A method for predicting the volumetric efficiency of a pump motor of special equipment in an extremely low temperature environment

Abstract

The application discloses a kind of ultra-low temperature environment special equipment pump motor volumetric efficiency prediction method, it is related to hydraulic transmission technical field, comprising the following steps: based on the structure parameters of pump motor, material thermal expansion characteristic and hydraulic oil viscosity-temperature characteristic, construct including key fit clearance calculation and leakage flow calculation pump motor volumetric efficiency parameterization model;Obtain the test data of pump motor under low temperature working condition, unknown parameter in pump motor volumetric efficiency parameterization model is identified using global optimization algorithm, to determine model parameter;The operating state parameter under target low temperature working condition is input to the pump motor volumetric efficiency parameterization model of having determined parameter, and the volumetric efficiency prediction value of pump motor under corresponding working condition is calculated.The application realizes the reliable prediction of high accuracy, high efficiency, low cost to pump motor volumetric efficiency under ultra-low temperature environment, with strong engineering practicability and popularization value.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic transmission technology, and in particular relates to a method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures. Background Technology

[0002] Pump motors, as hydraulic components that highly integrate hydraulic pumps and hydraulic motors, have advantages such as compact structure and high energy density. They have important applications in the field of special equipment, such as hydraulic actuation systems for spacecraft in the aerospace industry, aircraft operating in high-altitude and low-temperature environments, and polar rovers and ice surface operation platforms in polar scientific research equipment. Volumetric efficiency is a key indicator for evaluating pump motor performance, directly reflecting the degree of internal leakage and mechanical loss during energy conversion.

[0003] Under extremely low temperature conditions (such as below -30°C), the volumetric efficiency of pump motors is affected by a combination of complex factors. On the one hand, the viscosity of hydraulic oil increases sharply with decreasing temperature, leading to increased flow resistance, difficulty in oil suction, and even cavitation. On the other hand, the different metal and non-metal materials constituting the friction pair undergo non-uniform shrinkage deformation at low temperatures due to differences in their coefficients of thermal expansion, causing critical clearances to deviate from the design optimum and further exacerbating internal leakage. Traditional theoretical and empirical models based on normal temperature conditions or simplified assumptions are insufficient to accurately characterize this multi-factor, nonlinear coupling effect, resulting in significant deviations between predicted results and actual conditions.

[0004] Currently, the main method for obtaining reliable volumetric efficiency data of pump motors in low-temperature environments is to conduct physical tests using a precisely temperature-controlled low-temperature test bench. However, this method has many limitations: First, the test system is expensive to build, requiring high-power refrigeration equipment, special insulation structures, and low-temperature sensors, making maintenance difficult; second, the low-temperature test process is energy-intensive and time-consuming, with prolonged cooling and insulation, resulting in low testing efficiency; furthermore, when performing cold start or load tests under real low-temperature conditions, irreversible damage to the pump motor is easily caused by oil solidification, lubrication failure, etc., posing a significant test risk. Due to the aforementioned constraints of cost, time, and risk, existing tests are often limited to certain temperature points and load conditions, making it difficult to systematically obtain complete test datasets covering a wide temperature range and all operating conditions, thus severely restricting the construction and verification of high-precision low-temperature performance models.

[0005] Therefore, existing technologies lack a method for accurately predicting the volumetric efficiency of pump motors under extreme cryogenic conditions based on limited conventional test data or easily accessible basic characteristic parameters. This method needs to address the failure issues of traditional models at low temperatures, reduce reliance on expensive and high-risk physical tests, and provide a reliable tool for performance evaluation of pump motors during the design and service phases to meet the reliability requirements of special equipment in harsh cryogenic environments. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures, comprising the following steps: Based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, a parameterized model of the pump motor volumetric efficiency is constructed, including the calculation of the clearance of key mating pairs and the calculation of leakage flow. Test data of pump motor under low temperature conditions were obtained, and the unknown parameters in the volumetric efficiency parameterized model of pump motor were identified by a global optimization algorithm, thereby determining the model parameters. The operating parameters under the target low-temperature condition are input into the pump motor volumetric efficiency parameterization model with determined parameters, and the predicted value of the pump motor volumetric efficiency under the corresponding operating condition is calculated.

[0007] Optionally, a parameterized model of pump motor volumetric efficiency is constructed, including calculations of key mating clearances and leakage flow rates, specifically including: Based on the viscosity-temperature characteristics data of hydraulic oil, a refined model describing the oil properties that change with temperature is established. Calculate the dimensional deformation of mechanical parts caused by thermal expansion and contraction based on the material thermal expansion coefficient and temperature change of key components. Based on the dimensional deformation, the change in working clearance of the key pump motor mating parts at low temperature is calculated. The key pump motor mating parts include at least the axial mating parts of the cylinder block and the distributor plate, and the radial mating parts of the plunger and the cylinder bore. Based on the change in working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference on both sides of the mating pair, a leakage flow calculation model is established to calculate the total leakage flow. Based on the total leakage flow and the theoretical flow of the pump motor, a parameterized model of the pump motor volumetric efficiency is established.

[0008] Optionally, based on the dimensional deformation, the change in the working clearance of the key mating parts of the pump motor at low temperature is calculated, specifically as follows: When calculating the change in axial clearance between the cylinder block and the distributor plate, the difference between the axial shrinkage of the distributor plate and the axial shrinkage of the cylinder block is used, combined with the axial constraint state of the two. When calculating the change in radial clearance between the plunger and the cylinder bore, the calculation is based on half the difference between the shrinkage of the cylinder bore inner diameter and the shrinkage of the plunger outer diameter.

[0009] Optionally, based on the change in the working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference across the mating pair, a leakage flow calculation model is established, specifically as follows: A first term is constructed that is proportional to the cube of the axial clearance and the pressure difference and inversely proportional to the dynamic viscosity; a second term is proportional to the cube of the radial clearance and the pressure difference and inversely proportional to the dynamic viscosity; and a third term is related to the radial clearance and the pump input speed. The three terms are summed to obtain the leakage flow calculation model.

[0010] Optionally, test data of the pump motor under low-temperature conditions are obtained, and a global optimization algorithm is used to identify the unknown parameters in the parameterized model of the pump motor's volumetric efficiency, specifically including: Acquire performance test data of the pump motor under typical low temperature, low speed and high torque conditions. The performance test data shall include at least the pump input speed, motor output speed, high and low pressure side pressure, oil temperature and motor output flow. The measured value of volumetric efficiency is calculated based on the performance test data. A fitness function is constructed based on the error between the predicted value of volumetric efficiency output by the pump motor volumetric efficiency parameterization model and the measured value of volumetric efficiency. The differential evolution algorithm is used as the global optimization algorithm to iteratively optimize the leakage coefficient and clearance parameters included in the parameterized model of pump motor volumetric efficiency. By continuously evaluating and screening parameter combinations to minimize the fitness function, the optimal parameter solution set is obtained.

[0011] Optionally, the fitness function is the mean square error between the predicted volumetric efficiency and the measured volumetric efficiency, and a regularization constraint term for optimizing the magnitude of the parameter is introduced therein.

[0012] This invention also provides a volumetric efficiency prediction system for pump motors in special equipment operating in extremely low temperatures, used to implement the method, comprising: The model building module is used to construct a parameterized model of pump motor volumetric efficiency based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, including the calculation of key mating clearances and leakage flow. The parameter identification module, connected to the model building module, is used to acquire test data of the pump motor under low temperature conditions and to identify unknown parameters in the pump motor volumetric efficiency parameterized model using a global optimization algorithm, thereby determining the model parameters. The efficiency prediction module, connected to the parameter identification module, is used to input the operating status parameters under the target low temperature condition into the parameterized model of pump motor volumetric efficiency with determined parameters, and to calculate and output the predicted value of the pump motor volumetric efficiency under the corresponding operating condition.

[0013] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method.

[0016] Compared with the prior art, the present invention has the following advantages and technical effects: This invention constructs a parameterized model for pump motor volumetric efficiency that integrates the viscosity-temperature characteristics of oil and the thermal deformation of materials. This model systematically characterizes the coupled effects of the dramatic increase in oil viscosity and the non-uniform shrinkage of materials at extremely low temperatures on the critical clearances of pump motors. This model overcomes the shortcomings of traditional room-temperature or simplified models that fail due to physical mechanisms under extreme low-temperature conditions. It achieves a mechanistic description of the nonlinear relationship between viscosity, clearance, and leakage, thereby significantly improving the model fidelity and adaptability for volumetric efficiency prediction.

[0017] This invention employs a parameter identification method based on low-temperature test data and a global optimization algorithm, enabling high-precision inversion of key model parameters using only limited and readily available low-temperature test data. This approach avoids the reliance on large amounts of full-condition, wide-temperature-range test data found in traditional methods, significantly reducing the high costs, long cycles, and high equipment risks associated with conducting extreme low-temperature physical tests. It provides an effective technical path for achieving reliable predictions even in the absence of complete test data.

[0018] This invention establishes a parameterizable, identifiable, and computable integrated prediction process, forming a complete technical closed loop from mechanism modeling and parameter correction to operating condition prediction. This method not only provides quantitative analysis tools for the low-temperature adaptability design and optimization of pump motors during the R&D phase, reducing the number of trial-and-test iterations, but also supports performance evaluation and predictive maintenance based on actual operating conditions during the service phase, thereby comprehensively improving the reliability design and operational assurance capabilities of products throughout their entire lifecycle.

[0019] In summary, this invention achieves high-precision, high-efficiency, and low-cost reliable prediction of pump motor volumetric efficiency in extremely low-temperature environments through a modeling approach that integrates mechanism and data, a parameter identification strategy driven by limited data, and a systematic prediction process. It has strong engineering applicability and promotional value. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1This is a flowchart illustrating the overall process of predicting the volumetric efficiency of pump motors for special equipment in extremely low-temperature environments, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the construction of a parameterized model for the volumetric efficiency of a pump motor according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the parameter identification process for the pump motor volumetric efficiency parameterization model under low-temperature conditions, as described in an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0023] The following technical problems exist in the related technologies: ① Failure of Physical Modeling under Low-Temperature Conditions: Traditional pump motor volumetric efficiency prediction models are mostly based on assumptions of normal temperature or steady state, failing to fully consider the coupling effects of multiple physical fields under extreme low-temperature conditions. In low-temperature environments, the viscosity of the hydraulic oil inside the pump motor increases exponentially, leading to a sharp increase in flow resistance and difficulty in oil suction. At the same time, the different metallic materials (force transmission structural components) and non-metallic materials (seals) constituting the friction pair will undergo non-uniform shrinkage deformation due to differences in their coefficients of thermal expansion, resulting in complex changes in key mating clearances. This strong coupling effect between oil characteristics and mechanical deformation forms a highly nonlinear leakage mechanism, making traditional pump motor volumetric efficiency prediction models based on single field theory or empirical coefficients unable to accurately describe system behavior. The volumetric efficiency prediction results often deviate significantly from the measured values, losing their engineering guidance value.

[0024] ② Cost and Risk Control Issues of Physical Testing Verification Systems: Current methods for verifying the low-temperature characteristics of pump motors heavily rely on constructing complete low-temperature environment test rigs. These systems require high-power refrigeration equipment, special insulation structures, and low-temperature sensors, resulting in substantial initial investment and high maintenance costs. Each test involves a lengthy cooling, insulation, and recovery process, leading to significant energy and time costs. More seriously, when performing cold start and load tests on pump motors under real low-temperature conditions, oil solidification and lubrication failure can easily cause dry friction, directly resulting in irreversible damage such as scratches and seizing on the friction pairs, and even rendering the entire machine unusable. These risks and cost constraints make it difficult to conduct systematic, full-condition low-temperature testing of pump motors, hindering the acquisition of reliable data.

[0025] ③ The lack of high-precision extrapolation prediction methods based on limited data: In engineering practice, it is difficult to obtain massive amounts of cryogenic pump motor test data covering a wide temperature range and all operating conditions. Relying solely on conventional temperature data or theoretical calculations cannot accurately predict the extreme low-temperature performance of pump motors. This necessitates the development of a prediction method capable of reliably extrapolating to unknown cryogenic conditions based on limited known information (such as ambient temperature test data, material properties, and oil parameters). This method must both integrate physical mechanisms to ensure extrapolation stability and possess the ability to learn nonlinear relationships from the data, thereby achieving high-precision predictions even in the absence of a large amount of cryogenic experimental data. This has long been a technical bottleneck in this field.

[0026] ④ Lack of performance evaluation tools covering the entire lifecycle: The existing pump and motor development technology system lacks quantitative evaluation tools that can span the product development and service stages. During the pump and motor product design phase, it is impossible to accurately predict its low-temperature performance and optimize key parameters (such as fit clearance and material matching) in a virtual environment, leading to repeated, costly trial-and-test iterations. During the service phase, maintenance personnel lack the means to predict volumetric efficiency degradation based on the actual operating conditions of the pump and motor, making it difficult to implement accurate preventative maintenance and reliability assessments. This lack of tools creates blind spots in the low-temperature adaptability design and operational safety assurance of pump and motor products, increasing the technical risks and overall costs throughout the entire lifecycle.

[0027] Example 1 To solve the problems in the existing technology, such as Figure 1 As shown in the figure, this embodiment provides a method for predicting the volumetric efficiency of a pump motor in special equipment operating in extremely low temperatures, including the following steps: Based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, a parameterized model of the pump motor volumetric efficiency is constructed, including the calculation of the clearance of key mating pairs and the calculation of leakage flow. Test data of pump motor under low temperature conditions were obtained, and the unknown parameters in the volumetric efficiency parameterized model of pump motor were identified by a global optimization algorithm, thereby determining the model parameters. The operating parameters under the target low-temperature condition are input into the pump motor volumetric efficiency parameterization model with determined parameters, and the predicted value of the pump motor volumetric efficiency under the corresponding operating condition is calculated.

[0028] Feasible construction of a parameterized model for pump motor volumetric efficiency, including calculations of key mating clearances and leakage flow rates, specifically includes: Based on the viscosity-temperature characteristics data of hydraulic oil, a refined model describing the oil properties that change with temperature is established. Calculate the dimensional deformation of mechanical parts caused by thermal expansion and contraction based on the material thermal expansion coefficient and temperature change of key components. Based on the dimensional deformation, the change in working clearance of the key pump motor mating parts at low temperature is calculated. The key pump motor mating parts include at least the axial mating parts of the cylinder block and the distributor plate, and the radial mating parts of the plunger and the cylinder bore. Based on the change in working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference on both sides of the mating pair, a leakage flow calculation model is established to calculate the total leakage flow. Based on the total leakage flow and the theoretical flow of the pump motor, a parameterized model of the pump motor volumetric efficiency is established.

[0029] Furthermore, based on the aforementioned dimensional deformation, the change in the working clearance of the key mating parts of the pump motor at low temperature is calculated, specifically as follows: When calculating the change in axial clearance between the cylinder block and the distributor plate, the difference between the axial shrinkage of the distributor plate and the axial shrinkage of the cylinder block is used, combined with the axial constraint state of the two. When calculating the change in radial clearance between the plunger and the cylinder bore, the calculation is based on half the difference between the shrinkage of the cylinder bore inner diameter and the shrinkage of the plunger outer diameter.

[0030] Furthermore, based on the aforementioned change in working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference across the mating pair, a leakage flow calculation model is established, specifically as follows: A first term is constructed that is proportional to the cube of the axial clearance and the pressure difference and inversely proportional to the dynamic viscosity; a second term is proportional to the cube of the radial clearance and the pressure difference and inversely proportional to the dynamic viscosity; and a third term is related to the radial clearance and the pump input speed. The three terms are summed to obtain the leakage flow calculation model.

[0031] As a specific implementation method, to effectively predict the volumetric efficiency of pump motors under low-temperature conditions and ensure real-time calculation requirements, a "semi-empirical-semi-mechanistic" parameterized model of pump motor volumetric efficiency is first constructed. This model fully considers the key factors affecting the volumetric efficiency changes of pump motors under low-temperature conditions and consists of three mutually coupled sub-models: first, a refined model of oil characteristics including viscosity-temperature and viscosity-pressure relationships; second, a model of the micro-geometric deformation of the friction pair based on the analysis of the material's thermal expansion coefficient and transient temperature field; and finally, a dynamic gap leakage flow calculation model that uses the outputs of the above two as input boundary conditions. Through this structured modeling, it not only surpasses the traditional simplified model that only considers viscosity changes, but also realizes the dynamic simulation and quantitative characterization of the coupling effect of "material contraction leading to gap change → gap change affecting leakage".

[0032] like Figure 2 As shown, this parameterized model is modeled based on the following assumptions: Temperature uniformity assumption: It is assumed that the internal temperature of each component of the pump motor is uniform, but temperature differences may exist between different components; The deformation of the part is dominated by linear thermal expansion: Only the deformation caused by the linear thermal expansion (cold contraction) of the material is considered, and the complex deformation modes such as bending and warping caused by the internal temperature gradient of the part are ignored. This assumption is a reasonable approximation under small deformation and steady-state heat transfer of the part.

[0033] Simplified rigid constraints: Simplify complex assembly constraints into constraints of local key degrees of freedom. For example: Assume that the distributor plate can contract freely in the axial direction, but is restricted in the radial direction by bolts; the cylinder block is subject to certain constraints in both the axial and radial directions.

[0034] One-dimensional dominant clearance: For a specific leakage path, only the clearance variation along the dominant leakage direction is considered. For example, the clearance of a flow distribution pair is mainly considered in terms of axial clearance, while the clearance of a plunger pair is mainly considered in terms of radial clearance.

[0035] Furthermore, the calculation method for the clearance of key mating parts of the pump motor: Considering the change in clearance between mating pairs due to deformation of key internal components of the pump motor at low temperatures, and consequently the change in leakage, this paper aims to solve for the change in clearance caused by temperature variation. Based on the above assumptions, for any component inside the pump motor, if its material thermal expansion coefficient is α, starting from the reference assembly temperature... When the temperature changes to the operating temperature T, the amount of free thermal expansion (contraction) along the deformation characteristic direction L is... for: (1) when hour, This refers to the shrinkage of the external dimensions of a part under low-temperature operating conditions.

[0036] To calculate the oil leakage caused by deformation of internal parts of the pump motor, the focus is on approximating the working clearances of key friction pairs within the pump motor. Based on the pump motor's structure and operating principle, the main clearances affecting the volumetric efficiency of the pump motor are: the axial clearance between the pump motor cylinder and the distributor plate (causing approximately 70% of the total leakage) and the radial clearance between the pump motor plunger and the cylinder bore (causing approximately 25% of the total leakage, and this clearance is significantly affected by wear).

[0037] ① Calculation of the working clearance between the pump motor cylinder and the distributor plate: Assumptions: Pump motor cylinder (material expansion coefficient) The axial expansion of the bearing is constrained by the bearing and housing, and cannot be completely free. Therefore, a constraint factor is introduced. (A value of 1 indicates complete freedom, and a value of 0 indicates complete constraint). Distribution plate (material) The axial expansion of a component is mainly constrained by its back pressure and preload, and a constraint coefficient is also introduced. .

[0038] Then, the change in the working clearance between the pump motor cylinder and the distributor plate It is approximately the algebraic sum of the axial contractions of both: (2) The final working clearance between the pump motor cylinder and the distributor plate is ,in This is the assembly gap at room temperature.

[0039] In summary, under low temperature conditions ( ),like When both are under similar constraints, it indicates that the distribution plate shrinks more. At this time, the actual working clearance between the pump motor cylinder and the distribution plate increases, which will lead to increased leakage and reduced volumetric efficiency. Conversely, the clearance decreases, which may bring the risk of increased friction.

[0040] ② Calculation of radial clearance between pump motor plunger and cylinder bore: The radial clearance between the pump motor plunger and cylinder bore is also a key factor affecting leakage. This fit is a typical "bulb-shaft" fit. The variation in its radial clearance... It is approximately half the difference between the change in cylinder bore diameter and the change in plunger diameter.

[0041] (3) In the formula: The cylinder bore diameter at the reference temperature. The cylinder block operating temperature, The plunger diameter at the reference temperature. This refers to the operating temperature of the plunger.

[0042] When the pump motor operates at low temperatures, both the cylinder bore diameter and the plunger outer diameter will shrink, but if (That is, the cylinder block material has a larger coefficient of thermal expansion), so the cylinder bore diameter shrinks more, which leads to a decrease in radial clearance.

[0043] Furthermore, to calculate the leakage of key mating parts during pump motor operation, the change in oil viscosity at low temperatures must be fully considered. The kinematic viscosity of the oil inside the pump motor varies under different temperature conditions. Satisfy the following formula: (4) In the formula, This is the absolute temperature value of the oil, expressed in Kelvin (K). is a constant; coefficients m and b are key oil physical property parameters. Coefficient m determines the slope of the viscosity-temperature curve, reflecting the sensitivity of viscosity to temperature changes, while coefficient b determines the reference position of the curve in the coordinate system. Together, they define the viscosity-temperature characteristics of a specific oil.

[0044] To accurately characterize the viscosity-temperature characteristics of the pump motor oil, based on the measured viscosity-temperature characteristics data of the oil (since this embodiment focuses on the calculation and characterization of the volumetric efficiency of the pump motor under low temperature conditions, the pump motor oil viscosity-temperature characteristics test is carried out in three scenarios: -40℃, -25℃, and -10℃), the above formula is fitted with parameters to determine specific coefficients in the model, so as to obtain the working expression of oil viscosity at different temperatures.

[0045] When calculating the oil leakage rate in the critical mating parts of a pump motor, the dynamic viscosity of the oil is required as a key input parameter. For the oil inside the pump motor, its density can be characterized under different medium temperature conditions as follows: (5) In the formula, for Standard density below, It is the coefficient of volume expansion of oil products.

[0046] By combining the viscosity-temperature equation with the density-temperature correction formula, the dynamic viscosity of the oil at different temperatures can be accurately calculated as follows: (6) in, For temperature The dynamic viscosity of the oil is as follows; For temperature The density of the oil below.

[0047] Finally, the calculation of pump motor mating clearance leakage and volumetric efficiency is as follows: Existing methods for calculating oil leakage in pump-motor mating gaps primarily rely on the laminar flow leakage rate under pressure differentials in narrow gaps between parallel plates, cylindrical annular gaps, and gaps at the end faces of parallel disks. However, these methods neglect the influence of the surface morphology (surface roughness, manufacturing tolerances) of the pump-motor mating gaps on the leakage rate. This influence is significant at low temperatures (when the oil exhibits high viscosity). Therefore, this embodiment employs a parametric model to calculate the leakage rate of pump-motor mating gaps, integrating the influence of the mating surface morphology on the leakage rate into the leakage coefficients of each mating pair.

[0048] Total leakage of pump motor mating pair clearance Represented as a parameterized state-space model: (7) In the formula These are state variables (pump input speed, high-pressure side pressure, low-pressure side pressure). The leakage coefficient is the working clearance between the pump motor cylinder and the distributor plate. The leakage coefficient is the radial clearance between the pump motor plunger and the cylinder bore. The leakage coefficient caused by the piston's rotational motion.

[0049] The volumetric efficiency of a pump motor can be characterized as: (8) Where: theoretical flow rate , This is the pump's theoretical displacement. For the theoretical speed of the motor, This is the theoretical displacement of the motor. The swashplate inclination angle.

[0050] Feasible methods include acquiring test data of the pump motor under low-temperature conditions and using a global optimization algorithm to identify unknown parameters in the parameterized model of the pump motor's volumetric efficiency. Specifically, this includes: Acquire performance test data of the pump motor under typical low temperature, low speed and high torque conditions. The performance test data shall include at least the pump input speed, motor output speed, high and low pressure side pressure, oil temperature and motor output flow. The measured value of volumetric efficiency is calculated based on the performance test data. A fitness function is constructed based on the error between the predicted value of volumetric efficiency output by the pump motor volumetric efficiency parameterization model and the measured value of volumetric efficiency. The differential evolution algorithm is used as the global optimization algorithm to iteratively optimize the leakage coefficient and clearance parameters included in the parameterized model of pump motor volumetric efficiency. By continuously evaluating and screening parameter combinations to minimize the fitness function, the optimal parameter solution set is obtained.

[0051] Furthermore, the fitness function is the mean square error between the predicted volumetric efficiency and the measured volumetric efficiency, and a regularization constraint term for optimizing the magnitude of the parameter is introduced therein.

[0052] like Figure 3 As shown, as a specific implementation method, to achieve the parameterized calculation of pump motor volumetric efficiency with the core variables of mating pair clearance calculation, oil viscosity, and temperature, low-temperature test data from a pump motor test bench is used as the basis, and the model clearance parameters are inverted using a differential evolution algorithm. Considering that the significant increase in oil viscosity during pump motor operation at low temperatures will greatly reduce the lubrication characteristics of various internal parts of the pump motor; at the same time, the leakage of each mating pair clearance inside the pump motor is mainly affected by the clearance size, oil viscosity, and pressure difference across the clearance, and is not strongly correlated with the pump motor speed. Therefore, in order to avoid pump motor damage due to poor lubrication under high speed and high load conditions, pump motor performance data is collected under low speed and high torque conditions during the test, which can meet the inversion requirements of the unknown parameters of the model.

[0053] To ensure the accuracy of model parameter inversion, the completeness of pump motor experimental data acquisition must be guaranteed. The data to be collected for the pump motor experiment includes: pump input speed. (rpm), motor output speed (rpm), high-pressure side oil pressure (MPa), Low-pressure side oil pressure (MPa), oil temperature T (°C), swashplate angle (°), motor output flow rate (L / min).

[0054] The volumetric efficiency model of pump motors under cryogenic conditions exhibits significant nonlinearity, and parameter optimization is prone to getting trapped in local optima. Therefore, a differential evolution algorithm is employed to identify key parameters. This method possesses strong global search capabilities and stable generalization advantages, enabling higher accuracy and more reliable model parameter identification in complex cryogenic environments, ensuring the accuracy and robustness of the volumetric efficiency calculation model for pump motors under cryogenic conditions.

[0055] The main steps of this method are as follows: ① Determination of parameters to be identified: Based on the state-space model of the total leakage of the pump-motor mating pair clearance mentioned above, the parameters to be identified are: (The physical parameters of oil can be accurately characterized by experimental data on the determination of physical parameters of oil, therefore it is not necessary to identify the parameters in this section).

[0056] ② Fitness function construction: The fitness function is defined as the error between the model's predicted values ​​and the experimental values. (9) In the formula: For the number of experimental data points, Let the volumetric efficiency be the volumetric efficiency at the k-th experimental point. Calculate the volumetric efficiency value for the model. The regularization coefficient is . .

[0057] Based on this, the problem of model parameter identification can be transformed into an optimization problem, which can be finally expressed as: (10) ③ Construction of Differential Evolution Algorithm and Identification of Model Parameters: This step is the core iterative process for identifying the parameters of the pump motor volumetric efficiency model, and it uses the differential evolution algorithm (DE) as its basic framework. In the initialization phase, the algorithm randomly generates an initial population of size NP. , Each individual in the population is within the feasible region. The population is uniformly distributed (based on the number of parameters to be identified in the model, each individual in the population has a total of 5 parameters). In each generation, the algorithm performs classic differential evolution, that is, it uses an adaptive strategy to generate a mutation vector for each target vector: (11) In the formula: For adaptive scaling factor, , , Random selection is made within the population.

[0058] Subsequently, experimental vectors were generated through binary crossover. CR represents the crossover probability.

[0059] The selection operation is based on a greedy principle and selects the optimal population: Repeat the iterations until the optimal solution of the model is obtained, and the identification results of the unknown parameters of the volumetric efficiency calculation model of the pump motor under low temperature conditions can be obtained.

[0060] Based on the parameter identification results of the pump motor volume efficiency calculation model under low temperature conditions, each parameter is substituted into formulas (7) and (8) to construct a pump motor volume efficiency parameterization model that integrates the mechanism model and the data-driven method.

[0061] For predicting the volumetric efficiency of pump motors under extremely low temperature conditions, the operating parameters of the pump motor are used. Substituting these values ​​into the identified model in formula (8), the predicted volumetric efficiency of the pump motor under extreme operating conditions can be obtained. .

[0062] Compared with the prior art, this embodiment has the following advantages: Traditional methods for predicting the volumetric efficiency of pumps and motors mainly rely on empirical formulas or simplified models at room temperature. These methods become completely ineffective in extremely low-temperature environments due to drastic changes in physical properties, resulting in significant prediction errors and making it difficult to guide precise design. The high-precision prediction method proposed in this embodiment constructs a high-fidelity real-time prediction model for the volumetric efficiency of pumps and motors. It systematically integrates key influencing factors such as nonlinear material deformation, fluid temperature-viscosity effects, and the evolution of microscopic leakage channels under extremely low-temperature conditions, achieving accurate mapping of complex operating conditions. This enables designers to reliably quantify and analyze the performance of pumps and motors under extremely low-temperature conditions, providing an accurate data foundation for optimized design.

[0063] ② In the field of cryogenic performance research for pump motors, the traditional "design-prototype-experimentation" R&D model faces significant challenges: high cost of setting up the testing environment, high risk of the medium, long testing cycle, and difficulty in obtaining key internal data, leading to uncontrolled R&D costs and low iteration efficiency. This embodiment achieves a paradigm shift to "simulation-driven design" by establishing a high-fidelity digital virtual prototype. Different structural parameters, material selections, and operating condition combinations can be rapidly simulated and optimized during the design phase, transferring the vast majority of design verification work to the virtual environment.

[0064] ③ In extremely low temperature environments, traditional pump and motor design methods, lacking accurate predictive tools, often employ overly conservative design strategies, leading to a difficulty in balancing efficiency and reliability: either excessively reducing clearance to pursue low leakage, causing increased friction or even jamming; or improper clearance control resulting in a severe decrease in volumetric efficiency. This embodiment achieves scientific optimization of the "friction loss - leakage loss" trade-off point for pumps and motors under extremely low temperature conditions by accurately predicting the optimal operating clearance and dynamic deformation. Design guided by the prediction results can effectively improve the overall efficiency of the product under target operating conditions, enhancing the product's operational stability and lifespan in extreme environments from the design stage.

[0065] ④ Existing technologies lack a systematic understanding of the complex internal physical processes of pump motors under extremely low temperatures, leading to designs that rely heavily on experience and trial and error, making it difficult to establish inheritable design principles. This embodiment is not only a predictive tool but also constructs a complete mechanism analysis system for extremely low temperature operating conditions. Through decoupling analysis of the model, it quantifies and reveals the influence weights and interaction laws of key factors such as temperature field gradient, material shrinkage characteristics, and microscale flow on volumetric efficiency, forming a universally applicable design knowledge base. This shift from "experience-dependent" to "mechanism-transparent" enables enterprises to accumulate core design knowledge, establish technological barriers, and lay a solid theoretical foundation for subsequent product series development and new technology breakthroughs.

[0066] On the other hand, the present invention also provides a volumetric efficiency prediction system for pump motors in special equipment operating in extremely low temperatures, for implementing the method, comprising: The model building module is used to construct a parameterized model of pump motor volumetric efficiency based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, including the calculation of key mating clearances and leakage flow. The parameter identification module, connected to the model building module, is used to acquire test data of the pump motor under low temperature conditions and to identify unknown parameters in the pump motor volumetric efficiency parameterized model using a global optimization algorithm, thereby determining the model parameters. The efficiency prediction module, connected to the parameter identification module, is used to input the operating status parameters under the target low temperature condition into the parameterized model of pump motor volumetric efficiency with determined parameters, and to calculate and output the predicted value of the pump motor volumetric efficiency under the corresponding operating condition.

[0067] On the other hand, this embodiment also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0068] On the other hand, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0069] On the other hand, this embodiment also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method.

[0070] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the volumetric efficiency of a pump motor in special equipment operating in extremely low temperatures, characterized in that, Includes the following steps: Based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, a parameterized model of the pump motor volumetric efficiency is constructed, including the calculation of the clearance of key mating pairs and the calculation of leakage flow. The test data of the pump motor under low temperature conditions were obtained, and the unknown parameters in the volumetric efficiency parameterization model of the pump motor were identified by a global optimization algorithm, thereby determining the model parameters. The operating parameters under the target low-temperature condition are input into the pump motor volumetric efficiency parameterization model with determined parameters, and the predicted volumetric efficiency value of the pump motor under the corresponding operating condition is calculated.

2. The method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures, as described in claim 1, is characterized in that... Constructing a parameterized model of pump motor volumetric efficiency, including calculations of key mating clearances and leakage flow rates, specifically includes: Based on the viscosity-temperature characteristics data of hydraulic oil, a refined model describing the oil properties that change with temperature is established. Calculate the dimensional deformation of mechanical parts caused by thermal expansion and contraction based on the material thermal expansion coefficient and temperature change of key components. Based on the dimensional deformation, the change in working clearance of the key pump motor mating parts at low temperature is calculated. The key pump motor mating parts include at least the axial mating parts of the cylinder block and the distributor plate, and the radial mating parts of the plunger and the cylinder bore. Based on the change in working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference on both sides of the mating pair, a leakage flow calculation model is established to calculate the total leakage flow. Based on the total leakage flow and the theoretical flow of the pump motor, a parameterized model of the pump motor volumetric efficiency is established.

3. The method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures, as described in claim 2, is characterized in that... Based on the aforementioned dimensional deformation, the change in the working clearance of the key mating parts of the pump motor at low temperature is calculated, specifically as follows: When calculating the change in axial clearance between the cylinder block and the distributor plate, the difference between the axial shrinkage of the distributor plate and the axial shrinkage of the cylinder block is used, combined with the axial constraint state of the two. When calculating the change in radial clearance between the plunger and the cylinder bore, the calculation is based on half the difference between the shrinkage of the cylinder bore inner diameter and the shrinkage of the plunger outer diameter.

4. The method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures, as described in claim 2, is characterized in that... Based on the aforementioned change in working clearance, the dynamic viscosity of the oil determined by the refined oil characteristic model, and the pressure difference across the mating pair, a leakage flow calculation model is established, specifically as follows: A first term is constructed that is proportional to the cube of the axial clearance and the pressure difference and inversely proportional to the dynamic viscosity; a second term is proportional to the cube of the radial clearance and the pressure difference and inversely proportional to the dynamic viscosity; and a third term is related to the radial clearance and the pump input speed. The three terms are summed to obtain the leakage flow calculation model.

5. The method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures according to claim 1, characterized in that, Test data of the pump motor under low-temperature operating conditions were obtained, and a global optimization algorithm was used to identify the unknown parameters in the volumetric efficiency parameterization model of the pump motor. Specifically, this included: Acquire performance test data of the pump motor under typical low temperature, low speed and high torque conditions. The performance test data shall include at least the pump input speed, motor output speed, high and low pressure side pressure, oil temperature and motor output flow. The measured value of volumetric efficiency is calculated based on the performance test data. A fitness function is constructed based on the error between the predicted value of volumetric efficiency output by the pump motor volumetric efficiency parameterization model and the measured value of volumetric efficiency. The differential evolution algorithm is used as the global optimization algorithm to iteratively optimize the leakage coefficient and clearance parameters included in the parameterized model of pump motor volumetric efficiency. By continuously evaluating and screening parameter combinations to minimize the fitness function, the optimal parameter solution set is obtained.

6. The method for predicting the volumetric efficiency of pump motors in special equipment operating in extremely low temperatures, as described in claim 5, is characterized in that... The fitness function is the mean square error between the predicted volumetric efficiency and the measured volumetric efficiency, and a regularization constraint term for optimizing the magnitude of the parameter is introduced into it.

7. A volumetric efficiency prediction system for pump motors in special equipment operating in extremely low temperatures, characterized in that, For implementing the method of any one of claims 1-6, comprising: The model building module is used to construct a parameterized model of pump motor volumetric efficiency based on the structural parameters of the pump motor, the thermal expansion characteristics of the materials, and the viscosity-temperature characteristics of the hydraulic oil, including the calculation of key mating clearances and leakage flow. The parameter identification module, connected to the model building module, is used to acquire test data of the pump motor under low temperature conditions and to identify unknown parameters in the pump motor volumetric efficiency parameterized model using a global optimization algorithm, thereby determining the model parameters. The efficiency prediction module, connected to the parameter identification module, is used to input the operating status parameters under the target low temperature condition into the parameterized model of pump motor volumetric efficiency with determined parameters, and to calculate and output the predicted value of the pump motor volumetric efficiency under the corresponding operating condition.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-6.

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