Short vertical propulsion system mode conversion corridor solving method

Abstract

The invention discloses a short vertical propulsion system mode conversion corridor solving method, and aims to solve the convergence problem caused by improper selection of an iteration initial value of a propulsion system model in a corridor solving process, the method selects a known feasible control plan as an initial value, ensures that the model is convergent in the control plan, and solves the convergence problem caused by the improper selection of the iteration initial value of the propulsion system model. Then, the search range is gradually expanded, and non-convergence caused by initial value selection is avoided. Aiming at the problem of slow solving speed of a traversal method and an optimization method, the scheme converts an optimization problem into an equation solving problem, and realizes high-precision rapid solving of the corridor boundary by using the second-order convergence characteristic of a Newton method.

Description

Technical Field

[0001] The invention relates to a method for solving a mode conversion corridor of a short-to-vertical propulsion system, and belongs to the field of aeroengine control. Background Art

[0002] A short takeoff and vertical landing aircraft is an aircraft that can take off and land vertically or at a very short distance. This type of aircraft usually adopts a short takeoff and vertical landing takeoff and landing mode, referred to as a short-distance vertical aircraft. During takeoff, a short-distance vertical aircraft uses both wings and propulsion systems (such as main engines with thrust vector nozzles, lift fans or lift engines, etc.) to generate lift, and during vertical landing, it relies entirely on the propulsion system to generate lift. Short-distance vertical aircraft can take off and land flexibly, and can achieve fast flight. It has the advantages of conventional fixed-wing aircraft and helicopters. It can be carried on amphibious assault ships and small and medium-sized aircraft carriers. It is an important aviation equipment. Among them, the short-distance vertical propulsion system is the key to ensuring that fighter jets have short-distance / vertical takeoff and landing capabilities. The biggest difference between the short-distance vertical propulsion system and the existing conventional propulsion system is that in addition to generating the main thrust, it also needs to provide auxiliary lift to meet the lift requirements of the fighter jet during short-distance / vertical takeoff and landing, and provide auxiliary control torque to meet the fighter jet’s attitude control requirements during short-distance / vertical takeoff and landing.

[0003] The shaft driven lift fan (SDLF) supersonic short vertical propulsion system represented by F135-PW-600 can meet the bypass ratio (BPR) requirements of different modes, and has high thermodynamic cycle efficiency and propulsion system reusability. However, in the mode conversion of the propulsion system, the low-pressure shaft power extraction and the amount of external bypass air intake change greatly, which greatly affects the matching of the lift cruise engine. This process requires comprehensive adjustment of the tail nozzle throat area (A8), rear variable area bypass injector (RVABI) and other actuators, and greatly changes the low-pressure shaft transmission power and the amount of external bypass air intake under the premise of ensuring the stability of the temperature before the lift cruise engine turbine, the non-surge of the compression components, and the non-critical and non-reflow of the mixing chamber. Therefore, it is necessary to carry out the design of the mode conversion control plan of the short vertical propulsion system to achieve safe and smooth mode conversion.

[0004] Although the academic community has established a dynamic model of the short-to-vertical propulsion system, which can realize mode conversion simulation, the research on the mode conversion control plan is still very limited. For a long time, the mode conversion control plan of the short-to-vertical propulsion system has often been obtained by trial and error. These materials have failed to propose a scientific control plan design method, which not only lacks universality, but also often causes the propulsion system monitoring quantity to exceed the limit range. In recent years, there have been studies in this field to design control plans using optimization methods. However, the optimization method will make the propulsion system close to the safety boundary (that is, the control quantity boundary required to ensure safety during mode conversion). Therefore, the accuracy requirements for the model and control are high, and the robustness of the control plan is low, which hinders its practical application. In response to the above problems, we can learn from the concept of transition process corridors in the fields of tilt-rotor aircraft and tail-seat aircraft, determine the safety boundary of the control quantity during mode conversion, construct the mode conversion corridor of the propulsion system, and design a robust control plan away from each safety boundary.

[0005] However, compared with the transition process corridors in the fields of tilt-rotor aircraft and tail-seat aircraft, the mode transition corridor of the short vertical propulsion system has high dimensionality, large computational complexity, and the model has convergence problems. When using the traditional traversal method to solve the corridor, there is not only the problem of high time cost, but it is also easy to exceed the representation range of the propulsion system model, making it difficult to apply the existing solution methods to the solution of the mode transition corridor of the propulsion system. In order to overcome the above problems, in recent years, there have been literatures that use optimization methods to solve the corridor boundaries, which can solve the corridor boundaries with high accuracy and speed compared to the traversal method. However, when the research object model has convergence problems, or when it is necessary to solve the corridor near the boundary of the model representation range, the solution accuracy and speed of the optimization method will be significantly deteriorated, and it may even be difficult to use in practice. Summary of the invention

[0006] The present invention is aimed at the deficiencies in the prior art and proposes a method for solving the corridor of mode conversion of a short vertical propulsion system. In order to solve the convergence problem caused by improper selection of initial values ​​for propulsion system model iteration during the corridor solution process, this method selects a known feasible control plan as the initial value to ensure that the model converges within this control plan, and then gradually expands the search range to avoid non-convergence due to the selection of initial values. In order to solve the problem of slow solution speed of traversal method and optimization method, this scheme transforms the optimization problem into an equation solving problem, and uses the second-order convergence characteristics of Newton's method to achieve high-precision and rapid solution of the corridor boundary.

[0007] The definition of the steady-state mode transition corridor of the short-to-vertical propulsion system is: The steady-state mode transition corridor of the short-to-vertical propulsion system is the control input u that makes the steady-state model of the lift cruise engine meet the safety constraints in the mode transition p The collection U co .

[0008] The control input involved in the propulsion system mode conversion control plan mainly includes the clutch axial clamping force F cl , Rolling nozzle area A RN , fuel flow W f , tail nozzle throat area A 8 , the outer duct outlet area A 52 Among them, F cl and A RN Directly characterize the process of propulsion system mode conversion, generally as the scheduling quantity in the control plan, then the control plan can be expressed as:

[0009]

[0010] For the short-to-vertical propulsion system, there are five control inputs, and the transition corridor has five dimensions, which is difficult to study. Therefore, this paper makes the following simplification:

[0011] 1. The clutch disengagement (engagement) and the roll nozzle closing (opening) are the main purposes of the propulsion system mode conversion. The clutch clamping force and the roll nozzle area respectively represent the degree of clutch disengagement (engagement) and roll nozzle closing (opening), which directly reflect the progress of mode conversion. These two variables reflecting the process of mode conversion can construct a functional relationship with each other, eliminating one degree of freedom. Therefore, the original control plan can be transformed into:

[0012]

[0013] Among them, A RN =A RN (F cl ) is set as a linear relationship in this paper, and the proportionality coefficient is determined by the second design point.

[0014] 2. Due to the total temperature before the turbine T 4 The thermal efficiency is greatly affected, so it is hoped that by adjusting W f , so that T 4 Able to maintain T during mode switching 4,goal This move can further simplify the corridor and achieve a reduction in corridor dimensions.

[0015] At this point, only three of the original five control variables can be changed independently, namely, the tail nozzle throat area u 1 =A 8 、Outer duct outlet area u 2 =A 52 and clutch pressure u 3 =F cl These three variables can constitute the coordinate axes of the propulsion system mode transition corridor.

[0016] The purpose of the present invention is achieved through the following technical solutions.

[0017] The present invention proposes a method for solving the mode conversion corridor of a short vertical propulsion system. 1 is the x-axis, and the tail nozzle throat area u 2 is the y-axis, the outer duct outlet area u 3 The upper boundary of the short vertical propulsion system corridor on the z axis and the lower boundary The main steps include:

[0018] Step 1: Use variable substitution or other traditional methods to design a feasible mode transition control plan, where u 2 The control plan is u 20 (u 1 ),u 3 The control plan is u 30 (u 1 ), and set i=0, which is used to substitute into step 3 and step 4; go to step 2;

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

[0020]

[0021] Substitute into step 5 and proceed to step 3;

[0022] Step 3: Update the tail nozzle throat area u under the current cycle 2 =u 2,j :

[0023]

[0024] Substitute into step 5 and proceed to step 4;

[0025] Step 4: Given u 3 The initial value u 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: Use u 3 is the decision variable, u 3,1 As the initial value, use the constraint set interval solution method based on Newton's method to solve the constraint [y min-y yy max ]≤0 next u 3 The upper bound of and the lower bound Substitute into step 6, where y = y (u 1 ,u 2 ,u 3 ) is the output constraint of the propulsion system, y min and max are the lower and upper limits of the constrained output y respectively;

[0029] Step 6: Compare and The size of the constraint set determines whether it exists; if j <N u2 -1, then set j = j + 1 and go to step 3; if j = N u2 -1 and i <N u1 -1, then set i=i+1 and go to step 2; if j=N u2 -1 and i=N u1 -1, go to step 7;

[0030] Step 7: The solution process ends and outputs and where i = 0, 2, L, N u1 -1,j=0,2,L,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 monotone constraints C i (z)≤0,i=1,2,L,N c The upper bound z of the lower z max and the lower bound z min , mainly including the following steps:

[0032] Step 1: Determine the decision volume Initial value z 1 , monotone constraint function C i (z),i=1,2,L,N c , convergence tolerance tol and maximum number of iterations N iter,max , and substitute into step 2 to start z max Iterative calculation;

[0033] Step 2: Set k=1 and go to step 3;

[0034] Step 3: Approximate solution z based on the iteration in the previous step k , when k = 1, z k =z 1 , use the following formula to calculate N c possible iterative approximate solution increments Δzk,i And substitute into step 4:

[0035]

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

[0037] Δz k =minΔz k,i ,i∈S i (19)

[0038] Among them, S i In order to make C i The set of increasing i:

[0039]

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

[0041] I(z)@{i|C i (z)≥0} (21)

[0042] U is the complete set of i:

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

[0044] Step 5: Update the iterative approximate solution z k , substitute into step 6:

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

[0046] Step 6: The iterative convergence condition is:

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

[0048] If the number of iterations k is greater than the maximum number of iterations N iter,max , then return to step 1 and reset the initial value z 1 ; If the iterative convergence condition shown in formula (24) does not hold, return to step 3; if the iterative convergence condition shown in formula (24) holds, enter step 7 and start z min Iterative calculation;

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

[0050] Step 8: Approximate solution z based on the iteration in the previous stepk (When k = 1, z k =z 1 ), use formula (18) to calculate N c possible iterative approximate solution increments Δz k,i And substitute into step 9;

[0051] Step 9: Use the following formula to select the iterative approximate solution increment Δz k And substitute into step 10:

[0052] Δz k =maxΔz k,i ,i∈S d (25)

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

[0054]

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

[0056] I(z)@{i|C i (z)≥0} (27)

[0057] Step 10: Update the iterative approximate solution z according to equation (23) k And substitute into step 11;

[0058] Step 11: The iterative convergence condition is:

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

[0060] If the number of iterations k is greater than the maximum number of iterations N iter,max , then return to step 1 and reset the initial value z 1 ; If the iterative convergence condition shown in formula (28) does not hold, return to step 8; if the iterative convergence condition shown in formula (28) holds, proceed to step 12;

[0061] Step 12: The solution process ends and the obtained z is output min and z max . BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Solving process for the boundary of mode transition corridor for short vertical propulsion system;

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

[0064] Figure 3 This is the calculation result diagram of the mode conversion corridor of a short vertical propulsion system; DETAILED DESCRIPTION

[0065] The following is a mode conversion corridor established by taking a short vertical propulsion system as an object to illustrate the content of the present invention.

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

[0067] The following table gives the design point parameters of the propulsion system in horizontal flight mode:

[0068] Table 1 Horizontal flight mode design point parameters

[0069]

[0070]

[0071] The design parameters of the propulsion system hovering working point are shown in the following table:

[0072] Table 2 Design parameters of hovering working point

[0073]

[0074] Using this as a parameter, establish Figure 2 The component-level model of the short and vertical propulsion system is shown.

[0075] Step 2: Determine the main output and limiting values ​​of the model.

[0076]

[0077]

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

[0079] Assumption 1: A RN =A RN (F cl ) is set as a linear relationship, where A RN is the tumble nozzle area, F cl is the clutch clamping force, and the linear proportionality coefficient is determined by the second design point, which can determine the rolling nozzle area under any working condition and eliminate one corridor dimension;

[0080] Assumption 2: Total temperature before turbine T 4 Maintaining 2500K, the fuel flow rate under any operating condition can be determined, eliminating one corridor dimension;

[0081] Assumption 3: Assuming that the lift / cruise engine rotor speed changes little during mode transition, the rotor dynamics can be ignored, and the dynamic model degenerates into a steady-state model, which can determine the rotor speed under any operating condition and eliminate the two corridor dimensions.

[0082] At this point, the propulsion system model is reduced to three dimensions, and the clutch clamping force u 1 is the x-axis, and the tail nozzle throat area u 2 is the y-axis, the outer duct outlet area u 3 Establish a mode transition corridor for the short vertical propulsion system for the z-axis.

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

Claims

1. A method for solving the mode transition corridor of a short-to-vertical propulsion system, characterized in that The upper boundary of the short vertical propulsion system corridor can be solved with the clutch clamping force u1 as the x-axis, the tail nozzle throat area u2 as the y-axis, and the outer duct outlet area u3 as the z-axis. and the lower boundary And includes the following steps: Step 1: Use variable substitution or other traditional methods to design a feasible mode conversion control plan, where the control plan of u2 is u 20 (u1), the control plan of u3 is u 30 (u1), and set i = 0, for substitution into step 3 and step 4; go to step 2; Step 2: Set j = 0 and update the clutch clamping force u1 = u in the current cycle 1,i : Substitute into step 5 and proceed to step 3; Step 3: Update the tail nozzle throat area u2 = u under the current cycle 2,j : Substitute into step 5 and proceed to step 4; Step 4: Given the initial value u of u3 3,1 ; If j = 0, then u 3,1 =u 30 (u 1,i ); if j>0, then let: Used to substitute into step 5; Step 5: Take u3 as the decision variable, u 3,1 As the initial value, use the constraint set interval solution method based on Newton's method to solve the constraint [y min -y yy max ]≤0 the upper bound of u3 and lower bound Substitute into step 6, where y = y (u1, u2, u3) is the output constraint of the propulsion system, y min and max are the lower and upper limits of the constrained output y respectively; Step 6: Compare and The size of the constraint set determines whether it exists; if j <N u2 -1, then set j = j + 1 and go to step 3; if j = N u2 -1 and i <N u1 -1, then set i=i+1 and go to step 2; if j=N u2 -1 and i=N u1 -1, go to step 7; Step 7: The solution process ends and outputs and where i = 0, 2, L, N u1 -1,j=0,2,L,N u2 -1.

2. The corridor boundary solution method according to claim 2 is characterized in that The constraint set interval solution method based on Newton's method described in step 5 can be used to solve multiple monotone constraints C i (z)≤0,i=1,2,L,N c The upper bound z of the lower z max and the lower bound z min : Step 1: Determine the decision volume Initial value z1, monotone constraint function C i (z),i=1,2,L,N c , convergence tolerance tol and maximum number of iterations N iter,max , and substitute into step 2 to start z max Iterative calculation; Step 2: Set k=1 and go to step 3; Step 3: Approximate solution z based on the iteration in the previous step k , when k = 1, z k =z1, use the following formula to calculate N c possible iterative approximate solution increments Δz k,i And substitute into step 4: Step 4: Select the iterative approximate solution increment Δz using the following formula k And substitute into step 5: Δz k =minΔz k,i ,i∈S i (5) Among them, S i In order to make C i A collection of increasing i: I is the set of i that does not satisfy the constraints: I(z)@{i|C i (z)≥0} (7) U is the complete set of i: U={1,2,L,N c } (8) Step 5: Update the iterative approximate solution z k , substitute into step 6: with k+1 =with k +Δz k (9) Step 6: The iterative convergence condition is: |maxC i (With k )| <tol,i∈S i (10) If the number of iterations k is greater than the maximum number of iterations N iter,max , then return to step 1 and reset the initial value z1; if the iterative convergence condition shown in formula (10) does not hold, then return to step 3; if the iterative convergence condition shown in formula (10) holds, then enter step 7 to start z min Iterative calculation; Step 7: Set k=1 and go to step 8; Step 8: Approximate solution z based on the iteration in the previous step k (When k = 1, z k = z1), use formula (4) to calculate N c possible iterative approximate solution increments Δz k,i And substitute into step 9; Step 9: Use the following formula to select the iterative approximate solution increment Δz k And substitute into step 10: Δz k =maxΔz k,i ,i∈S d (11) Among them, S d In order to make C i The set of decreasing i: I is the set of i that does not satisfy the constraints: I(z)@{i|C i (z)≥0} (13) Step 10: Update the iterative approximate solution z according to equation (9) k And substitute into step 11; Step 11: The iterative convergence condition is: |max C i (z k )|<tol,i∈S d (14) If the number of iterations k is greater than the maximum number of iterations N iter,max , then return to step 1 and reset the initial value z1; if the iterative convergence condition shown in formula (14) does not hold, then return to step 8; if the iterative convergence condition shown in formula (14) holds, then proceed to step 12; Step 12: The solution process ends and the obtained z is output min and z max .

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