Method for sustainable aviation fuel turbofan engine fueling and combustion system optimization

Abstract

The application discloses a kind of sustainable aviation fuel turbofan engine oil supply and combustion system optimization method.The optimization method is based on digital twin structure system, and a variety of digital twin technologies are used between virtual model system and physical entity, including the following steps: the physical entity of turbofan engine and the virtual model system of turbofan engine are established, the virtual model system is operated, the iterative calculation improvement of the structure parameters of oil supply and combustion system is executed in the virtual model system, and the optimal structure parameters are obtained;According to the optimal structure parameters, the physical model is verified, and the structure parameters of the final oil supply and combustion system are determined;Determine the data of oil supply and combustion system in virtual model system;The actual processing and assembly of oil supply and combustion system are carried out, and the improved virtual model system after optimization is optimized by using the actual data of oil supply and combustion system;Perfect the optimized virtual model system.The application reduces optimization time and cost, and has high optimization improvement efficiency and good accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sustainable aviation fuel turbofan engine, and particularly to a sustainable aviation fuel turbofan engine oil supply and combustion system optimization method. BACKGROUND

[0002] The aero-engine is a typical complex product with complex characteristics in aspects of structural composition, key technology, performance requirement, manufacturing process, test and maintenance, project management, and working environment. The design and manufacturing of the aero-engine involve multiple disciplines and a large amount of information, and concentrate high-precision and advanced technologies in the industrial design, manufacturing and information industries, and the research and development cycle is very long. The traditional aero-engine development mode has been unable to meet the increasing demand for high quality, high performance and low cost of the engine, and the digital and intelligent development mode driven by new information technology is the future development trend. The design structure, manufacturing process and performance index of the aero turbofan engine are very high, and the aero turbofan engine is the mainstream aviation power for civil aviation and military aircraft. The development of virtual design and intelligent manufacturing technology of the aero turbofan engine is very important.

[0003] According to the calculation of the carbon emission in the whole life cycle, the sustainable aviation fuel (SAF) can effectively reduce the carbon dioxide emission by 80%. The aero turbofan engine currently uses 100% SAF, and faces some challenges in the oil supply and combustion system. For example, the SAF without aromatic compounds can cause the shrinkage of the sealing ring of the aero-engine oil supply system, thereby causing a serious oil leakage phenomenon. Therefore, at the present stage, the SAF must be used in mixture with the traditional aviation fuel, and the mixing ratio should not be higher than 50%. At present, the mixing ratio of the SAF is generally about 10%. Therefore, in order to improve the mixing ratio of the SAF, the sealing problem of the oil supply system for the SAF must be solved. For example, the SAF with different viscosity and density has different atomization quality, and the original combustion chamber of the combustion system cannot fully adapt to the SAF, which may cause the mismatching of the atomization performance, the unstable combustion and other phenomena, and the related parameters of the combustion chamber need to be optimized.

[0004] However, the oil supply and combustion system encounters many new challenges in the adaptive design, manufacturing and performance improvement of the SAF. On the one hand, the improvement of the oil supply and combustion system becomes very complex due to the introduction of multiple new components, such as the oil supply sealing structure, the rotational flow atomization structure and the like. The design, manufacturing and assembly of these components need to meet the requirements of the lightweight and high safety of the aero-engine, and the transmission and sealing performance of the system structure after assembly is often not satisfactory. The related manufacturing and process need to be reasonably planned, and the data needs to be recorded and fed back in real time, the manufacturing process scheme needs to be iteratively improved, and intelligent manufacturing needs to be realized as much as possible. On the other hand, multiple uncertain system parameters, such as the length of the new sealing structure and the atomization structure, are introduced, which increases the difficulty of the multi-objective performance improvement.

[0005] Currently, most multi-objective improvements to the parameters of this system rely on repeated experiments, which consume a lot of time and money. Furthermore, the selection of parameter values ​​in these experiments can only be done intermittently, limiting the number of possible values ​​and making it impossible to obtain precise improvement values. Alternatively, virtual performance simulation models of the engine can be established, but due to the lack of real-time experimental data feedback and correction, the virtual and real data are not integrated for analysis, and the accuracy of the performance model needs improvement. Therefore, exploring rapid and effective improvement methods for the design, manufacturing, and performance of turbofan engine fuel supply and combustion systems for SAF (Self-Fueling Engine) systems is extremely challenging. Summary of the Invention

[0006] This invention aims to at least solve one of the technical problems existing in the prior art. Therefore, one objective of this invention is to propose an optimization method for the fuel supply and combustion system of a sustainable aviation fuel turbofan engine, which significantly saves time and cost in optimizing and improving the fuel supply and combustion system of a sustainable aviation fuel turbofan engine, achieving high optimization efficiency and accuracy.

[0007] The sustainable aviation fuel turbofan engine fuel supply and combustion system optimization method according to embodiments of the present invention is based on a digital twin structure system, wherein the digital twin structure system includes a virtual model system, physical entities, service systems, twin data, and interactive connections;

[0008] The physical entity includes actual entity data and actual entity processing, manufacturing, and assembly process data;

[0009] The virtual model system is used to perform virtual simulation of the physical entity. Various digital twin technologies are employed between the virtual model system and the physical entity. The virtual model system comprises four levels of models: a geometric model, a physical model, a behavioral model, and a rule model. The geometric model describes the shape, size, manufacturing, and assembly information of each component in the actual entity. The physical model simulates the physical properties of the actual entity. The behavioral model receives data from actual experiments conducted on the actual entity, responds to external drives and disturbances, and processes and adjusts the virtual model system. The rule model models and simulates the performance patterns or rules of the actual entity.

[0010] The service system is used to collect information from the virtual model system and the physical entity, and to perform fusion analysis on the information;

[0011] The twin data includes data generated by the physical entity, the virtual model system, and the service system. After being processed by the service system, the twin data is fed back to the virtual model system, the physical entity, and the service system, driving the operation of the digital twin structure system. The twin data is continuously updated and improved as real-time data is generated.

[0012] The interactive connection includes a plurality of different data transmission modules for connecting the physical entity, the virtual model system, the service system and the twin data to each other, so that the data of the physical entity, the virtual model system, the service system and the twin data can be exchanged and transmitted between each part;

[0013] The sustainable aviation fuel turbofan engine oil supply and combustion system optimization method comprises the following steps:

[0014] S1: Establish a virtual model system: test and measure the entity turbofan engine and the power, oil supply and combustion performance of the entity turbofan engine, obtain multi-dimensional data, form a physical entity of the turbofan engine, and then analyze the obtained multi-dimensional data and store it in the service system, wherein the entity turbofan engine uses sustainable aviation fuel or aviation fuel containing sustainable aviation fuel as fuel, and the entity turbofan engine includes an oil supply and combustion system; a turbofan engine simulation model and a sustainable aviation fuel combustion simulation model are established to form the virtual model system, and the service system inputs the obtained multi-dimensional data into the simulation correction module in the virtual model system;

[0015] S2: Virtual performance optimization: running the virtual model system, fusing and analyzing the simulation behavior, performance results of the virtual model system and the behavior data, performance data in the obtained multi-dimensional data, and performing iterative calculation improvement of the structure parameters of the oil supply and combustion system in the virtual model system to obtain optimal structure parameters; according to the optimal structure parameters, improving the structure parameters of the oil supply pipeline and atomization structure in the oil supply and combustion system in the physical model to form an improved physical model, verifying the oil supply flow and atomization characteristics and rotor system dynamics characteristics by using the improved physical model, and determining the final structure parameters of the oil supply and combustion system in the virtual model system;

[0016] S3: Virtual manufacturing simulation: according to the final structure parameters of the oil supply and combustion system in the virtual model system, determining the shape, size, assembly relationship and machining manufacturing data of the parts of the improved oil supply and combustion system in the virtual model system, obtaining an improved geometric model, and adding material and process attributes in the improved virtual model system;

[0017] S4: Real manufacturing process: according to the information provided by the improved virtual model system, the actual machining and assembly of the parts in the oil supply and combustion system are carried out, and an improved entity turbofan engine is obtained, and in this process, the actual shape, size, assembly relationship, machining manufacturing data, material and process data of the parts in the oil supply and combustion system are fed back to the service system to optimize the improved virtual model system.

[0018] S5: Real engine test: test the improved power, oil supply and combustion system performance of the entity turbofan engine, feed back the collected behavior data, performance data and environment data to the service system, fuse and analyze with the simulation data generated by the optimized virtual model system, and perfect the optimized virtual model system.

[0019] The sustainable aviation fuel turbofan engine oil supply and combustion system optimization method according to the embodiment of the present application has the following advantages: first, the present application is based on the digital twin structure system, the virtual model system and the physical entity adopt digital twin technology, and the accuracy of the virtual model system of the turbofan engine based on the digital twin structure system and the digital twin technology is higher; second, the high-accuracy virtual model system of the turbofan engine is used to simulate the multi-objective improvement process of the structure parameters of the oil supply and combustion system, which has the advantages of high efficiency, high reliability and high accuracy, can obtain accurate performance improvement results, save a lot of time and processing cost, simulate the process of processing, manufacturing and assembly, help to improve process management, improve processing and assembly efficiency, and reduce the waste rate, which is beneficial to realize the full qualification of parts in small batch manufacturing; third, the actual processing and manufacturing process of the oil supply and combustion system in the turbofan engine and the test results are fed back to the virtual model system to obtain a more perfect virtual model system, and the data of virtual-real interactive fusion analysis are recorded, which is beneficial to the inheritance of data information and has an important promoting effect on the development of engine engineering improvement technology.

[0020] In some embodiments, the digital twin technology adopted between the virtual model system and the physical entity includes structure twin, material twin, process twin, behavior twin, performance twin and environment twin;

[0021] Among them, the structure twin is the twin of the structure, shape and assembly relationship data of the physical entity and the virtual model system; the material twin is the twin of the physical properties and kinetic properties data of the materials used in each part of the physical entity and the virtual model system; the process twin is the twin of the processing and manufacturing and assembly process data of the physical entity and the virtual model system; the behavior twin is the twin of various response data made by the physical entity and the virtual model system to their own state or external environment; the performance twin is the twin of each performance parameter data of the physical entity and the virtual model system; the environment twin is the twin of the internal and external environment data in the physical entity and the virtual model system;

[0022] Correspondingly, the step S1 tests and measures the entity turbofan engine and the power, oil supply and combustion performance of the entity turbofan engine, and the multi-dimensional data obtained includes structure data, material data, process data, behavior data, performance data and environment data.

[0023] In some embodiments, the geometric model is established in CATIA software, the physical model is established in FLUENT software and ABAQUS software, the one-dimensional whole machine model of the behavior model and the rule model is established in GT-POWER software, and the three-dimensional combustion model of the behavior model and the rule model is established in Converge software.

[0024] In some embodiments, the service system is established in Labview data acquisition and analysis software and Access database.

[0025] In some embodiments, the step S2 is to perform fusion analysis on the simulation behavior, performance results of the virtual model system, and the behavior data and performance data in the obtained multi-dimensional data, specifically, to perform fusion analysis on the fuel supply pressure and flow data, sealing leakage data, atomization performance data, and combustion heat release law data in the simulation results of the virtual model system and in the obtained multi-dimensional data.

[0026] In some embodiments, the step S3 is to add material and process attributes to the improved virtual model system, specifically, to add material attributes to the geometric model, the physical model, and the one-dimensional whole machine model of the behavior model and the rule model.

[0027] In some embodiments, the process twin is the twin of process data of machining and assembly of the physical entity and the virtual model system, wherein the process data of machining and assembly includes machining method, machining tolerance, process reference, and process cost model.

[0028] In some embodiments, the step S2 is to perform iterative calculation improvement of the oil supply and combustion system structure parameters in the virtual model system to obtain optimal structure parameters, specifically including the following steps:

[0029] S201: Select the oil supply sealing structure length, the total volume of the oil supply pipeline, the fuel atomization structure length, and the diameter of the dilution hole of the combustion diffusion zone as optimization factors;

[0030] S202: Apply Latin hypercube sampling method to define the level range and test number of each optimization factor, take the sealing degree index reflecting the oil supply performance and the atomization coefficient and combustion efficiency index reflecting the atomization and combustion quality as responses, perform test calculation in the virtual model system and obtain test results, fit the response surface according to the test results, then evaluate the fitting quality of the response surface, if the fitting accuracy of the response surface is poor, increase the test number, repeat step S202, if the fitting accuracy of the response surface is good, proceed to step S203;

[0031] S203: Set the optimization target, apply the genetic algorithm to perform optimization calculations, obtain the optimization results of the fuel supply and combustion system structural parameters, round the optimization results, and then simulate the virtual model system with optimized structural parameters to determine whether the simulation results meet the performance requirements. If yes, the iterative calculation and improvement process ends and the optimal structural parameters are obtained. Otherwise, repeat steps S202 and S203.

[0032] In some embodiments, the step S202 of fitting the response surface based on the experimental results specifically involves fitting the response surface using ordinary least squares method based on the experimental results, and the fitting order is 2.

[0033] In some embodiments, the application of the genetic algorithm for optimization calculation in step S203 specifically includes the following steps:

[0034] The improvement targets are set as maximizing sealing, atomization coefficient, and combustion efficiency, with each target having equal weight. The maximum number of improvement iterations is set to 80-120. The convergence condition is that there is no change for 15-25 consecutive iterations. Each sampling sample consists of 30-50 individuals, with a mutation rate of 8%-12%. Crossover is performed through single-point crossover, and optimal elimination is used for selection. Mutation is carried out using a unified mutation method. Finally, the optimized values ​​of the length of the fuel supply sealing structure, the total volume of the fuel supply pipeline, the length of the fuel atomization structure, and the diameter of the dilution hole in the combustion diffusion zone can be obtained.

[0035] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0036] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0037] Figure 1 This is a flowchart illustrating the optimization method for the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to an embodiment of the present invention.

[0038] Figure 2 This is a structural block diagram of the digital twin structure system on which the embodiments of the present invention are based.

[0039] Figure 3 This is a schematic diagram illustrating the various digital twin technologies used between the virtual model system and the physical entity in this invention.

[0040] Figure 4 This is a flowchart illustrating the process of performing iterative calculations of the fuel supply and combustion system structural parameters in a virtual model system to obtain the optimal structural parameters in this embodiment of the invention.

[0041] Figure label:

[0042] Digital Twin Structure System 1000

[0043] Virtual model system 1 Physical entity 2 Service system 3 Twin data 4 Detailed Implementation

[0044] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0045] The following is combined Figures 1 to 4 This invention describes a method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine.

[0046] The sustainable aviation fuel turbofan engine fuel supply and combustion system optimization method according to an embodiment of the present invention is based on a digital twin structure system 1000, such as... Figure 2 As shown, the digital twin structure system 1000 includes a virtual model system 1, a physical entity 2, a service system 3, twin data 4, and interactive connections.

[0047] Among them, physical entity 2 includes actual entity data and actual entity processing, manufacturing and assembly process data; in this invention, physical entity 2 refers to objectively existing turbofan engine and fuel supply and combustion system data and fuel supply and combustion system processing, manufacturing and assembly process data. The turbofan engine includes multiple different sensors directly or indirectly arranged on the turbofan engine. The sensors are used to monitor the operating status and performance data of the turbofan engine and fuel supply and combustion system in real time.

[0048] Virtual model system 1 is used for virtual simulation of physical entity 2. Various digital twin technologies are employed between virtual model system 1 and physical entity 2, such as structural twins, material twins, process twins, behavioral twins, performance twins, and environmental twins. Virtual model system 1 comprises four levels of models: geometric model, physical model, behavioral model, and rule model. The geometric model describes the shape, size, manufacturing, and assembly information of each component in the actual entity and can be created using modeling software such as CATIA. The physical model simulates the physical properties of the actual entity; for example, in this invention, the physical model is used to simulate the characteristics of oil supply flow and atomization, and the dynamic characteristics of the rotor system. The physical model can be created using modeling software such as FLUENT and ABAQUS. The behavioral model receives data from actual experiments on the actual entity, responds to external drives and disturbances, processes and adjusts virtual model system 1, thereby making the calculations in virtual model system 1 more accurate and reliable. The rule model models and simulates the performance laws or rules of the actual entity, enabling virtual model system 1 to have rapid and accurate iterative improvement and prediction capabilities. Specifically, for example, a one-dimensional whole-machine model of the behavior model and rule model can be built in GT-POWER software, and a three-dimensional combustion model of the behavior model and rule model can be built in Converge software.

[0049] Service system 3 is used to collect information from virtual model system 1 and physical entity 2, and to perform fusion analysis on the information to provide precise control and reliable operation and maintenance services. For example, service system 3 will collect structural parameter information of the fuel supply and combustion system components in this invention, such as information on sealing structures, atomization enhancement structures, etc. Improvements and updates during the design process will also be described and recorded in service system 3. Specifically, service system 3 can be established in LabVIEW data acquisition and analysis software and an Access database.

[0050] Twin data 4 includes data generated by physical entity 2, virtual model system 1, and service system 3. After being processed by service system 3, twin data 4 is fed back to virtual model system 1, physical entity 2, and service system 3, driving the operation of digital twin structure system 1000. Twin data 4 is continuously updated and improved as real-time data is generated. In other words, twin data 4 is the core driver of the operation of digital twin structure system 1000.

[0051] The interactive connection includes multiple different data transmission modules used to connect physical entity 2, virtual model system 1, service system 3, and twin data 4, enabling the exchange and transfer of data between these components, forming an organic whole. The interconnection of physical entity 2, virtual model system 1, service system 3, and twin data 4 ensures the interactive transmission of twin data 4, guaranteeing consistency in the direction of iterative improvement across all components.

[0052] It is understood that the optimization method of the present invention is based on the digital twin structure system 1000. Various digital twin technologies are used between the virtual model system 1 and the physical entity 2. The fuel supply and combustion system of the sustainable aviation fuel turbofan engine is optimized based on the digital twin structure system 1000 and the digital twin technology, resulting in high optimization efficiency and high accuracy.

[0053] like Figure 1 As shown, the optimization method for the fuel supply and combustion system of a sustainable aviation fuel turbofan engine includes the following steps:

[0054] S1: Establishing a Virtual Model System 1: Experimental measurements are conducted on the physical turbofan engine and its power, fuel supply, and combustion performance to acquire multi-dimensional data, forming a physical entity 2 of the turbofan engine. The acquired multi-dimensional data is then analyzed and stored in a service system 3. The physical turbofan engine uses sustainable aviation fuel or aviation fuel containing sustainable aviation fuel as fuel, and includes a fuel supply and combustion system. In other words, this invention aims to improve and optimize the fuel supply and combustion system of a turbofan engine to adapt to using sustainable aviation fuel or aviation fuel containing sustainable aviation fuel as fuel. A turbofan engine simulation model and a sustainable aviation fuel combustion simulation model are established to form the virtual model system 1. The service system 3 inputs the acquired multi-dimensional data into the simulation correction module of the virtual model system 1 to improve its accuracy.

[0055] S2: Virtual Performance Optimization: Run Virtual Model System 1 and perform fusion analysis on the simulation behavior, performance results, and behavioral and performance data from the acquired multidimensional data to verify the accuracy of the twin simulation of Virtual Model System 1. Iterative calculations and improvements of the fuel supply and combustion system structural parameters are performed in Virtual Model System 1 to obtain optimal structural parameters. Here, the iterative calculation and improvement of the fuel supply and combustion system structural parameters in the turbofan engine adopts experimental design multi-objective improvement. Experimental design is a structurally systematic research method for studying the relationship between independent and dependent variables. Using experimental design in Virtual Model System 1 enables rapid and reliable multi-objective improvement of important structural parameters of the fuel supply and combustion system, yielding accurate performance improvement results and saving significant processing and testing time and costs. Simulations such as experimental design multi-objective improvement in Virtual Model System 1 have the advantages of high efficiency, high reliability, and high accuracy, while storing a large amount of effective data from the design and process improvement process, which plays an important role in promoting the development of engineering improvement technologies. Based on the optimal structural parameters, the structural parameters of the fuel supply pipeline and atomization structure in the fuel supply and combustion system are improved in the physical model to form an improved physical model. The improved physical model is then used to verify the fuel supply flow and atomization characteristics and the rotor system dynamics characteristics to determine the structural parameters of the fuel supply and combustion system in the final virtual model system 1. It should be noted that the latter half of step S2 is the verification of the physical model, which is mainly at the physical level, such as the verification of the basic models of fuel supply, atomization, and rotor dynamics, emphasizing the verification of subsystem models, while the first half of step S2 emphasizes the entire virtual model system 1.

[0056] S3: Virtual Manufacturing Simulation: Based on the structural parameters of the fuel supply and combustion system in the final virtual model system 1, determine the shape, size, assembly relationships, and manufacturing data of the components of the improved virtual model system 1. This yields an improved geometric model. For example, the processing, manufacturing, and assembly processes can be simulated to obtain assembly relationships and manufacturing data. Material and process attributes are then added to the improved virtual model system 1. Adding material and process attributes to the virtual model system 1 provides more accurate guidance for the actual processing and manufacturing of the fuel supply and combustion system. Simulating the processing, manufacturing, and assembly processes helps improve process management, increase processing and assembly efficiency, reduce scrap rates, and facilitates achieving full qualification of parts in small-batch manufacturing.

[0057] S4: Actual Manufacturing Process: Based on the information provided by the improved virtual model system 1, the components in the fuel supply and combustion system are actually processed, manufactured, and assembled to obtain the improved physical turbofan engine. During this process, the actual shape, size, assembly relationship, processing and manufacturing data, material and process data of the components in the fuel supply and combustion system of the physical turbofan engine are fed back to the service system 3 to optimize the improved virtual model system 1.

[0058] S5: Real Engine Testing: This test examines the performance of the improved physical turbofan engine's power, fuel supply, and combustion systems. The collected behavioral, performance, and environmental data are fed back to Service System 3 and integrated with the simulation data generated by the optimized virtual model system 1 to refine and improve the system. Using experimental data to feed back and correct the virtual model system 1 improves its accuracy, which is beneficial for subsequent product management and further optimization.

[0059] The optimization method for the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to embodiments of the present invention has the following advantages: First, the present invention is based on a digital twin structure system 1000. Digital twin technology is used between the virtual model system 1 and the physical entity 2, resulting in higher accuracy between the digital twin structure system 1000 and the virtual model system 1 of the turbofan engine built using digital twin technology. Second, using a highly accurate turbofan engine virtual model system 1 to simulate the multi-objective improvement process of the fuel supply and combustion system structural parameters has the advantages of high efficiency, high reliability, and high accuracy. It can obtain accurate performance improvement results, saving a significant amount of time and processing costs. Simulating the processing, manufacturing, and assembly processes helps improve process management, increase processing and assembly efficiency, and reduce scrap rates, which is beneficial for achieving full qualification of parts in small-batch manufacturing. Third, feeding back the actual processing and manufacturing process and test results of the fuel supply and combustion system in the turbofan engine to the virtual model system 1 yields a more complete virtual model system 1. Recording the data from the virtual-real interactive fusion analysis is beneficial for the inheritance of data information and plays an important role in promoting the development of engine engineering improvement technology.

[0060] In some embodiments, such as Figure 3 As shown, the various digital twin technologies used between the virtual model system 1 and the physical entity 2 include structural twins, material twins, process twins, behavioral twins, performance twins, and environmental twins;

[0061] Among them, structural twin is a twin of the structure, shape and assembly relationship data of physical entity 2 and virtual model system 1. Specifically, the geometric model in virtual model system 1 can reflect the structure and assembly relationship of the parts in the turbofan engine. The geometric model can be modified and transmitted to physical entity 2 through service system 3 to guide the processing, manufacturing and assembly of turbofan engine. At the same time, the real-time data of physical entity 2 will also be fed back to service system 3.

[0062] Material twins are twins of the physical and dynamic properties of the materials used in each component of the physical entity 2 and the virtual model system 1. Specifically, the material information of the actual tested fuel supply and combustion system is fed back to the service system 3 for data fusion analysis, and then transmitted to the virtual model system 1. The material information in the virtual model system 1 can guide the processing and manufacturing of the fuel supply and combustion system.

[0063] Process twins are twins of the manufacturing and assembly process data of physical entity 2 and virtual model system 1. Specifically, the process data of the fuel supply and combustion system is fed back to the geometric model through service system 3, and the process information in the geometric model is corrected based on this. The improved geometric model can simulate the actual manufacturing process of the fuel supply and combustion system and guide the manufacturing of the fuel supply and combustion system.

[0064] Behavioral twins are twins of the various response data of physical entity 2 and virtual model system 1 to their own state or external environment. Specifically, twin mirrors of the power, fuel supply and combustion behavior of turbofan engines are established in the physical model and behavioral model. The data describing the power, fuel supply and combustion behavior of turbofan engines come from various data collected by sensors in actual turbofan engines. Various behavioral information collected by sensors can be fed back to the behavioral model and the behavior can be reproduced in the behavioral model and rule model. The predictive analysis of power and fuel supply and combustion behavior in virtual model system 1 can guide the improvement of fuel supply and combustion system design schemes.

[0065] The performance twin is a twin of the performance parameters of the physical entity 2 and the virtual model system 1. Specifically, the performance data obtained by the test equipment and sensors is fed back to the one-dimensional whole machine model of the behavior model and rule model through the service system 3, and is fused and analyzed with the performance simulation data of the virtual model system 1. Finally, the performance of the fuel supply and combustion system is reproduced and predicted in the virtual model system 1, and the improvement of the fuel supply and combustion system design scheme is guided by the prediction results.

[0066] Environmental twinning refers to the creation of a mirror image of the internal and external environmental data of the physical entity 2 and the virtual model system 1. Specifically, it involves establishing a mirror image of the turbofan engine's operating environment within the one-dimensional whole-machine model of the behavioral and rule models. This includes the internal and external environmental conditions during the actual turbofan engine testing process. The external environmental conditions mainly include data such as air pressure, temperature, humidity, noise, and vibration intensity, while the internal environmental conditions mainly include data such as temperature and pressure. Environmental twinning helps the virtual model system 1 improve the accuracy of its performance predictions for the turbofan engine.

[0067] Correspondingly, in step S1, experimental measurements are performed on the physical turbofan engine and its power, fuel supply, and combustion performance. The acquired multidimensional data includes structural data, material data, process data, behavioral data, performance data, and environmental data. This facilitates the acquisition of a highly accurate virtual model system 1 and high-quality improvement and optimization results.

[0068] In some embodiments, the geometric model is established in CATIA software, the physical model is established in FLUENT and ABAQUS software, the one-dimensional whole-machine model of the behavioral and rule-based models is established in GT-POWER software, and the three-dimensional combustion model of the behavioral and rule-based models is established in Converge software, resulting in good simulation performance. The three-dimensional combustion model of the behavioral and rule-based models may include only the simulation model of the fuel supply and combustion system.

[0069] In some embodiments, service system 3 is built on LabVIEW data acquisition and analysis software and an Access database, and its performance is good.

[0070] In some embodiments, step S2 involves fusing and analyzing the simulation behavior and performance results of the virtual model system 1 with the behavioral and performance data from the acquired multidimensional data. Specifically, this involves fusing and analyzing the fuel supply pressure and flow rate data, seal leakage data, atomization performance data, and combustion heat release law data from the simulation results of the virtual model system 1 and the acquired multidimensional data. This is beneficial for verifying the accuracy of the simulation twin between the turbofan engine simulation model and the sustainable aviation fuel combustion simulation model.

[0071] In some embodiments, adding material and process attributes to the improved virtual model system 1 in step S3 specifically includes adding material attributes to the one-dimensional whole-machine model of the geometric model, physical model, behavioral model, and rule model. It is understood that adding material attributes to the geometric model enables a one-to-one correspondence between the geometric model of the fuel supply and combustion system and the entity of the fuel supply and combustion system in terms of the materials used; adding material attributes to the physical model improves the accuracy of calculations such as the transmission flow characteristics of the fuel supply pipeline and the dynamics of the rotor system in this invention; adding material attributes to the behavioral and rule models makes the calculations more accurate and reliable, and the improvement strategies more complete.

[0072] In some embodiments, the process twin is a twin of the manufacturing and assembly process data of the physical entity 2 and the virtual model system 1. The manufacturing and assembly process data includes processing methods, processing tolerances, process benchmarks, and process cost models. This allows for a higher degree of simulation and accuracy in the established virtual model system 1. Furthermore, it facilitates comprehensive and efficient guidance of the actual processing of the fuel supply and combustion system, helps improve process management, increases processing and assembly efficiency, reduces scrap rates, and promotes full qualification of parts in small-batch manufacturing.

[0073] In some embodiments, step S2 involves iterative calculation and improvement of the fuel supply and combustion system structural parameters in the virtual model system 1 to obtain optimal structural parameters, such as... Figure 4 As shown, the specific steps include the following:

[0074] S201: Select the length of the fuel supply sealing structure, the total volume of the fuel supply pipeline, the length of the fuel atomization structure, and the diameter of the dilution hole in the combustion diffusion zone as optimization factors; that is, select the parameters of the fuel supply and combustion system as optimization factors.

[0075] S202: The Latin hypercube sampling method is applied to define the level range and number of trials for each optimization factor. The sealing performance index (reflecting fuel supply performance) and the atomization coefficient and combustion efficiency index (reflecting atomization and combustion quality) are used as responses. Experimental calculations are performed in virtual model system 1 to obtain experimental results. A response surface is fitted based on the experimental results, and the fitting quality of the response surface is evaluated. If the fitting accuracy of the response surface is poor, the number of trials is increased, and step S202 is repeated. If the fitting accuracy of the response surface is good, step S203 is performed. Specifically, for example, the Adj.R-sqr index can be used to evaluate the fitting quality of the response surface. The Adj.R-sqr index has high reliability and can eliminate the influence of overfitting. The Adj.R-sqr values ​​of the response surfaces for sealing performance, atomization coefficient, and combustion efficiency are all above 0.8, indicating a high quality of response fitting, which can be applied to subsequent optimization calculations.

[0076] S203: Set optimization objectives, apply a genetic algorithm for optimization calculations to obtain optimized results for the structural parameters of the fuel supply and combustion system, round the optimization results, and then simulate the virtual model system 1 with optimized structural parameters. Determine whether the simulation results meet the performance requirements. If yes, end the iterative calculation and improvement process to obtain the optimal structural parameters; otherwise, repeat steps S202 and S203. It is understood that this invention uses experimental design methods in the virtual model system 1 to perform multi-objective improvement on important structural parameters of the fuel supply and combustion system. The improvement process is fast and reliable, yields accurate performance improvement results, and saves significant processing and testing time and costs.

[0077] In some embodiments, the step S202 of fitting the response surface based on the experimental results specifically involves fitting the response surface using ordinary least squares method based on the experimental results, and the fitting order is 2.

[0078] In some embodiments, the application of the genetic algorithm for optimization calculation in step S203 specifically includes the following steps:

[0079] The improvement targets are set as maximizing sealing, atomization coefficient, and combustion efficiency, with each target having equal weight. The maximum number of improvement iterations is set to 80-120. The convergence condition is that there is no change for 15-25 consecutive iterations. Each sampling sample consists of 30-50 individuals, with a mutation rate of 8%-12%. Crossover is performed through single-point crossover, and optimal elimination is used for selection. Mutation is carried out using a unified mutation method. Finally, the optimized values ​​of fuel supply sealing structure length, total fuel supply pipeline volume, fuel atomization structure length, and diameter of dilution holes in the combustion diffusion zone are obtained. This yields the optimized results of the fuel supply and combustion system structural parameters, with good optimization effect.

[0080] Preferably, the improvement targets are set as maximizing sealing degree, maximizing atomization coefficient, and maximizing combustion efficiency, with each improvement target having equal weight. The maximum number of improvement iterations is set to 100, and the convergence condition is that there is no change for 20 consecutive iterations. Each sampling is 40 individuals, the mutation rate is 10%, and hybridization is performed through single-point crossover. The optimal elimination system is used for selection, and the mutation is performed in a unified mutation mode. Finally, the optimized values ​​of fuel supply sealing structure length, total fuel supply pipeline volume, fuel atomization structure length, and diameter of dilution holes in the combustion diffusion zone can be obtained.

[0081] Optionally, the improvement targets are set as maximizing sealing, maximizing atomization coefficient, and maximizing combustion efficiency, with each target having equal weight. The maximum number of improvement iterations is set to 80, and the convergence condition is that there is no change for 15 consecutive iterations. Each sampling is 30 individuals, the mutation rate is 8%, and hybridization is performed through single-point crossover. The optimal elimination system is used for selection, and the mutation is performed using a unified mutation method. Finally, the optimized values ​​of the fuel supply sealing structure length, the total volume of the fuel supply pipeline, the fuel atomization structure length, and the diameter of the dilution hole in the combustion diffusion zone can be obtained. That is, the optimized results of the fuel supply and combustion system structural parameters are obtained, and the optimization effect is good.

[0082] Optionally, the improvement targets are set as maximizing sealing, maximizing atomization coefficient, and maximizing combustion efficiency, with each target having equal weight. The maximum number of improvement iterations is set to 120, and the convergence condition is that there is no change for 25 consecutive iterations. Each sample consists of 50 individuals, with a mutation rate of 12%. Crossover is performed through single-point crossover, and the optimal elimination system is used for selection. Mutation is carried out using a unified mutation method. Finally, the optimized values ​​of the fuel supply sealing structure length, the total volume of the fuel supply pipeline, the fuel atomization structure length, and the diameter of the dilution hole in the combustion diffusion zone can be obtained. This yields the optimized results of the fuel supply and combustion system structural parameters, with good optimization effect.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0084] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine, characterized in that, The digital twin architecture system includes a virtual model system, physical entities, service systems, twin data, and interactive connections. The physical entity includes actual entity data and actual entity processing, manufacturing, and assembly process data; The virtual model system is used to perform virtual simulation of the physical entity. Various digital twin technologies are employed between the virtual model system and the physical entity. The virtual model system comprises four levels of models: a geometric model, a physical model, a behavioral model, and a rule model. The geometric model describes the shape, size, manufacturing, and assembly information of each component in the actual entity. The physical model simulates the physical properties of the actual entity. The behavioral model receives data from actual experiments conducted on the actual entity, responds to external drives and disturbances, and processes and adjusts the virtual model system. The rule model models and simulates the performance patterns or rules of the actual entity. The service system is used to collect information from the virtual model system and the physical entity, and to perform fusion analysis on the information; The twin data includes data generated by the physical entity, the virtual model system, and the service system. After being processed by the service system, the twin data is fed back to the virtual model system, the physical entity, and the service system, driving the operation of the digital twin structure system. The twin data is continuously updated and improved as real-time data is generated. The interactive connection includes multiple different data transmission modules for interconnecting the physical entity, the virtual model system, the service system, and the twin data, so that data of the physical entity, the virtual model system, the service system, and the twin data can be exchanged and transmitted between the various parts; The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine includes the following steps: S1: Establish a virtual model system: Conduct experimental measurements on the physical turbofan engine and its power, fuel supply, and combustion performance to acquire multi-dimensional data, forming a physical entity of the turbofan engine. Then, analyze the acquired multi-dimensional data and store it in the service system. The physical turbofan engine uses sustainable aviation fuel or aviation fuel containing sustainable aviation fuel as fuel, and includes a fuel supply and combustion system. Establish a turbofan engine simulation model and a sustainable aviation fuel combustion simulation model to form the virtual model system. The service system inputs the acquired multi-dimensional data into the simulation correction module of the virtual model system. S2: Virtual Performance Optimization: Run the virtual model system, and perform fusion analysis on the simulation behavior and performance results of the virtual model system and the behavioral and performance data in the acquired multidimensional data. Perform iterative calculation and improvement of the structural parameters of the fuel supply and combustion system in the virtual model system to obtain the optimal structural parameters. Based on the optimal structural parameters, improve the structural parameters of the fuel supply pipeline and atomization structure in the fuel supply and combustion system in the physical model to form an improved physical model. Use the improved physical model to verify the fuel supply flow and atomization characteristics and the dynamic characteristics of the rotor system, and determine the final structural parameters of the fuel supply and combustion system in the virtual model system. S3: Virtual Manufacturing Simulation: Based on the structural parameters of the fuel supply and combustion system in the final virtual model system, determine the shape, size, assembly relationship and processing data of the components of the fuel supply and combustion system in the improved virtual model system, obtain the improved geometric model, and add material and process attributes to the improved virtual model system; S4: Actual manufacturing process: Based on the information provided by the improved virtual model system, the components in the fuel supply and combustion system are actually processed, manufactured and assembled to obtain the improved physical turbofan engine. During this process, the actual shape, size, assembly relationship, processing and manufacturing data, material and process data of the components in the fuel supply and combustion system are fed back to the service system to optimize the improved virtual model system. S5: Real Engine Test: Test the power, fuel supply and combustion system performance of the improved physical turbofan engine, and feed back the collected behavioral data, performance data and environmental data to the service system. Then, perform fusion analysis with the simulation data generated by the optimized virtual model system to improve the optimized virtual model system.

2. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 1, characterized in that, The various digital twin technologies used between the virtual model system and the physical entity include structural twins, material twins, process twins, behavioral twins, performance twins, and environmental twins. Among them, structural twin is a twin of the structure, shape, and assembly relationship data between the physical entity and the virtual model system; material twin is a twin of the physical and dynamic properties of the materials used in each component of the physical entity and the virtual model system; process twin is a twin of the manufacturing and assembly process data of the physical entity and the virtual model system; behavioral twin is a twin of the various response data of the physical entity and the virtual model system to their own state or external environment; performance twin is a twin of the performance parameters of the physical entity and the virtual model system; and environmental twin is a twin of the internal and external environmental data of the physical entity and the virtual model system. Correspondingly, in step S1, the physical turbofan engine and its power, fuel supply and combustion performance are tested and measured, and the obtained multidimensional data includes structural data, material data, process data, behavior data, performance data and environmental data.

3. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 1, characterized in that, The geometric model was established in CATIA software, the physical model was established in FLUENT and ABAQUS software, the one-dimensional whole machine model of the behavioral model and the rule model was established in GT-POWER software, and the three-dimensional combustion model of the behavioral model and the rule model was established in Converge software.

4. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 1, characterized in that, The service system is built on LabVIEW data acquisition and analysis software and an Access database.

5. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 1, characterized in that, In step S2, the simulation behavior and performance results of the virtual model system are fused and analyzed with the behavioral data and performance data in the acquired multidimensional data. Specifically, the simulation results of the virtual model system and the oil supply pressure and flow data, sealing leakage data, atomization performance data, and combustion heat release law data in the acquired multidimensional data are fused and analyzed.

6. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 1, characterized in that, The step S3, which involves adding material and process attributes to the improved virtual model system, specifically includes adding material attributes to the one-dimensional whole machine model of the geometric model, the physical model, the behavioral model, and the rule model.

7. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 2, characterized in that, The process twin is a twin of the physical entity and the processing, manufacturing and assembly process data of the virtual model system, wherein the processing, manufacturing and assembly process data includes processing methods, processing tolerances, process benchmarks and process cost models.

8. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to any one of claims 1-7, characterized in that, Step S2, which involves iteratively calculating and improving the structural parameters of the fuel supply and combustion system in the virtual model system to obtain the optimal structural parameters, specifically includes the following steps: S201: Select the length of the fuel supply sealing structure, the total volume of the fuel supply pipeline, the length of the fuel atomization structure, and the diameter of the dilution hole in the combustion diffusion zone as optimization factors; S202: The Latin hypercube sampling method is applied to define the level range and number of tests for each optimization factor. The sealing index, which reflects the fuel supply performance, and the atomization coefficient and combustion efficiency index, which reflect the atomization and combustion quality, are used as responses. Experimental calculations are performed in the virtual model system to obtain experimental results. The response surface is fitted based on the experimental results, and then the fitting quality of the response surface is evaluated. If the fitting accuracy of the response surface is poor, the number of tests is increased and step S202 is repeated. If the fitting accuracy of the response surface is good, step S203 is performed. S203: Set the optimization target, apply the genetic algorithm to perform optimization calculations, obtain the optimization results of the fuel supply and combustion system structural parameters, round the optimization results, and then simulate the virtual model system with optimized structural parameters to determine whether the simulation results meet the performance requirements. If yes, the iterative calculation improvement process ends and the optimal structural parameters are obtained. Otherwise, repeat steps S202 and S203.

9. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 8, characterized in that, The step S202 of fitting the response surface based on the experimental results specifically involves fitting the response surface using ordinary least squares method based on the experimental results, with the fitting order being 2.

10. The method for optimizing the fuel supply and combustion system of a sustainable aviation fuel turbofan engine according to claim 9, characterized in that, The optimization calculation using a genetic algorithm in step S203 specifically includes the following steps: The improvement targets are set as maximizing sealing, atomization coefficient, and combustion efficiency, with each target having equal weight. The maximum number of improvement iterations is set to 80-120. The convergence condition is that there is no change for 15-25 consecutive iterations. Each sampling sample consists of 30-50 individuals, with a mutation rate of 8%-12%. Crossover is performed through single-point crossover, and optimal elimination is used for selection. Mutation is carried out using a unified mutation method. Finally, the optimized values ​​of the length of the fuel supply sealing structure, the total volume of the fuel supply pipeline, the length of the fuel atomization structure, and the diameter of the dilution hole in the combustion diffusion zone can be obtained.

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