CN116341324A - Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method - Google Patents
Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method Download PDFInfo
- Publication number
- CN116341324A CN116341324A CN202310289884.6A CN202310289884A CN116341324A CN 116341324 A CN116341324 A CN 116341324A CN 202310289884 A CN202310289884 A CN 202310289884A CN 116341324 A CN116341324 A CN 116341324A
- Authority
- CN
- China
- Prior art keywords
- temperature
- superconducting cavity
- model
- flux density
- heat flux
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000001816 cooling Methods 0.000 title claims abstract description 43
- 230000004907 flux Effects 0.000 claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 230000005672 electromagnetic field Effects 0.000 claims abstract description 6
- 238000010791 quenching Methods 0.000 claims description 31
- 238000009826 distribution Methods 0.000 claims description 26
- 230000008569 process Effects 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 6
- 238000002939 conjugate gradient method Methods 0.000 claims description 6
- 238000009529 body temperature measurement Methods 0.000 claims description 2
- 239000000758 substrate Substances 0.000 claims 1
- 238000005259 measurement Methods 0.000 description 12
- 238000012360 testing method Methods 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 8
- 239000001307 helium Substances 0.000 description 7
- 229910052734 helium Inorganic materials 0.000 description 7
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 description 7
- 238000012546 transfer Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 239000007788 liquid Substances 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000002791 soaking Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 230000000704 physical effect Effects 0.000 description 3
- 238000009835 boiling Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 229920002678 cellulose Polymers 0.000 description 1
- 239000001913 cellulose Substances 0.000 description 1
- 230000009514 concussion Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000010842 industrial wastewater Substances 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000004065 wastewater treatment Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
The invention discloses a three-dimensional temperature field of a conduction cooling superconducting cavity and an electromagnetic loss reconstruction method, which comprises the following steps: 1) Establishing a forward model and a reverse model of the conduction cooling superconducting cavity; the calculation domain of the forward model comprises a superconducting cavity region omega for calculating a temperature field 1 Vacuum area Ω for calculating electromagnetic field 2 ,Ω 1 And omega 2 Is Γ 2 ;Γ 1 Is the superconducting cavity outer surface Γ 3 Is a side surface area of the superconducting cavity beam tube; according to Γ 1 、Γ 3 Temperature determination Γ at 3 Is used for determining the wall temperature boundary condition; will be gamma 1 The place is set as an adiabatic boundary condition; will be gamma 2 The heat flux density at the position is set as q; 2) Solving the forward model to obtain gamma 2 Heat flux density of different distributionOmega corresponding to degree 1 Temperature T of (2); a solving method of the reverse model is optimized according to T and q; 3) Reconstruction Γ using inverse model 2 A heat flux density function thereon; 4) Modifying Γ in a forward model based on a heat flux density function 2 Boundary conditions at which a three-dimensional temperature field is reconstructed.
Description
Technical Field
The invention relates to a three-dimensional temperature field and electromagnetic loss reconstruction technology of a conduction cooling superconducting cavity based on temperature field inversion, which is used for research and development test of the conduction cooling superconducting cavity, temperature monitoring and fault diagnosis during stable operation.
Background
Due to its excellent properties, the application of superconducting accelerators is gradually widened to industrial wastewater treatment, cellulose manufacture, and medical (isotope) production. Compared with the traditional liquid helium soaking cooling, the superconducting cavity is cooled by using a conduction cooling mode of a small refrigerator, and the superconducting cavity has the advantages of compact structure, high economy, reliability, easiness in maintenance and the like. These advantages make it of great advantage in industry and the like. As the cooling capacity of small refrigerators is generally small, about 2W@4.2K, there is a higher demand for the heat transfer capacity of the cooling structure. The reliable and efficient conduction cooling structure is a key factor for ensuring the temperature stability and normal operation of the superconducting cavity.
Unlike liquid helium soaking cooling, conduction cooling can lead to the unavoidable existence of a large temperature gradient of a superconducting cavity, and meanwhile, due to limited heat transfer capacity, the heat transfer speed of the superconducting cavity after local quench is obviously lower than boiling heat exchange during soaking, the heat transfer path is longer, the temperature of more areas can be increased, and the quench risk is increased.
In addition, accurate measurement of superconducting cavity electromagnetic loss is a key parameter for evaluating superconducting cavity performance (Q value). When liquid helium is used for soaking and cooling, the electromagnetic loss is measured by the flow of helium gas evaporated from a helium pool, and the measurement result of the method is generally high in reliability. The measurement of electromagnetic losses in superconducting cavities is also a critical loop when using conduction cooling, and the accuracy and reliability of new measurement schemes need to be evaluated. The work is not developed in China at present and is in a starting exploration stage.
At present, the low-temperature system of the superconducting accelerator is mainly a large helium low-temperature system, the superconducting cavity is soaked and cooled by liquid helium, the stable operation of the superconducting cavity is ensured by large cold quantity and efficient boiling heat transfer, and the heat transfer process of the superconducting cavity is not much concerned when in actual work, so that the technology has less application in the superconducting accelerator. The technology has many application scenarios in conduction cooling superconducting cavities.
Disclosure of Invention
In order to accurately judge the temperature distribution of the whole conduction cooling superconducting cavity in the cooling and quench process, the invention provides a three-dimensional temperature field of the conduction cooling superconducting cavity and an electromagnetic loss reconstruction method. The present invention has developed a technique for reverse reconstruction of the temperature field and boundary conditions (electromagnetic losses at the inner surface of the superconducting cavity) of the superconducting cavity in time and space coordinates by means of limited temperature sensor data. The temperature field and electromagnetic loss reconstruction technology of the conduction cooling superconducting cavity based on temperature field inversion can obtain richer physical parameters, such as electromagnetic loss and temperature distribution in the superconducting cavity, diffusion speed of a quench zone after quench and the like, according to limited measurement. The technology is applied to various fields including aerospace, metallurgy, engine manufacturing and other engineering fields, and equipment improvement and related process improvement are promoted.
The invention can be used for research and development test of the conduction cooling superconducting cavity, monitoring in the running process and fault diagnosis, and provides powerful software support for the development of the conduction cooling superconducting cavity technology.
The technical scheme of the invention is as follows:
a three-dimensional temperature field of a conduction cooling superconducting cavity and an electromagnetic loss reconstruction method comprise the following steps:
1) Establishing a forward model and a reverse model of the conduction cooling superconducting cavity; the calculation domain of the forward model comprises a superconducting cavity region omega for calculating a temperature field 1 Vacuum area Ω for calculating electromagnetic field 2 ,Ω 1 And omega 2 Is Γ 2 ;Γ 1 Is the outer surface of the superconducting cavity, on which a plurality of temperature sensors are arranged,Γ 3 Is a side surface area of the superconducting cavity beam tube; according to Γ 1 Measured values Γ of the sensor arranged thereon 3 Temperature measurements at interface locations determine Γ 3 A first type of boundary condition; will be gamma 1 The position is set as an adiabatic boundary condition, namely a second type boundary condition; will be gamma 2 The heat flux density at the position is set as q;
2) Solving the forward model by using a finite difference method to obtain gamma 2 Omega corresponding to heat flow density of different distribution 1 Temperature data T of (2); determining Γ for inverse model solution from the temperature data T and heat flux density data q 1 The position and iteration parameters of the sensors to be arranged are optimized, and the solving method of the reverse model is optimized;
3) T determined based on step 2) 1 Temperature measured by a temperature sensor on the boundary, reconstructing Γ using the inverse model 2 The heat flux density function q (x, y, z, t) is the electromagnetic loss distribution of the superconducting cavity;
4) Based on gamma 2 The heat flux density function q (x, y, z, t) on the forward model modifies Γ 2 And reconstructing a three-dimensional temperature field of the conduction cooling superconducting cavity by using the forward model.
Further, reconstructing Γ using the inverse model 2 Heat flux density function q (x, y, z, t) on Ω 1 The method of the internal temperature field is as follows:
21 Acquiring superconducting cavity outer surface T) 1 Temperature data measured by a temperature sensor arranged aboveT n Is->Temperature data after noise removal; then find the q (x, y, z, t) function in the solution space ψ such that the equation +.>Hold in pi; f is the reverse modelAn abstract function of the heat transfer equation; />Ψ=L 2 (0,t;H 1 (T 2 ) A) is provided; wherein N represents the number of temperature measuring points;
22 According to the q (x, y, z, t) function obtained in step 21) as Γ 2 Boundary conditions at; at Ω 1 Is solved by using a heat conduction equation (the boundary condition of the forward model is modified) to obtain omega 1 Temperature of the domain, finish Ω 1 And reconstructing the temperature field in the inner part.
Further, regularization is used to minimize the objective function Solving to obtain a heat flow density function q (x, y, z, t); wherein beta is more than or equal to 0.
Further, an iterative regularization calculation method adopting a conjugate gradient method is adopted to select an objective function when beta=0The heat flux density function q (x, y, z, t) is obtained by the minimization solution; the convergence criterion of the conjugate gradient method is that the residual error between the predicted temperature and the actual measured temperature of the inverse model is minimum.
Further, the reverse model is utilized to simulate the quench process of the conduction cooling superconducting cavity, and the method comprises the following steps:
61 Using the inverse model and Γ 1 Calculating the temperature and heat flux density value at each time t from the temperature data measured by the temperature sensor arranged above;
62 Calculating Γ) 2 The quench zone on the upper part locates the onset of quenchA location; marking position coordinates with the temperature higher than 9.2K according to the temperature calculated in the step 61), namely a quench zone;
63 Calculating the quench propagation speed according to the boundary position coordinates of the two-time quench areas;
64 According to the reconstructed temperature data, calculating to obtain the maximum temperature distribution and the temperature gradient of the superconducting cavity corresponding to the conducting structure and the total quench time of the superconducting cavity, and evaluating the heat conduction capacity of the conducting structure by taking the maximum temperature distribution and the temperature gradient as the standard.
Further, the cooling process of the conduction cooling superconducting cavity is simulated by using the reverse model, and the method comprises the following steps:
71 Using the inverse model and T 1 The temperature data measured by the temperature sensor arranged above calculate the temperature at each moment t, and the change relation of the temperature field of the whole superconducting cavity along with time is reversely constructed;
72 Calculating stress distribution of the superconducting cavity at each moment t according to the change relation of the temperature field of the superconducting cavity with time obtained in the step 71), and marking the position with the maximum stress;
73 Judging whether the stress distribution exceeds a set value, if so, giving an early warning; and depending on the specific value and location of the stress,
the heater power at the corresponding position is controlled, and the stress at the position is reduced.
The invention has the following advantages:
the temperature field and electromagnetic loss reconstruction technology of the conduction cooling superconducting cavity based on temperature field inversion can obtain richer physical parameters according to limited measurement, and provides a powerful tool for research, development and test and stable operation of the conduction cooling superconducting cavity. The temperature can be monitored in real time in the testing and running processes of the conduction cooling superconducting cavity, richer physical images are provided, and fault diagnosis can be performed. The application of the technology can optimize the layout of the sensor, provide reliable data support for the arrangement of the sensor and the temperature control of the superconducting cavity, and further reduce the experiment cost.
Drawings
FIG. 1 is a block diagram of a conduction cooled superconducting cavity.
FIG. 2 is a flow chart of the method of the present invention.
FIG. 3 is a schematic diagram of a quarter-domain calculation.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, which are given by way of illustration only and are not intended to limit the scope of the invention.
The conduction cooling superconducting cavity structure is shown in fig. 1, and the invention takes the superconducting cavity and the conduction cooling structure thereof as research objects to establish an inverse problem mathematical model based on a heat conduction equation, namely an inverse model. The specific technical scheme is as follows:
and (3) establishing a forward model and a reverse model, wherein the forward model is shown in fig. 2.
The computational domain of the model is shown in fig. 3:
characteristic 1:
in FIG. 3, Ω 1 Is superconducting cavity region, temperature field, Ω 2 Is the vacuum area inside the superconducting cavity, is electromagnetic field, Ω 1 And omega 2 Is Γ 2 ,Γ 2 The second type of boundary condition, i.e., adiabatic boundary condition, is the heat flux density q (x, y, z, t). T (T) 1 For the outer surface of the superconducting cavity, a certain number of temperature sensors are arranged, T 3 Since the electromagnetic loss of the area is generally small, the area is defined as a fixed wall temperature boundary condition, and the measured value of the arranged sensor is taken. Γ -shaped structure 2 There is electromagnetic loss, q (x, y, z, t).
Based on T 1 Temperature of boundary measurement is inverted to obtain T 2 Accurate heat flux density q (x, y, z, t) not directly measurable at boundary and whole temperature field Ω 1 The temperature distribution of (2) is an inverse problem of three-dimensional heat conduction. This problem assumes that the relevant physical parameters (i.e. the parameters of thermal conductivity, density, heat capacity, etc. of the material present in the thermal conduction equation) are known and accurate, and may lead to a non-unique resulting solution, i.e. q (x, y, z, t), based on limited measurement data. At the same time, the small measurement error of the sensor may cause a large deviation of the estimated value, so the problem is thatAn ill-posed problem.
At Ω 1 In the above, the heat conduction process can be described as:
where T represents a continuous function of temperature in relation to time and space coordinates, ρ, C, λ are density, heat capacity and thermal conductivity, respectively, and T is the final time. At low temperatures, the physical properties of the material vary greatly with temperature, so the physical properties are a function of temperature. q (x, y, z, t) represents a group defined as Γ 2 The heat flux density function, the value of which is a function of time and space coordinates, is the second kind of boundary condition of the equation; the problem is that it is appropriate that the solution is present and unique after the initial conditions and boundary conditions are determined.
Characteristic 2:
after the forward model is established, a finite difference method is used for solving a heat conduction equation of the forward model, and the heat flow density q and the temperature T under the determined boundary condition can be obtained. The data are used for numerical experiments to provide guidance for verification of the reverse model and selection of specific parameters. In addition, the location of the temperature point selection needs to be determined through numerical experiments to guide the sensor arrangement in experimental testing. The accurate forward model and the accurate solving method are the basis for providing accurate data, and the convergence standard of the numerical solution is required to be 10 -6 。
Γ in forward model 1 The temperature data at which is unknown, the forward model is modeled under a given determined boundary condition (Γ 1 ,Γ 2 ,Γ 3 ) After that, omega can be calculated 1 Temperature. Wherein Γ is 1 、Γ 3 Boundary conditions at the point remain unchanged, modify Γ 2 Q at. Omega thus obtained 1 Temperature and given q (Γ 2 Where) are in one-to-one correspondence.
Characteristic 3: reverse model creation and solution
The thermal conductivity anti-problems here are known physical properties, initial field temperature distribution and Γ 3 Temperature at the locationTime variation based on Γ 1 Reconstructing Γ from measured temperature data 2 The heat flux density functions q (x, y, z, t) and Ω 1 And (3) an internal overall temperature field. Measurement can only obtain Γ 1 Temperature data of finite place, which can be based on Γ using inverse model 1 The temperature data with limited positions are calculated to obtain q and omega 1 Temperature data on the domain. The physical parameters are properties of the material, and are determined by measurement or physical library. The initial field refers to Ω 1 The total temperature distribution of the domain at the initial moment, which is generally a stable field distribution, changes in temperature after the electromagnetic field is applied, and the initial field at this time is the temperature field immediately before the electromagnetic field is applied.
Assume thatT n Noiseless measured temperature data representing the actual physical process, < >>Actual observation data representing the upper bound of noise as delta, i.e. superconducting cavity outer surface Γ 1 Temperature data measured by a temperature sensor arranged above; />The temperature data after noise removal is T n . The inverse problem is to solve the following equation:
the solution is to find the appropriate q (x, y, z, t) function in the solution space ψ so that the abstract equation holds in ζ. F is an abstract function representing a heat conduction equation. q (x, y, z, t) is the superconducting cavity Γ 2 And the heat flow density value of the position (x, y, z) at the time t in the upper space-time coordinate. T (T) n And (3) withThe relation of (2) is:
in the actual measurement process, T n While q (x, y, z, t) is a function of both temperature and space. ψ, pi should be spatially square integrable:
Ψ=L 2 (0,t;H 1 (Γ 2 ))
wherein N represents the number of temperature measuring points, and i represents the number of measuring points; l (L) 2 Is a mathematical representation of the square integrable space.
For the inverse problem described above, the heat flux density function q (x, y, z, t) can be obtained by solving by minimizing the objective function using a regularization method. In this problem, the objective function is:
phi (q) contains the fitting error of the data and the concussion of the estimated quantity caused by discomfort. The estimation quality of the regularized solution based on Tikhonov has a great relation with the choice of beta. Here, an iterative regularization calculation method of conjugate gradient method is adopted, and an objective function when β=0 is selected. The objective function of interest at this time is reduced to:
when the conjugate gradient method is used, the regularization parameter in the optimization problem is the iteration number, and the discomfort of the inverse problem is processed by adopting iteration stopping. The convergence criterion of the method is that the residual error between the predicted temperature of the model and the actual measured temperature is minimum, and when the temperature residual error is approximately equal to the error, the iterative process is stopped. Contrast Γ 2 The influence rule of different heat flux densities on the iteration times is provided. Since the practical heat flux density is uniform with the approximate distribution of space, selecting q in the forward model is a function with similar choice distribution.
Solving to obtainAfter the corresponding q (x, y, z, t), q (x, y, z, t) is taken as Γ 2 Boundary conditions at Ω 1 Can be solved by using a heat conduction equation (forward model) to obtain omega 1 And (3) the temperature of the domain, and completing the three-dimensional temperature field reconstruction of the superconducting cavity.
Characteristic 4:
application of reverse model in cooling process
In the cooling process of the superconducting cavity, the gamma is 2 Since the heat flux density is 0, the problem is simplified and the solution is relatively easy. The temperature field change relation of the whole superconducting cavity along with time is reversely constructed through high-resolution temperature data, so that:
(1) Calculating stress distribution of the superconducting cavity at each moment, and marking the position with the maximum stress;
(2) Judging whether the stress distribution exceeds a set value, if so, giving an early warning;
(3) And controlling the power of the heater at the corresponding position according to the specific value and the position of the stress, and reducing the stress at the position.
And 5. The characteristics are as follows:
application of reverse model in quench process
Quench of the superconducting cavity generally occurs in testing and failure. During the test, the inverse model and the surface Γ are utilized 1 The temperature data measured by the temperature sensor arranged above may be:
(1) Calculating the temperature and heat flux density distribution q (x, y, z, t) at each moment;
(2) Calculating Γ 2 A quench zone on the upper part, and locating the position of the beginning of the quench; and marking position coordinates with the temperature higher than 9.2K according to the calculated temperature, namely a quench area. Since the temperature data is updated after each time intervalThe location of the quench zone is also updated in real time.
(3) Calculating the speed of quench propagation; the quench speed is calculated according to the boundary position coordinates of the two times of quench areas. Quench zone is Ω 3 (t),Ω 3 The boundary of (t) is denoted as S (x, y, z, t). Then>Quench rate of +.>
(4) And evaluating the heat conduction capacity of the conduction structure, and calculating the maximum temperature distribution, the temperature gradient and the total quench time of the superconducting cavity corresponding to the conduction structure according to the reconstructed temperature data, wherein the maximum temperature distribution, the temperature gradient and the total quench time of the superconducting cavity are used as the standard to evaluate the heat conduction capacity of the conduction structure.
Application of reverse model in normal operation:
(1) Monitoring the change in q (x, y, z, t) as a function of temperature;
(2) After the local temperature rise occurs, diagnosing the cause of the temperature rise, whether the electromagnetic loss causes the cooling capacity to be reduced or not;
(3) Early warning the fluctuation q (x, y, z, t) of electromagnetic loss, and setting an early warning value q 0 Reconstructed q (x, y, z, t) and q 0 The difference delta q is a monitoring quantity, and when delta q is higher than a set value, an early warning is sent out;
(4) And drawing a real-time three-dimensional temperature and electromagnetic loss distribution image.
The reverse model can be implanted into a low-temperature control system of the conduction cooling superconducting cavity after testing and perfecting relevant parameters, gives real-time temperature and electromagnetic loss inversion results, and provides powerful software support for testing and running of the conduction cooling superconducting cavity.
Although specific embodiments of the invention have been disclosed for illustrative purposes, it will be appreciated by those skilled in the art that the invention may be implemented with the help of a variety of examples: various alternatives, variations and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will have the scope indicated by the scope of the appended claims.
Claims (7)
1. A three-dimensional temperature field of a conduction cooling superconducting cavity and an electromagnetic loss reconstruction method comprise the following steps:
1) Establishing a forward model and a reverse model of the conduction cooling superconducting cavity; the calculation domain of the forward model comprises a superconducting cavity region omega for calculating a temperature field 1 Vacuum area Ω for calculating electromagnetic field 2 ,Ω 1 And omega 2 Is Γ 2 ;Γ 1 Is the outer surface of the superconducting cavity, on which a plurality of temperature sensors are arranged, Γ 3 Is a side surface area of the superconducting cavity beam tube; according to Γ 1 Measured values Γ of the sensor arranged thereon 3 Temperature measurements at interface locations determine Γ 3 A first type of boundary condition; will be gamma 1 The position is set as an adiabatic boundary condition, namely a second type boundary condition; will be gamma 2 The heat flux density at the position is set as q;
2) Solving the forward model by using a finite difference method to obtain gamma 2 Omega corresponding to heat flow density of different distribution 1 Temperature data T of (2); determining Γ for inverse model solution from the temperature data T and heat flux density data q 1 The position and iteration parameters of the sensors to be arranged are optimized, and the solving method of the reverse model is optimized;
3) Γ determined based on step 2) 1 Temperature measured by a temperature sensor on the boundary, reconstructing Γ using the inverse model 2 The heat flux density function q (x, y, z, t) is the electromagnetic loss distribution of the superconducting cavity;
4) Based on gamma 2 The heat flux density function q (x, y, z, t) on the forward model modifies Γ 2 Boundary conditions at the boundary conditions, reconstructing a three-dimensional temperature field of the conduction cooling superconducting cavity by using the forward model。
2. The method of claim 1, wherein Γ is reconstructed using the inverse model 2 Heat flux density function q (x, y, z, t) on Ω 1 The method of the internal temperature field is as follows:
21 Acquiring superconducting cavity outer surface Γ 1 Temperature data measured by a temperature sensor arranged aboveT n Is->Temperature data after noise removal; then find the q (x, y, z, t) function in the solution space ψ such that the equation +.>Hold in pi; f is an abstract function of the heat conduction equation in the reverse model; />Ψ=L 2 (0,t;H 1 (Γ 2 ) A) is provided; wherein N represents the number of temperature measuring points;
22 (r) as Γ) according to the q (x, y, z, t) function obtained in step 21) 2 Boundary conditions at; at Ω 1 Is solved by using a heat conduction equation to obtain omega 1 Temperature of the domain, finish Ω 1 And reconstructing the temperature field in the inner part.
4. A method according to claim 3The method is characterized in that an iterative regularization calculation method of a conjugate gradient method is adopted, and an objective function when beta=0 is selectedThe heat flux density function q (x, y, z, t) is obtained by the minimization solution; the convergence criterion of the conjugate gradient method is that the residual error between the predicted temperature and the actual measured temperature of the inverse model is minimum.
6. The method according to any one of claims 1 to 5, characterized in that the quench process of the conduction-cooled superconducting cavity is simulated by means of the inverse model by:
61 Using the inverse model and Γ 1 Calculating the temperature and heat flux density value at each time t from the temperature data measured by the temperature sensor arranged above;
62 Calculating Γ) 2 A quench zone on the upper part, and locating the position of the beginning of the quench; marking position coordinates with the temperature higher than 9.2K according to the temperature calculated in the step 61), namely a quench zone;
63 Calculating the quench propagation speed according to the boundary position coordinates of the two-time quench areas;
64 According to the reconstructed temperature data, calculating to obtain the maximum temperature distribution and the temperature gradient of the superconducting cavity corresponding to the conducting structure and the total quench time of the superconducting cavity, and evaluating the heat conduction capacity of the conducting structure by taking the maximum temperature distribution and the temperature gradient as the standard.
7. The method according to any one of claims 1 to 5, wherein the cooling process of the conduction cooling superconducting cavity is simulated by using the inverse model, and the method comprises the following steps:
71 Using the inverse model andT 1 the temperature data measured by the temperature sensor arranged above calculates the temperature at each instant t,
reversely constructing the change relation of the temperature field of the whole superconducting cavity along with time;
72 Calculating stress distribution of the superconducting cavity at each moment t according to the change relation of the temperature field of the superconducting cavity with time obtained in the step 71), and marking the position with the maximum stress;
73 Judging whether the stress distribution exceeds a set value, if so, giving an early warning; and controlling the heater power at the corresponding position according to the specific value and the position of the stress, and reducing the stress at the position.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310289884.6A CN116341324B (en) | 2023-03-23 | 2023-03-23 | Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310289884.6A CN116341324B (en) | 2023-03-23 | 2023-03-23 | Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116341324A true CN116341324A (en) | 2023-06-27 |
CN116341324B CN116341324B (en) | 2023-10-03 |
Family
ID=86887315
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310289884.6A Active CN116341324B (en) | 2023-03-23 | 2023-03-23 | Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116341324B (en) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2012074016A (en) * | 2010-08-30 | 2012-04-12 | Keio Gijuku | Electromagnetic, thermal and fluid analysis device |
WO2014029415A1 (en) * | 2012-08-20 | 2014-02-27 | Statoil Petroleum As | Temperature modeling constrained on geophysical data and kinematic restoration |
CN107623979A (en) * | 2017-09-26 | 2018-01-23 | 中国科学院高能物理研究所 | Superconductor cavity failure compensation method, apparatus, equipment and storage medium |
WO2019223368A1 (en) * | 2018-05-22 | 2019-11-28 | 深圳供电局有限公司 | Simulation model establishment method for high-temperature superconducting corc |
-
2023
- 2023-03-23 CN CN202310289884.6A patent/CN116341324B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2012074016A (en) * | 2010-08-30 | 2012-04-12 | Keio Gijuku | Electromagnetic, thermal and fluid analysis device |
WO2014029415A1 (en) * | 2012-08-20 | 2014-02-27 | Statoil Petroleum As | Temperature modeling constrained on geophysical data and kinematic restoration |
CN107623979A (en) * | 2017-09-26 | 2018-01-23 | 中国科学院高能物理研究所 | Superconductor cavity failure compensation method, apparatus, equipment and storage medium |
WO2019223368A1 (en) * | 2018-05-22 | 2019-11-28 | 深圳供电局有限公司 | Simulation model establishment method for high-temperature superconducting corc |
Non-Patent Citations (5)
Title |
---|
CHANG ZHENG-ZE等: "Coupled electromagnetic-fluid-thermal analysis of gas-cooled high power input coupler for a 166.6 MHz proof-of principle superconducting cavity", CRYOGENICS, pages 1 - 11 * |
JIANRONG ZHOU等: "A Novel Method to Improve the Spatial Resolution of GEM Neutron Detectors With a Stopping Layer", IEEE TRANSACTIONS ON NUCLEAR SCIENCE, pages 68 - 77 * |
LI MEI等: "Unsteady numerical simulation and optimization of 499.8 MHz superconducting cavity cooling process at the High Energy Photon Source (HEPS)", THERMAL SCIENCE AND ENGINEERING PROGRESS, pages 1 - 16 * |
周健荣等: "166.6 MHz超导腔加强筋结构的多物理场耦合仿真", 低温工程, pages 23 - 30 * |
朱柯宇等: "Spoke型超导腔非稳态降温过程模拟与热应力分析", 低温工程, pages 15 - 22 * |
Also Published As
Publication number | Publication date |
---|---|
CN116341324B (en) | 2023-10-03 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP4658818B2 (en) | Temperature estimation method and apparatus | |
CN104915493B (en) | A kind of travelling-wave tubes internal temperature flexible measurement method based on FEM model | |
CN116108604B (en) | Water supply network abnormality detection method, system, equipment and storage medium | |
EP4071655A1 (en) | Straight pipeline inner wall surface temperature measurement and transient identification method and computer terminal | |
CN105868465A (en) | LM correction method for identification of heat conductivity changing along with temperature | |
CN109540968A (en) | A kind of method of quantitative detection equipment interior three-dimensional defect | |
CN116050261A (en) | Method, system, equipment and storage medium for predicting hot spot temperature of oil immersed transformer | |
CN116341324B (en) | Three-dimensional temperature field of conduction cooling superconducting cavity and electromagnetic loss reconstruction method | |
CN111460548A (en) | Normal-state roller compacted concrete gravity dam combined damming safety assessment method | |
CN117686102A (en) | Active early warning method and device for abnormal temperature of transformer | |
Maslov | A Computational—Experimental Method for Determination of the Stress—Strain State of Thermally-Loaded Power Equipment according to Full-Scale Strain-Gauging Data | |
CN105974341A (en) | Magnetic flux testing device at extreme air temperature | |
CN111159936A (en) | Cable joint thermal field calculation method based on generalized time domain finite difference | |
CN111610216A (en) | Freezing and thawing environment moisture migration testing equipment | |
JP2008151739A (en) | Temperature estimation method and device | |
Yaparova et al. | Method for temperature measuring inside a cylindrical body based on surface measurements | |
Szabó-Gali et al. | Adaptive fireworks algorithm to solve 2d inverse heat conduction problem | |
CN112699514B (en) | Rapid calculation method for transient thermal stress of thermal stratification pipeline | |
Behrem et al. | Estimate of heat transfer coefficient during quenching steel in water | |
CN113283190A (en) | Simulation method and device of cylinder cover temperature field based on boiling heat exchange model | |
CN205749858U (en) | Magnetic flux test device under extreme temperature | |
Obana | Study on the State Prediction of a Pool-Cooled Large Superconducting Coil Using Machine Learning | |
Jin et al. | Prediction of boundary heat flux in mold by inverse problem method | |
Zhang et al. | Research on Mathematical Model of Inverse Heat Conduction Problem in Laminar Cooling Process | |
CN113552438B (en) | Method for predicting load capacity of transformer |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |