Digital twinborn simulation method of rail type gravity energy storage multi-working-condition test system

Abstract

The invention discloses a digital twinborn simulation method of a rail type gravity energy storage multi-working-condition test system, and belongs to the technical field of physical energy storage. According to the method, a digital twinning virtual model consistent with a track type gravity energy storage multi-working-condition test system in physical structure and parameter is constructed, physical test data are collected and input into the virtual model for simulation operation, virtual and real data are compared, the model is optimized, and finally the optimized model is used for multi-working-condition simulation. The core technical problem that an existing test system cannot comprehensively simulate the system performance under multi-parameter coupling is solved, comprehensive and accurate simulation data support can be provided for optimization design and engineering application of the rail type gravity energy storage system, and important practical value is achieved.

Description

Technical Field

[0001] This invention belongs to the field of physical energy storage technology, specifically relating to a digital twin simulation method for a multi-condition test system of orbital gravity energy storage. Background Technology

[0002] Against the backdrop of the global energy structure transitioning towards renewable energy, the intermittency and volatility of renewable energy sources such as wind and solar power pose a severe challenge to the stable operation of power grids, making energy storage technology crucial for addressing this issue. Gravity energy storage, as a novel physical energy storage technology, has gradually become a research hotspot due to its advantages such as large capacity, low cost, high efficiency, and long lifespan. As an innovative form of gravity energy storage technology, track-mounted gravity energy storage systems require comprehensive verification of key performance parameters such as track transportation methods, energy conversion efficiency, and operational stability through experimental systems.

[0003] However, a core technical problem exists in existing technologies: current gravity energy storage test systems can only test under single operating conditions or with a few parameters, failing to simulate comprehensive operating conditions under multi-parameter coupling. This is because the structural design and control methods of existing systems have limitations, preventing the simultaneous adjustment and linkage testing of key parameters such as track gradient, power station / drive station spacing, gravity block load, and train speed. For example, when testing energy conversion efficiency, if the track gradient and power station spacing cannot be changed simultaneously, it is difficult to accurately analyze their synergistic impact on efficiency; when verifying track transport stability, if the circular tipping and unloading process under different loads cannot be simulated, stability data under real operating conditions cannot be obtained. This limitation makes it difficult to make decisions based on comprehensive test data during the design optimization of track-mounted gravity energy storage systems, potentially leading to problems such as lower-than-expected energy conversion efficiency and insufficient operational stability in practical applications, severely hindering the engineering implementation of this technology. Summary of the Invention

[0004] In view of this, the technical solution of the present invention realizes comprehensive testing and simulation verification of the track-type gravity energy storage system under multiple working conditions by constructing a multi-parameter adjustable test system and a digital twin simulation model, providing comprehensive and reliable test data support for the system's optimized design and engineering application.

[0005] According to one aspect of the present invention, a digital twin simulation method for a multi-condition test system of track-mounted gravity energy storage is provided, comprising the following steps: constructing a digital twin virtual model: based on the physical structure and parameters of the multi-condition test system of track-mounted gravity energy storage, a corresponding digital twin virtual model is constructed in a virtual environment. The virtual model includes a virtual sub-model of the gravity energy storage test system, a virtual sub-model of the track transportation test system, and a virtual sub-model of the gravity block lifting test system. The structure, size, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they are coordinated and linked through a data interface; acquiring physical test data: conducting multi-condition tests in the physical test system and acquiring test parameters and performance index data, wherein the test parameters include the mass of the gravity block. The performance indicators include energy conversion efficiency, operational stability, and power generation. The system incorporates physical test parameters into a digital twin virtual model. Virtual simulation calculations simulate the system's operation under different conditions to obtain virtual simulation performance indicator data. A comparison of physical and virtual data is performed, and if the deviation exceeds a preset threshold, the parameters of the digital twin virtual model or the physical test parameters are adjusted until the deviation is within a preset range. Multi-condition simulation based on the optimized model is conducted using the optimized virtual model to simulate more operating conditions.

[0006] The construction of the digital twin virtual model includes geometric modeling, physical property modeling, kinematic and dynamic modeling, and data interface development. Geometric modeling uses laser scanning technology to obtain three-dimensional point cloud data of the physical test system, and after processing, a geometric model consistent with the physical system is constructed.

[0007] In the physical property modeling, corresponding physical property parameters are set in the digital twin virtual model according to the material of each component of the physical test system, including the density and elastic modulus of steel, the density and elastic modulus of rubber, and the friction coefficient between components, the friction coefficient between the track and the wheel, and the friction coefficient between the power station friction wheel and the train.

[0008] Among them, kinematic and dynamic modeling uses multibody dynamics simulation software to establish the kinematic and dynamic models of the experimental system, define the kinematic pairs between the components, including rotational pairs and prismatic pairs, set the driving forces and constraints, and simulate the motion process and force conditions of the system.

[0009] Among them, the data interface development adopts a communication protocol to realize data communication between the digital twin virtual model and the physical test system, ensuring that the physical test data can be transmitted to the virtual model in real time, and the simulation results of the virtual model can also be fed back to the physical test system.

[0010] The preset threshold is 5%; if the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the material property parameters, friction coefficient parameters, or track slope and power station spacing parameters of the digital twin virtual model are adjusted.

[0011] Among them, when conducting simulation tests using the optimized digital twin virtual model, different combinations of parameters such as the mass of the gravity block, the train speed, and the slope of the inclined track can be set within a preset range to predict the system performance.

[0012] The physical test data acquisition is carried out through the sensor group of the data acquisition and analysis system, which includes displacement sensors, torque sensors, voltage sensors, current sensors, speed sensors, acceleration sensors and weight sensors.

[0013] During the virtual simulation process, data such as energy conversion efficiency, train speed, and power station power generation are recorded in real time, and corresponding curves and charts are generated.

[0014] The simulation, based on the optimization model and multi-condition simulation, generates a simulation report. The report includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of the orbital gravity energy storage system.

[0015] According to another aspect of the present invention, a computer device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a digital twin simulation method for a multi-condition test system of orbital gravity energy storage as described in any of the preceding claims.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements a digital twin simulation method for a multi-condition test system of orbital gravity energy storage as described in any of the preceding claims.

[0017] It can be observed that the above solutions can construct digital twin virtual models. Based on the physical structure and parameters of the multi-condition test system for track-mounted gravity energy storage, corresponding digital twin virtual models can be built in a virtual environment. These virtual models include virtual sub-models of the gravity energy storage test system, track transportation test system, and gravity block lifting test system. The structure, dimensions, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they achieve collaborative linkage through data interfaces. Furthermore, physical test data can be collected, allowing multi-condition tests to be conducted within the physical test system to collect test parameters and performance index data. These test parameters include gravity block mass, inclined track gradient, train speed, and power station / The system can control the distance between driving stations, the number of train cars, the wheel diameter, material, and tension of the power station friction wheel. Performance indicators include energy conversion efficiency, operational stability, and power generation. Physical test parameters can be input into the digital twin virtual model via virtual simulation parameters. Virtual simulation calculations can be used to simulate the system's operation under different working conditions to obtain virtual simulation performance indicator data. Comparison of physical test and virtual simulation performance indicator data can be achieved through virtual-real data comparison and model optimization. If the deviation exceeds a preset threshold, the parameters of the digital twin virtual model or the physical test parameters are adjusted until the deviation is within a preset range. By employing multi-condition simulation based on an optimized model, and utilizing the optimized virtual model to conduct simulation experiments under more operating conditions, this invention adopts a digital twin virtual model that is consistent with the physical structure and parameters of the multi-condition test system for orbital gravity energy storage. Physical test data is collected and input into the virtual model for simulation calculations. The virtual and real data are compared and the model is optimized. Finally, the optimized model is used for multi-condition simulation, which solves the core technical problem that existing test systems cannot fully simulate the system performance under multi-parameter coupling. It can provide comprehensive and accurate simulation data support for the optimized design and engineering application of orbital gravity energy storage systems, and has important practical value.

[0018] Furthermore, in the above scheme, the construction of the digital twin virtual model can include geometric modeling, physical property modeling, kinematic and dynamic modeling, and data interface development. Geometric modeling uses laser scanning technology to obtain three-dimensional point cloud data of the physical experimental system. After processing, a geometric model consistent with the physical system is constructed. The advantage of this is that it can realize the construction of accurate geometric models by laser scanning, multi-dimensional modeling related to the characteristics of the physical system, and data interface to realize virtual-real interaction. It can efficiently simulate and analyze, reduce costs and improve efficiency, and lay the foundation for system research and optimization.

[0019] Furthermore, in the above scheme, when modeling physical properties, corresponding physical property parameters are set in the digital twin virtual model according to the material of each component of the physical test system, including the density and elastic modulus of steel, the density and elastic modulus of rubber, and the friction coefficients between components, between the track and the wheel, and between the power station friction wheel and the train. The advantage of this is that it can realize the setting of physical parameters and friction coefficients between components according to the material, so that the virtual model fits the physical characteristics of the entity, improves the simulation realism, ensures the accuracy of mechanical analysis, and provides a reliable basis for system performance evaluation.

[0020] Furthermore, in the above scheme, kinematic and dynamic modeling can utilize multibody dynamics simulation software to establish the kinematic and dynamic models of the experimental system, define the kinematic pairs between each component, including rotational pairs and prismatic pairs, set driving forces and constraints, and simulate the motion process and force conditions of the system. The advantage of this is that it can achieve modeling with multibody dynamics software, define kinematic pairs, driving forces and constraints, accurately reproduce the motion and force of the system, improve the accuracy of dynamic simulation, and provide support for analyzing motion laws and optimizing design.

[0021] Furthermore, in the above scheme, the data interface development can adopt a communication protocol to realize data communication between the digital twin virtual model and the physical test system, ensuring that physical test data can be transmitted to the virtual model in real time, and the simulation results of the virtual model can also be fed back to the physical test system. The advantage of this is that it can realize virtual and real data interaction through the communication protocol, ensuring that physical test data is transmitted to the virtual model in real time and simulation results are fed back to the system in a timely manner, achieving dynamic linkage and improving the timeliness of the model and the accuracy of system control.

[0022] Furthermore, in the above scheme, the preset threshold is 5%. If the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the material property parameters, friction coefficient parameters of the digital twin virtual model, or the track slope and power station spacing parameters of the physical test are adjusted. The advantage of this is that it can achieve the setting of a 5% deviation threshold, and adjust the model parameters or test parameters when the limit is exceeded, which can reduce the gap between virtual and real performance indicators, ensure the consistency between the digital twin model and the physical test, and improve the reliability of the model and the accuracy of the test.

[0023] Furthermore, the above scheme allows for simulation testing using the optimized digital twin virtual model. Different combinations of parameters, such as the mass of the gravity block, the train speed, and the slope of the inclined track, can be set to predict system performance. The advantage of this is that it enables efficient prediction of system performance through multi-parameter combination simulation using the optimized model, reducing the number of physical tests, lowering costs, providing accurate references for system optimization, and improving R&D efficiency.

[0024] Furthermore, in the above scheme, physical test data can be acquired through a sensor group of the data acquisition and analysis system. The sensor group includes displacement sensors, torque sensors, voltage sensors, current sensors, velocity sensors, acceleration sensors, and weight sensors. The advantage of this is that it can realize an acquisition system composed of multiple sensors, which can comprehensively capture key data such as displacement and torque, ensuring data integrity and accuracy, providing a reliable basis for model optimization and performance analysis, and supporting the efficient conduct of experiments.

[0025] Furthermore, in the above scheme, during the virtual simulation operation, data such as energy conversion efficiency, train speed, and power station power generation are recorded in real time, and corresponding curves and graphs are generated. The advantage of this is that it can realize the real-time recording of data such as energy conversion efficiency and generate curves and graphs, which can intuitively present the simulation process and results, facilitate the analysis of system performance patterns, and provide a clear basis for model optimization and experimental adjustment.

[0026] Furthermore, the above solution can generate a simulation report after multi-condition simulation based on the optimization model. The report includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of the orbital gravity energy storage system. The advantage of this is that it can generate a report containing parameters and data after multi-condition simulation, systematically present the results, provide a comprehensive reference for the optimized design of the orbital gravity energy storage system, and help make accurate decisions and improve performance. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating an embodiment of the digital twin simulation method for the multi-condition test system of track-type gravity energy storage of the present invention; Figure 2 This is a schematic diagram of the structure of an embodiment of the track-type gravity energy storage multi-condition test system of the present invention; Figure 3 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] This invention provides a digital twin simulation method for a multi-condition test system of orbital gravity energy storage. By constructing a test system with adjustable multi-parameters and a digital twin simulation model, it enables comprehensive testing and simulation verification of the orbital gravity energy storage system under multiple conditions, providing comprehensive and reliable test data support for the system's optimized design and engineering applications.

[0031] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the digital twin simulation method for the multi-condition test system of track-type gravity energy storage according to the present invention. It should be noted that if substantially the same results are obtained, the method of the present invention is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, the method includes the following steps: S1: Constructing a digital twin virtual model

[0032] Based on the physical structure and parameters of the multi-condition test system for track-type gravity energy storage, a corresponding digital twin virtual model is constructed in a virtual environment.

[0033] In this embodiment, constructing a digital twin virtual model may include geometric modeling: using laser scanning technology to scan the physical test system to obtain accurate three-dimensional point cloud data, and then using reverse engineering software to process the point cloud data to construct a geometric model that is completely consistent with the physical system, ensuring the dimensional accuracy of the model. For example, for the track in the gravity energy storage test system, its width, as well as the length and connection relationship of the upper horizontal rail, the ramp rail, and the lower horizontal rail, are accurately restored.

[0034] In this embodiment, constructing a digital twin virtual model may include physical property modeling: based on the material of each component of the physical test system, corresponding physical property parameters are set in the digital twin virtual model. For example, the density of a steel track is set to 7850 kg / m³, and its elastic modulus is set to 206 GPa; the density of a rubber friction wheel is set to 1200 kg / m³, and its elastic modulus is set to 1 MPa. Simultaneously, the friction coefficients between components are set: the friction coefficient between the track and the wheel is 0.02, and the friction coefficient between the power station friction wheel and the train is 0.3.

[0035] In this embodiment, constructing a digital twin virtual model can include kinematic and dynamic modeling: using multibody dynamics simulation software to establish the kinematic and dynamic models of the experimental system, defining the kinematic pairs between various components, such as the revolute pairs between the train wheels and the track, the revolute pairs of the power station friction wheels, and the prismatic pairs of the trolley traveling mechanism, setting driving forces and constraints, and simulating the motion process and force conditions of the system. For example, constraints are set for the train to move along the track, and driving forces are set for the power station to drive the train's movement.

[0036] In this embodiment, constructing a digital twin virtual model may include data interface development: developing a data interface between the digital twin virtual model and the physical test system, using a communication protocol to achieve data communication between the two, ensuring that physical test data can be transmitted to the virtual model in real time, and that the simulation results of the virtual model can also be fed back to the physical test system.

[0037] In this embodiment, the virtual model includes a virtual sub-model of a gravity energy storage test system, a virtual sub-model of a rail transport test system, and a virtual sub-model of a gravity block lifting test system. The structure, size, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they can coordinate and link through a data interface. S2: Physical Experiment Data Acquisition

[0038] Multi-condition tests are conducted in the physical testing system, and various parameters during the test process are collected by the sensor group of the data acquisition and analysis system.

[0039] The sensor group includes displacement sensors, torque sensors, voltage sensors, current sensors, speed sensors, acceleration sensors, and weight sensors, with a sampling frequency of, for example, 1000 Hz, to ensure accurate capture of dynamic data during the test.

[0040] In this embodiment, the physical test data acquisition may include: the mass of the gravity block (e.g., adjustable within the range of 500-2000 kg, with fine-tuning achieved by adding or removing bulk materials), the slope of the inclined rail (e.g., 10°, 15°, 20°), the train speed (1-5 m / s), the distance between the power station and the drive station (e.g., 5-15 m between power stations, 6-18 m between drive stations), the number of train cars (e.g., 1-5 cars), the wheel diameter of the friction wheel of the power station (e.g., 500 mm, 600 mm), the material (e.g., rubber, steel), and the tension (e.g., 500-2000 N), etc.

[0041] In this embodiment, the performance indicators collected in the physical test data acquisition may include: energy conversion efficiency (energy conversion efficiency = power generation / electrical energy consumed × 100%), operational stability (evaluated by vibration acceleration, vibration acceleration ≤ 0.5g is considered stable) and power generation, etc.

[0042] For example, in the energy conversion efficiency optimization test of the gravity energy storage test system, four gravity blocks (each with a mass of 1000 kg) can be installed on a three-car train. The track gradient is set to 15°, the distance between power stations is 10 m, the train speed is 3 m / s, the test system is started, and the data acquisition and analysis system collects data in real time, such as the train speed, voltage, current, torque, power generation of the power station motor, and vibration acceleration of the track. S3: Virtual Simulation Parameter Input

[0043] The physical test parameters collected in step S2 are input into the corresponding digital twin virtual sub-model, and the boundary conditions and initial conditions of the virtual simulation are set. For example, parameters such as the mass of the gravity block of 1000kg, the track slope of 15°, the train speed of 3m / s, the distance between power stations of 10m, and the number of train cars of 3 are input into the virtual sub-model of the gravity energy storage test system. S4: Virtual simulation computation

[0044] The digital twin virtual model is activated to perform virtual simulation calculations, simulating the operation of the orbital gravity energy storage system under different working conditions.

[0045] During the simulation, data such as energy conversion efficiency, train speed, and power station output are recorded in real time, and corresponding curves and charts are generated, such as energy conversion efficiency curves, speed curves, and torque curves.

[0046] For example, the virtual simulation of a train generating electricity on an uphill and downhill slope in a gravity energy storage test system recorded an energy conversion efficiency of 79.5%. S5: Comparison of Virtual and Real Data and Model Optimization

[0047] The physical test performance index data collected in step S2 is compared and analyzed with the virtual simulation performance index data obtained in step S4, and the deviation between the two is calculated.

[0048] In this embodiment, the comparison of virtual and real data and model optimization may include a preset threshold of 5%. If the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the parameters of the digital twin virtual model (such as material property parameters, friction coefficient parameters) or physical test parameters (such as track slope, power station spacing parameters) are adjusted, and steps S2-S4 are repeated until the deviation is within the preset range.

[0049] For example, the energy conversion efficiency of physical experiments is 80%, while that of virtual simulation is 79.5%, with a calculation deviation of 0.5%, which is within a preset threshold, such as 5%, indicating that the digital twin virtual model is accurate and reliable. S6: Multi-condition simulation based on optimization model

[0050] Using the digital twin virtual model optimized in step S5, more virtual simulation experiments were conducted for various working conditions.

[0051] In this embodiment, different combinations of parameters can be set, such as the mass of the gravity block being 500-2000 kg, the train speed being 1-5 m / s, and the slope of the inclined rail being 10°-20°, to predict the system performance.

[0052] After the simulation is completed, a simulation report is generated, which includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of orbital gravity energy storage systems.

[0053] For example, using the optimized model, simulations were conducted with parameter combinations such as a gravity block mass of 800 kg, a track slope of 10°, and a train speed of 2 m / s to predict the energy conversion efficiency under this condition, providing data support for system optimization.

[0054] It can be observed that, in this embodiment, a digital twin virtual model can be constructed. Based on the physical structure and parameters of the track-type gravity energy storage multi-condition test system, a corresponding digital twin virtual model can be built in a virtual environment. This virtual model includes a virtual sub-model of the gravity energy storage test system, a virtual sub-model of the track transportation test system, and a virtual sub-model of the gravity block lifting test system. The structure, dimensions, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they achieve collaborative linkage through a data interface. Furthermore, physical test data can be collected, and multi-condition tests can be conducted in the physical test system to collect test parameters and performance index data. These test parameters include the gravity block mass, the slope of the inclined track, the train speed, and the power station / The system can control the distance between driving stations, the number of train cars, the wheel diameter, material, and tension of the power station friction wheel. Performance indicators include energy conversion efficiency, operational stability, and power generation. Physical test parameters can be input into the digital twin virtual model via virtual simulation parameters. Virtual simulation calculations can be used to simulate the system's operation under different working conditions to obtain virtual simulation performance indicator data. Comparison of physical test and virtual simulation performance indicator data can be achieved through virtual-real data comparison and model optimization. If the deviation exceeds a preset threshold, the parameters of the digital twin virtual model or the physical test parameters are adjusted until the deviation is within a preset range. By employing multi-condition simulation based on an optimized model, and utilizing the optimized virtual model to conduct simulation experiments under more operating conditions, this invention adopts a digital twin virtual model that is consistent with the physical structure and parameters of the multi-condition test system for orbital gravity energy storage. Physical test data is collected and input into the virtual model for simulation calculations. The virtual and real data are compared and the model is optimized. Finally, the optimized model is used for multi-condition simulation, which solves the core technical problem that existing test systems cannot fully simulate the system performance under multi-parameter coupling. It can provide comprehensive and accurate simulation data support for the optimized design and engineering application of orbital gravity energy storage systems, and has important practical value.

[0055] Furthermore, in this embodiment, the construction of the digital twin virtual model may include geometric modeling, physical property modeling, kinematic and dynamic modeling, and data interface development. Geometric modeling uses laser scanning technology to obtain three-dimensional point cloud data of the physical experimental system. After processing, a geometric model consistent with the physical system is constructed. The advantage of this is that it can realize the construction of accurate geometric models by laser scanning, multi-dimensional modeling to associate with the characteristics of the physical system, and data interface to realize virtual-real interaction. It can efficiently simulate and analyze, reduce costs and improve efficiency, and lay the foundation for system research and optimization.

[0056] Furthermore, in this embodiment, when modeling physical properties, corresponding physical property parameters are set in the digital twin virtual model according to the material of each component of the physical test system, including the density and elastic modulus of steel, the density and elastic modulus of rubber, and the friction coefficients between components, the friction coefficients between the track and the wheel, and the friction coefficients between the power station friction wheel and the train. The advantage of this is that it is possible to set physical parameters and friction coefficients between components according to the material, so that the virtual model fits the physical characteristics of the entity, improves the simulation realism, ensures the accuracy of mechanical analysis, and provides a reliable basis for system performance evaluation.

[0057] Furthermore, in this embodiment, kinematic and dynamic modeling can be achieved by using multibody dynamics simulation software to establish the kinematic and dynamic models of the experimental system, defining the kinematic pairs between the components, including rotational pairs and prismatic pairs, setting driving forces and constraints, and simulating the motion process and force conditions of the system. The advantage of this is that it is possible to use multibody dynamics software to model and define kinematic pairs, driving forces and constraints, accurately reproduce the motion and force of the system, improve the accuracy of dynamic simulation, and provide support for analyzing motion laws and optimizing design.

[0058] Furthermore, in this embodiment, the data interface development can adopt a communication protocol to realize data communication between the digital twin virtual model and the physical test system, ensuring that physical test data can be transmitted to the virtual model in real time, and the simulation results of the virtual model can also be fed back to the physical test system. The advantage of this is that it can realize virtual and real data interaction through the communication protocol, ensuring that physical test data is transmitted to the virtual model in real time and simulation results are fed back to the system in a timely manner, achieving dynamic linkage and improving the timeliness of the model and the accuracy of system control.

[0059] Furthermore, in this embodiment, the preset threshold is 5%. If the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the material property parameters, friction coefficient parameters of the digital twin virtual model, or the track slope and power station spacing parameters of the physical test are adjusted. The advantage of this is that it can achieve the setting of a 5% deviation threshold, and adjust the model parameters or test parameters when the limit is exceeded, which can reduce the gap between virtual and real performance indicators, ensure the consistency between the digital twin model and the physical test, and improve the reliability of the model and the accuracy of the test.

[0060] Furthermore, in this embodiment, when conducting simulation experiments using the optimized digital twin virtual model, different combinations of parameters such as the mass of the gravity block, the train speed, and the slope of the inclined track can be set to predict system performance. The advantage of this is that it is possible to use the optimized model to efficiently predict system performance through multi-parameter combination simulation, reduce the number of physical tests, reduce costs, provide accurate references for system optimization, and improve R&D efficiency.

[0061] Furthermore, in this embodiment, physical test data acquisition can be performed through a sensor group of the data acquisition and analysis system. The sensor group includes displacement sensors, torque sensors, voltage sensors, current sensors, velocity sensors, acceleration sensors, and weight sensors. The advantage of this is that it enables the acquisition system composed of multiple sensors, which can comprehensively capture key data such as displacement and torque, ensuring data integrity and accuracy, providing a reliable basis for model optimization and performance analysis, and supporting the efficient conduct of experiments.

[0062] Furthermore, in this embodiment, during the virtual simulation operation, data such as energy conversion efficiency, train speed, and power station power generation are recorded in real time, and corresponding curves and graphs are generated. The advantage of this is that it can record data such as energy conversion efficiency in real time and generate curves and graphs, which can intuitively present the simulation process and results, facilitate the analysis of system performance patterns, and provide a clear basis for model optimization and experimental adjustment.

[0063] Furthermore, in this embodiment, a simulation report can be generated after multi-condition simulation based on the optimization model. The report includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of the orbital gravity energy storage system. The advantage of this is that it can generate a report containing parameters and data after multi-condition simulation, systematically present the results, provide a comprehensive reference for the optimized design of the orbital gravity energy storage system, and help make accurate decisions and improve performance.

[0064] This invention also provides a multi-condition test system for track-mounted gravity energy storage. By constructing a test system with adjustable multi-parameter parameters and a digital twin simulation model, it enables comprehensive testing and simulation verification of the track-mounted gravity energy storage system under multiple conditions, providing comprehensive and reliable test data support for the system's optimized design and engineering applications.

[0065] Please see Figure 2 , Figure 2 This is a schematic diagram of an embodiment of the track-type gravity energy storage multi-condition test system of the present invention. In this embodiment, the track-type gravity energy storage multi-condition test system 20 includes a gravity energy storage test system 21, a track transportation test system 22, a gravity block lifting test system 23, an electrical control system 24, and a data acquisition and analysis system 25.

[0066] The gravity energy storage test system 21 includes an upper horizontal rail, an inclined rail, a lower horizontal rail, a power station, a train, and a gravity block. The slope of the inclined rail can be switched between 10°, 15°, and 20°, and its two ends are connected to the upper and lower horizontal rails, respectively. The power stations are arranged on the upper horizontal rail, the inclined rail, and the lower horizontal rail, and the spacing between adjacent power stations is adjustable. The train can carry the gravity block and run along the track. Driven by the power station, it can go uphill, and under the action of gravity, it can go downhill along the inclined rail and drive the power station to generate electricity.

[0067] The track transport test system 22 includes an upper track, a drive station, a bucket train, a feeder, a rotary unloading area, a receiving trough, and a material conveyor belt. The upper track is a ring structure, including a lower horizontal section, a ramp section, and an upper horizontal section. The drive station is arranged on the upper track, and the spacing between adjacent drive stations is adjustable. The bucket train can run along the upper track, and the feeder can load materials onto the bucket train on the lower horizontal section. The rotary unloading area is equipped with a 360° spiral track. When the bucket train passes through this area, it completes the unloading. After receiving the materials, the receiving trough sends them back to the feeder via the material conveyor belt, realizing material circulation.

[0068] The gravity block lifting test system 23 includes a traveling body, a trolley traveling mechanism, a lifting mechanism, and a gripper device. The traveling body straddles the track, and the trolley traveling mechanism can drive the lifting mechanism and the gripper device to move laterally along the traveling body. The gripper device can grasp and release the gravity block and supports multi-gripper collaborative operation.

[0069] The electrical control system 24 is used to control the operating parameters of each test system, including train speed, start and stop of the power station / drive station, track gradient adjustment, and gripper device operation.

[0070] The data acquisition and analysis system 25 includes a sensor group and a data analysis module. The sensor group is used to collect various parameters during the test, such as track gradient, power station / drive station spacing, train speed, gravity block mass, motor speed, torque, power generation, vibration acceleration, etc. The data analysis module is used to process and analyze the collected data and generate a test report.

[0071] In this embodiment, the upper horizontal rail, inclined rail, and lower horizontal rail of the gravity energy storage test system 21 are all made of high-strength steel, with a rail width of 600mm. The inclined rail achieves slope switching via a hydraulic device, with a slope adjustment range of 10°-20° and an adjustment accuracy of ±0.5°. The power station spacing adjustment range is 5-15m, achieved by servo motors driving track displacement, with an adjustment accuracy of ±0.1m.

[0072] The train adopts a modular design, allowing for the combination of 1-5 carriages as needed. Each carriage can carry 4 gravity blocks, the mass of which can be adjusted within the range of 500-2000 kg. Fine-tuning of the mass can be achieved by adding or removing loose materials. The friction wheels of the power station use various materials and diameters, including rubber (500mm and 600mm diameter) and steel (500mm and 600mm diameter), with tension adjustable within the range of 500-2000 N.

[0073] In this embodiment, the upward track of the rail transport test system 22 adopts a circular design with a total length of 500m, and the track material is high-strength wear-resistant steel. The drive station spacing adjustment range is 6-18m, and the adjustment method is the same as that of the power station of the gravity energy storage test system 21. The discharge speed of the feeder can be adjusted within the range of 0.5-2m³ / h to match the travel speed of the bucket train and ensure that the material is evenly filled into each car.

[0074] The rotating unloading area features a spiral track with a radius of curvature of 8m and a gradually changing inclination angle, ensuring the smooth 360° rotation and unloading of the bucket train. The receiving trough has a capacity of 10m³, and the conveyor belt operates at a speed of 1-3m / s, adjustable according to the material receiving volume.

[0075] In this embodiment, the trolley body of the gravity block lifting test system 13 has a span of 15m, adopts a double-beam bridge structure, and is made of Q345B steel. The running speed of the trolley traveling mechanism is 0.5-3m / min, and the lifting speed of the lifting mechanism is 0.3-1.5m / min, both driven by variable frequency motors to achieve stepless speed regulation.

[0076] The gripper device is hydraulically driven, and the gripping force can be adjusted within the range of 1000-5000N to accommodate heavy blocks of different masses. Sensors are installed on the grippers to detect whether the grip is secure, ensuring safety during the lifting process.

[0077] In this embodiment, the electrical control system 24 uses a PLC (Programmable Logic Controller) as the core control unit and is equipped with a touch screen as the human-machine interface. It can display the operating parameters and status of each test system in real time, and can set operating parameters and start / stop the test system through the interface.

[0078] The electrical control system 24 has two modes: automatic control and manual control. In automatic mode, the system operates automatically according to the preset test plan; in manual mode, the operator can control the action of each device individually through buttons, which facilitates system debugging and troubleshooting.

[0079] In this embodiment, the sampling frequency of the sensor group in the data acquisition and analysis system 25 is 1000Hz to ensure accurate capture of dynamic data during the experiment. The data acquisition module uses a high-precision A / D converter with a conversion accuracy of 16 bits to ensure the accuracy of data acquisition.

[0080] The data analysis module uses professional data processing software to filter, reduce noise, and perform statistical analysis on the collected data, generating charts such as energy conversion efficiency curves, speed curves, and torque curves. It can also automatically generate test reports, which include test parameters, test data, analysis results, and conclusions and recommendations.

[0081] Each unit module of the track-type gravity energy storage multi-condition test system 20 can execute the corresponding steps in the above method embodiment. Therefore, the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details.

[0082] This invention also provides a computer device, such as... Figure 3 As shown, it includes: at least one processor 31; and a memory 32 communicatively connected to at least one processor 31; wherein the memory 32 stores instructions that can be executed by at least one processor 31, the instructions being executed by at least one processor 31 to enable at least one processor 31 to execute the above-described digital twin simulation method for the multi-condition test system of orbital gravity energy storage.

[0083] The memory 32 and processor 31 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 31 and memory 32. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 31 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 31.

[0084] Processor 31 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 32 can be used to store data used by processor 31 during operation.

[0085] The present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method embodiments.

[0086] It can be observed that the above solutions can construct digital twin virtual models. Based on the physical structure and parameters of the multi-condition test system for track-mounted gravity energy storage, corresponding digital twin virtual models can be built in a virtual environment. These virtual models include virtual sub-models of the gravity energy storage test system, track transportation test system, and gravity block lifting test system. The structure, dimensions, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they achieve collaborative linkage through data interfaces. Furthermore, physical test data can be collected, allowing multi-condition tests to be conducted within the physical test system to collect test parameters and performance index data. These test parameters include gravity block mass, inclined track gradient, train speed, and power station / The system can control the distance between driving stations, the number of train cars, the wheel diameter, material, and tension of the power station friction wheel. Performance indicators include energy conversion efficiency, operational stability, and power generation. Physical test parameters can be input into the digital twin virtual model via virtual simulation parameters. Virtual simulation calculations can be used to simulate the system's operation under different working conditions to obtain virtual simulation performance indicator data. Comparison of physical test and virtual simulation performance indicator data can be achieved through virtual-real data comparison and model optimization. If the deviation exceeds a preset threshold, the parameters of the digital twin virtual model or the physical test parameters are adjusted until the deviation is within a preset range. By employing multi-condition simulation based on an optimized model, and utilizing the optimized virtual model to conduct simulation experiments under more operating conditions, this invention adopts a digital twin virtual model that is consistent with the physical structure and parameters of the multi-condition test system for orbital gravity energy storage. Physical test data is collected and input into the virtual model for simulation calculations. The virtual and real data are compared and the model is optimized. Finally, the optimized model is used for multi-condition simulation, which solves the core technical problem that existing test systems cannot fully simulate the system performance under multi-parameter coupling. It can provide comprehensive and accurate simulation data support for the optimized design and engineering application of orbital gravity energy storage systems, and has important practical value.

[0087] Furthermore, in the above scheme, the construction of the digital twin virtual model can include geometric modeling, physical property modeling, kinematic and dynamic modeling, and data interface development. Geometric modeling uses laser scanning technology to obtain three-dimensional point cloud data of the physical experimental system. After processing, a geometric model consistent with the physical system is constructed. The advantage of this is that it can realize the construction of accurate geometric models by laser scanning, multi-dimensional modeling related to the characteristics of the physical system, and data interface to realize virtual-real interaction. It can efficiently simulate and analyze, reduce costs and improve efficiency, and lay the foundation for system research and optimization.

[0088] Furthermore, in the above scheme, when modeling physical properties, corresponding physical property parameters are set in the digital twin virtual model according to the material of each component of the physical test system, including the density and elastic modulus of steel, the density and elastic modulus of rubber, and the friction coefficients between components, between the track and the wheel, and between the power station friction wheel and the train. The advantage of this is that it can realize the setting of physical parameters and friction coefficients between components according to the material, so that the virtual model fits the physical characteristics of the entity, improves the simulation realism, ensures the accuracy of mechanical analysis, and provides a reliable basis for system performance evaluation.

[0089] Furthermore, in the above scheme, kinematic and dynamic modeling can utilize multibody dynamics simulation software to establish the kinematic and dynamic models of the experimental system, define the kinematic pairs between each component, including rotational pairs and prismatic pairs, set driving forces and constraints, and simulate the motion process and force conditions of the system. The advantage of this is that it can achieve modeling with multibody dynamics software, define kinematic pairs, driving forces and constraints, accurately reproduce the motion and force of the system, improve the accuracy of dynamic simulation, and provide support for analyzing motion laws and optimizing design.

[0090] Furthermore, in the above scheme, the data interface development can adopt a communication protocol to realize data communication between the digital twin virtual model and the physical test system, ensuring that physical test data can be transmitted to the virtual model in real time, and the simulation results of the virtual model can also be fed back to the physical test system. The advantage of this is that it can realize virtual and real data interaction through the communication protocol, ensuring that physical test data is transmitted to the virtual model in real time and simulation results are fed back to the system in a timely manner, achieving dynamic linkage and improving the timeliness of the model and the accuracy of system control.

[0091] Furthermore, in the above scheme, the preset threshold is 5%. If the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the material property parameters, friction coefficient parameters of the digital twin virtual model, or the track slope and power station spacing parameters of the physical test are adjusted. The advantage of this is that it can achieve the setting of a 5% deviation threshold, and adjust the model parameters or test parameters when the limit is exceeded, which can reduce the gap between virtual and real performance indicators, ensure the consistency between the digital twin model and the physical test, and improve the reliability of the model and the accuracy of the test.

[0092] Furthermore, the above scheme allows for simulation testing using the optimized digital twin virtual model. Different combinations of parameters, such as the mass of the gravity block, the train speed, and the slope of the inclined track, can be set to predict system performance. The advantage of this is that it enables efficient prediction of system performance through multi-parameter combination simulation using the optimized model, reducing the number of physical tests, lowering costs, providing accurate references for system optimization, and improving R&D efficiency.

[0093] Furthermore, in the above scheme, physical test data can be acquired through a sensor group of the data acquisition and analysis system. The sensor group includes displacement sensors, torque sensors, voltage sensors, current sensors, velocity sensors, acceleration sensors, and weight sensors. The advantage of this is that it can realize an acquisition system composed of multiple sensors, which can comprehensively capture key data such as displacement and torque, ensuring data integrity and accuracy, providing a reliable basis for model optimization and performance analysis, and supporting the efficient conduct of experiments.

[0094] Furthermore, in the above scheme, during the virtual simulation operation, data such as energy conversion efficiency, train speed, and power station power generation are recorded in real time, and corresponding curves and graphs are generated. The advantage of this is that it can realize the real-time recording of data such as energy conversion efficiency and generate curves and graphs, which can intuitively present the simulation process and results, facilitate the analysis of system performance patterns, and provide a clear basis for model optimization and experimental adjustment.

[0095] Furthermore, the above solution can generate a simulation report after multi-condition simulation based on the optimization model. The report includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of the orbital gravity energy storage system. The advantage of this is that it can generate a report containing parameters and data after multi-condition simulation, systematically present the results, provide a comprehensive reference for the optimized design of the orbital gravity energy storage system, and help make accurate decisions and improve performance.

[0096] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection of apparatuses or units, and may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A digital twin simulation method for a multi-condition test system of track-mounted gravity energy storage, characterized in that, Includes the following steps: A digital twin virtual model is constructed based on the physical structure and parameters of the multi-condition test system for track-mounted gravity energy storage. This virtual model includes virtual sub-models for the gravity energy storage test system, the track transportation test system, and the gravity block lifting test system. The structure, dimensions, and parameters of each virtual sub-model are consistent with the corresponding physical system, and they are interconnected through a data interface. Physical test data is acquired by conducting multi-condition tests within the physical test system, collecting test parameters and performance index data. Test parameters include gravity block mass, inclined track gradient, train speed, power station / drive station spacing, number of train cars, wheel diameter, material, and tension of the power station friction wheel. Performance indexes include energy conversion efficiency, operational stability, and power generation. Virtual simulation parameters are input into the digital twin virtual model. Virtual simulation calculations are performed to simulate the system's operation under different conditions and obtain virtual simulation performance index data. Comparison of virtual and real data and model optimization: Compare the performance index data of physical experiments and virtual simulations. If the deviation exceeds the preset threshold, adjust the parameters of the digital twin virtual model or the physical experiment parameters until the deviation is within the preset range. Multi-condition simulation based on an optimized model is used to conduct simulation experiments for more operating conditions using the optimized virtual model.

2. The method according to claim 1, characterized in that, The construction of the digital twin virtual model includes geometric modeling, physical property modeling, kinematic and dynamic modeling, and data interface development. Geometric modeling uses laser scanning technology to obtain three-dimensional point cloud data of the physical test system, and after processing, a geometric model consistent with the physical system is constructed.

3. The method according to claim 2, characterized in that, When modeling physical properties, corresponding physical property parameters are set in the digital twin virtual model according to the material of each component of the physical test system, including the density and elastic modulus of steel, the density and elastic modulus of rubber, and the friction coefficient between components, the friction coefficient between the track and the wheel, and the friction coefficient between the power station friction wheel and the train.

4. The method according to claim 1, characterized in that, Kinematic and dynamic modeling utilizes multibody dynamics simulation software to establish kinematic and dynamic models of the experimental system, define kinematic pairs between components, including rotary pairs and prismatic pairs, set driving forces and constraints, and simulate the motion process and force conditions of the system.

5. The method according to claim 1, characterized in that, The data interface development adopts a communication protocol to realize data communication between the digital twin virtual model and the physical test system, ensuring that physical test data can be transmitted to the virtual model in real time, and the simulation results of the virtual model can also be fed back to the physical test system.

6. The method according to claim 1, characterized in that, The preset threshold is 5%; if the deviation between the physical test performance index data and the virtual simulation performance index data exceeds 5%, the material property parameters, friction coefficient parameters, or track slope and power station spacing parameters of the digital twin virtual model are adjusted.

7. The method according to claim 1, characterized in that, When conducting simulation experiments using the optimized digital twin virtual model, different combinations of parameters such as the mass of the gravity block, the train speed, and the slope of the inclined track can be set within a preset range to predict the system performance.

8. The method according to claim 1, characterized in that, Physical experiment data acquisition is carried out through the sensor group of the data acquisition and analysis system, which includes displacement sensors, torque sensors, voltage sensors, current sensors, velocity sensors, acceleration sensors, and weight sensors.

9. The method according to claim 1, characterized in that, During the virtual simulation, data such as energy conversion efficiency, train speed, and power station power generation are recorded in real time, and corresponding curves and charts are generated.

10. The method according to claim 1, characterized in that, After multi-condition simulation based on the optimization model, a simulation report is generated. The report includes simulation parameters, simulation data, analysis results, and conclusions and recommendations, providing a reference for the optimized design of track-mounted gravity energy storage systems.

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