Method for simulating temperature field of line heating process based on mixing of heat source and cold source

Abstract

The invention discloses a line heating process temperature field simulation method based on mixing of a heat source and a cold source, and belongs to the field of numerical simulation. The method comprises the following steps: acquiring temperature fields of a sample steel plate under different process parameters by using an infrared thermal imager so as to test a temperature matrix and a distance weight of the sample steel plate under different process parameters of heating and water-cooling heat exchange; establishing a weighted Gaussian mixture heat source model, and executing heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrixes and the distance weights under different process parameters; and fitting the effective radiuses of heating and cooling based on the estimation results under different process parameters so as to obtain a Gaussian mixture heat source model under different process parameters. According to the scheme, the Gaussian mixture heat source model containing the heat source and the cold source is provided, the temperature field of the line heating process is simulated, and compared with simple boundary setting water cooling, the accuracy of a finite element calculation result is guaranteed, and the calculation time is saved.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, and in particular to a method for simulating the temperature field of a water-fire bending plate process based on the mixing of heat and cold sources. Background Technology

[0002] In shipbuilding production lines, processes such as layout, material marking, steel plate pretreatment, cutting, and welding have all been automated. However, the forming of curved steel plates has become a bottleneck in shipbuilding. The shipbuilding industry requires the forming of a large number of complex curved outer plates, and the efficiency and precision of this forming process are challenges in processing hyperboloid steel plates. Water-fire bending is a primary method for forming complex hyperboloid outer plates, but currently shipyards mainly rely on experienced workers operating manually, which suffers from low processing efficiency and large fluctuations in forming quality.

[0003] Existing numerical simulations of water-fire bending plates only use a Gaussian heat source to simulate the heat flow of the flame when calculating the temperature field. For the rapid cooling of the steel plate caused by water spraying, boundary conditions are set. However, this simple simulation method of setting water cooling as the heat transfer boundary condition has low calculation accuracy.

[0004] Therefore, there is an urgent need to provide a method for simulating the temperature field of water-fire bending plate process based on the mixing of heat and cold sources. Summary of the Invention

[0005] To address the problem that existing numerical simulation methods for water-fire bending plates use simple boundary settings and water cooling, resulting in very low calculation accuracy, this invention provides a method for simulating the temperature field of water-fire bending plate processes based on a mixture of heat and cold sources.

[0006] On the one hand, a method for simulating the temperature field of a water-fire bending plate process based on the mixing of heat and cold sources is provided, the method comprising: The temperature field of the sample steel plate under different process parameters was acquired using an infrared thermal imager in order to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange. A weighted Gaussian mixture heat source model is established to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters. Based on the estimation results under different process parameters, the effective radii of heating and cooling are fitted to obtain Gaussian mixed heat source models under different process parameters.

[0007] On the other hand, a temperature field simulation device for water-fire bending plate process based on the mixing of heat and cold sources is provided, used to implement the steps described in any method embodiment of the specification, the device comprising: The test unit is used to acquire the temperature field of the sample steel plate under different process parameters using an infrared thermal imager, so as to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange. The estimation unit is used to establish a weighted Gaussian mixture heat source model to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters. The fitting unit is used to fit the effective radii of heating and cooling based on the estimation results under different process parameters, so as to obtain the Gaussian mixed heat source model under different process parameters.

[0008] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the method described above.

[0009] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the method described above.

[0010] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0011] The technical solution provided by this invention can bring at least the following beneficial effects: By establishing a weighted Gaussian mixed heat source model that includes heat and cold sources, and by combining the temperature matrix and distance weights of the test sample steel plate under different process parameters of heating and water cooling heat transfer, the temperature field of the water-fire bending process is simulated. Compared with simple boundary setting water cooling, the weighted Gaussian mixed heat source model that includes heat and cold sources designed in this scheme not only ensures the accuracy of finite element calculation results, but also saves calculation time. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for simulating the temperature field of a water-fire bending plate process based on the mixing of heat and cold sources, provided by an embodiment of the present invention. Figure 2This is a comparison chart of temperature curves between a Gaussian mixed heat source model and a traditional single heat source model provided in an embodiment of the present invention; Figure 3 This is a simulation diagram of the heat flow distribution of a Gaussian mixed heat source model provided in an embodiment of the present invention; Figure 4 This is a temperature field simulation diagram of a Gaussian mixed heat source model provided in an embodiment of the present invention; Figure 5 This is a simulation diagram of the heat flow distribution of a traditional single heat source model provided in an embodiment of the present invention; Figure 6 This is a temperature field simulation diagram of a traditional single heat source model provided in an embodiment of the present invention; Figure 7 This is a structural diagram of a temperature field simulation device for a water-fire bending plate process based on the mixing of heat and cold sources, provided in an embodiment of the present invention. Figure 8 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] The following describes the specific implementation of the above concept.

[0016] Please refer to Figure 1 The present invention provides a method for simulating the temperature field of a water-fire bending process based on a mixture of heat and cold sources. The method includes: Step 100: Use an infrared thermal imager to collect the temperature field of the sample steel plate under different process parameters, so as to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange; Step 102: Establish a weighted Gaussian mixture heat source model to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters; Step 104: Fit the effective radii of heating and cooling based on the estimation results under different process parameters to obtain the Gaussian mixed heat source model under different process parameters.

[0017] In this embodiment of the invention, a weighted Gaussian mixed heat source model containing heat and cold sources is established. By combining the temperature matrix and distance weights of the test sample steel plate under different process parameters of heating and water cooling heat transfer, the temperature field of the water-fire bending process is simulated. Compared with simple boundary setting water cooling, the weighted Gaussian mixed heat source model containing heat and cold sources designed in this scheme not only ensures the accuracy of the finite element calculation results, but also saves calculation time.

[0018] The following description Figure 1 The execution method for each step is shown.

[0019] For step 100: In some implementations, step 100 may include: Multiple heating paths were marked on the sample steel plate, and temperature sensors were attached at 50mm, 100mm, and 150mm on each heating path. The temperature sensors were connected to a computer to record temperature changes during the heating process. The flow rates of propylene and oxygen cylinders were adjusted to the experimental values. The heating rate, water-fire distance, and water flow rate were also adjusted to the experimental values. Heating and water-cooling tests were conducted according to the above parameters. After the temperature stabilized, images of the sample steel plate were taken using an infrared thermal imager to obtain temperature images of each path under different process parameters. The process parameters included the flow rates of propylene and oxygen cylinders, heating rate, water-fire distance, and water flow rate. Several experimental values ​​were obtained for each process parameter. The temperature images of each path under different process parameters are converted into a two-dimensional temperature matrix, and each pixel coordinate is used as a two-dimensional sample point to serve as the distance weight of the temperature value.

[0020] In this embodiment, the experimental values ​​of the process parameters include the flow rates of the propylene and oxygen cylinders. (1200 / 1300 / 1400 / 1500 / 1600) L / h, heating rate V (4 / 6 / 8) mm / s, water-fire spacing L (80 / 90 / 100) mm, and water flow rate (6 / 8 / 10) L / min, the experimental values ​​of each process parameter were arranged and combined to obtain temperature pictures under each combination of process parameters.

[0021] Regarding step 102: In some implementations, the weighted Gaussian mixed heat source model is established as follows: The density function of the weighted Gaussian mixture heat source model containing heat and cold sources is: In the formula, k represents the number of Gaussian heat sources, which is 2, representing the heat source and the cold source respectively. The mixing weights are the k-th Gaussian distribution. This is a simplified representation of the Gaussian mixed heat source model. Let be the expected value of the k-th Gaussian distribution, i.e., the effective radius.

[0022] The density function of the heat source is: In the formula, Let A be the flow rate of propylene, and A be the calorific value of propylene after complete combustion. For thermal efficiency, Let r be the effective radius of the heat source, and r be the distance from a point to the heating center. The density function of the cold source is: In the formula, Let C be the flow rate of the water, and C be the heat absorbed by the water as it heats up from 20°C to 100°C. For heat absorption efficiency, Let r be the effective radius of the cold source, and r be the distance from a certain point to the center of the water spray.

[0023] In this embodiment, considering that the temperature data points are generated by a weighted combination of two Gaussian distributions, namely the heat source and the cold source Gaussian distributions, the probability density function of the weighted Gaussian mixed heat source model containing the heat source and the cold source is a linear combination of the two Gaussian heat source distributions.

[0024] The Gaussian hybrid heat source model consists of two parts: a Gaussian model of the heat source simulating the temperature and heat flux density q1 of the propylene flame, and a Gaussian model of the cold source simulating the water impact on the cold source q2. Given a propylene flow rate, the heat flux density q1 is determined by the thermal efficiency and the effective radius of the heat source. Since thermal efficiency and the effective radius of the heat source are difficult to measure directly, a combination of numerical simulation and experimental methods is used to determine them. The temperature distribution on the surface of the plate under a specific operating condition is experimentally measured, and the thermal efficiency and effective radius of the Gaussian hybrid heat source model are adjusted accordingly. The same applies to the cold source. Therefore, the following key parameters need to be determined in the above heat source and cold source models: thermal efficiency... Effective radius of heat source Heat absorption efficiency and the effective radius of the cold source .

[0025] After obtaining the temperature matrix and distance weights of the sample steel plate under different process parameters of heating and water cooling, the effective radius of heating and cooling was estimated using the expectation-maximization algorithm.

[0026] Regarding step 104: In some implementations, step 104 may include: For temperature images under different process parameters, the following steps were performed: Based on the mean vectors of the heat source and cold source in the estimation results corresponding to the temperature image, the centers of the heat source and cold source are marked in the image; Using the covariance matrices of the heat source and cold source in the estimation results, draw the heat source ellipse and cold source ellipse in the image respectively; The Gaussian weights of the heat source and cold source in the estimation results are averaged to determine the target Gaussian weights of the heat source and the cold source. Based on the expected values ​​of the process parameters and Gaussian distributions of the heat source and cold source corresponding to the temperature image, the effective radii of heating and cooling are fitted to obtain the effective radius models of the heat source and cold source under different process parameters. Based on the effective radius model of heat source and cold source, the target Gaussian weight of heat source, the target Gaussian weight of cold source, the heating thermal efficiency, the cooling heat absorption efficiency, and the density functions of heat source and cold source, the Gaussian mixed heat source model under different process parameters is determined.

[0027] In this embodiment, each image can obtain two Gaussian weights π1 and π2, mean vectors μ1 and μ2 (i.e., the coordinates of the heat source center), and covariance matrices Σ1 and Σ2 (i.e., the orientation and size of the heat source distribution ellipse). The centers of the heat source and cold source are marked on the image using μ1 and μ2, and 1σ or 2σ ellipses are drawn using the covariance matrices Σ1 and Σ2 to complete the confirmation of the dual heat source model.

[0028] Averaging the weights π1 and π2 of the two Gaussian sources, the target Gaussian weights for the heat source and the cold source are determined to be 0.64 and 0.36, respectively. The efficiencies of flame heating and water-cooled impact heat transfer are also obtained. Based on the above methods, the effective radii of heating and cooling of a Gaussian mixed heat source with different process parameters such as flow rate, heating rate, water-fire distance, and water flow rate were determined. These radii are the expected values ​​of the Gaussian distributions of the heat source and cold source. Then, the effective radii of heating and cooling were fitted using the above parameters to obtain effective radius models of the heat source and cold source under different process parameters.

[0029] In some implementations, the effective radius models for the heat source and cold source under different process parameters are as follows: In the formula, and These are the effective radii of the heat source and the cold source, respectively. The flow rate of propylene. For water flow rate, For heating rate, The distance between water and fire.

[0030] In some implementations, the Gaussian mixed heat source model under different process parameters is as follows: In the formula, and These are the effective radii of the heat source and the cold source, respectively. The flow rate of propylene. For water flow rate, For heating rate, denoted as the water-fire distance, A as the calorific value of propylene complete combustion, r as the distance from a point to the heating center, and C as the heat absorbed by the water as it heats from 20°C to 100°C.

[0031] As can be seen, the Gaussian mixed heat source model of this scheme includes heat sources and cold sources. Compared with simple boundary setting water cooling, the weighted Gaussian mixed heat source model with heat sources and cold sources designed in this scheme not only ensures the accuracy of finite element calculation results, but also saves calculation time.

[0032] To verify the effectiveness of this scheme, the following temperature field simulation was conducted.

[0033] A 3D finite element model was established, with dimensions matching the actual test plate material (DH32). Temperature field simulations of the water-fire bending process were performed using both the proposed Gaussian hybrid heat source model (combining heat and cold sources) and a traditional single heat source Gaussian model with water-cooled boundaries. The temperature curves are shown below. Figure 2 As shown in the figure, the high point is the heating point of the Gaussian heat source, and the low point area is the cold source area and the cooling area. It can be seen from the figure that due to the addition of the cold source model, the temperature of the steel plate drops rapidly, and the temperature curve is more consistent with reality than the traditional model. Figure 3 , Figure 4 These represent the heat flux distribution and temperature field of the Gaussian mixed heat source model, respectively. Figure 5 , Figure 6 The figures show the heat flow distribution and temperature field of the traditional single heat source model, respectively. It can be seen that the Gaussian mixed heat source contains Gaussian distributions of both the heat source and the cold source. The temperature field around the cold source can more accurately and realistically simulate the temperature field of water-fire processes compared to the traditional single heat source model.

[0034] Please refer to Figure 7 This invention provides a temperature field simulation device for a water-fire bending process based on a mixture of heat and cold sources, used to implement the steps of any method embodiment in the specification. The device includes: Test unit 701 is used to collect the temperature field of the sample steel plate under different process parameters using an infrared thermal imager, so as to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange. The estimation unit 702 is used to establish a weighted Gaussian mixture heat source model to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters. Fitting unit 703 is used to fit the effective radius of heating and cooling based on the estimation results under different process parameters, so as to obtain the Gaussian mixed heat source model under different process parameters.

[0035] It should be noted that the temperature field simulation device for water-fire bending plate process based on heat source and cold source mixing provided in the above embodiments is only an example of the division of the above functional units. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. In addition, the above device embodiments and method embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0036] Embodiments of this application also provide a computer device, please refer to... Figure 8 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the water-fire bending plate process temperature field simulation method based on heat source and cold source mixing provided in the above method embodiments.

[0037] The embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the water-fire bending plate process temperature field simulation method based on heat source and cold source mixing provided in the above method embodiments.

[0038] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the above embodiments of the method for simulating the temperature field of a water-fire bending plate process based on the mixing of heat and cold sources.

[0039] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0040] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of the embodiments of this application.

[0041] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0042] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for simulating the temperature field of a water-fire bending plate process based on the mixing of heat and cold sources, characterized in that, The method includes: The temperature field of the sample steel plate under different process parameters was acquired using an infrared thermal imager in order to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange. A weighted Gaussian mixture heat source model is established to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters. Based on the estimation results under different process parameters, the effective radii of heating and cooling are fitted to obtain Gaussian mixed heat source models under different process parameters.

2. The method as described in claim 1, characterized in that, The method of using an infrared thermal imager to acquire the temperature field of the sample steel plate under different process parameters, in order to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat transfer, includes: Multiple heating paths were marked on the sample steel plate, and temperature sensors were attached at 50mm, 100mm, and 150mm on each heating path. The temperature sensors were connected to a computer to record temperature changes during the heating process. The flow rates of the propylene and oxygen cylinders were adjusted to the experimental values. The heating rate, water-fire distance, and water flow rate were also adjusted to the experimental values. Heating and water-cooling tests were conducted according to the above parameters. After the temperature stabilized, the sample steel plate was photographed using an infrared thermal imager to obtain temperature images of each path under different process parameters. The process parameters include the flow rates of the propylene and oxygen cylinders, the heating rate, the water-fire distance, and the water flow rate. Several experimental values ​​were obtained for each process parameter. The temperature images of each path under different process parameters are converted into a two-dimensional temperature matrix, and each pixel coordinate is used as a two-dimensional sample point to serve as the distance weight of the temperature value.

3. The method as described in claim 1, characterized in that, The weighted Gaussian mixed heat source model is established in the following way: The density function of the weighted Gaussian mixture heat source model containing heat and cold sources is: In the formula, k represents the number of Gaussian heat sources, which is 2, representing the heat source and the cold source respectively. The mixing weights are the k-th Gaussian distribution. This is a simplified representation of the Gaussian mixed heat source model. Let $\mathbf{k}$ be the expected value of the $k$-th Gaussian distribution, i.e., the effective radius. The density function of the heat source is: In the formula, Let A be the flow rate of propylene, and A be the calorific value of propylene after complete combustion. For thermal efficiency, Let r be the effective radius of the heat source, and r be the distance from a point to the heating center. The density function of the cold source is: In the formula, Let C be the flow rate of the water, and C be the heat absorbed by the water as it heats up from 20°C to 100°C. For heat absorption efficiency, Let r be the effective radius of the cold source, and r be the distance from a certain point to the center of the water spray.

4. The method according to claim 1, characterized in that, The effective radii of heating and cooling are fitted based on the estimation results under different process parameters to obtain Gaussian mixed heat source models under different process parameters, including: For temperature images under different process parameters, the following steps were performed: Based on the mean vectors of the heat source and cold source in the estimation results corresponding to the temperature image, the centers of the heat source and cold source are marked in the image; Using the covariance matrices of the heat source and cold source in the estimation results, draw the heat source ellipse and cold source ellipse in the image respectively; The Gaussian weights of the heat source and cold source in the estimation results are averaged to determine the target Gaussian weights of the heat source and the cold source. Based on the expected values ​​of the process parameters and Gaussian distributions of the heat source and cold source corresponding to the temperature image, the effective radii of heating and cooling are fitted to obtain the effective radius models of the heat source and cold source under different process parameters. Based on the effective radius model of heat source and cold source, the target Gaussian weight of heat source, the target Gaussian weight of cold source, the heating thermal efficiency, the cooling heat absorption efficiency, and the density functions of heat source and cold source, the Gaussian mixed heat source model under different process parameters is determined.

5. The method according to claim 4, characterized in that, The effective radius models for the heat source and cold source under different process parameters are as follows: In the formula, and These are the effective radii of the heat source and the cold source, respectively. The flow rate of propylene. For water flow rate, For heating rate, The distance between water and fire.

6. The method according to claim 4, characterized in that, The Gaussian mixed heat source model under different process parameters is as follows: In the formula, and These are the effective radii of the heat source and the cold source, respectively. The flow rate of propylene. For water flow rate, For heating rate, denoted as the water-fire distance, A as the calorific value of propylene complete combustion, r as the distance from a point to the heating center, and C as the heat absorbed by the water as it heats from 20°C to 100°C.

7. A temperature field simulation device for water-fire bending plate process based on heat source and cold source mixing, used to implement the steps of the method described in any one of claims 1-6, characterized in that, The device includes: The test unit is used to acquire the temperature field of the sample steel plate under different process parameters using an infrared thermal imager, so as to test the temperature matrix and distance weight of the sample steel plate under different process parameters of heating and water cooling heat exchange. The estimation unit is used to establish a weighted Gaussian mixture heat source model to perform heat source-cold source weighted Gaussian mixture model estimation on the sample points of the sample steel plate based on the temperature matrix and distance weight under different process parameters. The fitting unit is used to fit the effective radii of heating and cooling based on the estimation results under different process parameters, so as to obtain the Gaussian mixed heat source model under different process parameters.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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