An overall operation optimization method for an aero-engine thermal control system

Abstract

The application discloses an aero-engine thermal control system overall operation optimization method, comprising the following steps: S1, establishing an equivalent energy flow model of the system; S2, establishing a flow constraint equation describing power balance and resistance balance of working medium pressure distribution law; S3, constructing a mathematical model for overall optimization of the thermal system; S4, presetting initial values of unknown variables in a heat exchanger thermal resistance calculation formula; S5, solving numerical values of the remaining unknown variables in the mathematical model for overall optimization of the system; S6, updating the unknown variables in the heat exchanger thermal resistance calculation formula in S4 based on the calculated numerical values, and repeatedly executing S4-S5 until the unknown variables in the heat exchanger thermal resistance calculation formula converge in the updating process. The aero-engine thermal control system overall operation optimization method has the coupling relationship between various parameters in the system retained, the complexity and difficulty of the solving model are reduced, and the solving efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of aero-engines, and in particular to a method for optimizing the overall operation of an aero-engine thermal control system. Background Technology

[0002] Aero engines are highly complex and precise thermodynamic machines. As the heart of an aircraft, they not only power flight but also serve as a crucial driving force for the development of aviation. Every significant revolution in human aviation history is inextricably linked to advancements in aero engine technology. Engine thermal control technology is currently one of the key technologies for improving engine power density, fuel economy, and reliability, and it is also an important direction for the development of intelligent engine control technology. Advanced engine thermal control technology allows engines to operate within their optimal temperature range under various conditions, thereby improving fuel economy, power, passenger comfort, and emissions. Therefore, optimizing the operational strategies of the thermal control system during engine remode transitions is extremely important. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the overall operation of an aero-engine thermal control system. This system is based on an intermediate loop thermal management scheme, which establishes an intermediate loop to achieve heat exchange between fuel and lubricating oil inside the engine. While ensuring that the temperatures of the fuel and lubricating oil within the entire envelope do not exceed the limits, the system also dissipates heat. Optimization requires maintaining a constant total system energy consumption. By calculating the operating frequency of each oil pump in the system under coordinated action, the maximum heat dissipation rate of the system is increased, thereby maximizing the total heat exchange under the same boundary conditions.

[0004] To achieve the above objectives, the present invention provides a method for optimizing the overall operation of an aero-engine thermal control system, comprising the following steps:

[0005] S1. Construct an equivalent energy flow model based on Kirchhoff's laws to describe the overall laws of heat transfer and conversion as the heat transfer constraint equation of the system.

[0006] S2. Analyze the pressure change characteristics of the power equipment in the system, establish the functional relationship between the pressure change and mass flow rate of the working fluid after it flows through the pipeline and valves, and establish the flow constraint equations of dynamic balance and resistance balance to describe the pressure distribution law of the working fluid.

[0007] S3. Couple the constraint equations obtained in steps S1 and S2 to construct a mathematical model for optimizing the overall operation of the nonlinear thermodynamic system with the goal of maximizing the heat exchange of the system.

[0008] S4. Preset the initial values ​​of unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system;

[0009] S5. Solve the numerical values ​​of the remaining unknown variables in the mathematical model of the overall operation optimization of the nonlinear thermodynamic system through calculation;

[0010] S6. Based on the calculated values ​​of the remaining unknown variables in step S5, update the unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system preset in step S4. Repeat steps S4 to S5 until the unknown variables in the heat exchanger thermal resistance calculation formula converge during the update process.

[0011] Preferably, in step S1, an equivalent energy flow model of the system is established based on the thermal system process structure, and a set of governing equations describing the overall laws of heat transfer and conversion is constructed according to Kirchhoff's laws, including:

[0012]

[0013]

[0014]

[0015]

[0016] in:

[0017]

[0018] Where m is the mass flow rate of the working fluid; c p ρ is the specific heat capacity at constant pressure of the working fluid; Q is the heat transfer capacity of the heat exchanger; R is the thermal resistance of the heat exchanger; K is the heat transfer coefficient of the heat exchanger; A i The heat exchange area is T; the temperature is h; the subscripts h and c represent the hot and cold sides of the heat exchanger, respectively; and the subscript in represents the heat exchanger inlet.

[0019] Preferably, in step S2, the pressure change characteristics of the working fluid flowing through each component are analyzed, and the functional relationship between the pressure change and mass flow rate after the working fluid flows through each component is established. This results in the creation of flow constraint equations describing the pressure distribution law of the working fluid, including:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Where ω is the pump frequency; ρ is the density; g is the acceleration due to gravity; and a is the characteristic parameter of the pump and the pipeline network. i d i All are known variables.

[0028] Preferably, in step S3, a mathematical model for optimizing the overall operation of the nonlinear thermodynamic system is constructed, constrained by the coupled influence relationships of working fluid flow, heat transfer, and heat conversion, to obtain the optimization equations, including:

[0029]

[0030] In the formula P t The total energy consumption of the system is expressed in W; P1, P2, P3, P4, and P5 correspond to the energy consumption required by the variable frequency pumps P1-P5 in the system, respectively; α, β, and γ are the multipliers in the Lagrange multiplier method.

[0031] Based on the established mathematical model for the overall operation optimization of the nonlinear thermodynamic system, the operation optimization problem contains 22 constraint equations and 22 unknown variables, namely ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8, ω9, ω1, ω1, ω2 ... 10 , ω2, ω3, ω4, m1, m 11 m 12 m2, m3, m4, m 10 m 30 m 40 ,α,β1,β2,β3,β4,β5,β6,β7.

[0032] Preferably, in step S4, the unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system include: the mass flow rates of the cold and hot fluids in the heat exchanger and the operating frequency of the oil pump.

[0033] Therefore, the present invention employs the above-mentioned method for optimizing the overall operation of an aero-engine thermal control system, and its technical effects are as follows:

[0034] (1) The solution process of this invention uses the Lagrange multiplier method for multi-objective optimization, which preserves the coupling relationship between the parameters in the system. When constructing the system heat transfer constraint, the introduction of thermal resistance strips away the original nonlinear implicit coupling relationship between variables in the heat transfer process, reduces the complexity and difficulty of the solution model, and improves the solution efficiency.

[0035] (2) All constraint equations solved by this invention are inherent system constraints, which is fundamentally different from linear simplification of the nonlinear model of the system, thus further ensuring the accuracy of the calculation.

[0036] (3) The present invention can obtain the optimal operating conditions of the system under different working conditions by solving the problem, so that the system can obtain the maximum heat dissipation efficiency.

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the hierarchical divide-and-conquer solution method for the overall operation optimization solution model of an aero-engine thermal control system according to the present invention;

[0039] Figure 2 This is an experimental schematic diagram of an aero-engine thermal control system.

[0040] Figure 3 This is a global energy flow model for an aero-engine thermal control system.

[0041] Figure 4 This is a global flow model for an aero-engine thermal control system.

[0042] Figure 5 A flowchart for solving a mathematical model for optimizing the operation of an aero-engine thermal control system. Detailed Implementation

[0043] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0045] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0046] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. These other embodiments are also covered within the scope of protection of this invention.

[0047] It should also be understood that the specific embodiments described above are only used to explain the present invention, and the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0048] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0049] All prior art documents cited in this specification are incorporated herein by reference in their entirety and are therefore part of the disclosure of this invention.

[0050] Example 1

[0051] An experimental platform for the thermal control system of an aero-engine was built. The experimental principle diagram is shown below. Figure 2 As shown, step S1 involves establishing an overall energy flow model of the thermal system based on its process structure, as follows: Figure 3 As shown, by combining Kirchhoff's laws, the governing equations describing the overall laws governing heat transfer and conversion are derived:

[0052]

[0053]

[0054]

[0055]

[0056] in:

[0057]

[0058] The meanings of each variable are as follows:

[0059] G = mc p —Heat capacity flow of the working fluid, W / K;

[0060] m—mass flow rate of the working fluid, kg / s;

[0061] c p —Specific heat capacity at constant pressure of the working fluid, J / kg / K;

[0062] Q – Heat exchange capacity of the heat exchanger, in W;

[0063] R—thermal resistance of the heat exchanger, K / W;

[0064] K – Heat transfer coefficient of the heat exchanger, W / K / m 2 ;

[0065] A – Heat exchange area of ​​the heat exchanger, in meters (m²) 2 ;

[0066] T – Temperature, °C;

[0067] The subscripts h and c represent the hot side and cold side of the heat exchanger, respectively, and the subscript in represents the heat exchanger inlet;

[0068] The thermal conductivity KA of the heat exchanger in the system can be predicted from experimental data using an artificial neural network, and the inlet temperature of the heat exchanger is a known quantity.

[0069] Due to the introduction of thermal resistance in the energy flow model, the originally complex implicit nonlinear relationships between variables in the heat transfer process are decomposed into linear relationships between thermal resistance, heat transfer, and fluid inlet temperature difference, as well as explicit nonlinear relationships between thermal resistance, working fluid flow rate and specific heat capacity, and heat exchanger heat transfer area and heat transfer coefficient. Furthermore, because the nonlinear factors in the heat transfer process are all transferred to the explicit calculation formula of thermal resistance, the control equations of the energy flow model derived from circuit principles exhibit a regular linear form. Mathematically, both linear equations and explicit nonlinear equations are easy to solve. Therefore, the energy flow model helps improve the nonlinear properties of the system control equations, providing a new approach for designing more stable and faster mathematical model solving algorithms.

[0070] Step S2, establish the overall flow model of the system as follows: Figure 4 As shown, the pressure change characteristics of the working fluid flowing through each component are analyzed. The functional relationship between the pressure change and mass flow rate after the working fluid flows through each component is established, and a set of flow constraint equations describing the pressure distribution law of the working fluid, based on dynamic equilibrium and resistance equilibrium, is constructed.

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] The meanings of each variable are as follows:

[0079] ω — pump frequency, Hz;

[0080] ρ — density, kg·m -3 ;

[0081] g — acceleration due to gravity, m·s -2 ;

[0082] Characteristic parameters a of pumps and piping networks i d i All are known variables.

[0083] Step S3: Construct a mathematical model for the overall optimization of the thermodynamic system. The solution flowchart is as follows: Figure 5 As shown, the optimization equation is obtained by constraining the coupled influence of working fluid flow, heat transfer, and heat conversion.

[0084]

[0085] Based on the established system-wide optimization mathematical model, this operational optimization problem contains 22 constraint equations and 22 unknown variables, namely ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8, ω9, ω1, ω1, ω2 ...1, ω2, ω1, ω2, ω3, ω4, ω5, ω1, 10 , ω2, ω3, ω4, m1, m 11 m 12 m2, m3, m4, m 10 m 30 m 40 ,α,β1,β2,β3,β4,β5,β6,β7.

[0086] Step S4: Set the initial values ​​of the unknown variables in the heat exchanger thermal resistance calculation formula.

[0087] Step S5: Solve the numerical values ​​of the remaining unknown variables in the mathematical model of the overall optimization solution of the nonlinear thermodynamic system through calculation.

[0088] Step S6: Update the unknown variables in the heat exchanger thermal resistance calculation formula preset in S4 based on the calculated values, and repeat S4 to S5 until the unknown variables in the heat exchanger thermal resistance calculation formula converge during the update process.

[0089] Table 1 compares a set of experimental data before and after system optimization. The table shows that after optimization, the heat exchange capacity of the lubricating oil heat exchanger 1 increased by 2.8%, the heat exchange capacity of the lubricating oil heat exchanger 2 increased by 10.4%, the heat exchange capacity of heat exchanger #1 increased by 2.6%, the heat exchange capacity of heat exchanger #2 increased by 13.2%, and the total heat exchange capacity of the system increased by 8.6%. The experimental results verify the reliability of the optimization method.

[0090] Table 1 Optimization Results

[0091] equipment Heat exchange rate (W) before optimization Optimized heat exchange (W) growth rate Lubricating oil heat exchanger 1 1552 1596 2.8％ Oil-fired heat exchanger 2 3094 3414 10.4％ Heat exchanger #1 1037 1064 2.6％ Heat exchanger #2 2057 2328 13.2％ Total heat exchange 7740 8402 8.6％

[0092] Therefore, this invention adopts the above-mentioned overall operation optimization method for the thermal control system of an aero-engine, and uses the Lagrange multiplier method for multi-objective optimization. It retains the coupling relationship between the parameters in the system. When constructing the system heat transfer constraints, the introduction of thermal resistance removes the original nonlinear implicit coupling relationship between variables in the heat transfer process, reducing the complexity and difficulty of the solution model and improving the solution efficiency. All the constraint equations solved are inherent system constraints, which is fundamentally different from linear simplification of the nonlinear model of the system, thereby further ensuring the accuracy of the calculation.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the overall operation of an aero-engine thermal control system, characterized in that, Includes the following steps: S1. Construct an equivalent energy level based on Kirchhoff's laws to describe the overall laws governing heat transfer and conversion. The flow model serves as the heat transfer constraint equation for the system. S2. Analyze the pressure change characteristics of the power equipment in the system, establish the functional relationship between the pressure change and mass flow rate of the working fluid after it flows through the pipeline and valves, and establish the flow constraint equations of dynamic balance and resistance balance to describe the pressure distribution law of the working fluid. S3. Couple the constraint equations obtained in steps S1 and S2 to construct a mathematical model for optimizing the overall operation of the nonlinear thermodynamic system with the goal of maximizing the heat exchange of the system. S4. Preset the initial values ​​of unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system; S5. Solve the numerical values ​​of the remaining unknown variables in the mathematical model of the overall operation optimization of the nonlinear thermodynamic system through calculation; S6. Based on the calculated values ​​of the remaining unknown variables in step S5, update the unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system preset in step S4. Repeat steps S4 to S5 until the unknown variables in the heat exchanger thermal resistance calculation formula converge during the update process. In step S1, an equivalent energy flow model of the system is established based on the thermodynamic system process structure. A set of governing equations describing the overall laws governing heat transfer and conversion is constructed according to Kirchhoff's laws, including: （1） （2） （3） （4） in: （ i =1,2,3,4） （5） in, m The mass flow rate of the working fluid; c p The specific heat capacity at constant pressure of the working fluid; Q The heat exchanger's heat transfer capacity; R The thermal resistance of the heat exchanger; K The heat transfer coefficient of the heat exchanger; A i The heat exchange area of ​​the heat exchanger; T The value represents temperature; the subscripts h and c represent the hot and cold sides of the heat exchanger, respectively, and the subscript in represents the heat exchanger inlet. In step S2, the pressure change characteristics of the working fluid flowing through each component are analyzed, and the functional relationship between the pressure change and mass flow rate after the working fluid flows through each component is established. Flow constraint equations describing the pressure distribution law of the working fluid, including dynamic equilibrium and resistance equilibrium, are then constructed. （6） （7） （8） （9） （10） （11） （12） in, ρ Density; characteristic parameters of pumps and piping networks a i , d i All are known variables; In step S3, a mathematical model for the overall operation optimization of the nonlinear thermodynamic system is constructed, constrained by the coupled influence of working fluid flow, heat transfer, and heat conversion, resulting in optimization equations, including: （13） In the formula P t Total system energy consumption, in watts (W). P 1 , P 2 , P 3 , P 4 , P 5 These correspond to the energy consumption requirements of variable frequency pumps P1-P5 in the system, respectively; α 、β The multiplier in the Lagrange multiplier method; Based on the established mathematical model for the overall operation optimization of the nonlinear thermodynamic system, the operation optimization problem contains 22 constraint equations and 22 unknown variables, namely... ω 1 ω 10 ω 2 ω 30 ω 40 、m 1 、m 11 、m 12 、m 2 、m 3 、m 4 、m 10 、m 30 、m 40 、α、 β 1 、β 2 、β 3 、β 4 、β 5 、β 6 、β 7.

2. The method for optimizing the overall operation of an aero-engine thermal control system according to claim 1, characterized in that, In step S4, the unknown variables in the mathematical model for optimizing the overall operation of the nonlinear thermodynamic system include: the mass flow rates of the cold and hot fluids in the heat exchanger and the operating frequency of the oil pump.

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