Gradient Temperature Boundary Active Fidelity Preservation Method Based on Distributed Multi-Level Spatiotemporal Adaptive Regulation

Abstract

This invention provides an active fidelity method for gradient temperature boundary based on distributed multi-level spatiotemporal adaptive control. The method includes constructing a distributed multi-level collaborative control system, arranging a multi-layered composite thermal boundary structure and distributed sensors and driving circuits; pre-setting the target value of the gradient temperature distribution inside the sample; calculating the required heating or cooling power for each zone through a spatiotemporal adaptive closed-loop control module, and dynamically adjusting the power output of each heating / cooling module; and eliminating temperature inhomogeneity and time lag effects in each zone through multi-point spatiotemporal data fusion and online deviation correction technology, ensuring high-fidelity reproduction and long-term stable control of the temperature boundary in each zone. This method significantly improves the reproduction accuracy and spatiotemporal consistency of thermal boundaries in model experiments; greatly enhances temperature control accuracy, dynamic response speed, and anti-interference capability; effectively reduces the interference of thermal boundary distortion and edge effects on experimental results; and improves the similarity, repeatability, and reliability of multi-field coupled model experiments.

Description

Technical Field

[0001] This invention relates to geotechnical engineering technology, specifically to an active fidelity method for gradient temperature boundaries based on distributed multi-level spatiotemporal adaptive control. Background Technology

[0002] In physical simulation and centrifuge model testing of geotechnical engineering in cold regions, accurately reproducing the temperature field distribution in the natural environment is the core prerequisite for ensuring the reliability of test results. Especially for large-scale projects in high-altitude and cold regions, constructing a gradient temperature boundary that changes nonlinearly along the depth direction is the basis for simulating the real thermal-hydraulic-mechanical multi-field coupling process.

[0003] However, existing model chamber temperature control technologies face multiple technical bottlenecks in achieving this goal:

[0004] First, traditional temperature control methods mostly rely on overall environmental cooling (such as air conditioning systems) or heating from a single cold / heat source. This extensive temperature control mode can only achieve a uniform distribution of the temperature field inside the chamber, but cannot form a specific temperature gradient in space. As a result, it is impossible to simulate the natural law of ground temperature change with depth in permafrost areas, which seriously affects the similarity of model tests. Although some small devices can achieve vertical temperature gradients by setting different temperatures at the top and bottom, their control precision is insufficient, resulting in a large deviation between the constructed gradient and the actual ground temperature change law, which is still difficult to meet the similarity requirements of model tests.

[0005] Secondly, existing technologies generally lack the ability to actively and accurately control the thermal boundary. Conventional PID control or passive insulation methods often exhibit response lag and insufficient control precision when faced with complex nonlinear heat conduction and external environmental disturbances, easily leading to "thermal boundary distortion," which causes a significant deviation between the actual temperature field inside the sample and the preset target field. More importantly, the lack of a feedback adjustment mechanism based on multi-point real-time monitoring data makes it impossible to adaptively correct according to the dynamic changes in the internal temperature of the sample. As a result, the temperature boundary is prone to drift during long-term experiments, making it difficult to maintain a high-fidelity gradient temperature field.

[0006] In summary, existing technologies have significant shortcomings in terms of the flexibility of temperature control modes, the accuracy of boundaries, and the stability of the system. There is an urgent need for an innovative method that can actively construct, precisely regulate, and maintain a stable gradient temperature boundary over a long period of time to address the pain points of existing technologies, such as "inability to achieve gradient distribution, lack of precise boundary control, and lack of real-time feedback adjustment." Summary of the Invention

[0007] This invention is made to solve the above problems, and aims to provide a gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control.

[0008] This invention provides a gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control, characterized by the following steps:

[0009] S1, construct a distributed multi-level collaborative control system, arrange a multi-layer composite thermal boundary structure in the model box, the middle layer of which contains a heating / cooling module array and a temperature-controlled water-cooled power supply, and divide the model box into multiple independent temperature control zones along the depth direction, and deploy multiple temperature sensors in each zone, connecting the heating / cooling module array, temperature-controlled water-cooled power supply and temperature sensors to the central controller in the system.

[0010] S2, preset the target value of gradient temperature distribution inside the sample in the model box according to the working conditions of the model prototype and the test requirements;

[0011] S3, based on the real-time temperature data of each zone collected by the temperature sensor, uses the spatiotemporal adaptive closed-loop control module built into the central controller to calculate the heating or cooling power required for each zone, and adjusts the power output of the heating / cooling modules of each zone in real time, actively generating and stably maintaining the preset gradient temperature distribution.

[0012] S4. During the experiment, real-time temperature data of each zone is monitored. Multi-point spatiotemporal data fusion and online deviation correction technology are used to eliminate temperature inhomogeneity, temperature discontinuity between zones and time lag effects, so as to ensure high-fidelity reproduction and long-term stable control of temperature boundaries of each zone.

[0013] The gradient temperature boundary active fidelity method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following features: wherein, in step S1, the multi-layer composite thermal boundary structure includes: a high thermal conductivity metal plate, a heating / cooling module array and a high performance thermal insulation layer arranged sequentially from the inside to the outside in the model box, wherein the heating / cooling module array is arranged in partitions according to the position of each temperature control zone.

[0014] The gradient temperature boundary active fidelity method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following features: wherein the high thermal conductivity metal plate is one of copper plate or aluminum alloy plate, the heating / cooling module is one of thermoelectric element or semiconductor temperature control chip, and the high performance heat insulation layer is polyurethane foam material.

[0015] The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following features: in step S2, the target value of gradient temperature distribution includes: spatial temperature gradient direction, gradient magnitude and time change sequence.

[0016] The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following features: In step S3, the spatiotemporal adaptive closed-loop control module adopts a fuzzy PID control algorithm to calculate the required heating or cooling power output value of each partition according to the spatial deviation, time deviation and rate of change of the real-time temperature data and the target value of the gradient temperature distribution, and dynamically adjusts the output power of the heating / cooling module of each partition to the heating or cooling power output value.

[0017] The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control provided by this invention may also have the following features: In step S4, the step of eliminating the uneven temperature of each partition, the discontinuity of temperature between partitions and the time lag effect through multi-point spatiotemporal data fusion and deviation online correction technology includes: using a weighted average algorithm to fuse multiple real-time temperature data in each partition; when the temperature deviation between the real-time temperature data of a partition and the target value of the gradient temperature distribution of that partition exceeds a preset value of ±0.3℃, the central controller automatically triggers correction control, and fine-tunes the power output of that partition through the heating / cooling module, thereby adjusting the temperature of each partition and eliminating the spatial temperature difference between each partition.

[0018] The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following features: In step S1, while deploying temperature sensors in each partition, an independent sensor driving circuit is set in each partition and connected to the central controller. The sensor driving circuit includes a power amplifier and a signal conditioning module.

[0019] The gradient temperature boundary active fidelity method based on distributed multi-level spatiotemporal adaptive regulation provided by the present invention may also have the following features: each temperature-controlled water-cooled power supply independently supplies power to its respective partition, the temperature-controlled water-cooled power supply includes a heating / cooling module array drive circuit and a forced water-cooling heat dissipation module, the forced water-cooling heat dissipation module includes a circulating water pump, a radiator and a fan.

[0020] The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control provided by the present invention may also have the following feature: wherein the temperature sensor adopts one of thermocouple, resistance temperature detector and optical fiber.

[0021] Compared with the prior art, the functions and effects of the present invention include:

[0022] 1) The gradient temperature boundary active fidelity method based on distributed multi-level spatiotemporal adaptive control of the present invention, through a distributed multi-level collaborative control system and a spatiotemporal adaptive closed-loop control module, can actively construct and maintain the preset gradient temperature distribution inside and at the boundary of the model box with high fidelity, significantly improving the reproduction accuracy and spatiotemporal consistency of the thermal boundary of the model test.

[0023] 2) This invention adopts a multi-zone independent temperature control and distributed collaborative regulation strategy, combined with multi-point spatiotemporal data fusion and online deviation correction technology, which can significantly improve temperature control accuracy, dynamic response speed and anti-interference ability, and effectively eliminate spatial temperature differences and time lag effects.

[0024] 3) This invention can effectively reduce the interference of thermal boundary distortion and edge effects on experimental results, and improve the similarity, repeatability and reliability of multi-field coupled model experiments. Attached Figure Description

[0025] Figure 1 This is a flowchart of the gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the multilayer composite thermal boundary structure in an embodiment of the present invention.

[0027] In the figure: 1. Multi-layer composite thermal boundary structure; 11. High thermal conductivity metal plate; 12. Heating / cooling module; 13. High performance thermal insulation layer. Detailed Implementation

[0028] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0029] To make the technical means, creative features, objectives and effects of this invention easier to understand, the following embodiments, in conjunction with the accompanying drawings, specifically illustrate the gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control of this invention.

[0030] Figure 1 This is a flowchart of the gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control in this embodiment.

[0031] like Figure 1 As shown: This embodiment provides a gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control, including the following steps S1-S4.

[0032] S1. Construct a distributed multi-level collaborative control system. Arrange a multi-layer composite thermal boundary structure 1 inside the model box and divide the model box into multiple independent temperature control zones along the depth direction. Arrange a temperature-controlled water-cooled power supply and multiple temperature sensors in each zone. Connect the heating / cooling module 12 and the temperature sensors to the central controller in the system.

[0033] Figure 2 This is a schematic diagram of the multilayer composite thermal boundary structure 1 in this embodiment.

[0034] Specifically, such as Figure 2 As shown: The multi-layer composite thermal boundary structure 1 includes a high thermal conductivity metal plate 11, a heating / cooling module 12 array, and a high-performance thermal insulation layer 13 arranged sequentially from the inside to the outside within the model box.

[0035] Furthermore, in this embodiment, the inner high thermal conductivity metal plate 11 is made of copper or aluminum alloy with a thickness of 3-5 mm; the middle heating / cooling module 12 uses TEC thermoelectric elements, or semiconductor temperature control chips, with a spacing of 10-20 mm between modules; the high-performance insulation layer 13 uses polyurethane foam with a thickness of 20-30 mm. This structure enables rapid heat conduction and effective isolation, reducing the interference of the external environment on the internal temperature field.

[0036] In this embodiment, the model box has a depth of 1000mm, and the sidewalls are divided into 10 independent temperature control zones along the depth direction, each zone being 100mm high. The independent temperature control zones are divided along the depth direction at equal or non-equal intervals to accommodate different gradient rates. The heating / cooling module array 12 is arranged according to the spatial location of each temperature control zone, and each module can work independently or collaboratively.

[0037] Five high-precision temperature sensors are deployed on the inner wall of each temperature-controlled zone. The temperature sensors employ one of the following: thermocouples, resistance temperature detectors (RTDs), or optical fibers. In this embodiment, T-type thermocouples or PT100 RTDs are selected for real-time monitoring of the zone's average temperature. Simultaneously, each zone is equipped with an independent sensor drive circuit, which includes a power amplifier and a signal conditioning module. All sensors and their drive circuits are connected to the central controller, enabling the system to support independent control of each zone and cross-zone collaborative scheduling.

[0038] Then, a multi-channel temperature-controlled water-cooled power supply is constructed to provide independent power to each zone. This temperature-controlled water-cooled power supply has independent voltage / current regulation functions, integrates a heating / cooling module array drive circuit and a forced water-cooling heat dissipation module, and is connected to the central controller to form a thermoelectric synergistic joint control channel. Among them, the forced water-cooling heat dissipation module includes a circulating water pump, radiator and fan, used to remove excess heat.

[0039] S2, based on the prototype working conditions and test requirements, preset the target value of the gradient temperature distribution inside the sample in the model box, including: the direction of the spatial temperature gradient, the magnitude of the gradient, and the time change sequence.

[0040] In this embodiment: the temperature of the top (0-100mm) is set to 15℃, the temperature of the bottom (900-1000mm) is set to -5℃, and the target temperature of the middle partitions is set by linear interpolation: partition 1 (0-100mm) 15℃, partition 2 (100-200mm) 13℃, partition 3 (200-300mm) 11℃, partition 4 (300-400mm) 9℃, partition 5 (400-500mm) 7℃, partition 6 (500-600mm) 5℃, partition 7 (600-700mm) 3℃, partition 8 (700-800mm) 1℃, partition 9 (800-900mm) -1℃, and partition 10 (900-1000mm) -5℃. Alternatively, the target temperature can be set for the intermediate zones using non-linear interpolation: Zone 1 (0-100mm) 15℃, Zone 2 (100-200mm) 10℃, Zone 3 (200-300mm) 6℃, Zone 4 (300-400mm) 3℃, Zone 5 (400-500mm) 1℃, Zone 6 (500-600mm) -1℃, Zone 7 (600-700mm) -2℃, Zone 8 (700-800mm) -3℃, Zone 9 (800-900mm) -4℃, Zone 10 (900-1000mm) -5℃.

[0041] S3, based on the real-time temperature data of each zone collected by the temperature sensor, uses the spatiotemporal adaptive closed-loop control module built into the central controller to calculate the heating or cooling power required for each zone, and adjusts the power output of the heating / cooling module 12 of each zone in real time, actively generating and stably maintaining the preset gradient temperature distribution.

[0042] Specifically, the spatiotemporal adaptive closed-loop control module employs a fuzzy PID control algorithm. It uses the spatial and temporal deviations and their rates of change between the real-time temperature data of each zone and the target value of the gradient temperature distribution as input variables. Based on the spatiotemporal coupled temperature change characteristics of each zone, it adaptively adjusts the proportional, integral, and derivative parameters online and outputs corresponding heating or cooling power control signals. Then, it dynamically adjusts the output power of the heating / cooling modules 12 in each zone to the heating or cooling power output value. The target value of the gradient temperature distribution is a dynamic gradient temperature field that varies with spatial location and time.

[0043] Furthermore, the first The temperature deviation between zones is: ,in For temperature deviation, For the target temperature, This is the real-time temperature. When When: The current temperature is below the target value, heating power needs to be increased; when At this time: The current temperature is higher than the target value, requiring a reduction in heating or an increase in cooling. The deviation rate is: ,when When: The deviation is increasing, indicating that the temperature is deviating further from the target and requires stronger adjustment; when Time: As the deviation decreases, it indicates that the system is approaching the target, and the adjustment intensity can be reduced. Power output is controlled by various parameters: ,in This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. The algorithm coefficients are dynamically adjusted in real time based on the deviation and the rate of change of the deviation to actively adjust them in order to maintain the fidelity of the gradient temperature boundary.

[0044] S4. During the experiment, real-time temperature data of each zone is monitored. Multi-point spatiotemporal data fusion and online deviation correction technology are used to eliminate temperature inhomogeneity, temperature discontinuity between zones and time lag effects, so as to ensure high-fidelity reproduction and long-term stable control of temperature boundaries of each zone.

[0045] In step S4, multi-point spatiotemporal data fusion and online deviation correction technology are used to eliminate temperature inhomogeneity in each zone, temperature discontinuity between zones, and time lag effects, ensuring high-fidelity reproduction and long-term stable control of temperature boundaries in each zone.

[0046] Specifically, the system continuously monitors the real-time temperature data of each zone, performing multi-point data fusion processing every 10 minutes. This processing includes using a weighted average algorithm to fuse the real-time temperature data from five temperature sensors within each zone, eliminating spatial temperature differences. When the real-time temperature data of a zone deviates from the target value of its gradient temperature distribution by more than a preset value of ±0.3℃, the system automatically triggers correction control. The central controller fine-tunes the power output of the heating / cooling module 12 in that zone, thereby adjusting the temperature of each zone. The system supports long-term operation and can preset multiple gradient temperature modes (such as linear gradient, nonlinear gradient, and piecewise gradient) according to different experimental requirements. The central controller enables rapid switching and automatic adjustment between different modes.

[0047] The role and effect of the embodiments

[0048] The gradient temperature boundary active fidelity method based on distributed multi-level spatiotemporal adaptive control in this embodiment can actively construct and maintain the preset gradient temperature distribution inside and at the boundary of the model box with high fidelity through a distributed multi-level collaborative control system and a spatiotemporal adaptive closed-loop control module, which significantly improves the reproduction accuracy and spatiotemporal consistency of the thermal boundary of the model test.

[0049] The method in this embodiment adopts a multi-zone independent temperature control and distributed collaborative control strategy, combined with multi-point spatiotemporal data fusion and online deviation correction technology, which can significantly improve temperature control accuracy, dynamic response speed and anti-interference ability, and effectively eliminate spatial temperature differences and time lag effects.

[0050] The method in this embodiment can effectively reduce the interference of thermal boundary distortion and edge effects on experimental results, and improve the similarity, repeatability and reliability of multi-field coupled model experiments.

[0051] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control, characterized in that, Includes the following steps: S1. Construct a distributed multi-level collaborative control system. Arrange a multi-layer composite thermal boundary structure in the model box. The middle layer contains a heating / cooling module array and a temperature-controlled water-cooled power supply. Divide the model box into multiple independent temperature control zones along the depth direction. Arrange multiple temperature sensors in each zone. Connect the heating / cooling module array, the temperature-controlled water-cooled power supply and the temperature sensors to the central controller in the system. S2, preset the target value of the gradient temperature distribution inside the sample in the model box according to the working conditions of the model prototype and the test requirements; S3. Based on the real-time temperature data of each zone collected by the temperature sensor, the spatiotemporal adaptive closed-loop control module built into the central controller calculates the heating or cooling power required for each zone, and adjusts the power output of the heating / cooling module of each zone in real time to actively generate and stably maintain the preset gradient temperature distribution. S4. During the experiment, real-time temperature data of each zone is monitored. Multi-point spatiotemporal data fusion and online deviation correction technology are used to eliminate temperature inhomogeneity, temperature discontinuity between zones and time lag effects, so as to ensure high-fidelity reproduction and long-term stable control of temperature boundaries of each zone.

2. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: In step S1, the multi-layer composite thermal boundary structure includes: a high thermal conductivity metal plate, a heating / cooling module array, and a high-performance thermal insulation layer arranged sequentially from the inside to the outside of the model box boundary, wherein the heating / cooling module array is arranged in zones according to the location of each temperature control zone.

3. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 2, characterized in that: in, The high thermal conductivity metal plate is made of either copper or aluminum alloy, the heating / cooling module is made of either a thermoelectric element or a semiconductor temperature control chip, and the high-performance insulation layer is made of polyurethane foam.

4. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: In step S2, the target value of the gradient temperature distribution includes: the direction of the spatial temperature gradient, the magnitude of the gradient, and the time variation sequence.

5. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: In step S3, the spatiotemporal adaptive closed-loop control module uses a fuzzy PID control algorithm to calculate the required heating or cooling power output value for each zone based on the spatial deviation, time deviation, and rate of change between the real-time temperature data and the target value of the gradient temperature distribution. The module then dynamically adjusts the output power of the heating / cooling module in each zone to the required heating or cooling power output value.

6. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: In step S4, the steps of eliminating uneven temperature in each zone, discontinuous temperature between zones, and time lag effect through multi-point spatiotemporal data fusion and deviation online correction technology include: using a weighted average algorithm to fuse multiple real-time temperature data in each zone; when the temperature deviation between the real-time temperature data of a zone and the target value of the gradient temperature distribution of that zone exceeds a preset value of ±0.3℃, the system automatically triggers correction control; the central controller adjusts the temperature of each zone by fine-tuning the power output of the heating / cooling module of that zone.

7. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: In step S1, while temperature sensors are installed in each zone, an independent sensor drive circuit is set up in each zone and connected to the central controller. The sensor drive circuit includes a power amplifier and a signal conditioning module.

8. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: in, Each temperature-controlled water-cooled power supply provides independent power to its respective zone. The temperature-controlled water-cooled power supply includes a heating / cooling module array drive circuit and a forced water-cooling heat dissipation module. The forced water-cooling heat dissipation module includes a circulating water pump, a radiator, and a fan.

9. The gradient temperature boundary active fidelity preservation method based on distributed multi-level spatiotemporal adaptive control according to claim 1, characterized in that: in, The temperature sensor is one of the following: thermocouple, resistance temperature detector (RTD), and optical fiber.

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