TPMS heat exchanger based on intelligent response of phase change material

Abstract

The invention discloses a TPMS heat exchanger based on intelligent response of a phase change material, and relates to the technical field of heat exchange and heat energy management, the TPMS heat exchanger comprises a heat storage unit, a sensor group and a controller; the heat storage unit is internally packaged with a three-period extremely-small-curved-surface-based stepped hole composite heat storage material and is used for absorbing redundant heat and generating phase change when a heat load is increased; the sensor group is used for monitoring flow resistance parameters and heat transfer parameters in real time; the controller identifies the phase change state and the phase change degree of the heat storage material through a built-in data driving model according to the monitoring data, and generates a self-adaptive control signal to adjust system operation parameters; the three-period minimum curved surface structure is optimized through the data driving method, active heat management is achieved based on phase change state feedback, and the comprehensive performance of the heat exchange system for coping with dynamic heat loads can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of heat exchange and thermal energy management technology, specifically a TPMS heat exchanger based on intelligent response of phase change materials. Background Technology

[0002] Modern high-tech equipment often faces severe transient thermal shock during operation. In fields such as aerospace and energy, rapid changes in thermal load can lead to decreased equipment performance, shortened lifespan, or even thermal failure accidents.

[0003] To address the problem of transient thermal shock, phase change materials (PCMs) have attracted widespread attention due to their ability to absorb or release large amounts of latent heat. However, traditional PCMs suffer from inherent drawbacks such as low thermal conductivity and slow thermal response. Existing technologies enhance heat transfer by adding highly thermally conductive nanoparticles, embedding metal foams, or designing finned structures, but these methods still suffer from problems such as discontinuous heat transfer paths, poor structural controllability, and high interfacial thermal resistance with the PCM, and they lack the ability to actively sense the phase change process.

[0004] In recent years, the three-period minimal surface structure (TPMS) has been applied to heat exchanger design due to its unique topological characteristics. Some studies have used it as a skeleton to fill phase change materials to construct efficient heat transfer networks. However, most existing applications treat the TPMS structure as a static component with fixed parameters, failing to fully utilize its geometrically programmable potential for condition-adaptive optimization design, and also failing to achieve deep integration with intelligent control systems.

[0005] Therefore, there is a need for a heat exchange system that can optimize structural design according to operating conditions, accurately sense phase change state in real time, and achieve adaptive intelligent control based on sensing data, in order to solve the problems of poor structural adaptability, insufficient sensing ability of phase change process, and inability to achieve active closed-loop thermal management in existing technologies. Summary of the Invention

[0006] The purpose of this invention is to provide a TPMS heat exchanger based on intelligent response of phase change materials to solve the problems raised in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a TPMS heat exchanger based on the intelligent response of phase change materials. The TPMS heat exchanger includes a heat storage unit, a sensor group, and a controller; TPMS refers to a three-dimensional surface structure with periodic repeating units, which is used to improve heat exchange efficiency. The thermal storage unit is equipped with a porous skeleton composed of three periodic minimal curved surfaces. The through space formed by the porous skeleton is filled with a stepped porous composite thermal storage material. The stepped porous composite thermal storage material is used to absorb excess heat and undergo phase change when the system heat load increases. The sensor array is configured to monitor the flow resistance and heat transfer parameters of the thermal storage unit in real time. The controller is communicatively connected to the sensor group and is configured to: receive real-time monitoring data from the sensor group; identify the phase change state and degree of phase change of the thermal storage material based on the real-time monitoring data through a built-in data-driven model; and generate an adaptive control signal to adjust the operating parameters of the system according to the identified phase change state and degree of phase change.

[0008] The three-period minimum surfaces within the thermal storage unit are Primitive surfaces. Units with decreasing porosity from inlet to outlet along the flow direction are interconnected to form a gradient structure. The expression for the Primitive surface is: cosX + cosY + cosZ = c(z), where X = 2απx, Y = 2βπy, Z = 2γπz, x, y, and z represent rectangular coordinates, α, β, and γ control the size of the three-period minimum surface units, and c(z) controls the unit porosity. The porosity varies uniformly along the z-axis of the flow direction, and the expression for c(z) is: c(z) = c min +(c max -c min )×[(zz min ) / (z max -z min )]. Among them, c min For the set minimum porosity, c max For the set maximum porosity, z min The minimum value of the z-axis coordinate, z max This represents the maximum value of the z-axis coordinate.

[0009] The sensor set includes a pressure sensor set for monitoring the pressure difference between the inlet and outlet of the thermal storage unit and a temperature sensor set for monitoring the temperature; the flow resistance parameter is a flow resistance value calculated based on the pressure difference between the inlet and outlet, and is calculated using the following formula: R f =ΔP / q v , where R f Here are the flow resistance parameters, ΔP is the inlet and outlet pressure difference, and q is the flow resistance parameter. v The volumetric flow rate is used; the heat transfer parameters include the inlet temperature, outlet temperature, and temperature distribution inside the heat storage material of the heat storage unit.

[0010] The data-driven model is a Long Short-Term Memory (LSTM) neural network trained using a machine learning algorithm. The LSM takes the time-series data of the flow resistance and heat transfer parameters as input and outputs the liquid phase fraction or remaining heat storage capacity of the thermal storage material. The time-series feature vector received by the input layer of the LSM is: X t =[R f (t),T in (t),Tout (t),T1(t),...,T n (t),dR f / dt,dT / dt,…], where X t Let R be a time series feature vector, where t is the time index. f (t) represents the current resistance value at time t, T in (t) represents the inlet temperature at time t, T out (t) represents the outlet temperature at time t, where T1(t),...,T n (t) represents the temperature measurement values ​​at n different locations inside the thermal storage unit at time t, and dR f / dt is the rate of change of flow resistance, and dT / dt is the rate of change of temperature.

[0011] The liquid phase fraction φ(t) satisfies 0 ≤ φ(t) ≤ 1; the remaining thermal storage capacity C res (t) is calculated using the following formula: C res (t)=m p ×L f ×(1-φ(t)), where C res (t) represents the remaining thermal storage capacity at time t, m p L represents the total mass of the phase change material. f Let φ(t) be the latent heat of phase change, and let φ(t) be the liquid phase fraction at time t.

[0012] The controller is further configured to: when the liquid phase ratio φ(t) output by the data-driven model exceeds a first preset threshold φ th or the remaining thermal storage capacity C res (t) is lower than the second preset threshold C th When the system is under high heat load, it is determined that the system is in a high heat load state and a corresponding control signal is generated.

[0013] The adaptive control signal is generated based on the PID control algorithm, specifically: Where u(t) is the control signal, and e(t) = φ target -φ(t) is the difference between the actual state and the desired state of the system at time t, φ target K is the target liquid fraction. p K i K d These are the PID control parameters.

[0014] The adaptive control signal is used to perform at least one of the following operations: adjusting the flow rate of the circulating working fluid in the system; starting or adjusting the power of the auxiliary cooling device; triggering a system overheat warning; and reducing the power output of the heat source equipment.

[0015] The material of the three-period minimal surface is a metallic material or a ceramic material, and the metallic material is at least one of stainless steel, aluminum alloy or mold steel.

[0016] The phase change material in the stepped porous composite thermal storage material is paraffin or an inorganic salt phase change material.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention provides an intelligent heat exchange solution integrating advanced materials, intelligent sensing, and adaptive control, realizing a fundamental shift from passive heat exchange to active closed-loop thermal management. By real-time monitoring of flow resistance and heat transfer parameters using a sensor array, and combining this with a data-driven model to directly identify the liquid phase fraction or remaining thermal storage capacity of the stepped-pore composite thermal storage material, the phase change state can be accurately sensed in real time, providing precise data for adaptive control. This overcomes the limitations of traditional methods, which can only indirectly infer the phase change state through temperature distribution, and suffer from limited accuracy and real-time performance.

[0018] 2. This invention employs a data-driven approach to optimize the design of the three-period minimum surface, no longer relying on a limited number of fixed topology forms. By setting a gradient structure with porosity decreasing sequentially from inlet to outlet along the flow direction, the distribution of the tiered porous composite thermal storage material becomes more adaptable to temperature gradient changes, resulting in more balanced heat transfer. It can generate the optimal flow channel topology for specific operating conditions, significantly improving the scientific nature and adaptability of the design, and overcoming the shortcomings of existing TPMS structures as static components with fixed parameters, which cannot be optimized for adaptability to operating conditions.

[0019] 3. This invention generates adaptive control signals based on a proportional-integral-derivative (PID) control algorithm. When the liquid phase fraction exceeds a first preset threshold or the remaining heat storage capacity falls below a second preset threshold, a high heat load state is determined, and operations such as adjusting the working fluid flow rate, initiating auxiliary cooling, triggering early warnings, or reducing heat source power are executed. Compared to traditional temperature control methods based on fixed thresholds, this invention can reduce the system's response delay to sudden changes in heat load by 50%-65% and reduce the dynamic fluctuation amplitude of key measuring points by 60%-75%, significantly improving the overall performance of the heat exchange system in handling complex dynamic conditions. Attached Figure Description

[0020] Figure 1 This is an isometric view of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention; Figure 2 This is a top view of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention; Figure 3 This is a cross-sectional view of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention; Figure 4This is a front view of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention; Figure 5 This is a partial schematic diagram of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention; Figure 6 This is a three-dimensional structural diagram of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention. Figure 7 This is a sectional view of the three-period minimum surface of a TPMS heat exchanger based on intelligent response of phase change materials according to the present invention. Figure 8 This is a flowchart of a data-driven model and intelligent control design method for a TPMS heat exchanger based on the intelligent response of phase change materials, according to the present invention. Explanation of reference numerals in the attached figures: 1. Thermal storage unit; 2. Inlet pipe; 3. Outlet pipe; 4. Pressure sensor group; 5. Temperature sensor group; 6. Controller; 7. Heat exchanger shell; 401. Inlet pressure sensor; 402. Outlet pressure sensor; 501. Inlet temperature sensor; 502. Outlet temperature sensor; 503. Internal temperature sensor array. Detailed Implementation

[0021] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example: Figures 1-8 As shown, this invention provides a technical solution: a TPMS heat exchanger based on the intelligent response of phase change materials. like Figures 1 to 4 As shown, this invention provides a TPMS heat exchanger based on intelligent response of phase change materials, comprising three core parts: a heat storage unit 1, a sensor group, and a controller 6.

[0023] The thermal storage unit 1, as the core heat transfer-storage component of the heat exchanger, has an internal porous framework composed of three-period minimal curved surfaces, filled with phase change thermal storage material. A sensor array monitors the flow resistance and heat transfer parameters of the thermal storage unit 1 in real time and transmits the data to the controller 6. Based on the sensor data, the controller 6 uses a built-in data-driven model to identify the phase change state and degree of the tiered porous composite thermal storage material, and generates control signals accordingly to adjust the system's operating parameters. Figure 1As shown, the thermal storage unit 1 is connected to the external thermal management system through the inlet pipe 2 and the outlet pipe 3. A pressure sensor group 4 and a temperature sensor group 5 are arranged on the thermal storage unit 1. The controller 6 is connected to each sensor through signal lines and to the actuator through control lines. The actuator includes a flow regulating valve and a heat exchanger housing 7.

[0024] like Figure 5 As shown, the three-period minimal surface inside thermal storage unit 1 uses a Primitive surface as its basic topology. Horizontally, this structure consists of multiple identical Primitive units arranged in a periodic array; along the fluid flow direction, the porosity of the units decreases sequentially from the inlet to the outlet, forming a gradient structure. Figure 6 and Figure 7 As shown, the expression for the Primitive surface is cosX + cosY + cosZ = c(z), where X = 2απx, Y = 2βπy, Z = 2γπz, x, y, and z represent rectangular coordinates, α, β, and γ are used to control the size of the three-period minimum surface element, and c(z) is used to control the porosity. To make the porosity increase uniformly along the z-axis, the expression for c(z) is c(z) = c min +(c max -c min )×[(zz min ) / (z max -z min )], where c min For the set minimum porosity, c max For the set maximum porosity, z min The minimum value of the z-axis coordinate, z max The z-axis coordinate represents the maximum value. The material of the three-period minimum surface is 316L stainless steel, and the tiered perforated composite thermal storage material is paraffin wax. When the high-temperature working fluid flows through thermal storage unit 1, the heat is rapidly conducted to the tiered perforated composite thermal storage material through the three-period minimum surface. After absorbing the heat, the phase change material undergoes a solid-liquid phase change, converting sensible heat into latent heat for storage. When the working fluid temperature decreases or the heat load decreases, the phase change material releases the stored heat, undergoing a liquid-solid phase change, thus playing a role in "peak shaving and valley filling".

[0025] The sensor group includes pressure sensor group 4 and temperature sensor group 5. For example... Figure 4 As shown, a pressure sensor array is configured with one sensor at the inlet and one at the outlet of thermal storage unit 1, namely inlet pressure sensor 401 and outlet pressure sensor 402, for measuring the pressure difference between the inlet and outlet. The flow resistance parameter R is calculated using the following formula. f =ΔP / q v , where R f Here are the flow resistance parameters, ΔP is the inlet and outlet pressure difference, and q is the flow resistance parameter. vThis represents the volumetric flow rate. Changes in flow resistance reflect alterations in flow characteristics caused by changes in the state of the phase change material. When the phase change material melts, the effective cross-sectional area of ​​the flow channel decreases, and the flow resistance increases; the opposite occurs during solidification. For example... Figure 5 As shown, the temperature sensor group 5 includes an inlet temperature sensor 501, an outlet temperature sensor 502, and an internal temperature sensor array 503. The inlet temperature sensor 501 measures the inlet temperature of the working fluid, the outlet temperature sensor 502 measures the outlet temperature of the working fluid, and the internal temperature sensor array 503 arranges multiple temperature probes at key locations within the three-period minimal surface to measure the internal temperature distribution of the phase change material. The temperature distribution data is used to calculate heat transfer parameters, including heat transfer, heat transfer coefficient, and the position of the phase change front.

[0026] like Figure 8 As shown, the data-driven model is constructed using a long short-term memory neural network, with the input layer receiving time-series data sequences from sensors. The time-series feature vector is X. t =[R f (t),T in (t),T out (t),T1(t),...,T n (t),dR f [ / dt,dT / dt,…], where X t Let R be a time series feature vector, where t is the time index. f (t) represents the current resistance value at time t, T in (t) represents the inlet temperature at time t, T out (t) represents the outlet temperature at time t, where T1(t),...,T n (t) represents the temperature measurement values ​​at n different locations inside thermal storage unit 1 at time t, dR f / dt represents the rate of change of flow resistance, and dT / dt represents the rate of change of temperature. The model output is the liquid phase fraction φ(t), satisfying 0≤φ(t)≤1, and the remaining thermal storage capacity C. res (t), C is calculated using the following formula. res (t)=m p ×L f ×(1-φ(t)), where C res (t) represents the remaining thermal storage capacity at time t, m p L represents the total mass of the phase change material. f This is the latent heat of phase transition.

[0027] The intelligent control logic is as follows: First, sensor data is collected in real time and filtered and normalized; then, the processed data is input into a long short-term memory neural network model, which outputs the current liquid fraction φ(t) and the remaining capacity C. res (t); when φ(t) exceeds the first preset threshold φ th or the remaining thermal storage capacity Cres (t) is lower than the second preset threshold C th At that time, the system is determined to be in a high heat load state; finally, an adaptive control signal is generated based on the decision result, which is based on the PID control algorithm, specifically as follows: Where u(t) is the control signal, and e(t) = φ target -φ(t) is the difference between the actual state and the desired state of the system at time t, φ target K is the target liquid fraction. p K i K d These are the PID control parameters. The adaptive control signal is used to perform at least one of the following operations: adjusting the flow rate of the circulating working fluid in the system; starting or adjusting the power of the auxiliary cooling device; triggering a system overheat warning; or reducing the power output of the heat source equipment. Controller 6 records the effect of each control operation and uses a reinforcement learning algorithm to optimize the control strategy and model parameters online.

[0028] The TPMS heat exchanger of the present invention can be manufactured using the following process: 316L stainless steel three-period minimal curved surface is directly formed using metal 3D printing technology, with a minimum feature size of 0.3mm; molten paraffin is injected into the micropores of the three-period minimal curved surface under vacuum, and capillary force is used to ensure complete filling; sensor mounting holes are reserved during the printing process, or integrated using micromachining technology in post-processing; end cap sealing is performed using laser welding or diffusion welding; and the heat storage unit 1 is assembled with pipelines, sensors, controller 6, etc., into a complete system.

[0029] Based on theoretical analysis and preliminary simulation verification of the control system, compared with the traditional temperature control method based on fixed threshold, the feedforward-feedback composite control strategy based on phase change state prediction adopted in this invention can reduce the system's response delay to sudden changes in heat load by 50%-65%, while reducing the dynamic fluctuation amplitude of key measuring points by 60%-75%.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A TPMS heat exchanger based on intelligent response of phase change materials, characterized in that: The TPMS heat exchanger includes: a heat storage unit (1), a sensor group, and a controller (6). The thermal storage unit (1) is connected to an external thermal management system through an inlet pipe (2) and an outlet pipe (3). The thermal storage unit (1) is provided with a porous skeleton composed of three periodic minimal curved surfaces and is filled with phase change thermal storage material. The porous skeleton and the phase change thermal storage material together form a stepped pore composite thermal storage structure. The sensor group is configured to monitor the flow resistance parameters and heat transfer parameters of the thermal storage unit (1) in real time. The controller (6) is communicatively connected to the sensor group and is configured to: receive real-time monitoring data from the sensor group; identify the phase change state and degree of phase change of the thermal storage material based on the real-time monitoring data through a built-in data-driven model; and generate an adaptive control signal to adjust the operating parameters of the system according to the identified phase change state and degree of phase change.

2. The TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The sensor group includes a pressure sensor group (4) for monitoring the pressure difference between the inlet and outlet of the thermal storage unit (1) and a temperature sensor group (5) for monitoring the temperature; the pressure sensor group (4) includes an inlet pressure sensor (401) and an outlet pressure sensor (402) for monitoring the pressure difference between the inlet and outlet of the thermal storage unit (1); the temperature sensor group (5) includes an inlet temperature sensor (501), an outlet temperature sensor (502) and an internal temperature sensor array (503) for monitoring the inlet temperature, outlet temperature and temperature distribution inside the thermal storage material of the thermal storage unit (1); the flow resistance parameter is the flow resistance value calculated based on the pressure difference between the inlet and outlet; the heat transfer parameter includes the inlet temperature, outlet temperature and temperature distribution inside the thermal storage material of the thermal storage unit (1).

3. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The three-period minimum surface in the thermal storage unit (1) is a Primitive surface. The units with decreasing porosity from the inlet to the outlet along the flow direction are interconnected to form a gradient structure.

4. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The data-driven model is a long short-term memory neural network trained by a machine learning algorithm. The long short-term memory neural network takes the time-series data of the flow resistance parameters and heat transfer parameters as input and outputs the liquid phase fraction or remaining heat storage capacity of the stepped-pore composite thermal storage material.

5. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 4, characterized in that: The controller (6) is further configured to: determine that the system is in a high heat load state when the liquid phase ratio output by the data-driven model exceeds a first preset threshold or the remaining heat storage capacity is lower than a second preset threshold, and generate a corresponding control signal.

6. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 5, characterized in that: The adaptive control signal is generated based on the proportional-integral-derivative (PID) control algorithm.

7. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The controller (6) is connected to the actuator via a control line. The actuator includes a flow regulating valve and a heat exchanger housing (7). The adaptive control signal is used to perform at least one of the following operations: regulate the flow rate of the circulating working fluid in the system; start or adjust the power of the auxiliary cooling device; trigger a system overheat warning; and reduce the power output of the heat source equipment.

8. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The material of the three-period minimal surface is a metallic material or a ceramic material, and the metallic material is at least one of stainless steel, aluminum alloy or mold steel.

9. A TPMS heat exchanger based on intelligent response of phase change materials according to claim 1, characterized in that: The phase change material in the stepped porous composite thermal storage material is paraffin or an inorganic salt phase change material.

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