A spacecraft active thermal protection control method, device, equipment, medium and product

Abstract

The application discloses a spacecraft active thermal protection control method, device, equipment, medium and product, and relates to the technical field of spacecraft thermal management. The method comprises the following steps: acquiring real-time thermal environment data outside a spacecraft; predicting the heat flow change trend of the outside of the spacecraft in a preset time period based on the real-time thermal environment data; and generating a regulation instruction for a heat dissipation execution component arranged outside the spacecraft according to the heat flow change trend, wherein the heat dissipation execution component comprises a porous zeolite matrix in which a liquid phase change working medium is stored, and the regulation instruction is used for controlling the transport process of the liquid phase change working medium in the porous zeolite matrix and the vaporization process of the liquid phase change working medium on the surface layer of the porous zeolite matrix. The method can realize active control of thermal protection.

Description

Technical Field

[0001] This application relates to the field of spacecraft thermal management technology, and in particular to a method, device, equipment, medium and product for active thermal protection control of spacecraft. Background Technology

[0002] Spacecraft face extreme and dynamically changing thermal environments during complex mission scenarios such as low Earth orbit operation and hypersonic reentry. Continuous high-temperature loads can not only affect the structural integrity of spacecraft, but may also cause internal electronic equipment and precision instruments to fail. Therefore, as a core component to ensure the safe operation of spacecraft, the performance of thermal protection systems directly determines the mission success rate and the reusability of spacecraft. Efficient and reliable thermal management technology has become an important research direction in the aerospace field.

[0003] Currently, three main technical solutions are used in the field of spacecraft thermal protection: first, passive thermal protection technology represented by ablation materials, which dissipates heat through the ablation of the material itself; second, heat capacity-based heat dissipation technology relying on phase change materials, which uses the latent heat of phase change to absorb heat and achieve temperature regulation; and third, traditional active cooling systems, which remove heat from the surface of the spacecraft through fluid loop circulation. In addition, some solutions employ heat pipe cooling technology, utilizing the high-efficiency heat transfer characteristics of heat pipes to achieve heat transfer. These technologies have been applied to some extent in the thermal protection of different types of spacecraft. However, all of these thermal protection technologies are passive response protections, which suffer from problems such as response lag.

[0004] Therefore, there is an urgent need for a method that can achieve active control of thermal protection. Summary of the Invention

[0005] This application provides a method, device, equipment, medium, and product for active thermal protection control of spacecraft, which can realize active control of thermal protection.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a spacecraft active thermal protection control method, including:

[0008] Acquire real-time thermal environment data outside the spacecraft;

[0009] Based on real-time thermal environment data, predict the trend of heat flow changes outside the spacecraft over a preset period of time;

[0010] Based on the trend of heat flow changes, control commands are generated for the heat dissipation execution components located on the outside of the spacecraft. The heat dissipation execution components include a porous zeolite matrix storing liquid phase change working fluid. The control commands are used to control the transport process of the liquid phase change working fluid in the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix.

[0011] In some possible implementations, the control instructions include control signals for adjusting the opening of the microstructure on the surface of the porous zeolite matrix to control the transport rate of the liquid phase change working fluid to the surface of the porous zeolite matrix.

[0012] Among some possible implementations, based on real-time thermal environment data, the trend of heat flow changes outside the spacecraft over a preset period of time is predicted, including:

[0013] Real-time thermal environment data is input into a pre-trained neural network prediction model to obtain the trend of heat flow changes outside the spacecraft over a preset period of time; the neural network prediction model is a long short-term memory network model.

[0014] In some possible implementations, control commands are generated for heat dissipation actuators located outside the spacecraft based on heat flow variation trends, including:

[0015] To obtain the physical parameters of the porous zeolite matrix and the physical properties of the liquid phase change working fluid;

[0016] Based on physical parameters, physical property parameters and heat flux variation trends, simulation calculations are performed using a multi-physics coupling model to obtain the first control parameter that matches the heat flux variation trend.

[0017] Based on the first control parameter, control commands are generated for the heat dissipation execution components located outside the spacecraft.

[0018] In some possible implementations, the multiphysics coupling model is a model that couples the heat transfer field, the flow field, and the phase change process.

[0019] In some possible implementations, the porous zeolite matrix has a gradient pore structure, with the pore size increasing along the flow direction of the gas flow.

[0020] Secondly, this application provides a spacecraft active thermal protection control device, comprising:

[0021] The acquisition module is used to acquire real-time thermal environment data outside the spacecraft;

[0022] The prediction module is used to predict the trend of heat flow changes outside the spacecraft over a preset period of time based on real-time thermal environment data;

[0023] The control module is used to generate control commands for the heat dissipation execution components located outside the spacecraft based on the heat flow change trend. The heat dissipation execution components include a porous zeolite matrix storing liquid phase change working fluid. The control commands are used to control the transport process of the liquid phase change working fluid in the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix.

[0024] Thirdly, this application provides a computing device, including a memory and a processor;

[0025] The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0026] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0027] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.

[0028] As can be seen from the above technical solution, this application has at least the following beneficial effects:

[0029] In this application, by acquiring real-time thermal environment data from the spacecraft's exterior, the instantaneous changes in the external thermal environment under complex flight conditions are accurately captured, providing high-quality data for subsequent heat flow prediction and laying a reliable data foundation for the entire control process. This effectively avoids the risk of inaccurate heat dissipation control due to inaccurate or incomplete data. Furthermore, based on real-time thermal environment data, the heat flow change trend outside the spacecraft over a preset period can be predicted, achieving accurate capture of the long-term dependence of heat flow changes and key characteristics such as peaks and fluctuations. This provides a decision-making basis for achieving intelligent heat dissipation with millisecond-level response. Finally, based on the heat flow change trend, control commands are generated for a heat dissipation execution component located outside the spacecraft, including a porous zeolite matrix storing liquid phase change working fluid. These commands control the transport process of the liquid phase change working fluid within the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix, enabling dynamic optimization of heat dissipation strategies according to different thermal environment scenarios. This solution provides a way to control heat flow changes by introducing a heat dissipation actuator made of a porous zeolite matrix, ultimately achieving active control of thermal protection.

[0030] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0031] Figure 1 An application environment diagram for a spacecraft active thermal protection control method provided in this application embodiment;

[0032] Figure 2 A schematic flowchart of an active thermal protection control method for spacecraft provided in this application embodiment;

[0033] Figure 3 A structural diagram of a spacecraft active thermal protection control device provided in this application embodiment;

[0034] Figure 4 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0035] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0036] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0038] Currently, in the field of spacecraft thermal management technology, the mainstream thermal protection solutions include ablation material heat protection, phase change material heat dissipation, traditional active cooling systems and heat pipe heat dissipation, which mainly rely on the material's own heat capacity, ablation consumption or simple fluid circulation to achieve heat control. Among them, porous zeolite, as a functional material with ultra-high specific surface area and controllable pore size, has attracted attention for its efficient water storage and directional transport characteristics, providing a material basis for breaking through traditional technical bottlenecks.

[0039] Existing technologies generally have shortcomings: ablation materials suffer from unavoidable mass loss and cannot meet the requirements for reusability; phase change materials have limited heat capacity and a cycle life of only 5 times, resulting in insufficient continuous heat dissipation capacity; traditional active cooling systems and heat pipe cooling have a high weight ratio and high energy consumption, and all solutions are passive response designs, lacking the ability to predict and dynamically control changes in heat flow, resulting in system response lag, prominent risk of local overheating, and difficulty in meeting the requirements of lightweight, efficient heat dissipation and long life. The root cause is that the material structural characteristics and intelligent control strategies have not been deeply integrated, making it impossible to adapt to the real-time changes of complex dynamic thermal environments.

[0040] In view of this, embodiments of this application provide an active thermal protection control method for spacecraft. In this method, real-time thermal environment data is first acquired through sensors deployed outside the spacecraft. Then, the heat flow change trend within a preset time period is predicted based on a pre-trained LSTM neural network model. Finally, by combining a multiphysics coupling model with the structural characteristics of a gradient porous zeolite matrix, control commands are generated to control the transport and vaporization process of the liquid phase change working fluid. Through a closed-loop logic of data acquisition, trend prediction, and accurate control, active control of thermal protection is achieved, effectively solving the problems of traditional technologies.

[0041] To make the technical solution of this application clearer and easier to understand, the application scenarios of the technical solution of this application are described below with reference to the accompanying drawings. Figure 1 As shown in the figure, this figure is a schematic diagram of an application scenario provided by an embodiment of this application.

[0042] In this application scenario, terminal 102 serves as the front-end device for data acquisition and command execution, while server 104 acts as the core data processing and decision-making hub. The two collaborate through a real-time communication link. Terminal 102 is integrated into the heat dissipation execution component (including a gradient porous zeolite matrix storing liquid phase change working fluid) outside the spacecraft. Its onboard temperature sensors, heat flux sensors, and other devices collect real-time thermal environment data (including dynamic parameters such as heat flux density, surface temperature, and ambient temperature) from outside the spacecraft. This real-time data is encrypted and uploaded to server 104 via a stable communication network, along with a data acquisition timestamp and sensor status identifier, ensuring the integrity and timeliness of the data acquired by server 104. After receiving real-time thermal environment data uploaded by terminal 102, server 104 first performs noise reduction and normalization preprocessing on the data to remove abnormal data points. Then, it calls the pre-trained LSTM neural network prediction model and inputs the preprocessed real-time data to accurately predict the trend of external heat flow changes of the spacecraft (including key features such as heat flow peak value, rise and fall amplitude, and stabilization duration) within a preset 5-second time period, with the prediction error controlled within the error threshold. After the prediction is completed, server 104 feeds back the heat flow change trend results to terminal 102 in real time, providing a scenario prediction basis for terminal 102 to receive subsequent control commands.

[0043] To make the technical solution of this application clearer and easier to understand, the following describes a spacecraft active thermal protection control method provided by an embodiment of this application, using server 104 as the execution subject, in conjunction with the above application scenario. Figure 2 As shown, this figure is a flowchart of an active thermal protection control method for spacecraft provided in an embodiment of this application. The active thermal protection control method for spacecraft includes:

[0044] S201. Acquire real-time thermal environment data of the spacecraft's exterior.

[0045] Real-time thermal environment data refers to the real-time measurement or calculation data of heat-related physical quantities such as temperature and heat flux density experienced by the external surface of a spacecraft during flight. It is usually collected by temperature sensors, heat flux sensors and other sensors placed on the external surface of the spacecraft.

[0046] For example, temperature and heat flux sensors installed in critical thermal protection areas of the spacecraft (such as the nose cone and wing leading edges) collect real-time thermal environment data, including temperature distribution and heat flux density, experienced by the spacecraft's outer surface during flight. These sensors transmit the collected analog or digital signals to server 104 via data bus or wireless transmission. Server 104 performs preprocessing on the received raw data, including filtering, calibration, and format unification, to eliminate noise and convert it into structured input that can be directly used by intelligent control algorithms. Through this process, real-time perception and data acquisition of the external thermal environment are achieved, providing an accurate and timely data foundation for subsequent intelligent prediction and control.

[0047] Optionally, the key thermal protection areas are the nose cone region (8-12μm aperture) and the wing leading edge region (15-20μm aperture). Three modules are arranged in the nose cone region and five modules are arranged in the wing leading edge region. Each module integrates four temperature sensors.

[0048] S202. Based on real-time thermal environment data, predict the trend of heat flow changes outside the spacecraft during a preset period.

[0049] One possible approach is to input real-time thermal environment data into a pre-trained neural network prediction model to obtain the trend of heat flow changes outside the spacecraft over a preset period of time; wherein the neural network prediction model is a long short-term memory network model.

[0050] The preset time period refers to a pre-defined window of time in the future, such as a time range of 5 seconds in the future, which is used to predict heat flow trends.

[0051] The heat flux change trend refers to the dynamic evolution of the external heat flux density of a spacecraft within a preset time period, including important characteristics such as the time of heat flux peak occurrence, the magnitude of value rise and fall, and the duration of stable state. It is an important basis for intelligent heat dissipation systems to formulate control strategies.

[0052] Neural network prediction models are mathematical models based on machine learning that can learn patterns of thermal environment change from historical data and be used to predict future trends.

[0053] Long Short-Term Memory (LSTM) network models are a special type of recurrent neural network that can capture long-term dependencies in time series data and are suitable for heat flow prediction tasks with time-series characteristics.

[0054] For example, after receiving real-time thermal environment data, server 104 first performs standardization processing on the time series data such as temperature and heat flux density, such as noise reduction and normalization, and constructs the input sequence using a sliding time window method, such as taking the most recent 5 seconds of historical data; then, the sequence is input into a Long Short-Term Memory (LSTM) network model that has been pre-trained using historical flight data and wind tunnel test data (such as temperature distribution data obtained under conditions of Mach number Ma=8, nose cone stagnation temperature decreasing from 2100K to 1530K, and wing leading edge temperature decreasing from 1850K to 1320K). Through its forget gate, input gate, and output gate, the temporal characteristics of heat flux changes are encoded and learned, capturing the characteristics of long-term dependencies in long-sequence data. The predicted value and trend of heat flux density within the next 5 seconds are output. After de-standardization and credibility assessment, the prediction results are transmitted to the control decision module in real time, providing a basis for advance control of the piezoelectric ceramic actuators of the spacecraft's intelligent heat dissipation system. This enables the actuators to predict heat flux changes and dynamically adjust the opening of the coating pores, realizing the transformation from passive response to active prediction and control, and significantly improving the adaptability and response speed of the heat dissipation system to complex thermal environments.

[0055] It should be noted that the training process of the neural network prediction model can include data preparation and preprocessing, model building and training, and model evaluation and solidification. In the data preparation and preprocessing stage, multiple data sources are collected, including measured data from hypersonic wind tunnel tests, thermal-fluid-phase change coupling data generated based on multiphysics simulation software, and thermal environment telemetry data from previous spacecraft missions. These raw data are cleaned, outliers are removed, and normalization is performed to unify the dimensions and accelerate model convergence. Subsequently, continuous time-series data are constructed into "sample-label" pairs: thermal environment data (temperature, heat flux density, pressure, etc.) from the first N time steps (e.g., 50 steps, representing 5 seconds of history) are used as input samples, and heat flux density values ​​from the next M time steps (e.g., 50 steps, representing the next 5 seconds) are used as prediction target labels.

[0056] During the model building and training phases, an LSTM network architecture is first initialized, typically containing several stacked LSTM layers to extract deep temporal features, followed by fully connected layers to output predicted sequences. During training, the preprocessed dataset is divided into training, validation, and test sets. In the training loop, the model's predicted output is calculated using the training set data through forward propagation. Then, a loss function such as mean squared error is used to quantify the difference between the predicted heat flow and the true label. Finally, the gradient of the loss relative to the model parameters (weights and biases) is calculated using the backpropagation algorithm, and these parameters are iteratively updated to minimize the loss function. Throughout this process, the validation set is used to monitor the model's performance on unseen data, preventing overfitting, and allowing for the tuning of hyperparameters (such as learning rate, number of network layers, and number of neurons).

[0057] During the model evaluation and solidification stages, once the model's performance on the validation set stabilizes and meets preset accuracy requirements (e.g., prediction error ≤ 8%), a separate test set is used for final evaluation to ensure its generalization ability. After training, the optimal model parameters and structure are solidified and saved, and lightweight processing (such as model pruning and quantization) is typically performed to generate a final prediction model that can be efficiently deployed in spacecraft embedded environments, providing a reliable algorithmic foundation for real-time on-orbit prediction.

[0058] S203. Based on the trend of heat flow changes, generate control commands for the heat dissipation execution components located outside the spacecraft.

[0059] The heat dissipation actuator includes a porous zeolite matrix storing a liquid phase change working fluid.

[0060] The heat dissipation actuator is an important component on the exterior of a spacecraft that performs active heat dissipation. It is based on a gradient porous zeolite matrix and contains a liquid phase change working fluid (water). Heat dissipation is achieved through the transport and vaporization of the working fluid, and it serves as the carrier for executing control commands.

[0061] The porous zeolite matrix has a gradient pore structure with pore size increasing along the flow direction of the airflow, such as 8-12 μm at the leading edge and 15-20 μm at the trailing edge. It is an important structure for heat dissipation actuators, with high water storage capacity and high specific surface area, providing a physical carrier for the storage and transportation of liquid phase change working fluids.

[0062] Liquid phase change working fluid is a heat transfer medium (such as water) stored in a porous zeolite matrix. It absorbs heat and vaporizes by utilizing the latent heat of phase change, thereby achieving efficient heat dissipation. Its physical properties directly affect transport and phase change efficiency.

[0063] The control commands include control signals for adjusting the opening degree of the microstructure on the surface of the porous zeolite matrix to control the transport rate of the liquid phase change working fluid to the surface of the porous zeolite matrix, for controlling the transport process of the liquid phase change working fluid in the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix, and for precisely controlling the working state of the heat dissipation actuator, including signals for adjusting the opening degree of the microstructure on the surface of the porous zeolite matrix, ultimately achieving dynamic control of the working fluid transport rate and vaporization process.

[0064] The microstructure opening degree of the porous zeolite matrix surface layer refers to the degree of pore opening and closing of the functional coating of the porous zeolite matrix (e.g., adjustable range 0-90%). By changing the opening degree, the transport rate of liquid phase change working fluid to the surface layer can be controlled, thereby regulating the vaporization heat absorption efficiency.

[0065] One possible approach is to obtain the physical parameters of a porous zeolite matrix and the physical properties of a liquid phase change working fluid; based on the physical parameters, physical properties, and heat flow variation trends, to perform simulation calculations using a multiphysics coupling model to obtain a first control parameter that matches the heat flow variation trend; and based on the first control parameter, to generate control commands for a heat dissipation execution component located outside the spacecraft.

[0066] Among them, the physical parameters of the porous zeolite matrix are key parameters that characterize the structural properties of the porous zeolite matrix, including gradient pore size distribution (e.g., 8-20 μm), matrix thickness (e.g., 5-20 mm), water storage capacity, thermal conductivity, BET (Brunauer–Emmett–Teller theory) specific surface area ≥500 m² / g, etc., which are the basic inputs for multiphysics coupling model simulation.

[0067] Optionally, the porous zeolite matrix has a three-layer composite structure, namely a matrix layer, a water storage layer, and a functional coating layer; wherein, the matrix layer is a zeolite molecular sieve framework with a thickness of 5-20 mm; the water storage layer is phase change working fluid water (latent heat ≥2000 kJ / kg); and the functional layer is a water-based porous coating with a thickness of 3-8 mm.

[0068] The physical properties of liquid phase change working fluids are parameters that characterize the thermophysical properties of liquid phase change working fluids, including latent heat of phase change, viscosity, surface tension, density, etc. (such as the latent heat of vaporization of water at temperature 2257 kJ / kg), which directly affect the transport and phase change process of the working fluid.

[0069] A multiphysics coupling model is a model that couples the heat transfer field, flow field, and phase change process, and can accurately simulate the synergistic mechanism of working fluid transport, heat transfer, and vaporization endothermy; optionally, the multiphysics coupling model satisfies: ,in, To absorb heat during vaporization, The mass flow rate of the water medium can be calculated based on the zeolite pore size distribution and capillary force model. It is the latent heat of vaporization of water.

[0070] The first control parameter refers to the optimal control parameter obtained through simulation calculation by a multi-physics coupling model, including the target opening degree of the surface microstructure and the threshold of the working fluid transport rate. Its characteristic is that it is precisely matched with the predicted heat flow change trend to ensure that the heat dissipation efficiency is dynamically adapted to the heat flow demand.

[0071] For example, firstly, the 5-second-level heat flow change trend of the spacecraft's exterior, output by the LSTM neural network, is acquired. Simultaneously, physical parameters of the porous zeolite matrix, such as gradient pore size distribution, matrix thickness, and water storage rate, are collected, along with the physical properties of the liquid phase change working fluid, such as latent heat of phase change, viscosity, and surface tension. All input data undergoes standardized preprocessing to ensure the data format is consistent with the model requirements. Then, a pre-defined multiphysics coupling model (integrating heat transfer field, flow field, and phase change process) is invoked. The pre-processed heat flow change trend, physical parameters, and physical properties are input into the model. Dynamic calculations are performed using COMSOL-MATLAB co-simulation to simulate the matching relationship between the working fluid transport rate, vaporization efficiency, and surface microstructure opening under different heat flow scenarios. The final output includes first control parameters that precisely match the trend of heat flow changes, such as the target opening degree of the surface microstructure and the threshold of the working fluid transport rate. Finally, based on the first control parameters, corresponding control commands can be generated. The purpose is to output a control signal to adjust the opening degree of the surface microstructure of the porous zeolite matrix. This signal controls the transport rate of the liquid phase change working fluid to the matrix surface, so that the vaporization process of the working fluid is synchronized with the predicted heat flow changes in real time. That is, the opening degree is increased in advance before the heat flow peak arrives to improve the transport and vaporization efficiency of the working fluid, and the opening degree is reduced when the heat flow decreases to avoid working fluid waste. Ultimately, adaptive heat dissipation control with a millisecond-level (≤10ms) response is achieved, ensuring that the temperature uniformity inside the chamber is controlled within the target range, such as within ±1.2°C, while maximizing the heat dissipation efficiency by 58% and the service life.

[0072] Based on the above, the spacecraft active thermal protection control method, by acquiring real-time thermal environment data from the spacecraft's exterior, achieves precise capture of instantaneous changes in the external thermal environment under complex flight conditions. This provides high-quality data for subsequent heat flow prediction, laying a reliable data foundation for the entire control process and effectively avoiding the risk of inaccurate heat dissipation control due to inaccurate or incomplete data. Furthermore, based on real-time thermal environment data, it can predict the heat flow change trend outside the spacecraft over a preset period, achieving precise capture of the long-term dependence of heat flow changes and key characteristics such as peaks and fluctuations, providing a decision-making basis for achieving millisecond-level intelligent heat dissipation. Finally, based on the heat flow change trend, it generates control commands for a heat dissipation execution component located outside the spacecraft, including a porous zeolite matrix storing liquid phase change working fluid, to control the transport process of the liquid phase change working fluid within the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix. This enables dynamic optimization of heat dissipation strategies according to different thermal environment scenarios. This solution provides a way to control heat flow changes by introducing a heat dissipation actuator made of a porous zeolite matrix, ultimately achieving active control of thermal protection.

[0073] The above text combined Figures 1 to 2The active thermal protection control method for spacecraft provided in the embodiments of this application has been described in detail. The apparatus and equipment provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0074] This application also provides an active thermal protection control device for spacecraft, such as... Figure 3 As shown in the figure, this is a schematic diagram of a spacecraft active thermal protection control device 300 provided in an embodiment of this application. The device includes:

[0075] The acquisition module 301 is used to acquire real-time thermal environment data outside the spacecraft;

[0076] Prediction module 302 is used to predict the trend of heat flow change outside the spacecraft over a preset period of time based on real-time thermal environment data;

[0077] The control module 303 is used to generate control commands for a heat dissipation execution component located outside the spacecraft based on the heat flow change trend. The heat dissipation execution component includes a porous zeolite matrix storing a liquid phase change working fluid. The control commands are used to control the transport process of the liquid phase change working fluid in the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix.

[0078] In some possible implementations, the control instructions include control signals for adjusting the opening of the microstructure on the surface of the porous zeolite matrix to control the transport rate of the liquid phase change working fluid to the surface of the porous zeolite matrix.

[0079] In some possible implementations, the prediction module 302 is specifically used for:

[0080] Real-time thermal environment data is input into a pre-trained neural network prediction model to obtain the trend of heat flow changes outside the spacecraft over a preset period of time; the neural network prediction model is a long short-term memory network model.

[0081] In some possible implementations, the control module 303 is specifically used for:

[0082] To obtain the physical parameters of the porous zeolite matrix and the physical properties of the liquid phase change working fluid;

[0083] Based on physical parameters, physical property parameters and heat flux variation trends, simulation calculations are performed using a multi-physics coupling model to obtain the first control parameter that matches the heat flux variation trend.

[0084] Based on the first control parameter, control commands are generated for the heat dissipation execution components located outside the spacecraft.

[0085] In some possible implementations, the multiphysics coupling model is a model that couples the heat transfer field, the flow field, and the phase change process.

[0086] In some possible implementations, the porous zeolite matrix has a gradient pore structure, with the pore size increasing along the flow direction of the gas flow.

[0087] The spacecraft active thermal protection control device according to the embodiments of this application can correspond to the execution of the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the spacecraft active thermal protection control device are respectively for implementing Figure 2 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0088] This application also provides a computing device. For example... Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 400 includes a bus 401, a processor 402, a communication interface 403, and a memory 404. The processor 402, the memory 404, and the communication interface 403 communicate with each other via the bus 401.

[0089] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0090] The processor 402 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0091] Communication interface 403 is used for communication with external devices.

[0092] Memory 404 may include volatile memory, such as random access memory (RAM). Memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0093] The memory 404 stores executable code, and the processor 402 executes the executable code to perform the aforementioned spacecraft active thermal protection control method.

[0094] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 When the modules or units of the spacecraft active thermal protection control device described in the embodiments are implemented by software, the execution... Figure 3 The software or program code required for the functions of each module / unit can be partially or entirely stored in memory 404. Processor 402 executes the program code corresponding to each unit stored in memory 404 to execute the aforementioned spacecraft active thermal protection control method.

[0095] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct a computing device to execute the aforementioned spacecraft active thermal protection control method.

[0096] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0097] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0098] When the computer program product is executed by a computer, the computer executes any of the aforementioned active thermal protection control methods for spacecraft. The computer program product can be a software installation package; when any of the aforementioned active thermal protection control methods for spacecraft needs to be used, the computer program product can be downloaded and executed on the computer.

[0099] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for active thermal protection control of spacecraft, characterized in that, The method includes: Acquire real-time thermal environment data outside the spacecraft; Based on the real-time thermal environment data, the trend of heat flow change outside the spacecraft during a preset period is predicted; To obtain the physical parameters of the porous zeolite matrix and the physical properties of the liquid phase change working fluid; Based on the physical parameters, physical property parameters and heat flux change trend, a first control parameter matching the heat flux change trend is obtained by simulation calculation through a multiphysics coupling model. Based on the first control parameter, control commands are generated for the heat dissipation execution components located outside the spacecraft; The heat dissipation actuator includes a porous zeolite matrix storing a liquid phase change working fluid. The control commands control the transport process of the liquid phase change working fluid within the porous zeolite matrix and its vaporization process on the surface of the porous zeolite matrix. The control commands include a control signal for adjusting the microstructure opening of the porous zeolite matrix surface to control the transport rate of the liquid phase change working fluid to the porous zeolite matrix surface. The porous zeolite matrix is ​​a three-layer composite structure, including a matrix layer, a water storage layer, and a functional coating. The multiphysics coupling model is a model that couples the heat transfer field, flow field, and phase change process, simulating the synergistic mechanism of working fluid transport, heat transfer, and vaporization endothermic reaction. The multiphysics coupling model satisfies... , To absorb heat during vaporization, The mass flow rate of the water medium. It is the latent heat of vaporization of water.

2. The method according to claim 1, characterized in that, Based on the real-time thermal environment data, predict the trend of heat flow changes outside the spacecraft over a preset period, including: The real-time thermal environment data is input into a pre-trained neural network prediction model to obtain the heat flow change trend of the spacecraft's exterior over a preset period; wherein, the neural network prediction model is a long short-term memory network model.

3. The method according to claim 1, characterized in that, The porous zeolite matrix has a gradient pore structure, with the pore size increasing along the flow direction of the airflow.

4. A spacecraft active thermal protection control device, characterized in that, The device includes: The acquisition module is used to acquire real-time thermal environment data outside the spacecraft; The prediction module is used to predict the trend of heat flow changes outside the spacecraft over a preset period of time based on the real-time thermal environment data. A control module is used to generate control commands for a heat dissipation execution component located outside the spacecraft based on the heat flow change trend; wherein the heat dissipation execution component includes a porous zeolite matrix storing a liquid phase change working fluid, and the control commands are used to control the transport process of the liquid phase change working fluid in the porous zeolite matrix and the vaporization process of the liquid phase change working fluid on the surface of the porous zeolite matrix. A control module is used to acquire the physical parameters of the porous zeolite matrix and the physical properties of the liquid phase change working fluid; based on the physical parameters, physical properties, and heat flow variation trends, simulation calculations are performed using a multiphysics coupling model to obtain a first control parameter that matches the heat flow variation trend; based on the first control parameter, control commands are generated for the heat dissipation execution components located outside the spacecraft; the control commands include control signals for adjusting the opening degree of the microstructure on the surface of the porous zeolite matrix to control the transport rate of the liquid phase change working fluid to the surface of the porous zeolite matrix; the multiphysics coupling model is a model that couples the heat transfer field, the flow field, and the phase change process.

5. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes one or more computer instructions, which, when executed by a computer, perform the method as described in any one of claims 1 to 3.

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