the actual demand, that is, when you want to be more cost-effective, the cost-effectiveness coefficient θ is used as the main basis for selection. If you want better temperature uniformity, the uniformity coefficient ξ is used as the main basis for selection. The same Ideally, if a faster heating and cooling rate is desired, v _upk and v _downk should be considered. If better temperature accuracy is desired, ess and σ minimization should be prioritized.

Compared with the prior art, the present invention has the following beneficial effects:

(1) The present invention completes the solution of the optimization data of the manufacturing parameters of the PCR base through the method of numerical analysis of the finite element model, which can meet the temperature performance requirements and greatly reduce the cost of various aspects during the research and development period, and is not limited by the environment, and has a relatively high performance. good adaptability.

(2) The present invention solves the problem that the numerical simulation of the finite element model of the PCR base only studies how to improve the temperature uniformity through steady-state analysis. The present invention can directly observe and analyze the temperature dynamic and static performance of the PCR base.

(3) The present invention adds sensing and control to the finite element numerical simulation, so that the simulation is very close to the real environment, and the simulation results have great guiding significance for actual research and development.

Description of drawings

FIG. 1 is a flowchart of a method according to an embodiment of the present invention.

FIG. 2 is a step curve diagram of heating and cooling according to an embodiment of the present invention.

FIG. 3 is an engineering drawing of a 96-well PCR base according to an embodiment of the present invention.

FIG. 4 is a flowchart of an initialization simulation condition according to an embodiment of the present invention.

FIG. 5 is a flow chart of setting a finite element initialization condition according to an embodiment of the present invention.

FIG. 6 is a design engineering diagram of a 96-well PCR base according to an embodiment of the present invention.

FIG. 7 is a diagram of a finite element geometric model of a 96-well base PCR base according to an embodiment of the present invention.

FIG. 8 is an example diagram of grid division of a 96-well PCR base according to an embodiment of the present invention.

FIG. 9 is a flowchart of a method for optimizing manufacturing parameters of a 96-well PCR base according to an embodiment of the present invention.

FIG. 10 is a steady-state thermal field diagram at a given temperature of 94° C. after optimization of the manufacturing parameters of the 96-well PCR base according to the embodiment of the present invention.

FIG. 11 is a steady-state thermal field diagram at a given 72° C. after optimization of the manufacturing parameters of the 96-well PCR base according to the embodiment of the present invention.

12 is a steady-state thermal field diagram at a given temperature of 55° C. after optimization of the manufacturing parameters of the 96-well PCR base according to the embodiment of the present invention

FIG. 13 is a dynamic curve diagram of the temperature of the measuring point after the manufacturing parameters of the 96-well PCR base are optimized according to the embodiment of the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

As shown in FIG. 1 , the present embodiment provides a method for optimizing the manufacturing parameters of a PCR base based on finite element model numerical simulation, including the following steps:

Step S1: Design the 3D model of the base: draw the 3D model of the PCR base according to the number and size of holes required for the actual design;

Step S2: Initialize simulation conditions;

Step S3: setting and simulating the initialization conditions of the finite element model;

Step S4: set the optimization method, and judge whether the constraint conditions are met, if so, continue to execute step S5, otherwise update the variables and return to step S3;

Step S5: save the result O _1k {0<k≤n ₂ ︱k∈Z} to the optimization set O;

Step S6: judging whether the stop condition formula (1) and formula (2) are met, if so, output the optimization set O, and carry out the optimization result processing to complete the optimization of the manufacturing parameters of the PCR base, otherwise update the variables and return to step S3;

As shown in FIG. 4 , in this embodiment, the step S2 specifically includes the following steps:

Step S21: Take the given PCR base static and dynamic design indicators as constraints, make the temperature sensing point ppb1 (the position of which is shown in the 396-well PCR base engineering drawing, and take the actual PCR base as the benchmark) Arrange it on any side wall of the sensor and take a center point.) Initialize the static and dynamic indicators of the PCR base as constraints, the steady-state error is _ess , the overshoot is σ, the heating rate is v _up , and the cooling rate is v _down , the temperature uniformity coefficient is ξ;

而降温区间超调量

升降温速率指的是从起始值到第一次达到给定温度值时温度变化速率，其中t₁为升温时间，t₂为降温时间，ΔT₁、ΔT₂为升降温期间温度变化量，由于本示意图起始温度为20，故ΔT₁＝T₁-20，ΔT₂＝T₄-T₂则升温速率

则同理降温速率

而对于温度均匀性评价指标本方法定义了一温度均匀性系数ξ，以96孔PCR基座为例，图3所示，点a为其基座上表面中心域点，点b为其基座上表面边缘端侧域点，温度均匀性偏差系数为

其中T_a1、T_b1指的是PCR基座升降温区间的上表面基座a点和b点温度值,即图2中的升降温时间t₁和t₂为区间；T_a2、T_b2则是指维持在给定温度进行反应的静态区间上表面中心域点和边缘域点温度值，其中静态区间指的是从一时刻温度达到稳态温度值且之后温度波动范围不超过5％到该温度区间反应结束为止，图2中t₃、t₄表示静态区间。λ则为动态过程与静态过程的权重系数，如果裸基座有限元模型仿真过程中PCR基座的温度存在较大稳态误差，则令λ＜1，同理若发现升降温速率较慢则取λ＞1，λ具体取值根据裸基座仿真中PCR基座温度性能动静态上存在不足的严重程度进行给定。Steady-state error _ess = a, overshoot σ = b, heating rate v _up = c, cooling rate v _down = d, temperature uniformity coefficient ξ = f; as shown in Figure 2, it is a step curve of heating and cooling In the figure, T ₁ and T ₄ are the given target temperature values in the heating and cooling intervals, respectively, T ₂ and T ₅ are the steady-state temperature values in the heating and cooling intervals, and the steady-state error _ess refers to the difference between the target temperature value and the steady-state temperature. The difference between the state temperature values, the steady-state error in the heating interval is _essup =T ₁ -T ₂ , and the steady-state error in the cooling interval is _essdown =T ₅ -T ₄ . The overshoot refers to the maximum degree to which the adjusted parameter dynamically deviates from the given value, in which T ₃ and T ₆ are the dynamic deviation from the maximum temperature value in the heating and cooling interval respectively, so the overshoot in the heating interval

The overshoot in the cooling range

The heating and cooling rate refers to the temperature change rate from the initial value to the first time when the given temperature value is reached, where t ₁ is the heating time, t ₂ is the cooling time, ΔT ₁ and ΔT ₂ are the temperature changes during the heating and cooling period, Since the initial temperature in this schematic diagram is 20, ΔT ₁ =T ₁ -20, ΔT ₂ =T ₄ -T ₂ is the heating rate

Similarly, the cooling rate

For the evaluation index of temperature uniformity, this method defines a temperature uniformity coefficient ξ. Taking a 96-well PCR susceptor as an example, as shown in Figure 3, point a is the center point of the upper surface of the susceptor, and point b is the susceptor The upper surface edge end side domain point, the temperature uniformity deviation coefficient is

Among them, T _a1 and T _b1 refer to the temperature values of points a and b on the upper surface of the base of the PCR base in the heating and cooling interval, that is, the heating and cooling times t ₁ and t ₂ in Figure 2 are the intervals; T _a2 and T _b2 are It refers to the temperature value of the surface center domain point and edge domain point on the static interval that is maintained at a given temperature for the reaction. t ₃ and t ₄ in Fig. 2 represent the static region until the reaction in the temperature region is completed. λ is the weight coefficient of the dynamic process and the static process. If there is a large steady-state error in the temperature of the PCR base during the simulation of the bare base finite element model, let λ < 1. Similarly, if the heating and cooling rate is found to be slow, then Take λ>1, and the specific value of λ is given according to the severity of the dynamic and static deficiencies of the PCR susceptor temperature performance in the bare susceptor simulation.

In the finite element simulation, the constraints are as in formula (1):

e _ss ≤a, σ≤b, v _up ≥c, v _down ≥d, ζ≤f (1)

Among them, a, b, c, d, and f all represent the design index coefficient; according to the performance of the existing PCR base, the index coefficient is formulated: 0≤a≤0.5, 0≤b≤10%, 2℃/s≤v _up ≤4℃/s, 1.5℃/s≤v _downn ≤3℃/s, 0.4≤ξ≤1;

Step S22: Initialize the manufacturing parameters to be optimized as variables V ₁ , V ₂ , V ₃ . . . V _n , wherein V ₁ refers to the type of base manufacturing material, V ₂ refers to the type of thermal insulation material, and V ₃ refers to the thickness of thermal insulation material, _{V 4} , .

According to the actual parameter selection range, the material type of V1 base can be selected from any metal with good thermal conductivity, such as (aluminum, copper), and the type of V2 thermal insulation material can be selected from materials with good thermal insulation performance, such as (foam, polyethylene) , aluminum silicate), the thickness of V3 insulation material is in line with the actual thickness range (0,100) [mm] to complete the setting of the optimization parameter step size;

Step S23: Carry out the realization of sensing and the selection of the control method, by adding the domain point probe ppb1 to the midpoint of any side wall of the PCR base to measure the real-time temperature sensing as the input of the adopted control algorithm, and the control method can include: PID control, fuzzy control, internal model control or Smith predictive control intelligent control algorithm as the system control scheme;

Step S24: formulate the optimized cost-effectiveness coefficient θ according to the actual manufacturing cost, and initialize the optimized cost-effectiveness coefficient θ, whose expression is as in formula (2) to define the weighting coefficients P ₁ , P of the variables V ₁ , V ₂ , V ₃ . . . V _n according to the manufacturing cost ₂ , _P3 ... _Pn ;

θ=P ₁ ×V ₁ +P ₂ ×V ₂ +P ₃ ×V ₃ +…+P _n ×V _n (2)

Step S25: Initialize stop conditions and simulation search times; set stop conditions as follows:

1) The number of running iterations reaches the maximum number of iterations: n ₁ >n ₂

2) The objective function F descends the gradient convergence: that is, F _k -F _k-1 <T

Among them, n ₁ is the number of optimization iterations of manufacturing parameter optimization data at this time, and n ₂ is the maximum number of iterations of data optimization based on computing resources in the actual optimization process, which can be 500 to 10,000. F _k represents the target value calculated by k iterations, and T is the set threshold constant, which is recommended to be set to 10 ^-6 .

As shown in FIG. 5, in this embodiment, the specific content of step S3 is:

Step S31: Set the 3D model:

Import the drawn bare pedestal model into COMSOL Multiphysics as C ₄ , and draw three rectangular parallelepipeds C ₁ , C ₂ , C ₃ in COMSOL Multiphysics; among them C ₁ , C ₂ , C ₃ and the bare pedestal C ₄ imported from outside The length, width and height are respectively (a ₁ , b ₁ , c ₁ ), (a ₂ , b ₂ , c ₂ ), (a ₃ , b ₃ , c ₃ ), (a ₄ , b ₄ , c ₄ ); Let the coordinates of the center point (x ₁ , y ₁ , z ₁ ) and (x ₂ , y ₂ , z ₂ ) of C ₁ and C ₂ agree with the coordinates of the center point of C ₄ (x ₄ , y ₄ , z ₄ ) , and the coordinates of the center point of C ₃ are (x ₄ , y ₄ , z ₄ +0.5c ₃ +0.5c ₄ ), and its relational expression is shown in formula (3):

a ₁ ≥a ₂ =a ₄ , b ₁ ≥b ₂ =b ₄ , c ₁ =c ₂ =c ₄ , (a ₁ -a ₂ )=(b ₁ -b ₂ ) (3)

Among them (a ₃ , b ₃ , c ₃ ) are set according to the size of the selected heat source, so 2*(a ₁ -a ₂ ) is the thickness of the thermal insulation material; let C ₁ and C ₂ form a difference set C ₅ is the base The thermal insulation material covered around it and the combination of C ₃ , C ₄ , and C ₅ are constructed to form a PCR base finite element geometric model with a heat source placed at the bottom of the bare base surrounded by the thermal insulation material; wherein, the C ₃ represents the heat source. , C ₄ represents the bare base, C ₅ represents the thermal insulation material;

Step S32: Analyze the heat transfer model of the PCR base, and select a physical field that can realize the temperature field characteristics of the PCR base in the COMSOL Multiphysics heat transfer module to perform finite element numerical simulation:

Due to the complex shape of the PCR base and no internal heat source, the thermal conductivity problem can only be described by the differential equation of thermal conductivity in the Cartesian coordinate system under the condition of steady state and no internal heat source, as shown in formula (4):

The boundary conditions for steady-state thermal analysis of the base include: the first type of boundary conditions on the bottom of the base, and the third type of boundary conditions for convection heat transfer on the sidewall of the base; because the base bottom is in contact with the heat source, when the temperature rises/drops to When the value is constant, the contact surface between the bottom surface of the base and the heat source maintains a constant temperature, and there is natural convection heat exchange between the side wall of the base and the air. The boundary conditions are shown in formula (5):

Among them, t _w is the base temperature, t _f is the ambient air temperature, h is the convective heat transfer coefficient, λ ₀ is the thermal conductivity coefficient, and n is the normal direction of the base side wall; The heat source P of the set heat source takes the measurement point ppb1 on the side wall of the base as the input to control the heat power P of the heat source and transfers the heat to the PCR base to complete the temperature change cycle; The solid heat transfer physics in the heat transfer module is used to complete the finite element numerical simulation;

Step S33: Complete the material type and parameter settings for the base, heat source, and thermal insulation material in the solid heat transfer physical field;

Use the built-in material library in COMSOL Multiphysics to search directly or define an empty material by yourself, you need to enter the constant pressure heat capacity C _p [J/(kg·K)], thermal conductivity λ ₀ [W/(m·K) ] and density ρ (kg/m ³ );

Step S34: Set the probe and heat source heat consumption rate settings in the solid heat transfer physics field:

Arrange a temperature sensor on any side wall of the PCR base and add a domain point probe ppb ₁ at the center of the side wall, and the position of the domain point probe is shown in Figure 3;

According to the selected control algorithm (in this example, the PID control algorithm is adopted), the control of the heat source heating power P is completed to change the size of the heat source heat consumption rate Q to realize the three-temperature zone cycle control; the heat source heat consumption rate Q is expressed as As shown in formula (6), the expression of P in this example is as shown in formula (7):

Among them, P is the heat source power, V is the heat source volume, T _ppb1 is the temperature value measured by the domain point probe ppb1, and T _i is the input temperature value; when the selected heat source is different, formula (6) can be modified according to its actual heating condition .

Step S35: Set the boundary conditions in the solid heat transfer physics field:

Set the natural convection heat exchange between the sidewall of the base and the air. If the base is bare, there is a natural convection heat transfer between the sidewall of the base and the air. There is no need to perform the calculation as in Equation (5). In the COMSOL Multiphysics Heat Flux Module Select external natural convection heat transfer, vertical wall, need to enter wall height L (m), external temperature T _ext (K), absolute pressure P _A (Pa) and select the fluid type as air; if there is thermal insulation material on the side wall of the base , the natural convection heat exchange between the side wall and the air can be ignored, so the temperature of the side wall of the thermal insulation material can be defined as room temperature T ₀ ; the upper surface of the base will be placed with a constant temperature of about 104°C during the actual PCR base reaction process. The cover prevents the volatilization of the reagent, so the upper surface is regarded as adiabatic;

For mesh division: use the free tetrahedron with the best geometric adaptability for mesh division; since the test tube hole in the PCR base is a complex part of the shape, in order to save computing resources, first create a free tetrahedron mesh The grid is drawn globally, and then the hole surface of the test tube and its connections are refined through the refinement function to complete the grid drawing;

Step S36: Configure the solver in the solid heat transfer physics field: select the transient solver, set the relative tolerance to the solver as 0.01; select the time step method as generalized α, select the step size as the intermediate level, and select the algebraic variable In the settings, the backward Euler method is selected for consistent initialization, and the initial step fraction is 0.001. Complete the solver configuration, and save the file in .m format to prepare for subsequent calls to the with Matlab interface;

Step S37: carry out finite element numerical simulation in the solid heat transfer physical field: carry out numerical simulation according to the set finite element model, obtain the dynamic thermal field distribution of the heat exchange process of the PCR base, and calculate the temperature value curve of the domain point probe ppb1 by calculating Steady-state error _ess , overshoot σ rise and fall, temperature rate v _up , v _down , and calculate the optimized cost performance coefficient ξ according to formula (2), and save all the results as output O _1k {V ₁ , V ₂ , V ₃ … V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z}.

In this embodiment, the specific content of the step S4 is:

Use COMSOL Multiphysics to generate the .m file of the finite element model set in step S35. Use the with MATLAB interface to perform the .m file on the .m file. Formula (1) is the constraint condition, (1) and (2) in S25 are the stopping conditions, and the number of searches is n ₂ T is the definition _of the set threshold constant; according to the set search strategy, the calculation result O _{1k {} V ₁ , V ₁ , V ₂ , V ₃ . . . V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z} output; determine whether the stop iteration condition (1) is satisfied , (2), if it is, exit the loop and go to step S5; otherwise, according to the constraint of formula (1), update the new simulation parameters and input step S3 to perform the finite element numerical simulation again.

In this embodiment, the set search strategy includes grid search, particle swarm algorithm, simulated annealing or ant colony algorithm. (This method and flowchart 1 are explained by genetic algorithm)

In this embodiment, the specific content of the step S6 is: judging whether the stopping conditions (1) and (2) are met, if so, stop the calculation, and output the optimized set O{V ₁ , V ₂ , V ₃ . . . V _n , _ess , σ, v _up , v _down , ξ, θ}, and process the optimization results to complete the optimization of the manufacturing parameters of the PCR base; otherwise, update the variables and return to step S3.

In this embodiment, the specific content of the optimization result processing is:

According to the calculation result, all output values O _1k {V ₁ , V ₂ , V ₃ . . . V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0< k≤n ₂ ︱k∈Z} input set O, reorder the set from large to small according to θ _k {0<k≤n ₂ ︱k∈Z}, and submit the output set O to the user; customized according to the user According to the optimization principle, in the set O, according to the actual demand, that is, when the cost performance is expected to be higher, the cost performance coefficient θ is used as the main basis for selection; if better temperature uniformity is desired, the uniformity coefficient ξ is used as the main basis for selection. Ideally, if a faster heating and cooling rate is desired, v _upk and v _downk should be considered. If better temperature accuracy is desired, _essk and σ _k should be considered for further screening to obtain the optimal manufacturing parameter results and complete the optimization.

Preferably, in this embodiment, a numerical simulation method is constructed through the commonly used finite element software COMSOL Multiphysics and its withMATLAB interface, and the actual temperature control process of the finite element model of the PCR base is given by the method of transient analysis, and manufacturing is carried out based on the results. The parameters are optimized to ensure the optimal dynamic and static performance of the PCR base during the actual thermal cycling process. The optimized manufacturing parameters obtained in this embodiment, such as the optimal size of the thermal insulation material, can be used to guide the actual susceptor processing under the condition that the temperature performance index of the PCR susceptor is satisfied, so as to ensure that it has good performance under the premise of maintaining a fast temperature response during the thermal cycle. thermal uniformity. In order to facilitate the understanding of the method of the present invention, we take the optimization of the manufacturing parameters of the 96-well PCR pedestal as an example, and use COMSOL Multiphysics and its withMATLAB interface to numerically solve the pedestal surrounding parameters that meet the requirements of the design indicators. The invention can greatly save time and material cost for the research and development of new PCR bases.

Taking the 96-well PCR base as an example for optimization, the 3D model design engineering drawing is shown in Figure 6.

(2) Initialize the simulation conditions and make the constraints (1) where a = 0.2, b = 5%, v _up = 2.5 °C/s, v _downn = 2 °C/s, ξ = 0.8, λ = 1.2; let the variable V _1. V ₂ and V ₃ are (aluminum, copper), (aluminum silicate cotton, polyethylene foam), (0,100,1); the heat source is carbon fiber heating plate, and the control algorithm is PID control algorithm; let it (2 ) where P ₁ , P ₂ , and P ₃ are 0.5, 2, and 3, respectively, and n ₂ is 1000 and T is 10 ^-6 in the stopping condition.

需先将其定义为代数I在式(7)中进行指代，然后再选用COMSOL Multiphysics数学物理场中全局微分和常微分方程进行I表达式的定义。(3) Complete the setting of finite element initialization conditions and numerical simulation. The finite element initialization conditions are set according to the method shown in step S2, and the initial ambient temperature of the model is set to 20 °C. The finite element geometric model of the PCR base is shown in Figure 7, and the imported material parameters are shown in Table 1. The domain point The position of the probe ppb1 is shown in Figure 3, the grid division example is shown in Figure 8, and the heat source power expression P is shown in formula (7), where T _ppb1 is the temperature value measured by the domain point probe ppb1, and T _i In order to input the temperature value, in the PCR base model, the three temperature zones are 94°C-55°C-72°C, respectively, so P ₉₄ , P ₅₅ , and P ₇₂ need to be defined respectively in CO MSOL Multiphysics. In this example, 0- 90s is the temperature zone of 94°C, 90-180s is the temperature zone of 55°C, and 180-240s is the temperature zone of 72°C. In the calculation of the heat source heat consumption rate Q in solid heat transfer, the input expression of power P is shown in formula (8). , when the heat source is selected to change, the formula is modified according to its actual heating situation. which cannot be directly defined

It needs to be defined as algebra I and referred to in equation (7), and then the global differential and ordinary differential equations in the mathematical physics field of COMSOL Multiphysics are used to define the expression of I.

P=P ₉₄ *(t＞=0&t＜=90)+P ₅₅ *(t＞90&t＜180)+P ₇₂ *(t＞=180&t＜=240) (8)

Table 1 Import material parameter table

⑷Complete the optimization method setting. _Generate the model _{.m file} , set the initial conditions, and use the particle swarm algorithm as _an example to search this time after the stopping conditions _. When σ _k , v _upk , v _downk , ξ _k , θ _k ){0<k≤n ₂ ︱k∈Z} satisfies the constraint condition (1), save the optimization result to the set O. In this method, the stopping condition is that the number of iterations exceeds n ₂ or the gradient of the objective function F descends to converge. After the iteration is completed, the dataset O is output to the user. The flow chart of the optimization method is shown in Figure 9. After optimization, a data set is obtained (aluminum, aluminum silicate wool, 40mm), and the thermal field map of the three-temperature zone of the PCR base is shown in Figure 10-12, and the measurement point of the base is the temperature at the ppb1 point in Figure 3. The dynamic curve is shown in Figure 13. According to the method in step S2, the steady-state error of the base can be calculated as _ess94 = 0.117°C, _ess55 = 0.130°C, _ess72 = 0.108°C, overshoot σ ₉₄ = 3.8%, σ ₅₅ =4.5%, σ ₇₂ =4.2%, the heating and cooling rates are v ₉₄ =2.96°C/s, v ₅₅ =2.13°C/s, v ₉₄ =2.64°C/s, ξ=0.74, which are in line with the pre-specified indicators, so the The optimized data set meets the requirements.

⑸Optimize result processing

The data set O is sorted according to the manufacturing optimization weight coefficient θ from large to small, and the one with the largest θ is selected as the optimization result for processing, and the optimized data set O is saved for subsequent experimental verification. Complete the optimization of manufacturing parameters.

Preferably, by using the finite element transient analysis method, the thermal field of the PCR pedestal can be monitored in this embodiment, which greatly fits the actual situation, and can also optimize the index according to the established pedestal manufacturing parameters and complete the optimization. Pedestal manufacturing parameter optimization data. Improve the dynamic and static performance of PCR base temperature. Meet work needs and greatly reduce the cost of research and development of new PCR instruments. By adding sensing and control to the finite element numerical simulation, a dynamic simulation closer to the real temperature control is realized.

The above descriptions are only preferred embodiments of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope of the present invention.

Claims (7)

1. a PCR base manufacturing parameter optimization method based on finite element model numerical simulation, is characterized in that: comprise the following steps: Step S1: Design the 3D model of the base: draw the 3D model of the PCR base according to the number and size of holes required for the actual design; Step S2: Initialize simulation conditions; Step S3: setting and simulating the initialization conditions of the finite element model; Step S4: set the optimization method, and judge whether the constraint conditions are met, if so, continue to execute step S5, otherwise update the variables and return to step S3; Step S5: save the result O _1k {0<k≤n ₂ ︱k∈Z} to the optimization set O; Step S6: Determine whether the stop condition formula (1) and formula (2) are met, if so, output the optimization set O, and process the optimization result to complete the optimization of the manufacturing parameters of the PCR base, otherwise update the variables and return to step S3. 2. a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation according to claim 1, is characterized in that: described step S2 specifically comprises the following steps: Step S21: initialize the static and dynamic indicators of the PCR base as constraints, the steady-state error is _ess , the overshoot is σ, the heating rate is v _up , the cooling rate is v _down , and the temperature uniformity coefficient is ξ; Steady-state error _ess = a, overshoot σ = b, heating rate v _up = c, cooling rate v _down = d, temperature uniformity coefficient ξ = f; and the constraints in the finite element simulation are as follows (1 ): e _ss ≤a, σ≤b, v _up ≥c, v _down ≥d, ζ≤f (1) Among them, a, b, c, d, and f all represent the design index coefficient; according to the performance of the existing PCR base, the index coefficient is formulated: 0≤a≤0.5, 0≤b≤10%, 2℃/s≤v _up ≤4℃/s, 1.5℃/s≤v _downn ≤3℃/s, 0.4≤ξ≤1; Step S22: Initialize the manufacturing parameters to be optimized as variables V ₁ , V ₂ , V ₃ . . . V _n , wherein V ₁ refers to the type of base manufacturing material, V ₂ refers to the type of thermal insulation material, and V ₃ refers to the thickness of thermal insulation material, V ₄ , ..., Vn refer to the parameters including the size of the base, the size of the test tube hole, and whether the surface of the plum blossom hole may have an impact on the PCR base; the step size corresponding to the variable is set according to the selection of the actual material type; Step S23: Carry out the realization of the sensing and the selection of the control method, by adding the domain point probe ppb1 on the side wall of the PCR base to measure the real-time temperature sensing as the input of the adopted control algorithm, and the control method can include PID control, fuzzy Control, internal model control or Smith predictive control intelligent control algorithm as the system control scheme; Step S24 : formulate the optimized cost-effectiveness coefficient θ according to the actual manufacturing cost, and initialize the optimized cost-effectiveness coefficient θ, whose expression is as in formula (2) according to the manufacturing cost to define the weight coefficient P1 _of the variables V ₁ , V ₂ , V ₃ , . . . , V _n , P ₂ , P ₃ , ..., P _n ; θ=P ₁ ×V ₁ +P ₂ ×V ₂ +P ₃ ×V ₃ +…+P _n ×V _n (2) Step S25: Initialize stop conditions and simulation search times; set stop conditions as follows: The number of running iterations reaches the maximum number of iterations: n ₁ >n ₂ The objective function F descends the gradient convergence: that is, F _k -F _k-1 <T Among them, n ₁ is the number of optimization iterations of the manufacturing parameter optimization data at this time, and n ₂ is the maximum number of data optimization iterations based on the computing resources in the actual optimization process, taking 500 to 10,000; F _k represents the calculated value obtained by k iterations The target value, T is the set threshold constant, set to 10 ^-6 . 3. a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation according to claim 2 is characterized in that: the concrete content of described step S3 is: Step S31: Set the 3D model: Import the drawn bare pedestal model into COMSOL Multiphysics, which is C ₄ , and draw three cuboids C ₁ , C ₂ , C ₃ in COMSOL Multiphysics; among them C ₁ , C ₂ , C ₃ and the bare pedestal imported from outside The length, width and height of C ₄ are respectively (a ₁ , b ₁ , c ₁ ), (a ₂ , b ₂ , c ₂ ), (a ₃ , b ₃ , c ₃ ), (a ₄ , b ₄ , c _{4 )} ); let the coordinates of the center point of C ₁ and C ₂ (x ₁ , y ₁ , z ₁ ), (x ₂ , y ₂ , z ₂ ) and the coordinates of the center point of C ₄ (x ₄ , y ₄ , z _{4 )} ) is consistent, and the coordinates of the center point of C ₃ are (x ₄ , y ₄ , z ₄ +0.5c ₃ +0.5c ₄ ), and its relational formula is shown in formula (3): a ₁ ≥a ₂ =a ₄ , b ₁ ≥b ₂ =b ₄ , c ₁ =c ₂ =c ₄ , (a ₁ -a ₂ )=(b ₁ -b ₂ ) (3) Among them (a ₃ , b ₃ , c ₃ ) are set according to the size of the selected heat source, so 2*(a ₁ -a ₂ ) is the thickness of the thermal insulation material; let C ₁ and C ₂ form a difference set C ₅ is the base The thermal insulation material covered around it and the combination of C ₃ , C ₄ , and C ₅ are constructed to form a PCR base finite element geometric model with a heat source placed at the bottom of the bare base surrounded by the thermal insulation material; wherein, the C ₃ represents the heat source. , C ₄ represents the bare base, C ₅ represents the thermal insulation material; Step S32: Analyze the heat transfer model of the PCR base, and select a physical field that can realize the temperature field characteristics of the PCR base in the COMSOL Multiphysics heat transfer module to perform finite element numerical simulation: Due to the complex shape of the PCR base and no internal heat source, its thermal conductivity problem is described by a differential equation of thermal conductivity in the Cartesian coordinate system under the condition of steady state and no internal heat source, as shown in formula (4):

The boundary conditions for steady-state thermal analysis of the base include: the first type of boundary conditions on the bottom of the base, and the third type of boundary conditions for convection heat transfer on the sidewall of the base; because the base bottom is in contact with the heat source, when the temperature rises/drops to When the value is constant, the contact surface between the bottom surface of the base and the heat source maintains a constant temperature, and there is natural convection heat exchange between the side wall of the base and the air. The boundary conditions are shown in formula (5):

Among them, t _w is the base temperature, t _f is the ambient air temperature, h is the convective heat transfer coefficient, λ ₀ is the thermal conductivity coefficient, and n is the normal direction of the base side wall; The heat source P of the set heat source takes the measuring point ppb1 on the side wall of the base as the input to control the heat power P of the heat source and transfers the heat to the PCR base to complete the temperature change cycle; The solid heat transfer physics in the Thermal Module is used to complete finite element numerical simulations; Step S33: Complete the material type and parameter settings for the base, heat source, and thermal insulation material in the solid heat transfer physical field; Use the built-in material library in COMSOL Multiphysics to search directly or define an empty material by yourself, you need to enter the constant pressure heat capacity C _p [J/(kg·K)], thermal conductivity λ ₀ [W/(m·K) ] and density ρ (kg/m ³ ); Step S34: Set the probe and heat source heat consumption rate settings in the solid heat transfer physics field: Arrange a temperature sensor on either side wall of the PCR base and add a domain point probe ppb ₁ at the center of the side wall; according to the selected control algorithm, it includes alternative PID control, fuzzy control, internal model control or Smith prediction control The intelligent control algorithm completes the control of the heat source heating power P to change the size of the heat source heat consumption rate Q to realize the three-temperature zone cycle control; the heat source heat consumption rate Q expression is shown in formula (6):

Among them, P is the heat source power, V is the heat source volume; Step S35: Set the boundary conditions in the solid heat transfer physics field: Set the natural convection heat exchange between the side wall of the base and the air. If the base is bare, there is natural convection heat exchange between the side wall of the base and the air. There is no need to perform the calculation as in Equation (5). In the heat flux module of COMSOL Multiphysics Select external natural convection heat transfer, vertical wall, need to enter wall height L (m), external temperature T _ext (K), absolute pressure P _A (Pa) and select the fluid type as air; if there is thermal insulation material on the side wall of the base , the natural convection heat exchange between the sidewall and the air can be ignored, so the temperature of the _sidewall of the thermal insulation material can be defined as room temperature T The cover prevents the volatilization of the reagent, so the upper surface is regarded as adiabatic; For mesh division: use the free tetrahedron with the best geometric adaptability for mesh division; since the test tube hole in the PCR base is a complex part of the shape, in order to save computing resources, first create a free tetrahedron mesh The grid is drawn globally, and then the hole surface of the test tube and its connections are refined through the refinement function to complete the grid drawing; Step S36: Configure the solver in the solid heat transfer physics field: select the transient solver, set the relative tolerance to the solver as 0.01; select the time step method as generalized α, select the step size as the intermediate level, and select the algebraic variable In the settings, the backward Euler method is selected for consistent initialization, and the initial step fraction is 0.001. Complete the solver configuration, and save the file in .m format to prepare for subsequent calls to the with Matlab interface; Step S37: carry out finite element numerical simulation in the solid heat transfer physical field: carry out numerical simulation according to the set finite element model, obtain the dynamic thermal field distribution of the heat exchange process of the PCR base, and calculate the temperature value curve of the domain point probe ppb1 by calculating Steady-state error _ess , overshoot σ, heating and cooling rates v _up , v _down ; calculate the optimized cost-effectiveness coefficient ξ according to formula (2), and save all the results as output O _1k {V ₁ , V ₂ , V ₃ , ... , V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z}. 4. a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation according to claim 3 is characterized in that: the concrete content of described step S4 is: Use COMSOL Multiphysics to generate the .m file of the finite element model set in step S35, and use the withMATLAB interface to execute the .m file on the .m file. Formula (1) is the constraint condition, (1) and (2) in S25 are the stopping conditions, and the number of searches is n ₂ , T is the definition _of the set threshold constant _; according to the set search strategy, the calculation result O _{1k {} V ₁ , V ₂ , V ₃ , ..., V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0<k≤n ₂ ︱k∈Z} output; determine whether the stop iteration condition ( 1), (2), if so, exit the loop and go to step S5; otherwise, according to the constraints of formula (1), update the new simulation parameters and input step S3 to perform the finite element numerical simulation again. 5. The method for optimizing the manufacturing parameters of a PCR base based on finite element model numerical simulation according to claim 4, wherein the set search strategy comprises grid search, particle swarm algorithm, simulated annealing or ant Group algorithm. 6. a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation according to claim 2, is characterized in that: the concrete content of described step S6 is: judge whether to satisfy stop condition (1), (2) ), if it is satisfied, stop the calculation, output the optimization set O{V ₁ , V ₂ , V ₃ ... V _n , _ess , σ, v _up , v _down , ξ, θ}, and process the optimization results to complete the PCR base The seat manufacturing parameters are optimized; otherwise, the variables are updated and return to step S3. 7. a kind of PCR base manufacturing parameter optimization method based on finite element model numerical simulation according to claim 5, is characterized in that: the specific content of described optimization result processing is: According to the calculation result, all output values O _1k {V ₁ , V ₂ , V ₃ . . . V _n , _essk , σ _k , v _upk , v _downk , ξ _k , θ _k }{0< k≤n ₂ ︱k∈Z} input set O, reorder the set from large to small according to θ _k {0<k≤n ₂ ︱k∈Z}, and submit the output set O to the user; customized according to the user In the set O, the most reasonable manufacturing parameters are selected according to the design requirements to complete the optimization. When you want a higher cost performance, use the cost-effectiveness coefficient θ as the main basis; if you want better temperature uniformity, use the uniformity coefficient ξ as the main basis; similarly, if you want a faster heating and cooling rate, consider v _upk , v _downk , if you want better temperature accuracy, you need to prioritize _ess and σ minimization.

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