Granary thermal modeling method based on jmag

Abstract

The invention discloses a jmag-based granary thermal modeling method, and relates to the technical field of grain temperature prediction. The method comprises the steps of obtaining basic information parameters of a granary and a grain pile, and segmenting the granary based on a dimension with the minimum length to obtain a plurality of units; a Geometric Editor module and a python script of jmag software are utilized to dynamically and finely adjust the unit side length of the granary, and a thermal model is obtained through thermal modeling; historical temperature data of the grain pile and coordinates of the temperature measuring points are input into the thermal model, an average absolute error is calculated after simulation, and the reference length proportion with the minimum error is selected as the final reference length; and adjusting the side length of the granary unit at the corner to a final reference length, adjusting the granary unit structure except the corner after segmentation to a cube according to the final reference length, obtaining the finally segmented granary units, carrying out thermal modeling on the finally segmented granary units one by one, and obtaining a final thermal model of each segmented granary unit.

Description

Technical Field

[0001] The present application relates to the technical field of grain temperature prediction, and in particular to a granary thermal modeling method based on JMAG. Background Art

[0002] At present, grain is affected by multiple temperatures during the storage process. The rise and fall of temperature will cause the migration of moisture inside the grain. Too high or too low moisture content will affect the quality and storage stability of the grain. Too high temperature will make mold and insect pests reach a suitable reproduction temperature. If no intervention is taken at this time, the high temperature will be maintained for a long time, causing the grain in the grain pile to mold and deteriorate, thereby causing grain loss. In addition, the increase in temperature will accelerate the respiration of the grain and increase the metabolic rate of the grain, while excessive respiration will cause the grain to deteriorate faster and its quality will decline.

[0003] The thermal modeling method is based on thermal principles such as heat conduction, convection, and radiation, and uses the warehouse's geometric structure, heat transfer characteristics and other data for simulation and prediction, such as the finite difference method (FDM) and the finite element method (FEM). It can analyze and model the actual thermal system, and conduct a virtual experiment based on the modeling, namely thermal simulation, by inputting a series of parameters and information data to calculate and predict the heat transformation of the thermal system. When the grain is stored in the granary, its interior is a thermal system, and the temperature transfer inside the grain pile follows the principle of heat conduction. Therefore, thermal modeling can be used to study the temperature changes inside the grain pile.

[0004] In the existing technology, when thermal modeling is performed on large granaries, the length and width of the granaries can reach more than 20 meters, and the parameters such as heat and air flow inside them vary significantly at different locations. Based on the complex environment inside the granaries mentioned above, the mainstream grain temperature prediction method in large granaries is usually based on data-driven models (such as LSTM, Transformer, etc.) for temperature prediction. The whole process includes multiple steps such as data collection, model establishment, training and prediction.

[0005] However, this type of method requires a sufficient amount of temperature sensor data for model pattern learning, which places high demands on the difficulty and accuracy of data collection and lacks interpretability in the underlying principles, making it impossible to model large granaries and effectively simulate the real grain pile environment, resulting in unreliable grain temperature prediction results. Summary of the invention

[0006] Based on this, it is necessary to provide a granary thermal modeling method based on JMAG to address the above technical problems.

[0007] This manual adopts the following technical solutions: This specification provides a granary thermal modeling method based on JMAG, including: Obtain basic information parameters of the granary and grain pile, including: the length, width and height of the granary, historical temperature data of the grain pile and the coordinates of the temperature measurement points; Based on the length of the granary, which includes three dimensions: length, width, and height, the dimension with the smallest length is selected as the reference length, and the granary is divided into the other two dimensions from a spatial perspective based on the reference length to obtain multiple divided granary units; Through the Geometry Editor module of the jmag modeling software, the dynamic fine-tuning amount and fine-tuning range of the granary unit side length are set based on the reference length, and the python loop script is called to dynamically fine-tune the granary unit structure at any corner after segmentation into cubes with multiple side lengths, and multiple dynamically fine-tuned granary unit thermal models are obtained through thermal modeling; The historical temperature data of the grain pile and the coordinates of the temperature measurement points are input into a plurality of dynamically fine-tuned grain silo unit thermal models, and the parameters of the grain silo unit thermal models are set, and the dynamically fine-tuned grain silo unit thermal models are simulated one by one, and the simulation results of each dynamically fine-tuned grain silo unit thermal model are obtained and the average absolute error between the simulation result and the true value is calculated, and a reference length ratio with the smallest error is selected as the final reference length of the grain silo unit side length; The granary unit structure at the corner after cutting is adjusted to a cube with the final reference length as the side length, and the granary unit structure except the corner after cutting is adjusted to a cube according to interpolation by using the length of the other two dimensions and the difference between the side lengths of the granary units at the corresponding corners, so as to obtain the final granary units after cutting and thermally model them one by one, and obtain the final thermal model of each granary unit after cutting.

[0008] Preferably, the dynamic fine-tuning amount and fine-tuning range of the unit side length of the granary specifically include: The dynamic fine-tuning amount is 5% of the reference length; The fine adjustment range is from 70% to 130% of the reference length.

[0009] Preferably, the parameters of the granary unit thermal model specifically include: material parameters, granary parameters and simulation parameters.

[0010] Preferably, the material parameters include: thermal conductivity and specific heat capacity.

[0011] Preferably, the granary parameters include: constant heat source density, boundary conditions and initial temperature.

[0012] Preferably, the constant heat source density is used to reflect the ability to generate or consume heat per unit time and per unit volume in the granary scene.

[0013] Preferably, the boundary conditions specifically include: The granary surface affected by the change of ambient temperature is selected, and all walls are set during segmentation and simulation; the connection between the two segmented granary units does not need to set boundary conditions; and the boundary conditions of the thermal model of the granary unit are set by the table of ambient temperature changes over time.

[0014] Preferably, the initial temperature is the grain pile temperature corresponding to the moment when the grain silo unit thermal model starts to simulate.

[0015] Preferably, the simulation parameters include: step length and number of steps; The number of steps is consistent with the number of temperature schedules set in the boundary conditions.

[0016] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: In the granary thermal modeling method based on jmag provided in this specification, firstly, based on the parameters of the three dimensions of the granary including length, width and height, the granary is statically divided from a spatial perspective to obtain a plurality of divided granary units; secondly, using a Python loop script, the granary unit structure at any corner is dynamically fine-tuned to a cube according to a specified ratio of the reference length and thermal modeling is performed to obtain thermal models of a plurality of granary units; the historical temperature data of the grain pile and the coordinates of the temperature measurement points are input into the granary unit thermal model, the granary is simulated by setting the thermal model parameters, and the final reference length is determined according to the simulation results, thereby completing the dynamic adjustment of the side length of the granary unit, and adjusting the side length of the divided granary unit except the corner to be close to a cube according to the difference between the lengths of the other two dimensions and the side lengths of the granary units at the corresponding corners.

[0017] Since the length and width of a large granary are large, they have a greater impact on the simulation results, and the two sides of the granary are more affected by the outside world than the middle, which will form a stronger temperature gradient. Therefore, the present invention is based on the collected granary size and adopts the idea of ​​static segmentation and dynamic fine-tuning to perform thermal modeling and thermal simulation on the large granary, so as to more accurately simulate the heat conduction process of the granary, improve the interpretability of the underlying principles, and then effectively simulate the real grain pile environment, so as to improve the accuracy of grain temperature prediction and provide strong scientific guidance for grain temperature decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of the process flow of the granary thermal modeling method based on JMAG provided for this specification; Figure 2Front cross-sectional view of the granary sensor based on the JMAG granary thermal modeling method provided for this specification; Figure 3 A side cross-sectional view of the granary sensor based on the JMAG granary thermal modeling method provided for this specification; Figure 4 Schematic diagram of granary modeling based on jmag's granary thermal modeling method provided for this instruction manual. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this application will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in the specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0021] Figure 1 This is a flow chart of a granary thermal modeling method based on JMAG in this specification, which specifically includes the following steps: S101: Obtain basic information parameters of the granary and the grain pile, including: the length of the granary in three dimensions: length, width and height, historical temperature data of the grain pile and coordinates of the temperature measurement points.

[0022] S102: Based on the lengths of the granary in three dimensions, namely, length, width and height, the dimension with the smallest length is selected as a reference length, and the granary is divided in the other two dimensions from a spatial perspective based on the reference length to obtain a plurality of divided granary units; Among them, when dividing the granary in the other two dimensions from a spatial perspective based on the reference length, it is not strictly required to divide the cube with the reference length as the side length, and the number of granary divisions should be as small as possible.

[0023] S103: Through the Geometry Editor module of the jmag modeling software, the dynamic fine-tuning amount and fine-tuning range of the granary unit side length are set based on the reference length, and the python loop script is called to dynamically fine-tune the granary unit structure at any corner after segmentation into cubes with multiple side lengths, and multiple dynamically fine-tuned granary unit thermal models are obtained through thermal modeling.

[0024] S104: Input the historical temperature data of the grain pile and the coordinates of the temperature measurement points into multiple dynamically fine-tuned granary unit thermal models, set the parameters of the granary unit thermal models, simulate the dynamically fine-tuned granary unit thermal models one by one, obtain the simulation results of each dynamically fine-tuned granary unit thermal model and calculate the average absolute error between the simulation result and the true value, and select the reference length ratio with the smallest error as the final reference length of the granary unit side length.

[0025] S105: The granary unit structure at the corner after segmentation is adjusted to a cube with the final reference length as the side length, and the granary unit structure except the corner after segmentation is adjusted to a cube according to interpolation by using the difference between the lengths of the other two dimensions and the side lengths of the granary units at the corresponding corners, so as to obtain the final granary units after segmentation and perform thermal modeling on them one by one, and obtain the final thermal model of each granary unit after segmentation, which specifically includes: Parameters of the grain silo unit thermal model, including: material parameters, grain silo parameters and simulation parameters; Material parameters, including: thermal conductivity and specific heat capacity; Granary parameters, including: constant heat source density, boundary conditions, and initial temperature; Simulation parameters, including: step size and number of steps.

[0026] Among them, thermal conductivity is a measure of the heat conductivity of grain piles. It refers to the amount of heat transferred through a unit horizontal cross-sectional area per unit time when the vertical downward temperature gradient is 1°C / m. It is specifically defined as: take two parallel planes with a distance of 1 meter and an area of ​​1 square meter inside the object perpendicular to the heat conduction direction. If the temperature difference between the two planes is 1K, the heat transferred from one plane to another within 1 second is defined as the thermal conductivity of the material, and its unit is watt-meter -1 ·open -1 (W·m -1 ·K -1 ); Specific Heat Capacity, also known as specific heat capacity, abbreviated as specific heat, is the heat capacity of a substance per unit mass, that is, the heat absorbed or released when a unit mass object changes unit temperature.

[0027] Constant heat source density refers to the rate of heat release or absorption per unit area or unit volume. In the case of a granary, it describes the ability of a grain pile to stably generate or consume heat per unit time and per unit volume. The boundary conditions are to select the granary surface affected by the change of ambient temperature, and set it for all walls during segmentation and simulation; among them, no boundary conditions need to be set at the connection between the two granary units; and the boundary conditions of the thermal model of the granary unit are set by the table of ambient temperature changes over time; The initial temperature is set to the grain pile temperature at the beginning of the simulation; Set the simulation step size and number of steps, and the number of steps should be consistent with the number of temperature schedules set in the boundary conditions.

[0028] This embodiment takes a large granary as an example, and completes the thermal modeling and simulation of the granary through the steps of the above method.

[0029] Collect basic information parameters of the target granary: The target granary is a large bungalow with a length of 29.35 meters, a width of 23.45 meters, and a grain stacking line height of 6 meters. Temperature measuring cables are laid in the granary. Each temperature measuring cable is equipped with four temperature sensors. The sensors are spaced 4.8 meters apart in the length direction of the granary, recorded as zones, with a total of 7 zones deployed; the longitudinal spacing in the width direction is 4.5 meters, recorded as points, with a total of 6 points deployed; the upper and lower spacing is 1.8 meters, recorded as layers, with a total of four layers deployed. For specific sensor point deployment, see Figure 2 and Figure 3 The historical grain temperature data collected by the temperature sensors at each collection point at around 9 o'clock every day are stored in days.

[0030] Modeling is performed based on the granary size collected in step S101, see Figure 4 . Static segmentation: Since the height of the granary (6 meters) is the minimum value among the three dimensions, the height is selected as the basis for segmentation. Width-direction segmentation (point direction): The width of the granary (23.45 meters) is divided equally. The granary unit after segmentation is close to a cube, and the number of granary units divided into the entire granary should be as small as possible, based on 3 equal parts, each part is 7.816 meters. Since the spacing between the temperature measurement points in the width direction is 4.5 meters, each segment can cover two columns of temperature measurement points after equal division to ensure the uniformity of data collection. Length-direction segmentation (area direction): Since the two sides of the granary in the length direction are more affected by the external environment than the middle area, and after equal division, a row of sensors will be segmented, so that it is located on the edge of the segmented unit block, affecting the simulation results, a non-uniform segmentation strategy is adopted: the length of the two sides is consistent with the width direction, and the length is set to 7.816 meters to ensure that the temperature measurement points of the left and right side segments are evenly distributed, and each segment contains two columns of temperature measurement points. Middle part: The remaining 13.718 meters is used as an independent block, which contains three columns of temperature measurement points to ensure the continuity of data sampling. Finally, the entire granary is divided into 9 unit blocks of 3 (width direction) × 3 (length direction).

[0031] Dynamic fine-tuning: Use Python scripts to fine-tune the length of the unit block near the corner of the granary, and simulate the modeling. The height is the benchmark and remains unchanged. 10% of the benchmark length of 6 meters, that is, 0.6 meters, is used as the fine-tuning amount to fine-tune the other two sides. The initial length is 7.816 meters after static segmentation, and it is reduced by 0.6 meters each time. The fine-tuning range is 5.4 meters to 7.816 meters. It can ensure that the number of sensors contained in the unit block in this range is consistent, and calculate the simulation error each time, and select the minimum error side length as the final side length of the unit block. Then the segmented blocks of the area at the intersection of the granary are consistent with the final unit blocks, and the unit block lengths of the remaining areas are divided equally by deducting the adjusted part from the total length to ensure that the unit blocks in the internal area of ​​the granary are still close to cubes.

[0032] Since there are many blocks, the simulation operation is carried out by taking the three blocks on the leftmost side of the granary as an example. Since grain is a poor conductor of heat, the thermal conductivity of wheat is 0.13~0.16W / (m·K), while the thermal conductivity of rice is 0.1436 W / (m·K) and the heat capacity is 1550J / (㎏·K); another paper mentioned that the thermal conductivity of grain is 0.159 W / (m·K) and the heat capacity is 1871J / (㎏·K); and the constant heat source density of grain is very small, even negligible. The parameter settings during our simulation refer to the above data, and the thermal conductivity is set to 0.159 W / (m·K) and the constant heat source density is set to 0.1. The simulation requires setting a lossy thermodynamic simulation case (thermal transient state study), and setting the simulation step and time interval. In the simulation conditions, set the initial temperature, that is, the temperature on the first day of the actual simulation, and the table of the ambient temperature changing with the time step. After setting the above parameters, the temperature measurement point is also added to the simulation model in the form of coordinates according to its position in the granary. The three-dimensional coordinates of the specific temperature measurement point can be imported into the Probes of the case to obtain the simulated temperature of the point in the simulation results.

[0033] The granary of this embodiment adopts dynamic segmentation modeling and non-dynamic segmentation modeling to simulate some days in February, April, and May 2024, as shown in Table 1, Table 2, and Table 3, which are the average errors between the two leftmost rows of temperature measurement points and the actual temperature measurement values ​​before and after the granary segmentation in February, April, and May respectively: Table 1 Average error between the two leftmost rows of temperature measurement points and the actual temperature measurement values ​​before and after the grain silo was divided in February

[0034] Table 2 Average error between the two leftmost rows of temperature measurement points and the actual temperature measurement values ​​before and after the grain silo was divided in April

[0035] Table 3 Average error between the two leftmost rows of temperature measurement points and the actual temperature measurement values ​​before and after the granary was divided in May

[0036] From the simulation results of some dates in the above three months, it can be seen that the error between the thermal modeling simulation results after segmentation and the measured values ​​is smaller, and it can better reflect the temperature changes of the temperature measuring points inside the granary.

[0037] In summary, the present invention statically divides the granary through benchmark parameters based on jmag, and uses a Python script program to dynamically fine-tune the granary units after static division, and the side length of each granary unit can be dynamically adjusted according to actual conditions, and the thermal modeling and thermal simulation of the granary are completed. This method can more accurately simulate the heat conduction process of the granary, improve the interpretability of the underlying principles, and then effectively simulate the real grain pile environment to improve the accuracy of grain temperature prediction, and provide powerful scientific guidance for grain temperature decision-making.

[0038] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. The granary thermal modeling method based on jmag is characterized by: The following steps are involved: Obtain basic information parameters of the granary and grain pile, including: the length, width and height of the granary, historical temperature data of the grain pile and the coordinates of the temperature measurement points; Based on the length of the granary, which includes three dimensions: length, width, and height, the dimension with the smallest length is selected as the reference length, and the granary is divided into the other two dimensions from a spatial perspective based on the reference length to obtain multiple divided granary units; Through the Geometry Editor module of the jmag modeling software, the dynamic fine-tuning amount and fine-tuning range of the granary unit side length are set based on the reference length, and the python loop script is called to dynamically fine-tune the granary unit structure at any corner after segmentation into cubes with multiple side lengths, and multiple dynamically fine-tuned granary unit thermal models are obtained through thermal modeling; The historical temperature data of the grain pile and the coordinates of the temperature measurement points are input into a plurality of dynamically fine-tuned grain silo unit thermal models, and the parameters of the grain silo unit thermal models are set, and the dynamically fine-tuned grain silo unit thermal models are simulated one by one, and the simulation results of each dynamically fine-tuned grain silo unit thermal model are obtained and the average absolute error between the simulation result and the true value is calculated, and a reference length ratio with the smallest error is selected as the final reference length of the grain silo unit side length; The granary unit structure at the corner after cutting is adjusted to a cube with the final reference length as the side length, and the granary unit structure except the corner after cutting is adjusted to a cube according to interpolation by using the length of the other two dimensions and the difference between the side lengths of the granary units at the corresponding corners, so as to obtain the final granary units after cutting and thermally model them one by one, and obtain the final thermal model of each granary unit after cutting.

2. The granary thermal modeling method based on jmag as claimed in claim 1 is characterized in that: The dynamic fine-tuning amount and fine-tuning range of the unit side length of the granary specifically include: The dynamic fine-tuning amount is 5% of the reference length; The fine adjustment range is from 70% to 130% of the reference length.

3. The granary thermal modeling method based on jmag as claimed in claim 1 is characterized in that: The parameters of the grain silo unit thermal model specifically include: material parameters, grain silo parameters and simulation parameters.

4. The granary thermal modeling method based on jmag as claimed in claim 3 is characterized in that: The material parameters include: thermal conductivity and specific heat capacity.

5. The granary thermal modeling method based on jmag as claimed in claim 4 is characterized in that: The granary parameters include: constant heat source density, boundary conditions and initial temperature.

6. The granary thermal modeling method based on jmag as claimed in claim 5 is characterized in that: The constant heat source density is used to reflect the ability to generate or consume heat per unit time and per unit volume in the granary scene.

7. The granary thermal modeling method based on jmag as claimed in claim 5 is characterized in that: The boundary conditions specifically include: The granary surface affected by the change of ambient temperature is selected, and all walls are set during segmentation and simulation; the connection between the two segmented granary units does not need to set boundary conditions; and the boundary conditions of the thermal model of the granary unit are set by the table of ambient temperature changes over time.

8. The granary thermal modeling method based on jmag as claimed in claim 5 is characterized in that: The initial temperature is the grain pile temperature corresponding to the moment when the grain silo unit thermal model starts to simulate.

9. The granary thermal modeling method based on jmag as claimed in claim 3 is characterized in that: The simulation parameters include: step length and number of steps; The number of steps is consistent with the number of temperature schedules set in the boundary conditions.

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