A method and system for analyzing ship methanol fuel supply data

Abstract

A method and system for analyzing methanol fuel supply data in ships, relating to the field of shipping energy efficiency management, is applied to ship methanol fuel supply systems. The method includes: acquiring a state dataset, methanol physical properties, and nitrogen physical properties within the methanol supply pipeline; calculating the difference between inlet and outlet pressure data to obtain a pressure differential curve; performing empirical mode decomposition on the pressure differential gradient curve to obtain multiple effective IMF components and calculating the bubble precipitation index within the supply pipeline; calculating the gas-liquid two-phase mass ratio within the supply pipeline using a pre-defined two-phase flow pressure drop model, and converting the gas-liquid two-phase mass ratio into a gas phase volume fraction based on nitrogen and methanol physical properties; and correcting the methanol mass flow rate detection value within the methanol supply pipeline based on the gas phase volume fraction to obtain the target methanol mass flow rate detection value. This solves the problem of decreased metering accuracy in ship methanol fuel supply pipelines due to microbubbles.

Description

Technical Field

[0001] This application relates to the field of shipping energy efficiency management, specifically to a method and system for analyzing data on ship methanol fuel supply. Background Technology

[0002] Methanol, as an alternative marine fuel, is increasingly being used in ocean-going and inland waterway vessels due to its advantages such as being clean, environmentally friendly, and easy to store and transport. Currently, in ship methanol fuel supply systems, nitrogen inerting technology is commonly used to ensure operational safety and prevent oxygen from the air from entering the methanol fuel tank and forming an explosive mixture. Specifically, nitrogen is continuously injected into the gas phase space of the methanol fuel tank to maintain a slightly positive pressure. The pressure difference between the inside and outside of the tank effectively isolates the fuel from air.

[0003] However, in actual operation, the supply system uses a high-pressure pump to deliver methanol from the fuel tank to the common rail, and finally to the main engine, generator, and other equipment. Under the pressure of the high-pressure pump, some nitrogen dissolves in the methanol fuel and enters the supply pipeline. If the ship encounters severe sea conditions, the output pressure of the high-pressure pump will be reduced to stabilize the ship, causing a sudden drop in pressure in the common rail. The nitrogen dissolved in the methanol will rapidly precipitate in a supersaturated state, forming tiny bubbles. These tiny bubbles cause a nonlinear change in the equivalent bulk elastic modulus of methanol, resulting in a detection deviation in the volumetric mass flow meter. The actual methanol mass flow rate supplied to the engine deviates from the control command value, thereby increasing the risk of ship loss of control. Summary of the Invention

[0004] To address the problem of decreased metering accuracy of methanol fuel in ship methanol supply pipelines due to microbubbles, this application provides a method and system for analyzing ship methanol fuel supply data.

[0005] Firstly, this application provides a method for analyzing ship methanol fuel supply data, applied to a ship methanol fuel supply system, the method comprising:

[0006] Acquire the status dataset, methanol physical property parameters, and nitrogen physical property parameters within the methanol supply pipeline. The status dataset includes inlet pressure data, outlet pressure data, and methanol mass flow rate detection values.

[0007] Calculate the difference between the inlet pressure data and the outlet pressure data to obtain the pressure difference curve;

[0008] Empirical mode decomposition is performed on the pressure gradient curve to obtain multiple effective IMF components, and the bubble precipitation index in the methanol supply pipeline is calculated based on the multiple effective IMF components.

[0009] Based on the bubble precipitation index, the current inlet pressure and outlet pressure values, and the methanol physical properties, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model. The gas-liquid two-phase mass ratio is then converted into a gas phase volume fraction based on the nitrogen physical properties and the methanol physical properties.

[0010] Based on the gas phase volume fraction, the methanol mass flow rate detection value in the methanol supply pipeline is corrected to obtain the target methanol mass flow rate detection value.

[0011] Optionally, the step of performing empirical mode decomposition on the pressure gradient curve to obtain multiple effective IMF components specifically involves:

[0012] Empirical mode decomposition was performed on the pressure difference curve to obtain multiple IMF components;

[0013] The correlation coefficients of the multiple IMF components and the pressure curve are calculated to obtain the correlation coefficients corresponding to each of the multiple IMF components.

[0014] The correlation coefficient of the first IMF component is compared with a preset correlation coefficient threshold, wherein the first IMF component is any one of the plurality of IMF components;

[0015] If the correlation coefficient of the first IMF component is greater than or equal to a preset correlation coefficient threshold, then the first IMF component is determined to be a valid IMF component.

[0016] Optionally, the step of calculating the bubble precipitation index in the methanol supply pipeline based on multiple effective IMF components specifically involves:

[0017] Perform Hilbert transform on the multiple effective IMF components to obtain the instantaneous energy distribution curves corresponding to each of the multiple effective IMF components;

[0018] Calculate the energy entropy of the instantaneous energy distribution curves of each effective IMF component;

[0019] The energy entropy of the instantaneous energy distribution curves of each effective IMF component is normalized to obtain the energy entropy weight of each effective IMF component.

[0020] Based on the energy entropy weights corresponding to each of the multiple effective IMF components, the instantaneous values ​​of the IMF components of the multiple effective IMF components at the current moment are weighted and summed to obtain the bubble precipitation intensity.

[0021] The bubble precipitation index is obtained by normalizing the ratio of the bubble precipitation intensity to the theoretical maximum bubble precipitation intensity.

[0022] Optionally, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model based on the bubble precipitation index, the current inlet and outlet pressure values, and the methanol physical properties. Specifically:

[0023] Based on the current inlet and outlet pressure values, determine the actual pressure drop of the methanol-nitrogen two-phase fluid after it flows through the methanol supply pipeline;

[0024] Based on the methanol physical properties, determine the theoretical pressure drop of pure methanol fluid flowing through the methanol supply pipeline;

[0025] Based on the bubble precipitation index, the target flow regime correlation constant of the methanol-nitrogen two-phase fluid in the methanol supply pipeline is calculated using a preset flow regime correlation constant matching formula.

[0026] Based on the actual pressure drop, the theoretical pressure drop, and the target flow regime constant, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model.

[0027] Optionally, the preset flow-related constant matching formula is specifically as follows:

[0028]

[0029] in, Let be the target flow state correlation constant at time t. The flow regime-related constants for pure methanol flow within a methanol supply pipeline. The flow regime-related constants for the methanol fluid in the methanol supply pipeline at which the bubble precipitation intensity is maximized. Let be the bubble precipitation index at time t. This is the rate adjustment coefficient for the change of the flow-related constant.

[0030] Optionally, the step of correcting the methanol mass flow rate detection value in the methanol supply pipeline based on the gas phase volume fraction to obtain the target methanol mass flow rate detection value specifically involves:

[0031] Obtain the type of flow meter used in the methanol supply pipeline;

[0032] From the preset flow meter measurement parameter correction model library, query the measurement parameter correction model corresponding to the type of flow meter used in the methanol supply pipeline;

[0033] Based on the gas phase volume fraction, the methanol mass flow rate detection value is corrected using the measurement parameter correction model to obtain the methanol mass flow rate target detection value.

[0034] Secondly, this application provides a ship methanol fuel supply data analysis system. The system is a ship methanol fuel supply system, comprising an acquisition module, a processing module, and an output module, wherein:

[0035] The acquisition module is used to acquire the state dataset, methanol physical property parameters and nitrogen physical property parameters in the methanol supply pipeline. The state dataset includes inlet pressure data, outlet pressure data and methanol mass flow rate detection value.

[0036] The processing module is used to calculate the difference between the inlet pressure data and the outlet pressure data to obtain a pressure difference curve; perform empirical mode decomposition on the pressure difference gradient curve to obtain multiple effective IMF components, and calculate the bubble precipitation index in the methanol supply pipeline based on the multiple effective IMF components.

[0037] Based on the bubble precipitation index, the current inlet pressure and outlet pressure values, and the methanol physical properties, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model. The gas-liquid two-phase mass ratio is then converted into a gas phase volume fraction based on the nitrogen physical properties and the methanol physical properties.

[0038] The output module is used to correct the methanol mass flow rate detection value in the methanol supply pipeline based on the gas phase volume fraction, so as to obtain the target methanol mass flow rate detection value.

[0039] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0040] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0041] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0042] This application first performs empirical mode decomposition on the pressure difference curve between the inlet and outlet of the methanol supply pipeline to obtain multiple IMF components. This allows for the identification of various factors causing pressure fluctuations within the pipeline from the pressure signal. Then, by calculating the energy entropy of each IMF component and weighted summing, a bubble release index is obtained, comprehensively characterizing the intensity of bubble release. It is understood that a larger bubble release index indicates a more intense nitrogen bubble release from the methanol fuel, and a greater impact on the equivalent bulk elastic modulus of methanol. To quantify the detection deviation caused by this impact on the mass flow metering device, this application further uses the bubble release index to clarify the methanol flow pattern within the pipeline, combined with the current inlet and outlet pressure values ​​of the supply pipeline and methanol physical properties. The volume fraction of the gas phase in the methanol supply pipeline is calculated using a classical two-phase flow pressure drop model based on methanol density and viscosity, nitrogen physical properties (nitrogen density and viscosity), and methanol mass flow rate. The gas phase volume fraction represents the proportion of gas in the methanol fuel flowing in the supply pipeline. The larger the gas phase volume fraction, the higher the proportion of gas, and the greater the detection deviation caused by the mass flow rate metering device (because it includes the gas phase volume). Therefore, the methanol mass flow rate detection value is corrected in real time using the gas phase volume fraction, thereby effectively eliminating the metering deviation caused by the precipitation of dissolved nitrogen in liquid methanol fuel under pressure transient conditions. This ensures accurate fuel supply and stable combustion of the methanol engine, thereby reducing the risk of ship loss of control in severe sea conditions. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a method for analyzing ship methanol fuel supply data provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of the structure of a ship methanol fuel supply data analysis system provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0046] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Output module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Currently, in order to prevent oxygen from the air from entering the methanol fuel tank and forming an explosive mixture, nitrogen inerting technology is commonly used in ship methanol fuel supply systems to ensure operational safety. This involves continuously filling the gas phase space of the methanol fuel tank with nitrogen to maintain a slightly positive pressure inside the tank. Due to the pressure difference between the inside and outside of the tank, the air is effectively isolated.

[0049] In actual operation, the supply system uses a high-pressure pump to transport methanol from the fuel tank to the common rail pipeline, and finally to the main engine, generator and other equipment. At this time, in order to achieve precise diversion of methanol to each equipment, mass flow metering devices are installed in multiple branches of the common rail pipeline to monitor the mass flow rate of methanol fuel in the pipeline at all times. The working principle of the mass flow metering device is based on the fact that when the fluid flows in the pipeline, it pushes the piston inside the metering device to make periodic movements. By statistically analyzing the piston's movement cycle and displacement, combined with the fluid density, the mass flow rate of the fluid is calculated.

[0050] However, when a ship encounters severe sea conditions and reduces the output pressure of the high-pressure pump to maintain its attitude, the pressure in the common rail and branch pipes will drop sharply. Nitrogen dissolved in methanol fuel under high pressure will quickly precipitate and form tiny bubbles. At this time, the fluid in the pipeline becomes a gas-liquid two-phase flow, causing a nonlinear change in the equivalent bulk elastic modulus of methanol. This results in gaseous interference in the fluid that drives the piston inside the metering device to make periodic movements, causing a deviation between the liquid methanol mass flow rate measured by the mass flow metering device and the actual liquid methanol mass flow rate. This, in turn, affects the accurate fuel supply and stable combustion of various equipment, increasing the risk of loss of control of the ship.

[0051] Therefore, to solve the above problems, this application provides a method for analyzing ship methanol fuel supply data. This method is applied to ship methanol fuel supply systems, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0052] S101. Obtain the state dataset, methanol physical property parameters, and nitrogen physical property parameters within the methanol supply pipeline. The state dataset includes inlet pressure data, outlet pressure data, and methanol mass flow rate detection values.

[0053] In the above steps, for any methanol supply pipeline among the multiple branch pipelines of the common rail pipeline, a first pressure sensor is installed at its inlet to detect the pipeline pressure at the inlet, and a second pressure sensor is installed at its outlet to detect the pipeline pressure at the outlet; at the same time, a mass flow meter is installed between the inlet and outlet of the methanol supply pipeline to measure the mass flow rate of methanol flowing through the pipeline in real time.

[0054] Then, during the operation of the ship's methanol fuel supply system, the first pressure sensor, the second pressure sensor, and the mass flow meter continuously upload the real-time collected inlet pressure data, outlet pressure data, and methanol mass flow detection value to the ship's methanol fuel supply system to obtain the status dataset within the methanol supply pipeline.

[0055] In addition, a temperature sensor is installed in the methanol supply pipeline to detect the methanol temperature T. Then, based on a pre-established density-temperature relationship table, the methanol density at temperature T is found, and based on a pre-established viscosity-temperature relationship table, the methanol viscosity at temperature T is found. At this point, the methanol viscosity and methanol density are the methanol physical property parameters. Simultaneously, based on a pre-established pressure-temperature-density relationship table, the nitrogen density at the current pressure P and temperature T is found, and based on a pre-established pressure-temperature-viscosity relationship table, the nitrogen viscosity at the current pressure P and temperature T is found. At this point, the nitrogen density and nitrogen viscosity are the nitrogen physical property parameters. Here, the current pressure P is the average of the inlet and outlet pressures of the methanol supply pipeline.

[0056] S102. Calculate the difference between the inlet pressure data and the outlet pressure data to obtain the pressure difference curve.

[0057] In the above steps, when the methanol fuel containing dissolved nitrogen flows in the supply pipeline, if the pressure in the supply pipeline drops, the nitrogen dissolved in the methanol fuel will be released, causing irregular fluctuations in the pressure in the pipeline. Therefore, this application calculates the difference between the inlet pressure data and the outlet pressure data point by point after aligning the time sequence to obtain the pressure difference curve, thereby determining the pressure fluctuation after the methanol flows through the supply pipeline. At this time, the more violent the fluctuation of the pressure difference curve, the more violent the nitrogen release intensity.

[0058] S103. Perform empirical mode decomposition on the pressure difference curve to obtain multiple effective IMF components, and calculate the bubble precipitation index in the methanol supply pipeline based on the multiple effective IMF components.

[0059] In the above steps, when detecting the pressure signal in the pipeline, not only will nitrogen evolution cause fluctuations in the pressure signal, but also various background interferences such as high-pressure pump pulsation and inherent pipeline vibration will also cause fluctuations in the pressure signal. However, these background interferences will not change the elastic modulus of methanol when it flows in the pipeline. At this time, if the differential pressure curve is used directly for subsequent analysis, it will be difficult to accurately quantify the impact of nitrogen evolution on flow measurement.

[0060] Therefore, in order to separate the fluctuation components caused by nitrogen evolution from the pressure difference curve, this application first performs empirical mode decomposition on the pressure difference curve to obtain multiple IMF components. Since empirical mode decomposition is a conventional technique for those skilled in the art, it will not be described in detail here.

[0061] Then, since these IMF components include both IMF components characterizing nitrogen evolution and IMF components characterizing background interference, this application, in order to screen out the gas-phase IMF components related to nitrogen evolution from multiple IMF components, first calculates the Pearson correlation coefficient between each IMF component and the original pressure difference curve. It should be noted that the more intense the nitrogen evolution, the greater its contribution to the pressure difference curve, and the higher the correlation coefficient between the corresponding IMF component and the pressure difference curve. Therefore, this application compares the Pearson correlation coefficient between each IMF component and the original pressure difference curve with a preset correlation coefficient threshold. If the Pearson correlation coefficient between a certain IMF component and the original pressure difference curve is greater than or equal to the preset correlation coefficient threshold, then that IMF component is considered a valid IMF component. This filters out irrelevant interference components with extremely low correlation to the original pressure difference curve, retaining only the IMF components characterizing nitrogen evolution and the IMF components characterizing background interference with certain regularity. Then, Hilbert correlation coefficients are applied to multiple valid IMF components. The instantaneous energy distribution curves of each effective IMF component are obtained through a transform. Then, the energy entropy of each instantaneous energy distribution curve is calculated using the Shannon entropy formula. Energy entropy quantifies the uniformity of signal energy distribution on the time axis. In the instantaneous energy distribution curve corresponding to background interference, due to its certain regularity, its energy is generally concentrated near specific phase points or time points, so its energy entropy is low. However, in the instantaneous energy distribution curve corresponding to nitrogen evolution, since the evolution phenomenon is a random event, its occurrence time and intensity are uncertain. Therefore, its energy distribution on the time axis is dispersed, and its corresponding energy entropy is high. Moreover, the more intense the nitrogen evolution, the stronger the randomness, and the higher the energy entropy. Therefore, this application normalizes the energy entropy of each effective IMF component to obtain the energy entropy weight of each effective IMF component. This weight reflects the degree of characterization of bubble evolution by each component within the entire time window. That is, the component with large energy entropy is assigned a higher weight, and the component with small energy entropy is assigned a lower weight. Then, at each current time t, the instantaneous value of each effective IMF component is weighted and summed with its corresponding energy entropy weight to obtain the bubble precipitation intensity in the methanol supply pipeline at that time, as follows:

[0062]

[0063] in, Let be the intensity of bubble precipitation in the methanol supply pipeline at the current time t. The energy entropy weight of the i-th effective IMF component. Let be the instantaneous value of the i-th effective IMF component at the current time t, and n be the total number of effective IMF components.

[0064] Through the above weighted calculation method, the background interference component, due to its small energy entropy and low weight, effectively suppresses its contribution to the bubble precipitation index; while the nitrogen precipitation related component, due to its large energy entropy and high weight, is fully preserved and enhanced in the weighted summation, so that the final bubble precipitation intensity can accurately reflect the true intensity of nitrogen bubble precipitation at the current moment, providing a reliable data basis for subsequent quantification of the deviation caused by bubble precipitation to flow measurement. Finally, the bubble precipitation intensity is normalized by the ratio of the theoretical maximum bubble precipitation intensity to obtain the bubble precipitation index with a value range of [0,1].

[0065] S104. Based on the bubble precipitation index, the current inlet pressure and outlet pressure values, and the physical properties of methanol, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model. The gas-liquid two-phase mass ratio is then converted into a gas phase volume fraction based on the physical properties of nitrogen and methanol.

[0066] In the above steps, the bubble release index characterizes the intensity of nitrogen release in the methanol supply pipeline. The larger the bubble release index, the more nitrogen is released from the methanol. Nitrogen forms bubbles after being released from methanol. As the number of bubbles increases, the flow pattern of the fluid in the supply pipeline gradually changes from single-phase to gas-liquid two-phase, resulting in a change in the equivalent bulk elastic modulus of methanol. At this time, when the metering device measures the methanol mass flow rate in the supply pipeline, it may mistakenly include the bubbles as part of the methanol mass flow rate, thus causing a deviation from the actual liquid methanol mass flow rate. Therefore, in order to quantify the influence of bubbles on the deviation of the methanol mass flow rate detection value, this application uses a preset two-phase flow pressure drop model to calculate the gas phase volume fraction in the methanol supply pipeline, thereby determining the gas-liquid ratio of the fluid in the supply pipeline, and thus providing an accurate correction basis for the subsequent correction of the methanol mass flow rate detection value. Specifically:

[0067] Since most methanol supply pipelines on ships are straight pipelines with uniform cross-sections, the two-phase flow pressure drop model adopted in this application is the classic Lockhart-Martinelli model. The core idea of ​​this model is that there is a definite proportional relationship between the actual pressure drop of the gas-liquid two-phase fluid in the pipeline and the pressure drop of the assumed pure liquid phase flow. This proportional relationship is related to the mass ratio and physical property parameters of the gas and liquid phases, as follows:

[0068] (1)

[0069] in, Let X be the two-phase friction multiplier, X be the mass ratio of the gas and liquid phases, and C be the flow regime correlation constant.

[0070] In this model, the two-phase friction multiplier is the ratio of the actual pressure drop of the gas-liquid two-phase fluid in the pipe to the pressure drop of the assumed pure liquid phase flow. The actual pressure drop of the gas-liquid two-phase fluid in the pipe can be calculated in this application by dividing the difference between the outlet pressure and inlet pressure of the current supply pipe by the length of the supply pipe, as follows:

[0071]

[0072] in, The pressure drop of the methanol-nitrogen two-phase fluid after flowing through a supply pipe of length L is given. , These represent the difference between the current outlet pressure and the inlet pressure of the supply pipeline. This represents the current length of the supply pipeline.

[0073] Assuming the pressure drop of pure methanol flow can be obtained from the theoretical formula of single-phase flow frictional pressure drop, the Darcy-Weisbach equation can be used in this application, as follows:

[0074]

[0075] in, Let be the pressure drop of pure methanol after flowing through a supply pipe of length L, f be the frictional resistance coefficient experienced by pure methanol as it flows in the supply pipe, and D be the inner diameter of the supply pipe. The density of methanol, This refers to the viscosity of methanol.

[0076] Therefore, / You can get Then Substituting into formula (1), we solve for X. The positive root obtained is the mass ratio of methanol-nitrogen two-phase fluid in the current supply pipeline.

[0077] In the above process, the original definition of parameter X in the Lockhart-Martinelli model is the square root of the ratio of the pressure drop gradient when the liquid phase flows through the pipe alone to the pressure drop gradient when the gas phase flows through the pipe alone. However, in actual methanol supply pipelines, since nitrogen is dispersed in liquid methanol in the form of bubbles, there is no situation where the gas phase flows alone. Therefore, X cannot be directly calculated using the original definition. Thus, this application adopts the core relationship of the Lockhart-Martinelli model, the two-phase friction multiplier. The functional relationship with X, through measurable X is obtained by inversely solving for X (i.e., the ratio of the actual two-phase flow pressure drop to the assumed pure liquid phase pressure drop), thereby bypassing the unmeasurable pressure drop of the gas phase flow alone and indirectly achieving accurate calculation of the mass ratio of methanol-nitrogen two-phase fluid.

[0078] Then, the gas phase mass fraction is solved in reverse according to the conversion relationship between the gas-liquid two-phase mass ratio and the gas phase mass fraction in the Lockhart-Martinelli model, as follows:

[0079]

[0080] in, This represents the mass fraction of the gas phase in the fluid currently supplied through the pipeline. , These are the densities of nitrogen and methanol, respectively. , Here, denoted as nitrogen viscosity and methanol viscosity, respectively; X represents the gas-liquid two-phase mass ratio of the fluid currently supplied in the pipeline; and n is the Reynolds constant.

[0081] Finally, the gas phase mass fraction in the fluid currently supplied to the pipeline is converted into a gas phase volume fraction, as follows:

[0082]

[0083] in, This refers to the gas phase volume fraction. This refers to the gas phase mass fraction. , These are the densities of nitrogen and methanol, respectively.

[0084] In one possible implementation, in the Lockhart-Martinelli model, the flow regime correlation constant C is highly correlated with the intensity of the gas-liquid two-phase interaction in the fluid. Specifically, when the bubble content is low, the gas-liquid two-phase interaction is weak, and the flow regime correlation constant C has a small value; when the bubble content is high, the gas-liquid two-phase interaction is strong, and the flow regime correlation constant C has a large value. Therefore, in order to improve the accuracy of the calculation of the mass ratio of methanol-nitrogen two-phase fluid, this application obtains the flow regime correlation constant of pure methanol flowing in a methanol supply pipeline that has been pre-calibrated in the laboratory. And the flow regime-related constants when the bubble precipitation intensity in the methanol fluid within the methanol supply pipeline is at its maximum. Then, based on the bubble precipitation index at the current moment, a preset flow regime correlation constant matching formula is used to determine the target flow regime correlation constant. This ensures that the target flow regime correlation constant better reflects the current flow state of the gas-liquid two-phase fluid in the methanol supply pipeline. At this point, the target flow regime correlation constant is then substituted into the Lockhart-Martinelli model to solve for the gas-liquid two-phase mass ratio X, thereby improving the accuracy of the methanol-nitrogen two-phase fluid mass ratio calculation. The specific flow regime correlation constant matching formula is as follows:

[0085]

[0086] in, Let be the target flow state correlation constant at time t. The flow regime-related constants for pure methanol flow within a methanol supply pipeline. The flow regime-related constants for the methanol fluid in the methanol supply pipeline at which the bubble precipitation intensity is maximized. Let be the bubble precipitation index at time t. This is the rate adjustment coefficient for the change of the flow-related constant.

[0087] In the above formula, when the bubble precipitation index is close to 0, the target flow regime correlation constant approaches 0. This corresponds to a state with extremely low bubble content and weak gas-liquid interaction; when the bubble precipitation index approaches 1, the target flow regime correlation constant approaches... This corresponds to a state where the bubble content is saturated and the interaction between the gas and liquid phases is relatively strong.

[0088] S105. Based on the gas phase volume fraction, the methanol mass flow rate detection value in the methanol supply pipeline is corrected to obtain the target methanol mass flow rate detection value.

[0089] In the above steps, after calculating the gas phase volume fraction in the methanol supply pipeline at the current moment, since this parameter characterizes the volume ratio of bubbles in the flowing medium, the larger the gas phase volume fraction, the higher the bubble content, and the greater the detection deviation caused by the mass flow metering device. Therefore, this application further uses the gas phase volume fraction to correct the methanol mass flow detection value to eliminate the measurement error caused by the presence of bubbles, and obtain the true liquid methanol mass flow rate at the current moment. Specifically:

[0090] This application first identifies the type of flow meter used in the current methanol supply pipeline. Then, it searches for the corresponding measurement parameter correction model for the current methanol supply pipeline's flow meter type from a pre-set flow meter measurement parameter correction model library. Next, it substitutes the gas phase volume fraction into the measurement parameter correction model for calculation, obtaining the corrected methanol mass flow rate target detection value. The flow meter type in this application includes, but is not limited to, volumetric mass flow meters and Coriolis mass flow meters. Specifically, taking a volumetric mass flow meter as an example, its measurement parameter correction model is as follows:

[0091]

[0092] Where Q is the corrected target detection value for methanol mass flow rate. The target detection value for methanol mass flow rate before correction. This represents the gas phase volume fraction.

[0093] At this point, the corrected methanol mass flow rate target detection value eliminates the false contribution of bubble volume and truly reflects the current liquid methanol mass flow rate entering each device, thus providing reliable feedback input for the precise fuel supply system and reducing the risk of ship navigation loss of control.

[0094] Reference Figure 2 This application also provides a ship methanol fuel supply data analysis system, which is a ship methanol fuel supply system. The system includes an acquisition module 1, a processing module 2, and an output module 3, wherein:

[0095] The acquisition module 1 is used to acquire the state dataset, methanol physical property parameters and nitrogen physical property parameters in the methanol supply pipeline. The state dataset includes inlet pressure data, outlet pressure data and methanol mass flow detection value.

[0096] The processing module 2 is used to calculate the difference between the inlet pressure data and the outlet pressure data to obtain a pressure difference curve; perform empirical mode decomposition on the pressure difference gradient curve to obtain multiple effective IMF components, and calculate the bubble precipitation index in the methanol supply pipeline based on the multiple effective IMF components.

[0097] Based on the bubble precipitation index, the current inlet pressure and outlet pressure values, and the methanol physical properties, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model. The gas-liquid two-phase mass ratio is then converted into a gas phase volume fraction based on the nitrogen physical properties and the methanol physical properties.

[0098] The output module 3 is used to correct the methanol mass flow rate detection value in the methanol supply pipeline according to the gas phase volume fraction, so as to obtain the methanol mass flow rate target detection value.

[0099] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0100] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0101] The communication bus 302 is used to enable communication between these components.

[0102] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0103] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0104] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0105] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for analyzing ship methanol fuel supply data.

[0106] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for analyzing ship methanol fuel supply data. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0108] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0112] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0113] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for analyzing ship methanol fuel supply data, characterized in that, The method is applied to a methanol fuel supply system of a ship, and the method comprises the following steps: Obtaining a state data set in a methanol supply pipeline, methanol physical parameters and nitrogen physical parameters, wherein the state data set comprises inlet pressure data, outlet pressure data and a methanol mass flow detection value; Calculating a difference between the inlet pressure data and the outlet pressure data to obtain a pressure difference curve; Performing empirical mode decomposition on the pressure difference gradient curve to obtain a plurality of effective IMF components, and calculating a bubble precipitation index in the methanol supply pipeline according to the plurality of effective IMF components; According to the bubble precipitation index, the current inlet pressure value and the outlet pressure value, and the methanol physical parameters, a preset two-phase flow pressure drop model is used to calculate a gas-liquid two-phase mass ratio in the methanol supply pipeline, and the gas-liquid two-phase mass ratio is converted into a gas phase volume fraction according to the nitrogen physical parameters and the methanol physical parameters; According to the gas phase volume fraction, the methanol mass flow detection value in the methanol supply pipeline is corrected to obtain a methanol mass flow target detection value.

2. The method of claim 1, wherein, The empirical mode decomposition of the pressure difference gradient curve obtains a plurality of effective IMF components, and the specific process is as follows: Performing empirical mode decomposition on the pressure difference curve to obtain a plurality of IMF components; Respectively calculating the correlation coefficients of the plurality of IMF components and the pressure curve to obtain a plurality of correlation coefficients corresponding to the plurality of IMF components; Comparing the correlation coefficient of a first IMF component with a preset correlation coefficient threshold, wherein the first IMF component is any one of the plurality of IMF components; If the correlation coefficient of the first IMF component is greater than or equal to the preset correlation coefficient threshold, the first IMF component is determined as an effective IMF component.

3. The method of claim 1, wherein, The bubble precipitation index in the methanol supply pipeline is calculated according to the plurality of effective IMF components, and the specific process is as follows: Performing Hilbert transform on the plurality of effective IMF components to obtain a plurality of instantaneous energy distribution curves corresponding to the plurality of effective IMF components; Calculating the energy entropy of the instantaneous energy distribution curve of each effective IMF component; Normalizing the energy entropy of the instantaneous energy distribution curve of each effective IMF component to obtain the energy entropy weight of each effective IMF component; Based on the energy entropy weight corresponding to each of the plurality of effective IMF components, the IMF component instantaneous values of the plurality of effective IMF components at the current time are weighted and summed to obtain a bubble precipitation intensity; The bubble precipitation intensity is normalized by the ratio of the theoretical maximum bubble precipitation intensity to obtain the bubble precipitation index.

4. The method of claim 1, wherein, According to the bubble precipitation index, the current inlet pressure value and the outlet pressure value, and the methanol physical parameters, a preset two-phase flow pressure drop model is used to calculate a gas-liquid two-phase mass ratio in the methanol supply pipeline, and the specific process is as follows: According to the current inlet pressure value and the outlet pressure value, the actual pressure drop of the methanol-nitrogen two-phase fluid flowing through the methanol supply pipeline is determined; According to the methanol physical parameters, the theoretical pressure drop of the pure methanol fluid flowing through the methanol supply pipeline is determined; Based on the bubble precipitation index, the target flow regime correlation constant of the methanol-nitrogen two-phase fluid in the methanol supply pipeline is calculated using a preset flow regime correlation constant matching formula. Based on the actual pressure drop, the theoretical pressure drop, and the target flow regime constant, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model.

5. The method of claim 4, wherein, The preset flow-related constant matching formula is as follows: wherein, is a flow regime dependent constant at time t, is a flow regime dependent constant when pure methanol is flowing in the methanol supply conduit, is a flow regime dependent constant when the bubble detachment intensity is the greatest in the methanol fluid flowing in the methanol supply conduit, is a bubble detachment index at time t, is a flow regime dependent constant change rate adjustment coefficient.

6. The method of claim 1, wherein, The step of correcting the methanol mass flow rate detection value in the methanol supply pipeline based on the gas phase volume fraction to obtain the target methanol mass flow rate detection value is as follows: Obtain the type of flow meter used in the methanol supply pipeline; From the preset flow meter measurement parameter correction model library, query the measurement parameter correction model corresponding to the type of flow meter used in the methanol supply pipeline; Based on the gas phase volume fraction, the methanol mass flow rate detection value is corrected using the measurement parameter correction model to obtain the methanol mass flow rate target detection value.

7. A marine methanol fuel supply data analysis system characterized by, The system is a ship methanol fuel supply system, which includes an acquisition module (1), a processing module (2), and an output module (3), wherein: The acquisition module (1) is used to acquire the state dataset, methanol physical property parameters and nitrogen physical property parameters in the methanol supply pipeline. The state dataset includes inlet pressure data, outlet pressure data and methanol mass flow detection value. The processing module (2) is used to calculate the difference between the inlet pressure data and the outlet pressure data to obtain the pressure difference curve; perform empirical mode decomposition on the pressure difference gradient curve to obtain multiple effective IMF components, and calculate the bubble precipitation index in the methanol supply pipeline based on the multiple effective IMF components. Based on the bubble precipitation index, the current inlet pressure and outlet pressure values, and the methanol physical properties, the gas-liquid two-phase mass ratio in the methanol supply pipeline is calculated using a preset two-phase flow pressure drop model. The gas-liquid two-phase mass ratio is then converted into a gas phase volume fraction based on the nitrogen physical properties and the methanol physical properties. The output module (3) is used to correct the methanol mass flow rate detection value in the methanol supply pipeline according to the gas phase volume fraction, so as to obtain the methanol mass flow rate target detection value.

8. An electronic device, comprising: The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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