Design method of aviation electric fuel pump based on structure optimization and intelligent control

Abstract

The present invention discloses a design method for an aviation electric fuel pump based on structural optimization and intelligent control, which belongs to the field of intelligent optimization design and control. By optimizing the structure of the gear pump, the bearing design is simplified and the diffusion zone and the unloading groove are ignored, so that it is more in line with the quality requirements in actual applications. The structural parameters are optimized using the sparrow search algorithm, thereby improving the quality and efficiency of the gear pump. By adding splines between the motor end and the gear pump end of the gear shaft, rotational isolation of the two ends is achieved, effectively solving the problem of the electric fuel pump getting stuck during the load-waiting startup phase. By redesigning the housing and comprehensively considering issues such as weight, leakage and torque, the overall design of the electric fuel pump is optimized. The super-helical direct torque control method is adopted to achieve rapid torque response and precise flow control of the electric fuel pump. The method optimizes the gain parameters of the controller through the sparrow search algorithm, thereby improving the control accuracy and steady-state performance of the system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent optimization design and control technology, and in particular to a design method for an aviation electric fuel pump based on structural optimization and intelligent control. Background Art

[0002] Currently, the fuel systems of modern large passenger aircraft mostly use electric fuel pumps with a variable displacement pump structure driven by a high-speed induction motor. This type of electric pump has some significant disadvantages, especially low energy efficiency at partial load, wear and noise caused by high speed.

[0003] The Sparrow Search Algorithm (SSA), as an emerging intelligent optimization algorithm, can be applied in the design and optimization of electric fuel pumps. By simulating the behavior of sparrows in foraging and escaping predators, SSA possesses powerful global search capabilities and can avoid falling into local optimal solutions when solving complex design optimization problems. Especially in the multi-dimensional, multi-objective optimization of electric fuel pumps, SSA can demonstrate faster convergence speed and higher stability. In addition, SSA can take into account multiple performance indicators during the design process, such as the optimization of parameters such as flow control accuracy, noise and vibration, thereby improving the overall performance of the pump body. Compared with traditional optimization methods, SSA effectively improves design efficiency and optimization quality, and provides new ideas for the efficient design and performance improvement of aviation electric fuel pumps.

[0004] As a core subsystem of aircraft engines, the fuel pump control system ensures the appropriate fuel flow and pressure to the combustion chamber under varying engine operating conditions, thereby ensuring smooth and safe aircraft operation under various flight conditions. Therefore, its control performance directly determines the safety and reliability of the entire flight system. Researching high-performance control algorithms to achieve fast and precise control is essential.

[0005] The superiority of electric fuel pumps has long been widely recognized both at home and abroad. However, electric fuel pumps for aviation still need to have technical characteristics such as fast response speed, high steady-state accuracy, wide adjustment range, strong reliability and high power density. These requirements have become difficult problems that need to be solved urgently by current technology. Summary of the Invention

[0006] The present invention provides an aviation electric fuel pump design method based on structural optimization and intelligent control. It deeply analyzes the flow characteristics of the electric fuel pump, designs a high-performance intelligent flow control system, further optimizes its structure, and combines it with intelligent optimization algorithms such as the sparrow search algorithm for innovative design. This will help promote the application and performance improvement of electric fuel pumps.

[0007] An embodiment of the present invention provides a design method for an aviation electric fuel pump based on structural optimization and intelligent control, comprising the following steps:

[0008] Step 1: To optimize the quality of an aviation electric fuel pump, the principle of the gear pump is analyzed. The strength of the gears and shaft, the minimum oil film thickness, and the design flow rate are determined to establish constraints. The overall quality of the gear pump is used as the optimization objective function to optimize the design parameters. The sparrow search algorithm is used to obtain the optimal gear pump parameter configuration and its corresponding overall quality, thereby achieving structural optimization of the gear pump.

[0009] Step 2: To address the problem of stuck aircraft electric fuel pumps, the company comprehensively considered connection, positioning, strength requirements, as well as weight, leakage, and torque issues. The company analyzed the gear shaft, bearings, left and right housings, and splines to optimize the overall design of the aircraft electric fuel pump.

[0010] Step 3: To address the problem of fast and precise speed control of the electric fuel pump, a superhelical direct torque electric fuel pump control method is used based on a sparrow search algorithm to control the electric fuel pump to meet the flow and torque response requirements. The speed is controlled by an outer loop PI controller, and the inner loop superhelical flux / torque controller controls the flux and torque respectively.

[0011] Step 4: Conduct characteristic analysis based on the optimized electric fuel pump prototype, perform no-load displacement testing and load characteristic analysis, adjust the speed and outlet pressure, obtain the characteristic curve, and verify the electric fuel pump structure optimization and control method.

[0012] Optionally, in one embodiment of the present invention, step 1 specifically includes:

[0013] Assuming the same gear parameters and the volume between gear teeth is equal to the volume of the gear teeth, the gear pump bearing is simplified and the diffusion zone and unloading groove are ignored:

[0014]

[0015] S 轴 =2πr z 2

[0016]

[0017] The overall mass of the gear pump is obtained as:

[0018] M=ρ1(2bS 轴 +BS 齿轮 )+2bρ2S 轴承

[0019] Taking the overall quality of the fuel pump as the optimization objective function, we can find the following:

[0020] minM(m,z,B,b)

[0021]

[0022] The sparrow search algorithm is used to obtain the optimal gear pump parameter configuration and its corresponding overall quality, thereby realizing the structural optimization of the gear pump.

[0023] Optionally, in one embodiment of the present invention, step 2 specifically includes:

[0024] A spline was added between the motor end of the gear shaft and the gear pump end to isolate the rotation of the two ends. The housing was redesigned, and weight, leakage, and torque issues were comprehensively considered. The final optimized overall design of the electric fuel pump was obtained:

[0025] While keeping the rotation, the size of the stator and the impeller unchanged, the length of the driving gear shaft at the left end of the impeller is lengthened to facilitate the installation of the bearing, and the right half of the casing is matched with the original end cover with stud screws; the left side of the bearing is positioned axially with a washer, and the right side is positioned axially with the casing; the driving gear shaft is connected to the left and right sides with splines; the gear pump is connected to the left half of the casing with stud screws; the left and right sides of the oil circuit are connected to the cavity where the rotor is located and the gear pump respectively, and the fuel is sucked into the oil circuit through the rotation of the rotor, enters the gear pump, and then flows out from the upper channel of the left casing.

[0026] Optionally, in one embodiment of the present invention, step 2 further includes:

[0027] Use deep groove ball bearings and add 1mm washers between bearings;

[0028] Add 7mm undercut after spline;

[0029] The O-ring is the external sealing ring of the long screw.

[0030] Optionally, in one embodiment of the present invention, step 3 further includes:

[0031] Following the principle of direct torque control, one loop controls the flux, one loop controls the torque, and the outer loop controls the speed. The outer loop uses a PI controller, and the inner loop uses a super-helical flux / torque controller.

[0032] The super-helical flux controller is designed as follows:

[0033]

[0034] Among them, the sliding mode variable is the error of the flux amplitude, and the gain K pd and K id Satisfy stability conditions;

[0035] The super-helical torque controller is designed to:

[0036]

[0037] Among them, the sliding mode variable is the torque error, and the gain K pq and K iq Satisfy stability conditions;

[0038] In the sparrow search algorithm, the position of the sparrow population is represented by the matrix X:

[0039]

[0040] Where n is the number of sparrows, d is the dimension of the variable to be optimized, and the fitness value of each sparrow is expressed as:

[0041]

[0042] F X Each row in represents the fitness value of each sparrow;

[0043] The sparrow with the highest fitness value is regarded as the producer. During the iteration process, the producer's position is updated as follows:

[0044]

[0045] Where t is the current iteration number, j = 1, 2, ..., d, is the value of the j-th dimension variable of the i-th sparrow at the t-th iteration, iter max is the maximum number of iterations, α∈(0,1] is a random number, R2(R2∈[0,1]) and ST(ST∈[0.5,1.0]) are the alarm value and safety threshold respectively, Q is a random number that obeys the normal distribution, and L is a 1×d matrix where all elements are 1;

[0046] The follower's location is updated as follows:

[0047]

[0048] Among them, X P is the optimal position among producers, X worst is the current global worst position, A is a 1×d matrix, in which all elements are randomly 1 or -1, A + =A T (AA T ) -1 ;

[0049] Randomly select an alert person from the entire sparrow population, and the position update formula of the alert person is:

[0050]

[0051] Among them, X best is the global optimal position, β~Ν(0,1) is the parameter for controlling the step size, K∈[-1,1] is a random number, f i is the current sparrow fitness value, f g and f w are the fitness values ​​of the global optimal position and the worst position respectively, and ε is a constant;

[0052] The flow rate and torque errors are used as the control targets of the sparrow search algorithm to select the parameter gains of the controller in the loop, thereby ultimately determining the parameters in the controller.

[0053] Optionally, in one embodiment of the present invention, step 4 further includes:

[0054] Carry out characteristic analysis based on the optimized electric fuel pump prototype: verify whether the improved and optimized electric fuel pump can operate stably without failure, and whether its stability and dynamic performance meet the design requirements; verify whether the performance indicators of the electric fuel pump's fuel supply and the fuel supply pulsation meet the design requirements; and test the reliability and stability of the electric fuel pump's operation.

[0055] Optionally, in one embodiment of the present invention, in step 4, the characteristic analysis platform uses a turbine flowmeter to measure the fuel flow rate. The principle of the turbine flowmeter for measuring flow rate is: using the fluid to flow through the turbine to drive the turbine to rotate. When the turbine rotates, the sensor will output a pulse signal that is proportional to the flow rate. By measuring the frequency of the pulse signal and performing a numerical transformation, the flow value can be obtained.

[0056] Optionally, in one embodiment of the present invention, in step 4, the characteristic analysis process is as follows:

[0057] The starting pump group is equipped with a motor. After the gear pump oil inlet draws oil from the oil tank, it generates working oil flow through the oil outlet. The pressure sensor displays the working pressure of the pump outlet, and the flow sensor displays the working oil flow rate of the pump outlet. The back pressure valve is adjusted to control the pump workload. The pump speed is controlled by adjusting the power supply current of the built-in motor. Finally, a complete control system is obtained. The following no-load displacement test and load test are performed for characteristic analysis.

[0058] The design method of an aviation electric fuel pump based on structural optimization and intelligent control according to an embodiment of the present invention has the following beneficial effects:

[0059] (1) The present invention optimizes the gear pump structure, simplifies the bearing design, and ignores the pressure diffusion area and unloading groove, making it more consistent with the quality requirements of practical applications. The sparrow search algorithm is used to optimize the structural parameters, thereby improving the quality and efficiency of the gear pump.

[0060] (2) This invention achieves rotational isolation between the motor and gear pump ends of the gear shaft by adding a spline, effectively resolving the problem of the electric fuel pump getting stuck during the load-waiting startup phase. The overall design of the electric fuel pump was optimized by redesigning the housing and comprehensively considering weight, leakage, and torque.

[0061] (3) This invention uses a super-helical direct torque control method to achieve rapid torque response and precise flow control of the electric fuel pump. This method optimizes the controller's gain parameters using a sparrow search algorithm, improving the system's control accuracy and steady-state performance.

[0062] (4) The present invention designs a test system based on a turbine flowmeter and multiple sensors, which can monitor the flow rate, pressure and other performance parameters of the electric fuel pump in real time, effectively verifying the stability, fuel supply and dynamic performance of the pump, and ensuring that it meets the design requirements.

[0063] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0065] Figure 1 This is the overall structural diagram of the electric fuel pump before improvement;

[0066] Figure 2 Schematic diagram of washers added for bearing diameter;

[0067] Figure 3 This is a schematic diagram of the tool recess behind the spline;

[0068] Figure 4 Schematic diagram of the replaced sealing ring;

[0069] Figure 5 This is the overall design diagram of the optimized electric fuel pump;

[0070] Figure 6 This is the overall control framework diagram of the electric fuel pump;

[0071] Figure 7 Flowchart for the sparrow search algorithm;

[0072] Figure 8 Schematic diagram of the aviation electric fuel pump test system;

[0073] Figure 9 This is the relationship diagram of flow rate versus speed in the electric fuel pump test experiment;

[0074] Figure 10This is a diagram showing the speed change after adding back pressure in the electric fuel pump test experiment;

[0075] Figure 11 This is a graph showing the relationship between power and back pressure during the electric fuel pump test. DETAILED DESCRIPTION

[0076] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0077] The design method of an aviation electric fuel pump based on structural optimization and intelligent control includes the following steps:

[0078] Step 1: Aiming at the quality optimization problem of aviation electric fuel pump, the principle of gear pump is analyzed, the strength of gear and shaft, minimum oil film thickness, and design flow rate are determined to establish constraint conditions, and the overall quality of gear pump is used as the optimization objective function to optimize the design parameters. The sparrow search algorithm is used to obtain the optimal gear pump parameter configuration and its corresponding overall quality, thereby realizing the structural optimization of gear pump.

[0079] In order to reduce the mass of the aviation electric fuel pump as much as possible, the principle of the gear pump is first analyzed, and the strength of the gear and shaft, the minimum oil film thickness, the design flow rate and other constraints are determined. The overall mass of the gear pump is used as the objective function to optimize the design parameters. The optimization algorithm is used to output the optimal gear pump parameter configuration and its corresponding overall mass. According to the optimization results, the parameter characteristics of the gear are obtained. The gear pump instantaneous flow rate, theoretical oil supply, oil supply pulsation and the influence of each parameter on the oil supply are analyzed. The overall mass of the gear pump is optimized using the sparrow search algorithm.

[0080] Step 2: To address the stuck problem of the aviation electric fuel pump, the gear shaft, bearings, left and right housings, and splines were analyzed, taking into account connection, positioning, strength requirements, as well as weight, leakage, and torque issues, to achieve overall design optimization of the aviation electric fuel pump.

[0081] To prevent the aviation electric fuel pump from getting stuck, an in-depth analysis of the electric fuel pump's principles was conducted to optimize the overall design of the aircraft electric fuel pump. This involved optimizing the housing, gear shaft, splines, bearings, seals, and other components. The optimized CAD overall design drawings and component drawings of the aircraft electric fuel pump were obtained.

[0082] In step 3, to address the problem of fast and accurate control of the electric fuel pump speed, a superhelical direct torque electric fuel pump control method is used based on the sparrow search algorithm to control the electric fuel pump to meet the response requirements of flow and torque. The speed is controlled by the PI controller in the outer loop, and the superhelical flux / torque controller in the inner loop controls the flux and torque respectively.

[0083] This invention uses superhelical direct torque control to control an electric fuel pump, meeting both flow and torque response requirements. An outer-loop PI controller controls speed, while an inner-loop superhelical flux / torque controller controls flux and torque, respectively. A sparrow search algorithm is used to optimize controller gain parameters to reduce flow and torque errors, thereby improving control accuracy.

[0084] Step 4: Conduct characteristic analysis based on the optimized electric fuel pump prototype, perform no-load displacement testing and load characteristic analysis, adjust the speed and outlet pressure, obtain the characteristic curve, and verify the electric fuel pump structure optimization and control method.

[0085] An electric fuel pump performance analysis was conducted to verify its stability, fuel delivery performance, and operational reliability. Aviation kerosene was used as fuel, and the pump's inlet and outlet pressures were regulated by controlling the relief and throttle valves. Key data were measured using turbine flowmeters, pressure sensors, and temperature sensors, while the pump speed was regulated by the main controller. The tests consisted of both no-load displacement and loaded tests, analyzing outlet pressure, flow rate, and volumetric efficiency to evaluate pump performance at various speeds and loads.

[0086] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description is given below with reference to the accompanying drawings and specific embodiments.

[0087] (1) Optimization design of gear pump structure for quality issues

[0088] For the convenience of research, it is assumed that the gear parameters are consistent and the volume between gear teeth is equal to the volume of the gear teeth.

[0089] The gear pump bearing is now simplified, ignoring the expansion zone and unloading groove. The simplified gear pump is as follows Figure 1 shown.

[0090]

[0091] S 轴 =2πr z 2

[0092]

[0093] Where, is the pressure angle, r is the pitch circle radius, r a is the tooth top radius, S 轴 is the cross-sectional area of ​​the shaft, S 轴承 is the cross-sectional area of ​​the bearing, S 齿轮 is the cross-sectional area of ​​the gear, r f is the tooth root circle radius;

[0094] The total mass of the main components of the gear pump can be obtained as:

[0095] M=ρ1(2bS 轴 +BS 齿轮 )+2bρ2S 轴承

[0096] Where M is the overall mass of the gear pump, ρ1 is the density of the gear and shaft, b is the bearing width, B is the tooth width, and ρ2 is the bearing density;

[0097] Taking the overall quality of the fuel pump as the objective function for optimization, the following mathematical description can be obtained:

[0098] minM(m,z,B,b)

[0099]

[0100] Where m is the module, z is the number of teeth, N* is a positive integer, Q 需求 is the required fuel flow, Q is the actual fuel flow, F is the bearing load, d z is the bearing diameter, [p] is the bearing pressure constraint, n is the gear speed, [pv] is the bearing load capacity constraint, ρ is the fuel density, μ is the Poisson's ratio, E is the elastic modulus, [σ] H is the gear contact fatigue constraint, [σ] F is the gear bending strength constraint, K is the load factor, T is the torque of the driven wheel due to the hydraulic pressure, and Y F is the tooth form coefficient, z1 is the number of teeth of the driving gear, α g1 is the engagement angle, m 标准 is the standard modulus, d f is the journal diameter, D is the minimum diameter;

[0101] The sparrow search algorithm is used and Matlab is used for optimization calculation to obtain the optimized parameters.

[0102] (2) Overall design optimization of aviation electric fuel pumps to address the problem of jamming

[0103] In practical applications, motor rotation can cause vibration or movement. Since the motor and gear pump are connected by a single shaft, the active gear shaft, this can affect the gear pump's operation. Furthermore, the motor and gear pump have different speed requirements. If the speed is too high, a localized high-pressure area will form at the gear pump inlet, increasing end-face leakage and causing underfill losses.

[0104] To address this issue, the present invention adds a spline between the motor end and the gear pump end of the gear shaft, isolating the rotation of the two ends. This solves the problem of the electric pump stalling during the load-free startup phase, which can lead to pump seizure. Simultaneously, the housing was redesigned, taking into account weight, leakage, and torque considerations, ultimately resulting in an optimized overall design for the electric fuel pump.

[0105] 1) Maintaining the same rotor, stator, and impeller dimensions, extend the drive gear shaft on the left side of the impeller to accommodate the bearing. The right half of the housing mates with the original end cap using stud screws. The bearing is positioned axially with a washer on the left side and the housing on the right. The drive gear shaft is splined on both sides. The gear pump is connected to the left half of the housing via stud screws. The left and right oil passages are connected to the rotor cavity and the gear pump, respectively. Fuel is drawn into the oil passage by the rotor's rotation, enters the gear pump, and then flows out through the upper channel of the left housing.

[0106] 2) Improve and optimize. The key points of optimization are as follows:

[0107] 1. The gear pump is enclosed in the left housing, with a wide gap to ensure adequate cooling. The left and right housings are connected by four long hexagonal screws. A sealing ring is placed outside the screw circle to prevent fuel leakage through the housing gap.

[0108] 2. The oil line is punched out from the side and enters the cavity where the gear pump is located.

[0109] 3. The left side of the bearing is still axially positioned by the step, and the right side shaft length is shortened. Considering assembly issues, a retaining ring is selected for the right side axial positioning and is tightly pressed against the impeller.

[0110] 3) The improvements to the details are as follows:

[0111] 1. Choose deep groove ball bearings and add 1mm washers between the bearings. Figure 2 shown.

[0112] 2. Add a 7mm undercut after the spline, such as Figure 3 shown.

[0113] 3. The O-ring is the external sealing ring of the long screw. It has a simple design and a self-sealing function. Its sealing effect is stable and it is relatively easy to install. Figure 4 shown.

[0114] The final improved overall design is as follows Figure 5 shown.

[0115] (3) Research on optimization control method of aviation electric fuel pump

[0116] The present invention regards the pump as the load of the motor, so the control of the electric fuel pump is actually the control of the motor. At present, direct torque control has become a mature motor control scheme like vector control. Moreover, direct torque control does not require complex coordinate transformation, can achieve fast torque response, and its structure is simple and easy to apply. Therefore, super helical direct torque control is used to control the electric fuel pump to ensure that it meets the flow and torque response requirements. The basic control framework is as follows Figure 6 As shown in Figure 1. Following the principle of direct torque control, one loop controls flux, one loop controls torque, and the outer loop controls speed. The outer loop uses a PI controller, while the inner loop uses a superhelical flux / torque controller.

[0117] The super-helical flux controller is designed as follows:

[0118]

[0119] Among them, the sliding mode variable is the error of the flux amplitude, and the gain K pd and K id Satisfy the stability condition, u sd is the control input, s d is the sliding mode variable, u sd1 is the intermediate variable, K pd , K id These are parameters that need to be adjusted and are all greater than 0.

[0120] The super-helical torque controller is designed to:

[0121]

[0122] Among them, the sliding mode variable is the torque error, and the gain K pq and K iq Satisfy the stability condition, u sq is the control input, s q is the sliding mode variable, u sq1 is the intermediate variable, K pq , K iq These are parameters that need to be adjusted and are all greater than 0.

[0123] The sparrow search algorithm is often used in various optimization problems. In the sparrow search algorithm, the position of the sparrow population is represented by the matrix X.

[0124]

[0125] Where n is the number of sparrows and d is the dimension of the variable to be optimized. The fitness value of each sparrow can be expressed as:

[0126]

[0127] F X Each row in represents the fitness value of each sparrow.

[0128] Sparrows are divided into producers and followers. Producers are responsible for finding food and providing the colony with information about foraging areas; followers use finders to obtain food. For simplicity, we can idealize the behavior of sparrows and formulate corresponding rules. Sparrows with higher fitness values ​​are considered producers, as they have better access to food resources. During the iteration process, the position of producers is updated as follows:

[0129]

[0130] Where t represents the current iteration number, j = 1, 2, ..., d, Iter represents the value of the j-th dimension variable of the i-th sparrow at the t-th iteration. max Represents the maximum number of iterations, α∈(0,1] is a random number. R2(R2∈[0,1]) and ST(ST∈[0.5,1.0]) represent the alarm value and safety threshold respectively. Q is a random number that follows a normal distribution, and L is a 1×d matrix where all elements are 1.

[0131] The follower's position update method is defined as:

[0132]

[0133] Among them, X P represents the optimal position among producers, X worst Represents the current global worst position, A represents a 1×d matrix, in which all elements are randomly 1 or -1, A + =A T (AA T ) -1 .

[0134] Assume that in the simulation experiment, some sparrows are able to recognize the approach of danger (such as natural enemies). These sparrows are called alerters. They are randomly selected from the entire sparrow population. The position update formula of the alerters is:

[0135]

[0136] Among them, X best is the global optimal position. β~Ν(0,1) is the parameter that controls the step size. K∈[-1,1] is a random number. fi is the current sparrow fitness value, f g and f w are the fitness values ​​of the global optimal position and the worst position respectively. ε is a small constant to avoid the denominator being 0. The process of the sparrow search algorithm is as follows Figure 7 shown.

[0137] Since the gains in both loops of the controller need to be further determined, the flow and torque errors are used as the control targets of the sparrow search algorithm to select the parameter gains of the controllers in the loops, thereby ultimately determining the parameters in the controllers and achieving better control effects.

[0138] (4) Research on test methods for aviation electric fuel pumps

[0139] The main objectives of the electric fuel pump characteristic analysis conducted by the present invention include the following aspects:

[0140] 1. Verify whether the improved and optimized electric fuel pump can operate stably without failure, and whether its stability and dynamic performance meet the design requirements.

[0141] 2. Verify whether the performance indicators of the fuel supply of the electric fuel pump and the fuel supply pulsation meet the design requirements.

[0142] 3. Check the reliability and stability of the electric fuel pump operation.

[0143] In the experiment, aviation kerosene was used as fuel. This fuel was drawn from the fuel tank by a boost pump, then transferred through a filter to an electric fuel pump. Finally, it was returned to the tank via a throttle valve and turbine flowmeter. The inlet pressure of the electric fuel pump was varied by controlling the opening of the relief valve, while the discharge pressure was similarly varied by controlling the opening of the throttle valve. The inlet pressure test equipment had a measurement range of 0–1 MPa and generated data of 4–20 mA. At the outlet, the pressure test equipment had a measurement range of 0–6 MPa and also generated data of 4–20 mA. The turbine flowmeter had a measurement range of 0.09733–11.51494 L / min and generated a 0–5 V pulse signal. The fuel temperature was measured by a resistance temperature sensor with a measurement range of -20°C to 350°C. After signal conditioning, the sensor's output signal was input into the main controller. Based on the sensor data and the flow control program, the main controller issued commands to control the speed of the electric fuel pump. The schematic diagram of the test system is as follows: Figure 8 As shown, among them, 1. oil tank, 2. oil filter, 3. tested pump group, 4. pressure sensor, 5. turbine flowmeter, 6. back pressure valve.

[0144] The characteristic analysis platform uses a turbine flowmeter to measure fuel flow. The principle of the turbine flowmeter for measuring flow is to use the fluid to flow through the turbine to drive the turbine to rotate. When the turbine rotates, the sensor will output a pulse signal that is proportional to the flow rate. By measuring the frequency of the pulse signal and performing a numerical transformation, the flow value can be obtained.

[0145] The feature analysis process is as follows:

[0146] The gear pump draws oil from the tank at its inlet and produces a working oil flow through its outlet. A pressure sensor indicates the pump outlet operating pressure, while a flow sensor indicates the oil flow rate. Adjusting the backpressure valve controls the pump's operating load. The pump's speed is controlled by adjusting the current supplied to the internal motor. The result is a complete control system, which was then used to analyze its characteristics during the following no-load displacement and load tests.

[0147] 1) No-load displacement test

[0148] With the electric fuel pump unloaded, ensure stable and reliable operation. Next, measure the pump flow rate at speeds ranging from 1,000 to 10,000 rpm. The theoretical flow rate is the product of displacement and speed, and the error between the theoretical and measured flow rates is calculated.

[0149] Theoretically, as the speed increases, the pump outlet flow rate increases in steps, corresponding to the speed. Figure 9 The relationship between flow rate and speed under no-load condition is shown. It can be seen that the flow rate increases with the increase of speed, but after about 4000 rpm, the flow rate increases more slowly.

[0150] 2) Load characteristics analysis

[0151] When the electric fuel pump is under load, the pump performance test is carried out to obtain the pump outlet pressure, motor power, theoretical flow rate, measured flow rate and volumetric efficiency. Volumetric efficiency is the ratio of measured flow rate to theoretical flow rate. Figure 10 、 Figure 11 shown. Figure 10 The figure shows the change in speed after adding back pressure. As the outlet pressure increases, the speed shows a downward trend.

[0152] Theoretically, when the pump speed increases, the pump outlet pressure remains unchanged, the motor power increases with the speed, the flow rate increases with the speed, and the measured flow rate is less than the theoretical flow rate, and the volumetric efficiency decreases with the speed. Figure 11 The variation of motor power with back pressure is shown. When the outlet pressure increases, the motor power increases with the increase of outlet pressure.

[0153] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0154] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0155] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

Claims

1. A design method for an aviation electric fuel pump based on structural optimization and intelligent control, characterized in that: The following steps are involved: Step 1: To optimize the quality of an aviation electric fuel pump, the principle of the gear pump is analyzed. The strength of the gears and shaft, the minimum oil film thickness, and the design flow rate are determined to establish constraints. The overall quality of the gear pump is used as the optimization objective function to optimize the design parameters. The sparrow search algorithm is used to obtain the optimal gear pump parameter configuration and its corresponding overall quality, thereby achieving structural optimization of the gear pump. Step 2: To address the problem of stuck aircraft electric fuel pumps, the company comprehensively considered connection, positioning, strength requirements, as well as weight, leakage, and torque issues. The company analyzed the gear shaft, bearings, left and right housings, and splines to optimize the overall design of the aircraft electric fuel pump. Step 3: To address the problem of fast and precise speed control of the electric fuel pump, a superhelical direct torque electric fuel pump control method is used based on a sparrow search algorithm to control the electric fuel pump to meet the flow and torque response requirements. The speed is controlled by an outer loop PI controller, and the inner loop superhelical flux / torque controller controls the flux and torque respectively. Step 4: Conduct characteristic analysis on the optimized electric fuel pump prototype, perform no-load displacement testing and load characteristic analysis, adjust the speed and outlet pressure, and obtain characteristic curves to verify the electric fuel pump structure optimization and control method. Step 1 specifically includes: Assuming the same gear parameters and the volume between gear teeth is equal to the volume of the gear teeth, the gear pump bearing is simplified and the diffusion zone and unloading groove are ignored: S 轴 ＝2πr z 2 Where, is the pressure angle, r is the pitch circle radius, r a is the tooth top radius, S 轴 is the cross-sectional area of ​​the shaft, S 轴承 is the cross-sectional area of ​​the bearing, S 齿轮 is the cross-sectional area of ​​the gear, r f is the tooth root circle radius; The overall mass of the gear pump is obtained as: M=ρ1(2bS 轴 +BS 齿轮 )+2bρ2S 轴承 Where M is the overall mass of the gear pump, ρ1 is the density of the gear and shaft, b is the bearing width, B is the tooth width, and ρ2 is the bearing density; Taking the overall quality of the fuel pump as the optimization objective function, we can find the following: minM(m,z,B,b) Where m is the module, z is the number of teeth, N* is a positive integer, Q 需求 is the required fuel flow, Q is the actual fuel flow, F is the bearing load, d z is the bearing diameter, [p] is the bearing pressure constraint, n is the gear speed, [pv] is the bearing load capacity constraint, ρ is the fuel density, μ is the Poisson's ratio, E is the elastic modulus, [σ] H is the gear contact fatigue constraint, [σ] F is the gear bending strength constraint, K is the load factor, T is the torque of the driven wheel due to the hydraulic pressure, and Y F is the tooth form coefficient, z1 is the number of teeth of the driving gear, α g1 is the engagement angle, m 标准 is the standard modulus, d f is the journal diameter, D is the minimum diameter; The sparrow search algorithm is used to obtain the optimal gear pump parameter configuration and its corresponding overall quality, thereby achieving structural optimization of the gear pump; Step 2 specifically includes: A spline was added between the motor end of the gear shaft and the gear pump end to isolate the rotation of the two ends. The housing was redesigned, and weight, leakage, and torque issues were comprehensively considered. The final optimized overall design of the electric fuel pump was obtained: While keeping the rotation, the size of the stator and the impeller unchanged, the length of the driving gear shaft at the left end of the impeller is lengthened to facilitate the installation of the bearing, and the right half of the casing is matched with the original end cover with stud screws; the left side of the bearing is positioned axially with a washer, and the right side is positioned axially with the casing; the driving gear shaft is connected to the left and right sides with splines; the gear pump is connected to the left half of the casing with stud screws; the left and right sides of the oil circuit are connected to the cavity where the rotor is located and the gear pump respectively, and the fuel is sucked into the oil circuit through the rotation of the rotor, enters the gear pump, and then flows out from the upper channel of the left casing.

2. The method according to claim 1, characterized in that The step 2 further comprises: Use deep groove ball bearings and add 1mm washers between bearings; Add 7mm undercut after spline; The O-ring is the external sealing ring of the long screw.

3. The method according to claim 1, characterized in that The step 3 further comprises: Following the principle of direct torque control, one loop controls the flux, one loop controls the torque, and the outer loop controls the speed. The outer loop uses a PI controller, and the inner loop uses a super-helical flux / torque controller. The super-helical flux controller is designed as follows: Among them, the sliding mode variable is the error of the flux amplitude, and the gain K pd and K id Satisfy the stability condition, u sd is the control input, s d is the sliding mode variable, u sd1 is the intermediate variable, K pd , K id are parameters that need to be adjusted, all greater than 0; The super-helical torque controller is designed to: Among them, the sliding mode variable is the torque error, and the gain K pq and K iq Satisfy the stability condition, u sq is the control input, s q is the sliding mode variable, u sq1 is the intermediate variable, K pq , K iq are parameters that need to be adjusted, all greater than 0; In the sparrow search algorithm, the position of the sparrow population is represented by the matrix X: Where n is the number of sparrows, d is the dimension of the variable to be optimized, and the fitness value of each sparrow is expressed as: F X Each row in represents the fitness value of each sparrow; The sparrow with the highest fitness value is regarded as the producer. During the iteration process, the producer's position is updated as follows: Where t is the current iteration number, j = 1, 2, ..., d, is the value of the j-th dimension variable of the i-th sparrow at the t-th iteration, iter max is the maximum number of iterations, α∈(0,1] is a random number, R2(R2∈[0,1]) and ST(ST∈[0.5,1.0]) are the alarm value and safety threshold respectively, Q is a random number that obeys the normal distribution, and L is a 1×d matrix where all elements are 1; The follower's location is updated as follows: Among them, X P is the optimal position among producers, X worst is the current global worst position, A is a 1×d matrix, in which all elements are randomly 1 or -1, A + =A T (AA T ) -1 ; Randomly select an alert person from the entire sparrow population, and the position update formula of the alert person is: Among them, X best is the global optimal position, β~Ν(0,1) is the parameter for controlling the step size, K∈[-1,1] is a random number, f i is the current sparrow fitness value, f g and f w are the fitness values ​​of the global optimal position and the worst position respectively, and ε is a constant; The flow rate and torque errors are used as the control targets of the sparrow search algorithm to select the parameter gains of the controller in the loop, thereby ultimately determining the parameters in the controller.

4. The method according to claim 1, wherein The step 4 further comprises: Carry out characteristic analysis based on the optimized electric fuel pump prototype: verify whether the improved and optimized electric fuel pump can operate stably without failure, and whether its stability and dynamic performance meet the design requirements; verify whether the performance indicators of the electric fuel pump's fuel supply and the fuel supply pulsation meet the design requirements; and test the reliability and stability of the electric fuel pump's operation.

5. The method according to claim 4, characterized in that In step 4, the characteristic analysis platform uses a turbine flowmeter to measure the fuel flow. The principle of the turbine flowmeter for measuring flow is: the fluid flows through the turbine to drive the turbine to rotate. When the turbine rotates, the sensor outputs a pulse signal that is proportional to the flow rate. The frequency of the pulse signal is measured and then numerically converted to obtain the flow value.

6. The method according to claim 5, characterized in that In step 4, the characteristic analysis process is as follows: The starting pump group is equipped with a motor. After the gear pump oil inlet draws oil from the oil tank, it generates working oil flow through the oil outlet. The pressure sensor displays the working pressure of the pump outlet, and the flow sensor displays the working oil flow rate of the pump outlet. The back pressure valve is adjusted to control the pump workload. The pump speed is controlled by adjusting the power supply current of the built-in motor. Finally, a complete control system is obtained. The following no-load displacement test and load test are performed for characteristic analysis.

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