A method for solving the mode switching corridor in a short vertical propulsion system

Abstract

This patent discloses a method for solving corridors in short vertical propulsion systems. Addressing the convergence problem caused by improper initial value selection during corridor solving, this method selects a known feasible control plan as the initial value, ensuring model convergence within this plan. Then, it gradually expands the search range to avoid non-convergence due to initial value selection. To address the slow solution speed of traversal and optimization methods, this scheme transforms the optimization problem into an equation-solving problem, utilizing the second-order convergence property of Newton's method to achieve high-precision and rapid solution of corridor boundaries.

Description

Technical Field

[0001] This invention relates to a method for solving the mode transition corridor of a short vertical propulsion system, which belongs to the field of aero-engine control. Background Technology

[0002] Short takeoff and vertical landing (STOVL) aircraft are aircraft capable of taking off and landing vertically or over very short distances. These aircraft typically employ a short takeoff and vertical landing (STOVL) mode, hence the name STOVL. During takeoff, STOVL aircraft utilize both their wings and propulsion system (such as a main engine with thrust vectoring nozzles, a lift fan, or a lift engine) to generate lift, while during vertical landing, they rely entirely on the propulsion system for lift. STOVL aircraft offer flexible takeoff and landing capabilities and high-speed flight, combining the advantages of conventional fixed-wing aircraft and helicopters. They can be deployed on amphibious assault ships and small to medium-sized aircraft carriers, making them an important type of aviation equipment. The STOVL propulsion system is crucial for ensuring the short takeoff and vertical landing (STOVL) capability of fighter jets. The biggest difference between the STOVL propulsion system and conventional propulsion systems is that, in addition to generating primary thrust, it must also provide auxiliary lift to meet the lift requirements during STOVL and provide auxiliary control torque to meet the attitude control requirements during STOVL.

[0003] Shaft-driven lift fan (SDLF) supersonic short vertical propulsion systems, exemplified by the F135-PW-600, can meet the bypass ratio (BPR) requirements of different modes, exhibiting high thermodynamic cycle efficiency and propulsion system reusability. However, during propulsion mode transitions, the low-pressure shaft power extraction and bypass bleed air volume undergo significant changes, greatly impacting the matching of the lift cruise engine. This process requires comprehensive adjustment of actuators such as the nozzle throat area (A8) and the rear variable area bypass injector (RVABI) to substantially alter the low-pressure shaft power transmission and bypass bleed air volume while ensuring safety constraints such as stable turbine inlet temperature, no surge in the compression components, and no criticality or backflow in the mixing chamber. Therefore, it is necessary to design a mode transition control plan for the SDLF propulsion system to achieve safe and smooth mode transitions.

[0004] Although dynamic models of stub-vertical-short-span propulsion systems have been established, enabling mode transition simulations, research on mode transition control plans remains limited. For a long time, mode transition control plans for stub-vertical-short-span propulsion systems have often been obtained through trial and error. These methods fail to offer scientific control plan design approaches, lacking universality and often resulting in propulsion system monitoring variables exceeding limits. In recent years, research has emerged using optimization methods to design control plans. However, optimization methods force the propulsion system to adhere closely to safety boundaries (i.e., the control variable boundaries required for safety during mode transitions), thus demanding high model and control accuracy and exhibiting low robustness, hindering practical applications. To address these issues, the concept of a transition corridor, borrowed from tiltrotor and tail-seat aircraft, can be used to determine the safety boundaries of control variables during mode transitions, construct mode transition corridors for the propulsion system, and design robust control plans that are far removed from these safety boundaries.

[0005] However, compared to the transition corridors in tiltrotor aircraft and tail-seat aircraft, the mode transition corridors in short vertical propulsion systems have high dimensionality, high computational cost, and convergence issues. When using traditional ergonomic methods to solve the corridor, not only is the time cost high, but it also easily exceeds the representation range of the propulsion system model, making existing methods unsuitable for solving mode transition corridors. To overcome these problems, recent literature has explored optimization methods for solving corridor boundaries, offering higher accuracy and faster solutions compared to ergonomic methods. However, when the research object model has convergence issues, or when the corridor needs to be solved near the boundary of the model's representation range, the accuracy and speed of optimization methods deteriorate significantly, making them practically unusable. Summary of the Invention

[0006] This invention addresses the shortcomings of existing technologies by proposing a method for solving corridors in short vertical propulsion systems. To address the convergence problem caused by improper initial value selection during corridor solving, this method selects a known feasible control plan as the initial value, ensuring model convergence within this plan. The search range is then gradually expanded to avoid non-convergence due to initial value selection. To address the slow solution speed of traversal and optimization methods, this scheme transforms the optimization problem into an equation-solving problem, utilizing the second-order convergence property of Newton's method to achieve high-precision and rapid solution of corridor boundaries.

[0007] The steady-state mode transition corridor of a short vertical and vertical propulsion system is defined as follows: The steady-state mode transition corridor of a short vertical and vertical propulsion system is the control input u that ensures the safety constraints are met by the steady-state model of the lift cruise engine during mode transitions. p set U co .

[0008] The control inputs involved in the propulsion system mode switching control plan mainly include the clutch axial clamping force F. cl Rolling nozzle area A RN Fuel flow rate W f The nozzle throat area is A8, and the outer bypass duct outlet area is A. 52 Among them, F cl and A RN The process of driving system mode transition is directly represented and is generally used as a scheduling variable in the control plan. The control plan can then be expressed as:

[0009]

[0010] For short vertical propulsion systems, there are five control inputs, resulting in a five-dimensional transition corridor, which makes research difficult. Therefore, this paper simplifies the process as follows:

[0011] 1. Clutch disengagement (engagement) and nozzle closure (opening) are the primary objectives of propulsion system mode switching. Clutch clamping force and nozzle area represent the degree of clutch disengagement (engagement) and nozzle closure (opening), respectively, directly reflecting the progress of mode switching. These two variables reflecting the mode switching process can be functionally related, eliminating one degree of freedom. Therefore, the original control plan can be simplified to:

[0012]

[0013] Among them, A RN =A RN (F cl In this paper, the relationship is assumed to be linear, and the proportionality coefficient is determined by the second design point.

[0014] 2. Since the total temperature before the turbine, T4, has a significant impact on thermal efficiency, it is desirable to adjust W... f This allows T4 to maintain T during mode transitions. 4,goal This move can further simplify the corridor, reducing its dimensionality.

[0015] At this point, only three of the original five control variables can change independently: the nozzle throat area u1 = A8, the bypass duct outlet area u2 = A8, and so on. 52 Clutch clamping force u3=F cl These three variables can form the coordinate axes of the propulsion system mode transition corridor.

[0016] The objective of this invention is achieved through the following technical solution.

[0017] This invention proposes a method for solving the mode switching corridor of a short vertical propulsion system. It can solve for the upper boundary of the short vertical propulsion system corridor with the clutch clamping force u1 as the x-axis, the nozzle throat area u2 as the y-axis, and the outer bypass duct outlet area u3 as the z-axis. and lower boundary The main steps include:

[0018] Step 1: Design a feasible mode transition control plan using the variable substitution method or other traditional methods, where the control plan for u2 is u 20 The control plan for (u1) and u3 is u 30 (u1), and set i = 0, to be substituted into steps 3 and 4; proceed to step 2;

[0019] Step 2: Set j = 0, and update the clutch clamping force u1 = u in the current cycle. 1,i :

[0020]

[0021] Used to substitute into step 5, proceeding to step 3;

[0022] Step 3: Update the nozzle throat area u2 = u in the current cycle. 2,j :

[0023]

[0024] Used to substitute into step 5, proceeding to step 4;

[0025] Step 4: Given an initial value u for u3 3,1 If j = 0, then u 3,1 =u 30 (u 1,i If j>0, then let:

[0026]

[0027] Used to substitute into step 5;

[0028] Step 5: Using u3 as the decision variable, u 3,1 As initial values, the constraint set interval solution method based on Newton's method is used to solve the problem within the constraint [y]. min -y yy max The upper bound of u3 ≤ 0 and the lower realm Substitute this into step 6, where y = y(u1, u2, u3) is the output constraint of the propulsion system, y min and y max These are the lower and upper limits of the constraint output quantity y, respectively;

[0029] Step 6: Comparison and The size of j determines whether the constraint set exists; if j <N u2 If j = N, then let j = j + 1 and proceed to step 3; if j = N u2 -1 and i <N u1 If j = N, then let i = i + 1 and proceed to step 2; u2 -1 and i=N u1 If the value is -1, proceed to step 7;

[0030] Step 7: The solution process terminates, and the output is released. and Where i = 0, 2, ..., N u1 -1, j = 0, 2, ..., N u2 -1.

[0031] In the above solution method, the constraint set interval solution method based on Newton's method described in step 5 can be used to solve multiple monotonic constraints C. i (z)≤0,i=1,2,L,N c The upper bound of the lower z max and the lower bound z min It mainly includes the following steps:

[0032] Step 1: Determine the decision quantity Initial value z1, monotonic constraint function C i (z), i = 1, 2, ..., N c Convergence tolerance (tol) and maximum number of iterations (N) iter,max And substitute it into step 2 to start z max Iterative calculation;

[0033] Step 2: Set k = 1, then proceed to Step 3;

[0034] Step 3: Based on the iterative approximate solution z from the previous step k When k=1, z k =z1, calculate N using the following formula c The increment Δz of one possible iterative approximate solution k,i And substitute it into step 4:

[0035]

[0036] Step 4: Select the increment Δz of the iterative approximate solution using the following formula. k And substitute it into step 5:

[0037] Δz k =minΔz k,i ,i∈S i (19)

[0038] Among them, Si To make C i The set of increasing i:

[0039]

[0040] I is the set of i that do not satisfy the constraints:

[0041] I(z)@{i|C i (z)≥0} (21)

[0042] The complete set of U for i:

[0043] U = {1, 2, L, N} c} (twenty two)

[0044] Step 5: Update the approximate solution z k Substitute into step 6:

[0045] z k+1 =z k +Δz k (twenty three)

[0046] Step 6: The convergence condition for the iteration is:

[0047] |maxC i (z k )| <tol,i∈S i (twenty four)

[0048] If the iteration number k is greater than the maximum iteration number N iter,max If the iteration convergence condition shown in equation (24) is not met, return to step 3; if the iteration convergence condition shown in equation (24) is met, proceed to step 7 to start z. min Iterative calculation;

[0049] Step 7: Set k = 1 and proceed to step 8;

[0050] Step 8: Based on the iterative approximate solution z from the previous step k (when k=1 z) k =z1), use equation (18) to calculate N c The increment Δz of one possible iterative approximate solution k,i And substitute it into step 9;

[0051] Step 9: Select the increment Δz of the iterative approximate solution using the following formula. k And substitute it into step 10:

[0052] Δz k =maxΔz k,i ,i∈S d (25)

[0053] Among them, S d To make C i The set of decreasing i:

[0054]

[0055] I is the set of i that do not satisfy the constraints:

[0056] I(z)@{i|C i (z)≥0} (27)

[0057] Step 10: Update the approximate solution z according to equation (23). k Substitute this into step 11;

[0058] Step 11: The convergence condition for the iteration is:

[0059] |maxC i (z k )| <tol,i∈S d (28)

[0060] If the iteration number k is greater than the maximum iteration number N iter,max If the iteration convergence condition shown in equation (28) is not met, return to step 8; if the iteration convergence condition shown in equation (28) is met, proceed to step 12.

[0061] Step 12: The solution process terminates, and the obtained z is output. min and z max . Attached Figure Description

[0062] Figure 1 The process for solving the corridor boundary in the mode conversion of a short vertical propulsion system;

[0063] Figure 2 This is a configuration diagram of a short vertical propulsion system;

[0064] Figure 3 A diagram showing the calculation results of the mode switching corridor for a short vertical propulsion system. Detailed Implementation

[0065] The following section uses a short vertical propulsion system as an example to establish its mode conversion corridor, thereby illustrating the content of this invention.

[0066] Step 1: Determine the model parameters and establish a component-level model of the short vertical propulsion system.

[0067] The table below shows the design point parameters for the propulsion system in horizontal flight mode:

[0068] Table 1 Design Point Parameters for Horizontal Flight Mode

[0069]

[0070]

[0071] The design parameters for the hovering operating point of the propulsion system are shown in the table below:

[0072] Table 2 Design parameters for hovering working point

[0073]

[0074] Using these parameters, establish as follows Figure 2 The figure shows a component-level model of a short vertical propulsion system.

[0075] Step 2: Determine the main outputs and limits of the model.

[0076]

[0077]

[0078] Step 3: Make assumptions about the mode conversion process so that the mode conversion corridor can be reduced to three dimensions.

[0079] Assumption 1: A RN =A RN (F cl Let A be a linear relationship, where A RN F is the area of ​​the rolling nozzle. cl The linear proportional coefficient is determined by the second design point for the clutch clamping force, thereby determining the rolling nozzle area under any working condition and eliminating a corridor dimension.

[0080] Assumption 2: The total temperature T4 before the turbine is kept at 2500K, so that the fuel flow rate under any operating condition can be determined and a corridor dimension can be eliminated;

[0081] Assumption 3: Assuming that the change in rotor speed of the lift / cruise engine is small during mode switching, the rotor dynamics can be ignored, and the dynamic model degenerates into a steady-state model. This allows the rotor speed under any operating condition to be determined, eliminating the two corridor dimensions.

[0082] At this point, the degrees of freedom of the propulsion system model are reduced to three dimensions. A mode conversion corridor for the short vertical propulsion system can be established with the clutch clamping force u1 as the x-axis, the nozzle throat area u2 as the y-axis, and the bypass duct outlet area u3 as the z-axis.

[0083] Step 4: Use as follows Figure 1 The solution method for the mode switching corridor of the short vertical propulsion system shown is as follows: Figure 3 The mode conversion corridor is shown.

Claims

1. A method for solving the mode-switching corridor in a short vertical propulsion system, characterized in that, The clutch clamping force can be solved. x-axis represents the area of ​​the nozzle throat. The y-axis represents the area of ​​the outer bypass duct outlet. The upper boundary of the corridor for the short vertical propulsion system along the z-axis and lower boundary And includes the following steps: Step 1: Design a feasible mode transition control plan using the variable substitution method or other traditional methods, whereby... The control plan is , The control plan is and set This is used to substitute into steps 3 and 4; proceed to step 2; Step 2: Set And update the clutch clamping force in the current cycle. : (1) Used to substitute into step 5, proceeding to step 3; Step 3: Update the nozzle throat area in the current cycle. : (2) Used to substitute into step 5, proceeding to step 4; Step 4: Given initial value ;like ,but ;like Then let: (3) Used to substitute into step 5; Step 5: with As decision variables, Using the initial values, a constraint set interval solution method based on Newton's method is used to solve the problem within the constraints. Down The upper realm and the lower realm And substitute it into step 6, where To advance the output constraints of the system, and These are the constraint output quantities. The lower and upper limits; Step 6: Comparison and The size of the constraint set determines whether it exists; if Then let And proceed to step 3; if and Then let And proceed to step 2; if and Then proceed to step 7; Step 7: The solution process terminates, and the output is released. and ,in , .

2. The corridor boundary solution method according to claim 1, characterized in that, The constraint set interval solution method based on Newton's method described in step 5 can be used to solve multiple monotonic constraints. Down The upper realm and the lower realm : Step 1: Determine the decision quantity initial value Monotonic constraint function Convergence tolerance and maximum number of iterations And substitute it into step 2 to begin. Iterative calculation; Step 2: Let Proceed to step 3; Step 3: Based on the iterative approximate solution from the previous step ,when hour Calculate using the following formula Increment of one possible iterative approximate solution And substitute it into step 4: (4) Step 4: Select the increment of the iterative approximate solution using the following formula. And substitute it into step 5: (5) in, In order to make Increasing i The set of: (6) For unsatisfied constraints i The set of: (7) for i The complete collection: (8) Step 5: Update the approximate solution Substitute into step 6: (9) Step 6: The convergence condition for the iteration is: (10) If the number of iterations It has exceeded the maximum number of iterations. Then return to step 1 and reset the initial values ​​for the iteration. If the iterative convergence condition shown in equation (10) is not met, return to step 3; if the iterative convergence condition shown in equation (10) is met, proceed to step 7. Iterative calculation; Step 7: Let Then proceed to step 8; Step 8: Based on the iterative approximate solution from the previous step ,when hour Calculate using equation (4) Increment of one possible iterative approximate solution And substitute it into step 9; Step 9: Select the increment of the iterative approximate solution using the following formula. And substitute it into step 10: (11) in, In order to make Decreasing i The set of: (12) For unsatisfied constraints i The set of: (13) Step 10: Update the approximate solution according to equation (9). Substitute this into step 11; Step 11: The convergence condition for the iteration is: (14) If the number of iterations It has exceeded the maximum number of iterations. Then return to step 1 and reset the initial values ​​for the iteration. If the iterative convergence condition shown in equation (14) is not met, return to step 8; if the iterative convergence condition shown in equation (14) is met, proceed to step 12. Step 12: The solution process terminates, and the output is obtained. and .

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