Improved volume method modeling of variable cycle engine based on adaptive virtual cavity

Abstract

The application discloses a kind of based on self-adapting virtual cavity's variable cycle engine improved volume method modeling, comprising the following steps: based on traditional variable cycle engine volume dynamics model, according to the smooth degree of each component common work line selection reference model;Design to establish the evaluation index of model virtual cavity under different simulation step length advantage and disadvantage;Based on particle swarm optimization algorithm design engine model virtual cavity self-adapting optimization framework, and determine optimal virtual cavity;According to the optimal virtual cavity obtained, optimize variable cycle engine model, improve calculation real-time performance.The virtual cavity evaluation index established by the application can intuitively evaluate the advantages and disadvantages of virtual cavity, and further reflect the volume effect in the dynamic working process of engine;The self-adapting optimization framework designed optimizes to determine the optimal virtual cavity, effectively alleviates the contradiction between model real-time performance and cavity effect, greatly improves the real-time performance of variable cycle engine improved volume dynamics model.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine modeling technology, specifically relating to an improved volumetric method for modeling variable-cycle engines based on an adaptive virtual cavity. Background Technology

[0002] To further ensure the development of aero-engines towards higher thrust-to-weight ratios, higher performance, and higher economy, it is essential to consider the effectiveness and reliability of control law design and health management. Aero-engine component-level models serve as the cornerstone of this research, and improving their real-time performance and accuracy is currently a research hotspot in the aerospace field. Existing aero-engine component-level modeling methods establish mathematical models that reflect the engine's operation under all envelope and all conditions based on the engine's working principles and following the laws of aerodynamics and thermodynamics. The solution methods for these models mainly include iterative methods and volumetric dynamics methods.

[0003] The basic principle of iterative methods is to establish three types of nonlinear equations—flow continuity equation, static pressure balance equation, and power balance equation—based on the working relationships between various components, and then solve them using iterative algorithms. Early iterative algorithms mainly included the parametric loop method and the equilibrium loop method. However, with the increasing complexity of aero-engine structures, these two iterative methods generally cannot meet the requirements of real-time simulation. Current iterative algorithms mainly include the N+1 point residual method, the Newton-Raphson method, and the Broyden method. Among them, the Newton-Raphson method has high computational efficiency and good convergence, making it one of the most widely used methods in the field of aero-engine modeling. The simulation accuracy and real-time performance of aero-engine models established based on iterative methods largely depend on the selection of initial values, and the models have significant shortcomings in reflecting the high-frequency characteristics and actual physical processes of engine dynamics. To address these issues, the volumetric dynamics method was developed. Originally proposed by AJ Fawke, this method posits that the flow rate is unbalanced during engine dynamic operation, and the accumulation effect of mass and energy within the cavity leads to pressure fluctuations. Appropriate selection of the cavity location can effectively update the pressure ratio and temperature of various engine components. Since its inception, the volumetric dynamics method has developed rapidly, with scholars both domestically and internationally establishing a series of engine component-level models, ranging from turboshaft engines to variable cycle engines, based on this method. A comparison of simulation results from engine models established using the two methods reveals that the model based on the Newton-Raphson iteration method exhibits better real-time performance due to its support for larger dynamic simulation step sizes (20-25 ms), while the model based on the volumetric dynamics method, with smaller dynamic simulation step sizes (1-2 ms), more accurately reflects the high-frequency physical characteristics of the engine during operation.

[0004] This invention takes a variable cycle engine as an example and conducts research based on the component-level model established by volumetric dynamics. It finds that the real-time performance of the volumetric dynamics model is greatly improved under a large simulation step size. However, the advantage of the volumetric dynamics method in reflecting the high-frequency physical characteristics of the engine during operation gradually becomes less obvious. Through further research, it is found that adjusting the cavity size can effectively alleviate the above contradiction. Since the final selected cavity size may not match the actual cavity size of the engine, it is called a virtual cavity.

[0005] Overall, although scholars at home and abroad have conducted a great deal of research on establishing component-level models of variable cycle engines based on volume dynamics, so far no method has been able to effectively balance the contradiction between the real-time performance of dynamic model simulation and the volume effect. Summary of the Invention

[0006] The purpose of this invention is to resolve the contradiction between the real-time performance and volumetric effect of variable cycle engine models based on the volumetric dynamics method. It provides an improved volumetric modeling method for variable cycle engines based on an adaptive virtual cavity. The aim is to improve the optimization effect and efficiency of the adaptive optimization frame by establishing a virtual cavity evaluation index based on a selected benchmark model, and ultimately improve the real-time performance of the variable cycle engine volumetric dynamics model while maintaining the volumetric effect.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an improved volumetric method modeling of a variable cycle engine based on an adaptive virtual cavity, comprising:

[0008] Step 1: Based on the variable cycle engine volume dynamics model, select the benchmark model according to the smoothness of the common working line of each rotating component;

[0009] Step 2: Based on the benchmark model described in Step 1, design and establish virtual cavity evaluation indexes for the volumetric dynamics model of the variable cycle engine under different simulation step sizes;

[0010] Step 3: Based on the virtual cavity evaluation index and particle swarm optimization algorithm described in Step 3, design an adaptive optimization architecture for the virtual cavity of the engine model, and determine the optimal virtual cavity;

[0011] Step 4: Optimize the variable cycle engine volume dynamics model based on the optimal virtual cavity obtained in Step 3.

[0012] Furthermore, step 1 specifically includes:

[0013] Step 1.1: Set the simulation step size of the variable cycle engine volume dynamics model to 1ms, and give the operating conditions and control parameters to make each rotating component of the model enter a stable operating state;

[0014] Step 1.2: By adjusting the cavity size corresponding to the variable cycle engine volumetric dynamics model, the common working line of each rotating component is changed. The changes in the rotating components include the fan, core drive fan CDFS, high-pressure compressor Comp, high-pressure turbine Hturb and low-pressure turbine Lturb;

[0015] Step 1.3: Repeat steps 1.1 to 1.2 k times. When the common working line of all rotating components changes smoothly without abrupt changes during dynamic operation, select the variable cycle engine model at this time as the reference model M. ref .

[0016] Furthermore, step 2 specifically includes:

[0017] Step 2.1: Increase the simulation step size of the variable cycle engine volumetric dynamics model to obtain the current model M. cur ;

[0018] Step 2.2: Based on the benchmark model M ref And the current model M cur The average cosine similarity of the common working lines of each rotating component is used to design a virtual cavity evaluation index to assess the comprehensive performance of virtual cavity balance real-time and volume effect.

[0019] Furthermore, the formula for the virtual cavity evaluation index is as follows:

[0020]

[0021] Where n represents the number of points taken on the dynamic working line when calculating cosine similarity in a single calculation, W represents the mass flow rate through the component, π represents the pressure ratio of the rotating component, the subscript x represents the point on the dynamic working line of the benchmark model, the subscript y represents the point on the dynamic working line of the current model under the same flow rate, θ is the angle between the tangents of the two points, and the closer the calculation result is to 1, the higher the correlation between the two.

[0022] Furthermore, step 3 specifically includes:

[0023] Step 3.1: Under the same operating conditions and control parameters, select different simulation step sizes and different virtual cavity sizes. Take 1000 points evenly along the common working line of each rotating component according to the flow rate. Combined with the virtual cavity evaluation index, set the optimization index as follows:

[0024] J = cos(θ) Fan +cos(θ) CDFS +cos(θ) Comp +cos(θ) HTurb +cos(θ) LTurb

[0025] The larger the optimization index, the higher the similarity between the common working line of the current model's dynamic process and the benchmark model, and the better the effect of the selected virtual cavity.

[0026] Step 3.2: The cavities corresponding to each rotating component in the variable cycle engine volumetric dynamics model constitute a target search space of dimension N. The target search space contains m particles, where the i-th particle represents a set of virtual cavities called X. i =(x i1 ,x i2 ,x i3 ,x i4 ,x i5 The velocity of the i-th particle (the largest change in each iteration) is denoted as V. i =(v i1 ,v i2 ,v i3 ,v i4 ,v i5 The optimal position found so far by the i-th particle is called the individual extreme value p. best =(p i1 ,p i2 ,p i3 ,p i4 ,p i5 The optimal position found so far by the entire particle swarm is the global extremum g. best = (g1, g2, g3, g4, g5). After finding the individual and global maxima based on the particle swarm optimization algorithm, the particles update their own velocities and positions;

[0027] Step 3.3: When the iteration satisfies the maximum optimization index J, the optimal virtual cavity is obtained.

[0028] Furthermore, the particle updates its velocity and position as follows:

[0029] v ij (t+1)=v ij (t)+c1r1(t)[p j (t)-x ij (t)]+c2r2(t)[g j (t)-x ij (t)]

[0030] x ij (t+1)=x ij (t)+v ij (t+1)

[0031] Where t represents time, p represents the individual extreme value, g represents the global extreme value, c1 and c2 are learning factors; r1 and r2 are uniformly random numbers in the range [0,1], increasing the randomness of particle flight; v ij It is the particle velocity, v ij ∈[0,v max ].

[0032] Furthermore, step 4 specifically includes:

[0033] The cavity size in the variable cycle engine volumetric dynamics model is set to the optimal virtual cavity X. best This is to optimize the real-time performance of the volumetric dynamics model calculation for the variable cycle engine.

[0034] Beneficial effects:

[0035] 1. The virtual cavity evaluation index established by this invention can intuitively evaluate the impact of the virtual cavity on the common working line of each rotating component of the engine, providing an effective performance index for the optimization process.

[0036] 2. The virtual cavity adaptive optimization architecture established in this invention can solve the complex coupling relationship between cavity adjustments and greatly improve the efficiency of obtaining the optimal virtual cavity.

[0037] 3. The present invention proposes an improved volumetric modeling method for variable cycle engines based on an adaptive virtual cavity. While maintaining the volumetric effect, it greatly improves the real-time performance of the volumetric dynamics model calculation for variable cycle engines. This has a positive effect on promoting the application of airborne models of variable cycle engines and improving the overall performance of engines. Attached Figure Description

[0038] Figure 1 This is a flowchart of the present invention.

[0039] Figure 2 This is a schematic diagram of a variable cycle engine.

[0040] Figure 3 This is a schematic diagram illustrating the calculation of evaluation indicators for virtual cavities.

[0041] Figure 4 It is the dynamic operating line of the fan before adopting the optimal virtual cavity.

[0042] Figure 5 It is the dynamic operating line of the fan after adopting the optimal virtual cavity.

[0043] Figure 6 This is a comparison of the calculation time of the model under different simulation step sizes. Detailed Implementation

[0044] The invention will now be further explained with reference to the accompanying drawings.

[0045] like Figure 1 As shown, this invention provides an improved volumetric method for modeling variable cycle engines based on an adaptive virtual cavity, comprising:

[0046] Step 1: Based on the volumetric dynamics model of the variable cycle engine, a benchmark model is selected according to the smoothness of the common working line of each rotating component.

[0047] In step 1, the simulation step size of the variable cycle engine volume dynamics model is set to 1ms. Under this simulation step size, the volume dynamics model has a significant volume effect, and the common working line of each rotating component changes relatively smoothly during the engine's dynamic process. Therefore, a benchmark model is selected based on this.

[0048] Next, by adjusting the cavity size corresponding to the variable cycle engine volumetric dynamics model, the common operating lines of the fan, core drive fan (CDFS), high-pressure compressor (Comp), high-pressure turbine (Hturb), and low-pressure turbine (Lturb) are changed. The changes.

[0049] Finally, steps 1.1 to 1.2 are repeated k times. When the common working line of each rotating component changes smoothly without abrupt changes during dynamic operation, the corresponding variable cycle engine model is taken as the baseline model M. ref .

[0050] Step 2: Based on the benchmark model described in Step 1, design and establish evaluation indicators for the virtual cavity of the variable cycle engine volume dynamics model under different simulation step sizes.

[0051] In step 2, the simulation step size of the variable cycle engine volumetric dynamics model is increased to obtain the current model M. cur .

[0052] Based on the benchmark model M ref And the current model M cur The average cosine similarity of the common working lines of all rotating components is used to design a virtual cavity evaluation index to assess the comprehensive performance of the virtual cavity in terms of real-time balance and volume effect. The formula for the virtual cavity evaluation index is as follows:

[0053]

[0054] In the formula, n represents the number of points taken on the dynamic working line when calculating the cosine similarity in a single calculation, W represents the mass flow rate through the component, π represents the pressure ratio of the rotating component, the subscript x represents the point on the dynamic working line of the benchmark model, the subscript y represents the point on the dynamic working line of the current model under the same flow rate, θ is the angle between the tangents of the two points, and the closer the calculation result is to 1, the higher the correlation between the two.

[0055] Step 3: Based on the virtual cavity evaluation index and particle swarm optimization algorithm described in Step 3, design the adaptive optimization architecture of the virtual cavity of the engine model and determine the optimal virtual cavity.

[0056] In step 3, under different simulation step sizes and different virtual cavity sizes, 1000 points are uniformly selected from the common working line of each rotating component according to the flow rate. Based on the virtual cavity evaluation index, the optimization index is set as follows:

[0057] J = cos(θ) Fan +cos(θ) CDFS +cos(θ) Comp +cos(θ) HTurb +cos(θ) LTurb

[0058] It is the sum of the average cosine similarity of each rotating component. The larger the optimization index, the higher the similarity between the current model's common working line and the benchmark model, and the better the effect.

[0059] Next, the cavities corresponding to the rotating components of the variable cycle engine volumetric dynamics model constitute a target search space of dimension N. The target search space contains m particles, where the i-th particle represents a set of virtual cavities called X. i =(x i1 ,x i2 ,x i3 ,x i4 ,x i5 The velocity of the i-th particle (the largest change in each iteration) is denoted as V. i =(v i1 ,v i2 ,v i3 ,v i4 ,v i5 The optimal position found so far by the i-th particle is called the individual extreme value p. best =(p i1 ,p i2 ,p i3 ,p i4 ,p i5 The optimal position found so far by the entire particle swarm is the global extremum g. best = (g1, g2, g3, g4, g5). After finding the individual and global maxima based on the particle swarm optimization algorithm, the particles update their velocities and positions as follows:

[0060] v ij (t+1)=v ij (t)+c1r1(t)[p j(t)-x ij (t)]+c2r2(t)[g j (t)-x ij (t)]

[0061] x ij (t+1)=x ij (t)+v ij (t+1)

[0062] In the formula, t represents time, p represents the individual extreme value, g represents the global extreme value, c1 and c2 are learning factors; r1 and r2 are uniformly random numbers in the range [0,1], which increases the randomness of particle flight; v ij It is the particle velocity, v ij ∈[0,v max ].

[0063] Finally, when the iteration satisfies the maximum optimization index, the optimal virtual cavity is obtained.

[0064] Step 4: Optimize the variable cycle engine model based on the optimal virtual cavity obtained in Step 3 to improve the real-time performance of the calculation.

[0065] In step 4, the cavity size in the variable cycle engine volumetric dynamics model is set to the optimal virtual cavity to optimize the variable cycle engine volumetric dynamics model. This maintains the cavity effect under larger simulation steps and improves the real-time performance of the model calculation without affecting the model's simulation accuracy.

[0066] The embodiment of this invention is a certain type of dual-bypass variable cycle engine, which contains two rotor components: a high-pressure shaft and a low-pressure shaft. Its structural schematic diagram and cavity location are shown below. Figure 2 As shown.

[0067] Step 1 in this embodiment is specifically as follows:

[0068] First, based on the engine input given in step 1.1, the simulation step size of the engine model is set to step = 1ms. At the operating point with altitude H = 0km and Mach number Ma = 0, the initial fuel flow rate W is given. f Control parameters such as the initial tail nozzle area A8 are used to bring the engine component-level model into a stable operating state.

[0069] Next, the fuel flow rate W of the engine component-level model is set. f The deceleration process during normal engine operation was simulated by linearly decreasing from 1.588 kg / s to 1.2704 kg / s within 10 seconds. By adjusting the cavity size corresponding to the variable cycle engine volume dynamics model, the changes in the common working line of the fan, core drive fan CDFS, high pressure compressor Comp, high pressure turbine Hturb, and low pressure turbine Lturb were observed.

[0070] Subsequently, the above steps are repeated until the common working line of each component changes smoothly without abrupt changes, and the corresponding variable cycle engine model is selected as the reference model M. ref The final selected baseline model cavity configuration is shown in Table 1.

[0071] Step 2 in this embodiment is specifically as follows:

[0072] Under the same operating conditions and control parameters, the dynamic simulation step size of the variable cycle engine volumetric dynamics model was set to 2ms, 5ms, and 10ms, respectively, to obtain the current model M. cur and the common working line of all rotating components;

[0073] Subsequently, evaluation metrics for the virtual cavity were designed. The mean cosine similarity was used to assess the similarity of the common working lines of the rotating components between the baseline model and the current model. A schematic diagram of the virtual cavity evaluation metric calculation is shown below. Figure 3 As shown in the figure, the black curve represents the dynamic working line of the baseline model, and the gray curve represents the dynamic working line of the current model. The calculation formula for the virtual cavity evaluation index is as follows:

[0074]

[0075] In the formula, W represents the mass flow rate through the component, π represents the pressure ratio of the rotating component, the subscript x represents a point on the dynamic working line of the reference model, the subscript y represents a point on the dynamic working line of the current model under the same flow rate, θ is the angle between the tangents of the two points, and the closer the calculation result is to 1, the higher the correlation between the two.

[0076] Step 3 in this embodiment is specifically as follows:

[0077] For each rotating component, 1000 points are uniformly selected along the common working line based on flow rate under different simulation step sizes and virtual cavity sizes (i.e., n = 1000 in the above formula), and their average cosine similarity is calculated. The optimization index J is set as:

[0078] J = cos(θ) Fan +cos(θ) CDFS +cos(θ) Comp +cos(θ) HTurb +cos(θ) LTurb

[0079] It is the sum of the average cosine similarity of each rotating component. The larger the optimization index, the higher the similarity between the current model's common working line and the benchmark model, and the better the effect.

[0080] Subsequently, the optimal virtual cavity X is searched in a target search space of dimension N=5 based on the particle swarm optimization algorithm. bestThe optimal virtual cavity found is shown in Table 1.

[0081] Table 1. Virtual cavity characteristics under different simulation step sizes

[0082]

[0083] Step 4 in this embodiment is specifically as follows:

[0084] Based on the optimization results in Table 1, the cavity size in the variable cycle engine volume dynamics model was modified and verified through simulation, thereby optimizing the variable cycle engine volume dynamics model.

[0085] Figure 4 This is the dynamic operating line of the fan under different simulation step sizes before optimizing the virtual cavity.

[0086] Figure 5 It is the dynamic operating line of the fan under different simulation step sizes after optimizing the virtual cavity.

[0087] Simulation results show that before using the optimal virtual cavity, as the simulation step size increases, the dynamic operating line gradually deviates from the optimal state, and the volumetric characteristics gradually weaken. However, after using the optimal virtual cavity, the fan dynamic operating line under simulation step sizes of 2ms and 5ms almost coincides with the baseline model. When the simulation step size is 10ms, although it is impossible to completely reach the optimal state by adjusting the virtual cavity, there is still a significant improvement.

[0088] Figure 6 The comparison of the computation time of the variable cycle engine iterative model and the volume dynamics model shows that increasing the simulation step size can indeed reduce the computation time of the volume method model. The improved volume dynamics modeling method based on adaptive virtual cavity proposed in this invention can effectively improve the real-time performance of the model calculation while maintaining the volume effect.

[0089] In summary, the virtual cavity evaluation index established in this invention can intuitively reflect the impact of changes in the size of the virtual cavity on the common working line of various rotating components of the engine; the designed virtual cavity adaptive optimization architecture greatly improves the efficiency of obtaining the optimal virtual cavity; and the improved volume dynamics model significantly improves the real-time performance of the volume dynamics model calculation for the variable cycle engine, which has a positive promoting effect on the application of the airborne model of the variable cycle engine and the improvement of the overall engine performance.

[0090] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An improved volumetric dynamics modeling method for a variable-cycle engine with an adaptive virtual cavity, characterized in that, include: Step 1: Based on the variable cycle engine volume dynamics model, select the benchmark model according to the smoothness of the common working line of each rotating component; Step 2: Based on the benchmark model described in Step 1, design and establish virtual cavity evaluation indexes for the volumetric dynamics model of the variable cycle engine under different simulation step sizes; Step 3: Based on the virtual cavity evaluation index and particle swarm optimization algorithm described in Step 2, design an adaptive optimization architecture for the virtual cavity of the engine model, and determine the optimal virtual cavity; Step 4: Optimize the variable cycle engine volumetric dynamics model based on the optimal virtual cavity obtained in Step 3; Step 1 specifically includes: Step 1.1: Set the simulation step size of the variable cycle engine volume dynamics model to 1ms, and give the operating conditions and control parameters to make each rotating component of the model enter a stable operating state; Step 1.2: By adjusting the cavity size corresponding to the variable cycle engine volumetric dynamics model, the common working line of each rotating component is changed. The changes in the rotating components, including the fan. Core drive fan High-pressure air compressor High-pressure turbine and low-pressure turbine ; Step 1.3: Repeat steps 1.1 to 1.2 k times. When the common working line of all rotating components changes smoothly without abrupt changes during dynamic operation, select the variable cycle engine model at this point as the reference model. ; Step 2 specifically includes: Step 2.1: Increase the simulation step size of the variable cycle engine volumetric dynamics model to obtain the current model. ; Step 2.2: Based on the benchmark model and the current model The average cosine similarity of the common working lines of each rotating component is used to design a virtual cavity evaluation index to assess the comprehensive performance of virtual cavity balance real-time and volume effect. The formula for the virtual cavity evaluation index is: ， in, This represents the number of points taken on the dynamic working line during a single calculation of cosine similarity. This indicates the mass flow rate through the component. Indicates the pressure ratio of the rotating component, subscript The subscript represents a point on the dynamic working line of the baseline model. This represents a point on the dynamic workline under the current model and the same flow rate. The angle between the tangents at two points is given; the closer the calculated result is to 1, the higher the correlation between the two points. Step 3 specifically includes: Step 3.1: Under the same operating conditions and control parameters, select different simulation step sizes and different virtual cavity sizes. Take 1000 points evenly along the common working line of each rotating component according to the flow rate. Combined with the virtual cavity evaluation index, set the optimization index as follows: ， The larger the optimization index, the higher the similarity between the common working line of the current model's dynamic process and the benchmark model, and the better the effect of the selected virtual cavity. Step 3.2: The cavities corresponding to each rotating component in the volumetric dynamics model of the variable cycle engine constitute a dimension. The target search space contains m particles, where the nth particle is the nth particle. Each particle represents a set of virtual cavities. , No. The velocity of each particle is expressed as , No. The optimal position found by an individual particle so far is called the individual extreme value. The optimal location found so far by the entire particle swarm is the global extremum. After finding the individual and global maxima based on the particle swarm optimization algorithm, the particles update their velocity and position. Step 3.3: When the iteration satisfies the optimization metric When the value is at its maximum, the optimal virtual cavity is obtained.

2. The improved volumetric dynamics modeling method for a variable-cycle engine with an adaptive virtual cavity according to claim 1, characterized in that, The particle updates its velocity and position in the following way: ， ， in, Indicates time, Represents an individual extreme value. Represents the global extremum. and For learning factors; and for Uniformly distributed random numbers within a certain range increase the randomness of particle flight; It is particle velocity. .

3. The improved volumetric dynamics modeling method for a variable-cycle engine with an adaptive virtual cavity according to claim 1, characterized in that, Step 4 specifically involves: The cavity size in the variable cycle engine volumetric dynamics model is set to the optimal virtual cavity. This is to optimize the real-time performance of the volumetric dynamics model calculation for the variable cycle engine.

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