A method, equipment, and medium for predicting liquid phase penetration distance under engine operating conditions.

Abstract

This application relates to a method, device, and medium for predicting the liquid phase penetration distance under engine operating conditions. The method involves determining the first in-cylinder temperature and pressure at the end of the intake manifold based on the engine's actual operating parameters; calculating the second in-cylinder temperature and pressure corresponding to the injection moment based on the first temperature and pressure; determining the mixture mass enhancement function based on the second temperature and pressure; introducing the mixture mass enhancement function to improve the phenomenological model to obtain a liquid phase penetration distance prediction model; constructing a single-variable data set by selecting test conditions and results with different target parameters using a single-variable method; and fitting the single-variable data set to the liquid phase penetration distance prediction model to obtain empirical coefficients corresponding to different target parameters, thereby predicting the liquid phase penetration distance. This method rapidly predicts the spray liquid phase penetration distance under actual engine operating conditions, and the entire implementation is simple and easily replicated.

Description

Technical Field

[0001] This application mainly relates to the field of engine fuel injection technology, and in particular to a method, device and medium for predicting the liquid phase penetration distance under engine operating conditions. Background Technology

[0002] Spray penetration capability is a core parameter in engine combustion system design. Too short a penetration distance leads to fuel accumulation near the injector, causing localized overly rich combustion and soot generation; too long a penetration distance causes fuel to hit the combustion chamber walls, increasing wall heat load, heat transfer loss, and unburned HC (hydrocarbon) emissions. Therefore, accurately predicting in-cylinder spray penetration characteristics is crucial for matching injector parameters with the combustion chamber structure, and plays a decisive role in improving engine power, fuel economy, and emissions performance.

[0003] Existing phenomenological models for spray penetration distance are mostly based on empirical fitting of diesel data within a limited operating range. Their empirical coefficients do not consider the unique physicochemical properties of alternative fuels (such as methanol and liquid ammonia) (e.g., differences in volatility due to low boiling point and high latent heat of vaporization). Directly applying existing models will lead to large deviations in the prediction of spray penetration distance for alternative fuels, failing to guide the optimization of injection parameters for alternative fuel engines and hindering the development of combustion systems. Summary of the Invention

[0004] One objective of this application is to provide a method, device, and medium for predicting liquid phase penetration distance under engine operating conditions, thereby addressing the problems in the prior art where the empirical coefficients of the prediction models do not take into account the unique physicochemical properties of alternative fuels, the prediction deviations are large, and the models cannot guide the optimization of injection parameters for alternative fuel engines, thus hindering the development of combustion systems.

[0005] According to one aspect of this application, a method for predicting liquid phase penetration distance under engine operating conditions is provided, the method comprising:

[0006] The first in-cylinder temperature and first pressure at the end of intake are determined based on the actual operating parameters of the engine.

[0007] Based on the first temperature and the first pressure, calculate the second in-cylinder temperature and the second pressure corresponding to the injection time.

[0008] Based on the second temperature and the second pressure, the mixture gas growth function is determined, and the mixture gas growth function is introduced to improve the phenomenological model, resulting in a liquid phase penetration distance prediction model.

[0009] By controlling for different target parameters and selecting test conditions and results using the single variable method, a single variable dataset is constructed.

[0010] Based on the liquid phase penetration distance prediction model, the single variable data set is fitted to obtain the empirical coefficients corresponding to different target parameters in order to predict the liquid phase penetration distance.

[0011] Optionally, the actual operating condition parameters include engine power, engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, stoichiometric air-fuel ratio, air-cooled intake air temperature, and fuel lower heating value.

[0012] Optionally, determining the first in-cylinder temperature and first pressure at the end of intake based on the engine's actual operating parameters includes:

[0013] The first in-cylinder temperature at the end of the intake is determined based on the intake temperature after air cooling.

[0014] The total mass of in-cylinder air at the end of intake is determined based on the engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, and stoichiometric air-fuel ratio.

[0015] The cylinder volume is determined based on the cylinder bore, stroke, engine compression ratio, and piston distance from top dead center.

[0016] The first pressure inside the cylinder at the time of intake termination is determined based on the first temperature, the total mass of air in the cylinder at the time of intake termination, and the cylinder volume.

[0017] Optionally, the step of calculating the second in-cylinder temperature and second pressure corresponding to the injection time based on the first temperature and the first pressure includes:

[0018] The second pressure inside the cylinder corresponding to the injection time is calculated based on the cylinder volume at the injection time, the cylinder volume at the intake valve closing time, and the first pressure.

[0019] The second temperature corresponding to the injection time is determined based on the first pressure, the second pressure, and the first temperature.

[0020] Optionally, the improvement of the phenomenological model by introducing the hybrid quality enhancement function includes:

[0021] Determine the fuel mass at any time by introducing the mixed gas mass increase function to improve the phenomenological model;

[0022] The function for determining the maximum liquid phase penetration distance of the spray is determined based on the improved phenomenological model, and the maximum liquid phase penetration distance function is used as the liquid phase penetration distance prediction model.

[0023] Optionally, the maximum liquid phase penetration distance function satisfies the following formula:

[0024] ;

[0025] in, Indicates the spray angle. Indicates the nozzle flow rate coefficient. Indicates the liquid density of fuel. Indicates gas density, Indicates the nozzle exit diameter. This represents the mixed-temperate increasing function.

[0026] Optionally, the mixture quality increase function satisfies the following formula:

[0027] ,in, , And b is an empirical coefficient. The value is obtained by correcting for the second pressure, where T is the in-cylinder gas temperature and is determined by the second temperature, and P is... sat is the saturated vapor pressure of fuel at the second temperature.

[0028] Optionally, the step of selecting test conditions and results with different target parameters using the single-variable method to construct a single-variable data set includes:

[0029] By controlling a single variable method and changing only the target parameters in the existing bulk material data, the corresponding test conditions and results can be obtained. The existing bulk material data includes nozzle diameter, gas pressure, gas temperature and fuel type.

[0030] Based on the test conditions and results, construct a single variable data set corresponding to the target parameter.

[0031] Optionally, the step of fitting the single-variable data set to the liquid phase penetration distance prediction model to obtain empirical coefficients corresponding to different target parameters includes:

[0032] If any target parameter in the existing ammunition capacity data is different from the actual operating conditions, obtain the single variable data group corresponding to that target parameter;

[0033] Based on the liquid phase penetration distance prediction model, the data set of the single variable is fitted to obtain the empirical coefficients corresponding to the target parameter.

[0034] Optionally, the prediction of the liquid phase penetration distance includes:

[0035] Based on the empirical coefficients, the spray penetration distance for operating conditions not covered by the data is predicted to obtain the predicted liquid phase penetration distance.

[0036] According to another aspect of this application, an electronic device is also provided, the electronic device comprising:

[0037] One or more processors; and

[0038] A memory storing computer-readable instructions, which, when executed, cause the processor to perform operations as described above.

[0039] According to another aspect of this application, a computer-readable storage medium is also provided, having stored thereon computer-readable instructions that can be executed by a processor to implement the method described above.

[0040] Compared with existing technologies, this application determines the first in-cylinder temperature and pressure at the end of the intake manifold based on the actual operating parameters of the engine; calculates the second in-cylinder temperature and pressure corresponding to the injection moment based on the first temperature and pressure; determines the mixture mass enhancement function based on the second temperature and pressure, and introduces the mixture mass enhancement function to improve the phenomenological model, obtaining a liquid phase penetration distance prediction model; constructs a single-variable data set by selecting test conditions and results with different target parameters through the single-variable control method; and performs data fitting on the single-variable data set based on the liquid phase penetration distance prediction model to obtain empirical coefficients corresponding to different target parameters, thereby predicting the liquid phase penetration distance. This overcomes the limitation of existing phenomenological models that cannot be directly applied to predict the liquid phase penetration distance of alternative fuels, rapidly predicts the spray liquid phase penetration distance under actual engine operating conditions, and the entire method is simple to implement and easy to replicate. Attached Figure Description

[0041] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings, wherein:

[0042] Figure 1 A schematic flowchart of a method for predicting liquid phase penetration distance under engine operating conditions according to one aspect of this application is shown.

[0043] Figure 2 This paper shows a comparison between the calculated aperture correction results and the experimental results according to an embodiment of this application;

[0044] Figure 3 This paper shows a comparison chart of gas pressure correction calculation results and experimental results in one embodiment of this application;

[0045] Figure 4 This diagram shows a comparison between the calculated gas temperature correction results and the experimental results according to an embodiment of this application.

[0046] Figure 5 This paper presents a comparison chart of the fuel type correction calculation results and experimental results in one embodiment of this application;

[0047] Figure 6A schematic diagram of a frame of an electronic device provided according to another aspect of this application is shown.

[0048] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0049] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein, and therefore this application is not limited to the specific embodiments disclosed below.

[0051] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0052] Figure 1 The diagram shows a flowchart of a method for predicting liquid phase penetration distance under engine operating conditions according to one aspect of this application. The method includes steps S11 to S15.

[0053] Step S11: Determine the first in-cylinder temperature and first pressure at the end of intake based on the actual operating parameters of the engine.

[0054] The actual operating parameters include engine power, engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, theoretical air-fuel ratio, intake air temperature after air cooling, and lower heating value of fuel.

[0055] Based on actual engine operating parameters (power P, speed N, load percentage Pct, equivalent effective fuel consumption rate BSFC, target excess air coefficient LAV, theoretical air-fuel ratio AFR) th and the intake air temperature T after air cooling af,CAC Calculate the first in-cylinder temperature (gas temperature T) at the intake manifold (IVC) end time. IVC ) and the first pressure (gas pressure P) IVC ).

[0056] Step S12: Calculate the second in-cylinder temperature and second pressure corresponding to the injection time based on the first temperature and the first pressure.

[0057] Based on the assumptions of adiabatic compression and the law of conservation of mass, the second in-cylinder temperature (gas temperature T) corresponding to the injection moment can be further calculated. inj ) and second pressure (gas pressure P) inj This provides realistic boundary conditions for predicting spray penetration distance.

[0058] Step S13: Determine the mixed gas mass enhancement function based on the second temperature and the second pressure, and introduce the mixed gas mass enhancement function to improve the phenomenological model to obtain the liquid phase penetration distance prediction model.

[0059] Existing diesel spray penetration models do not account for the limitations of alternative fuel property differences. The existing diesel spray penetration models are as follows:

[0060] ;

[0061] Where x(t) represents the axial penetration capability of the fuel. Indicates the spray angle. Indicates the nozzle flow rate coefficient. Indicates the injection pressure difference. d represents the gas density, and d represents the nozzle outlet diameter.

[0062] By introducing parameters characterizing fuel properties (such as fuel density and saturated vapor pressure), determining the mixture quality growth function, improving the model structure, and establishing a general prediction framework applicable to various fuels and operating conditions.

[0063] Step S14: Select test conditions and results with different target parameters by controlling the single variable method to construct a single variable data set.

[0064] Select a target parameter and set its different values, keeping all other variables constant to ensure that only the target parameter changes. Perform independent test conditions for each target parameter value, and then form a data set based on the results.

[0065] In one embodiment of this application, in step S14, the target parameter in the existing ammunition capacity data is changed only by controlling the single variable method to obtain the corresponding test conditions and results. The existing ammunition capacity data includes nozzle diameter, gas pressure, gas temperature and fuel type. A single variable data set corresponding to the target parameter is constructed based on the test conditions and results.

[0066] Constant-volume bomb testing is a core method for obtaining basic spray data. By simulating boundary conditions such as engine cylinder temperature and pressure, it can systematically measure the spray penetration distance, cone angle, and droplet distribution characteristics of different fuels. Based on the empirical coefficients of the phenomenological model fitted to the constant-volume bomb test data, the deviation between model predictions and actual operating conditions can be effectively reduced. Especially given the scarcity of alternative fuel spray data, there is an urgent need to establish a rapid prediction method for alternative fuel penetration distance to fill the data gap and support the design of efficient and clean combustion systems.

[0067] Specifically, the volumetric data includes nozzle diameter, gas temperature, fuel type, gas pressure, etc. The constant volumetric test data is screened by controlling a single variable: under the condition that other parameters in the existing volumetric data are fixed, only the target parameter is changed to construct a data group with independent variables. For example, when studying the effect of gas pressure on the penetration distance of the spray liquid phase, other parameters (fuel type, nozzle diameter, and gas temperature) are kept the same or similar, and test conditions and results with different target parameters (cylinder pressure) are selected to construct a data group to avoid the superposition of errors caused by the cross-interference of multiple factors.

[0068] Step S15: Based on the liquid phase penetration distance prediction model, perform data fitting on the single variable data set to obtain the empirical coefficients corresponding to different target parameters, so as to predict the liquid phase penetration distance.

[0069] Based on the improved phenomenological model, each single-variable data set is fitted separately to obtain empirical coefficients corresponding to different variables (such as nozzle diameter, gas pressure, gas temperature, and fuel type). After determining the empirical coefficients, the spray penetration distance for operating conditions not covered by the data (such as high pressure and high temperature, and alternative fuels) can be quickly predicted according to the empirical formula. While ensuring computational efficiency, this effectively solves the problems that the volumetric boundary cannot reach the cylinder boundary or that alternative fuel volumetric data is scarce.

[0070] In one embodiment of this application, in step S11, the first in-cylinder temperature at the end of the intake is determined based on the intake temperature after air cooling; the total mass of in-cylinder air at the end of the intake is determined based on the engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, and stoichiometric air-fuel ratio; the cylinder volume is determined based on the cylinder bore, stroke, engine compression ratio, and piston distance from top dead center; and the first in-cylinder pressure at the end of the intake is determined based on the first temperature, the total mass of in-cylinder air at the end of the intake, and the cylinder volume.

[0071] Input engine power P (kW), speed N (r / min), load percentage Pct (%), equivalent effective fuel consumption rate BSFC (g / (kW) h), target excess air coefficient (LAV), and stoichiometric air-fuel ratio (AFR) th Air-cooled intake temperature Taf,CAC (K) and the lower heating value of fuel (LHV) (MJ / kg) are used to calculate the in-cylinder thermal atmosphere at the end of intake (IVC), including the first temperature (gas temperature T). IVC ) and the first pressure (gas pressure P) IVC Among them, the gas temperature T IVC The intake air temperature T after air cooling can be used as an approximation. af,CAC (K), as in equation (1):

[0072] (1);

[0073] Gas pressure P IVC According to the ideal gas law, equation (2) is used to calculate:

[0074] (2);

[0075] in, V is the total mass of in-cylinder air at the IVC moment, calculated using equation (3); IVC The cylinder volume at IVC time is calculated using equation (4);

[0076] Rg is the gas constant, and the gas constant for air is 287 J / (kg·K).

[0077] [kg] (3);

[0078] [L] (4).

[0079] Where B is the cylinder bore, S is the stroke, CR is the engine compression ratio, and y is the distance from the piston to top dead center, calculated using equation (5):

[0080] [m] (5);

[0081] Where L is the length of the connecting rod. This represents the crank angle (in radians) corresponding to the IVC time, with 0 at top dead center.

[0082] In one embodiment of this application, in step S12, the cylinder internal volume at the injection time, the cylinder internal volume at the intake valve closing time, and the first pressure are used to calculate the second pressure in the cylinder corresponding to the injection time; and the second temperature corresponding to the injection time is determined based on the first pressure, the second pressure, and the first temperature.

[0083] Calculate the in-cylinder thermal atmosphere at the moment of fuel injection initiation, including the second temperature (gas temperature T). inj ) and second pressure (gas pressure P) injThe specific process is as follows: Ignoring heat transfer during the compression process, the in-cylinder air from the intake valve closing to the injection moment undergoes an adiabatic compression process. According to equation (6), the gas pressure P at the start of injection can be obtained. inj :

[0084] (6);

[0085] in, The adiabatic index of air is used. The adiabatic index of dry air is usually taken as 1.4, which can be adjusted according to temperature, humidity and other conditions. , These are the cylinder volumes at the injection moment and the cylinder volumes at the intake valve closing moment, respectively. The cylinder volume at the injection moment is V. inj The calculation method and the cylinder internal volume V at IVC time IVC same.

[0086] Based on the law of conservation of mass and the ideal gas law, the gas temperature T at the moment of injection can be calculated. inj See equation (7):

[0087] (7).

[0088] In one embodiment of this application, in step S13, the fuel mass at any time is determined by introducing the mixed gas mass enhancement function to improve the phenomenological model; based on the improved phenomenological model, the function for determining the maximum liquid phase penetration distance of the spray is determined, and the maximum liquid phase penetration distance function is used as the liquid phase penetration distance prediction model.

[0089] The maximum liquid phase penetration distance function satisfies the following formula:

[0090] ;

[0091] in, Indicates the spray angle. Indicates the nozzle flow rate coefficient. Indicates the liquid density of fuel. Indicates gas density, Indicates the nozzle exit diameter. This represents the mixed-temperate increasing function.

[0092] Specifically, the mixture gas growth function satisfies the following formula:

[0093] ,in, , And b is an empirical coefficient. The value is obtained by correcting for the second pressure, where T is the in-cylinder gas temperature and is determined by the second temperature, and P is... satis the saturated vapor pressure of fuel at the second temperature.

[0094] When the injection rate curve is rectangular, the axial fuel penetration capability x(t) can be expressed by equation (8):

[0095] (8);

[0096] in, For spraying half angle, The nozzle flow rate coefficient is... For injection pressure differential, d is the gas density, and d is the nozzle outlet diameter.

[0097] Differentiating equation (8) with respect to time yields the axial velocity u(t), as shown in equation (9):

[0098] (9);

[0099] The spray development process can be simplified as a superposition of fuel evaporation and a decrease in the mass of the air-fuel mixture, followed by air entrainment and an increase in the mass of the air-fuel mixture. A fuel evaporation function is defined. , representing the time t at which fuel diffuses due to evaporation. Evaporation into the outside air occurs at a velocity of zero; define the fuel entrainment function. At the same time, it draws in air. Assuming its velocity is the same as the velocity of the main fuel, define the mixture gas growth function. Then the fuel mass at any time t .

[0100] According to the law of conservation of momentum, we have:

[0101] (10).

[0102] Substituting equation (9) into equation (10) and eliminating u(t), we obtain equation (11):

[0103] (11).

[0104] Assume that the spray reaches its maximum liquid phase penetration distance at t=t0. Let t = t0, combine equations (8) and (11) to eliminate t0, and we can obtain the improved maximum liquid phase penetration distance. Formula (13):

[0105] (13);

[0106] Among them, the nozzle outlet velocity According to Bernoulli's principle, we can obtain:

[0107] (14);

[0108] in, The density of fuel oil in liquid form. The nozzle flow rate coefficient is... This refers to the injection pressure difference.

[0109] Substituting equation (14) into equation (13), we obtain equation (15):

[0110] (15);

[0111] Qualitative analysis shows that the higher the in-cylinder gas temperature T, the higher the saturated vapor pressure of the fuel. The higher the value, the stronger the fuel volatility, the longer the spray development time, and the greater the mass increase function of the mixture. The larger, that is:

[0112] (16);

[0113] When t=t0, the spray reaches its maximum liquid phase penetration distance. At this point, the mass increase function of the gas mixture is The higher the temperature, the stronger the fuel volatility, and the greater the liquid phase penetration distance of the spray. Time required The smaller, that is:

[0114] (17).

[0115] eliminate Equation (18) is obtained:

[0116] (18);

[0117] Among them, the empirical coefficient , , .

[0118] In one embodiment of this application, in step S15, if any target parameter in the existing bulk density data is different from the actual operating conditions, a single variable data set corresponding to the target parameter is obtained; the single variable data set is fitted based on the liquid phase penetration distance prediction model to obtain the empirical coefficient corresponding to the target parameter.

[0119] By interpolating and extrapolating empirical coefficients, the spray penetration distance for uncovered operating conditions (such as high pressure and high temperature, and alternative fuels) can be quickly predicted. Interpolation is used to fill the parameter gaps in the constant volume projectile test data, while extrapolation is extended to the actual operating conditions of the engine based on the variation law of empirical coefficients. While ensuring calculation efficiency, this effectively solves the problems that the volume projectile boundary cannot reach the cylinder boundary or that alternative fuel volume projectile data is scarce.

[0120] Specifically, if the existing volumetric data is different from the nozzle orifice diameter used in the actual operating conditions, then the data set of the single variable data with only different nozzle orifice diameters is fitted based on the maximum liquid phase penetration distance to obtain the empirical coefficient corresponding to the nozzle orifice diameter.

[0121] If the existing volumetric data differs from the gas pressure under the actual operating conditions, then data fitting is performed on the single variable data set where only the gas pressure differs based on the maximum liquid phase penetration distance to obtain the empirical coefficient corresponding to the gas pressure; if the existing volumetric data differs from the gas temperature under the actual operating conditions, then data fitting is performed on the single variable data set where only the gas temperature differs based on the maximum liquid phase penetration distance to obtain the empirical coefficient corresponding to the gas temperature; if the existing volumetric data differs from the fuel type used under the actual operating conditions, then data fitting is performed on the single variable data set where only the fuel type differs based on the maximum liquid phase penetration distance to obtain the empirical coefficient corresponding to the fuel type.

[0122] In one embodiment of this application, the spray penetration distance for conditions not covered by data is predicted based on the empirical coefficient to obtain the predicted liquid phase penetration distance.

[0123] During prediction, single-variable data sets corresponding to pore size, gas pressure, gas temperature, and fuel type are sequentially selected. The formula of the maximum liquid phase penetration distance model is then used for fitting to obtain corresponding empirical coefficients. These empirical coefficients are then substituted into the formula of the maximum liquid phase penetration distance model to predict the maximum liquid phase penetration distance. The specific process is as follows:

[0124] (1) Calculate the orifice diameter: If the orifice diameter used in the existing bulk material data is the same as that used in the predicted data, skip this step; otherwise, select bulk material data that differ only in orifice diameter to form a fitting data set (data volume ≥ 3). Combining the constant terms in equation (15) yields equation (19):

[0125] (19);

[0126] Based on equation (19), the empirical coefficient K is obtained by fitting the data. D Predicted maximum liquid phase penetration distance X liq :

[0127] (20).

[0128] Based on the spray liquid penetration test data of three groups with only different pore sizes, the prediction results of the maximum liquid penetration distance within a certain pore size change range are as Figure 2 shown.

[0129] (2) Perform gas pressure calculation: If the bomb data available and the predicted data are at the same gas pressure, skip this step; otherwise, select the bomb data with only different gas pressures to form a fitting data group (data volume ≥ 3). Combine the constant terms in Equation (15) and combine with the ideal gas state equation (2) to obtain Equation (21):

[0130] (21);

[0131] Based on Equation (21), fit the data. After obtaining the empirical coefficient Kp, predict the maximum liquid penetration distance X liq :

[0132] (22).

[0133] Based on the spray liquid penetration test data of three groups with only different gas pressures, the prediction results of the maximum liquid penetration distance within a certain gas pressure change range are as Figure 3 shown.

[0134] (3) Perform gas temperature calculation: If the bomb data available and the predicted data are at the same gas temperature, skip this step; otherwise, select the bomb data with only different gas temperatures to form a fitting data group (data volume ≥ 3). Combine the constant terms in Equation (15). The gas temperature mainly affects the maximum liquid penetration distance X through two aspects: gas density and the mixing gas quality increment function γ( liq ). Integrate the effects of the two aspects. The correction of the gas temperature is shown in Equation (23):

[0135] (23);

[0136] where the value of a is unknown. After determining the appropriate value of a, fit to obtain the empirical coefficient K T and then predict the maximum liquid penetration distance X liq :

[0137] (24).

[0138] Based on the spray liquid penetration test data of three groups with only different gas temperatures, the prediction results of the maximum liquid penetration distance within a certain gas temperature change range are as Figure 4 shown.

[0139] (4) Fuel type estimation: If the existing bomb capacity data and the predicted data use the same fuel, skip this step; otherwise, select bomb capacity data that differ only in fuel type. Combine the constant terms in equation (15). The fuel type is mainly determined by fuel density and the mixture mass increase function γ( The maximum liquid phase penetration distance X is affected by three aspects: the injection half-angle, the injection half-angle, and the injection half-angle. liq The effects are generated. Integrating the effects of the three aspects, the fuel type modification is shown in equation (25):

[0140] (25);

[0141] Where the value of b is unknown, after determining a suitable value of b, the empirical coefficient K is obtained through fitting. fuel Predicted maximum liquid phase penetration distance X liq :

[0142] (26).

[0143] The prediction of the maximum liquid phase penetration distance of methanol spray based on the maximum liquid phase penetration distance of three sets of diesel sprays, and the relative error between the prediction and the measured results of the maximum liquid phase penetration distance of methanol spray are as follows: Figure 5 As shown.

[0144] The method described in this application effectively addresses the significant discrepancy between constant-volume bomb data (up to 800K, 8MPa) and actual engine operating conditions (1000K, 18MPa) by improving traditional models and data fitting and extrapolation. This further enhances the guiding value of constant-volume bomb test data for engine combustion system design and development. It overcomes the limitation of existing phenomenological models that cannot be directly applied to predicting liquid phase penetration distance for alternative fuels, and can guide the combustion system design of engines using alternative fuels such as ammonia and methanol, helping to optimize combustion efficiency and control pollutant emissions. Based on constant-volume bomb data and an improved phenomenological model, it rapidly predicts the spray liquid phase penetration distance under actual engine operating conditions. The entire method is simple to implement and easy to replicate. This method provides a new research approach and concept for parameterized research on in-cylinder spray characteristics and combustion system design.

[0145] Figure 6 A schematic diagram of a framework of an electronic device according to another aspect of this application is shown, the electronic device including at least a processor 601 and a memory 602.

[0146] Processor 601 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 601 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 601 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 601 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 601 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0147] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 602 is used to store at least one instruction, which is executed by the processor 601 to implement a method for predicting the liquid penetration distance of an engine under operating conditions provided in the method embodiments of this application.

[0148] In some embodiments, the electronic device may also optionally include: a peripheral device interface and at least one peripheral device. The processor 601, memory 602, and peripheral device interface can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface via a bus, signal line, or circuit board. Indicatively, peripheral devices include, but are not limited to: radio frequency circuits, touch displays, audio circuits, and power supplies.

[0149] Of course, the electronic device may also include fewer or more components, and this embodiment does not limit this.

[0150] This application also provides a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to implement a method for predicting liquid penetration distance under engine operating conditions as described above.

[0151] When the method for predicting the liquid phase penetration distance under engine operating conditions is implemented as a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, a computer-readable storage medium may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media used for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0152] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or combinations thereof.

[0153] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0154] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0155] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0156] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0157] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

Claims

1. A method for predicting liquid phase penetration distance under engine operating conditions, characterized in that, The method includes: The first in-cylinder temperature and first pressure at the end of intake are determined based on the actual operating parameters of the engine. Based on the first temperature and the first pressure, calculate the second in-cylinder temperature and the second pressure corresponding to the injection time. Based on the second temperature and the second pressure, a mixture gas enhancement function is determined. This mixture gas enhancement function is then introduced to improve the phenomenological model, resulting in a liquid phase penetration distance prediction model. The mixture gas enhancement function satisfies the following formula: ,in, , And b is an empirical coefficient. The value is obtained by correcting for the second pressure, where T is the in-cylinder gas temperature and is determined by the second temperature, and P is... sat Let t0 be the saturated vapor pressure of the fuel at the second temperature, and t0 be the moment when the spray reaches its maximum liquid phase penetration distance. By controlling for different target parameters and selecting test conditions and results using the single variable method, a single variable dataset is constructed. Based on the liquid phase penetration distance prediction model, the single variable data set is fitted to obtain the empirical coefficients corresponding to different target parameters in order to predict the liquid phase penetration distance.

2. The method according to claim 1, characterized in that, The actual operating parameters include engine power, engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, stoichiometric air-fuel ratio, intake air temperature after air cooling, and lower heating value of fuel.

3. The method according to claim 2, characterized in that, The determination of the first in-cylinder temperature and first pressure at the end of intake based on the actual operating parameters of the engine includes: The first in-cylinder temperature at the end of the intake is determined based on the intake temperature after air cooling. The total mass of in-cylinder air at the end of intake is determined based on the engine speed, load percentage, equivalent effective fuel consumption rate, target excess air coefficient, and stoichiometric air-fuel ratio. The cylinder volume is determined based on the cylinder bore, stroke, engine compression ratio, and piston distance from top dead center. The first pressure inside the cylinder at the time of intake termination is determined based on the first temperature, the total mass of air in the cylinder at the time of intake termination, and the cylinder volume.

4. The method according to claim 1, characterized in that, The calculation of the second in-cylinder temperature and second pressure corresponding to the injection time based on the first temperature and the first pressure includes: The second pressure inside the cylinder corresponding to the injection time is calculated based on the cylinder volume at the injection time, the cylinder volume at the intake valve closing time, and the first pressure. The second temperature corresponding to the injection time is determined based on the first pressure, the second pressure, and the first temperature.

5. The method according to claim 1, characterized in that, The improvement of the phenomenological model by introducing the hybrid quality enhancement function includes: Determine the fuel mass at any time by introducing the mixed gas mass increase function to improve the phenomenological model; The function for determining the maximum liquid phase penetration distance of the spray is determined based on the improved phenomenological model, and the maximum liquid phase penetration distance function is used as the liquid phase penetration distance prediction model.

6. The method according to claim 5, characterized in that, The maximum liquid phase penetration distance function satisfies the following formula: ； in, Indicates the spray angle. Indicates the nozzle flow rate coefficient. Indicates the liquid density of fuel. Indicates gas density, Indicates the nozzle exit diameter. This represents the increasing function of mixed gases. This indicates the moment when the spray reaches its maximum liquid phase penetration distance.

7. The method according to claim 1, characterized in that, The method of selecting test conditions and results with different target parameters through the control of a single variable, and constructing a single-variable data set, includes: By controlling a single variable method and changing only the target parameters in the existing bulk material data, the corresponding test conditions and results can be obtained. The existing bulk material data includes nozzle diameter, gas pressure, gas temperature and fuel type. Based on the test conditions and results, construct a single variable data set corresponding to the target parameter.

8. The method according to claim 7, characterized in that, The step of fitting the single-variable data set to the liquid phase penetration distance prediction model to obtain empirical coefficients corresponding to different target parameters includes: If any target parameter in the existing ammunition capacity data is different from the actual operating conditions, obtain the single variable data group corresponding to that target parameter; Based on the liquid phase penetration prediction model, the data set of the single variable is fitted to obtain the empirical coefficients corresponding to the target parameter.

9. The method according to claim 1, characterized in that, The predicted liquid phase penetration distance includes: Based on the empirical coefficients, the spray penetration distance for operating conditions not covered by the data is predicted to obtain the predicted liquid phase penetration distance.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer-readable instructions, which, when executed, cause the processor to perform the operations of the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, The computer-readable instructions can be executed by a processor to implement the method as described in any one of claims 1 to 9.

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