Method for optimizing nozzle for combustion of biodiesel and n-butyl alcohol mixed fuel

Abstract

The invention relates to the technical field of combustion and emission control of internal combustion engines, and provides a method for optimizing a biodiesel and n-butanol mixed fuel combustion nozzle, which comprises the following steps of: establishing a three-dimensional calculation fluid dynamic model of a constant-volume combustion chamber, comprising an RNGk-epsilon turbulence model, a physical process sub-model, an SOOT emission prediction model and a laminar flame velocity calculation model; maximization of peak heat release rate and minimization of NOx emission and SOOT emission are taken as multiple objectives, an orthogonal test design method is adopted to construct a test scheme, and orthogonal test factors are NN and ND; based on an orthogonal test scheme, carrying out simulation calculation by utilizing a three-dimensional CFD model; and performing range analysis on the simulation result, and determining the primary and secondary sequence of the influence of each factor on the optimization target. According to the method, the interaction influence of the NN and the ND is considered at the same time, the primary and secondary influences of all parameters on the peak heat release rate, NOx and soot emission are scientifically quantified through range analysis, and the limitation of a traditional univariate method is overcome.

Description

Technical Field

[0001] The embodiments of the present invention generally relate to the field of internal combustion engine combustion and emission control technology, and more particularly to a method for nozzle optimization of biodiesel-n-butanol mixed fuel combustion. Background Technology

[0002] Profound changes in the global energy landscape and escalating geopolitical risks are driving an urgent global demand for fossil fuel alternatives. Biodiesel, as a highly promising renewable fuel, plays a crucial role in reducing particulate matter (PM), hydrocarbons (HC), carbon monoxide (CO), and carbon dioxide (C₂). While exhibiting significant advantages in terms of emissions, its inherent high viscosity, low volatility, and poor low-temperature fluidity severely limit its direct application in diesel engines.

[0003] To overcome this technical bottleneck and improve the combustion performance of biodiesel, mixing it with oxygenated alcohols (such as n-butanol) has proven to be an efficient and feasible approach. n-Butanol, with its high energy density, excellent miscibility with biodiesel, and resistance to nitrogen oxides (NOx)... The relatively mild impact of nitrogen oxides (NOx) and its high cetane number make it a key focus in current research on blended fuels. Existing studies have shown that adding n-butanol to biodiesel can effectively prolong the ignition delay, shorten the combustion duration, and reduce soot emissions; however, it also carries the risk of increasing nitrogen oxide emissions (N₂O₃). And the potential risks of increased HC emissions.

[0004] Current research largely focuses on the impact of operating parameters such as fuel mixture ratio and injection timing on the combustion process and pollutant emissions, but generally neglects the combined role of key injector nozzle geometric parameters—the number of nozzle holes (NN) and the nozzle diameter (ND)—in this process. This research gap is mainly attributed to the complexity and high cost of the related experiments; more importantly, different existing studies show significant discrepancies in their core conclusions, further highlighting the necessity and urgency of synergistic optimization of NN and ND parameters. Although orthogonal experimental design provides an efficient and systematic solution for such multi-factor optimization problems, this method has not been fully explored and applied in the optimization of injector nozzle geometric parameters, resulting in the underutilization of the optimization potential of these parameters. Summary of the Invention

[0005] To address the above issues, this invention applies orthogonal experimental design to the synergistic optimization of nozzle geometry parameters for biodiesel / n-butanol blended fuels. It also considers the interaction between NN and ND, and uses range analysis to scientifically quantify the primary and secondary influences of each parameter on peak heat release rate, NOx, and soot emissions, thus overcoming the limitations of traditional univariate methods.

[0006] According to an embodiment of the present invention, a method for nozzle optimization in the combustion of a biodiesel-n-butanol blend is provided.

[0007] In a first aspect of the invention, a method for nozzle optimization in the combustion of a biodiesel-n-butanol blend is provided. The method includes: Step S01: Establish a three-dimensional computational fluid dynamics model of the constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; Step S02: With the goal of maximizing peak heat release rate and minimizing NOx and SOOT emissions, an orthogonal experimental design method is used to construct an experimental scheme. The orthogonal experimental factors are NN and ND. Step S03: Based on the orthogonal experimental design, perform simulation calculations using a three-dimensional CFD model; Step S04: Perform range analysis on the simulation results to determine the order of importance of each factor's influence on the optimization objective.

[0008] Furthermore, the governing equations in the RNGk-ε turbulence model described in step S01 are: , , in, For turbulent kinetic energy, For turbulent dissipation rate, For fluid velocity, For turbulent viscosity, For turbulence generation term, , , , are model constants, and =1.0, =1.3, =1.42, =1.68.

[0009] Furthermore, the physical process sub-model described in step S01 specifically includes: droplet breakup using the KH-RT model to describe the initial breakup caused by aerodynamics and the secondary breakup induced by deceleration; combustion simulation using the SAGE detailed chemical reaction model to handle fuel oxidation and reaction kinetics; and NOx emission prediction using the extended Zeldowicz mechanism, including the following reactions: , , .

[0010] Furthermore, the SOOT emission prediction in step S01 adopts the Hiroyasu model, and the soot generation rate is expressed as: , in, =1000, =12500 kJ / mol, For carbon smoke quality, For fuel quality, For pressure, For temperature, is the gas constant.

[0011] Furthermore, the laminar flame velocity calculation model described in step S01 employs empirical formulas to accurately describe flame propagation: , in, For reference laminar flame velocity, Temperature of the unburned mixture. Reference temperature (298K). For pressure, The reference pressure is 0.1 MPa. The mass fraction of exhaust gas. and These are the temperature index and the pressure index, respectively, and are equivalent ratios. The function, and , .

[0012] Furthermore, in step S02, the NN is set with 4 holes: 4, 5, 6, and 7, and the ND is set with 4 holes: 0.15, 0.18, 0.21, and 0.24 mm. An L16 (4^5) orthogonal array is used to arrange 16 sets of experiments.

[0013] Furthermore, the simulation conditions described in step S03 are: ambient pressure 3 MPa, ambient temperature 900 K, injection pressure 80 MPa, and injection duration 2.5 ms; The data from the simulation calculations are as follows: Peak heat release rate: The maximum value extracted from the heat release rate curve, in J / s; Nitrogen oxide emissions: In-cylinder NOx concentration at the end of the simulation, in ppm; Carbon soot emissions: In-cylinder carbon soot concentration at the end of the simulation, in ppm.

[0014] In a second aspect of the invention, an apparatus for nozzle optimization of biodiesel-n-butanol blended fuel combustion is provided. The apparatus includes: Model building module: used to build a three-dimensional computational fluid dynamics model of a constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; The scheme construction module is used to construct experimental schemes with multiple objectives, namely maximizing peak heat release rate and minimizing NOx and SOOT emissions, using orthogonal experimental design method. The orthogonal experimental factors are NN and ND. Model calculation module: used for simulation calculations based on orthogonal experimental schemes using three-dimensional CFD models; Results Analysis Module: Used to perform range analysis on simulation results and determine the order of importance of each factor's influence on the optimization objective.

[0015] In a third aspect of the invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the method according to a first aspect of the invention.

[0016] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to a first aspect of the invention.

[0017] This invention applies orthogonal experimental design to the coordinated optimization of nozzle geometry parameters for biodiesel / n-butanol blended fuels, while also considering the interaction between NN and ND. Through range analysis, it scientifically quantifies the primary and secondary influences of each parameter on peak heat release rate, NOx, and soot emissions, overcoming the limitations of traditional univariate methods.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0019] Beneficial effects: 1. For the first time, the orthogonal experimental method was applied to the collaborative optimization of nozzle geometric parameters of biodiesel / n-butanol blended fuel. The influence of each parameter and its interaction on key combustion and emission indicators was scientifically quantified, effectively overcoming the limitations of traditional single-variable optimization methods. 2. A three-dimensional computational fluid dynamics model coupled with a validated simplified chemical reaction mechanism was established, and key sub-models such as the advanced KH-RT droplet breakup model, RNG k-ε turbulence model and SAGE combustion model were integrated, which significantly improved the accuracy and reliability of the simulation results and provided solid technical support for optimization research. 3. Through optimization, the optimal combination of nozzle geometric parameters under specific working conditions was successfully obtained, which can achieve an extremely high peak heat release rate while keeping NOx and carbon emissions at extremely low levels, thus alleviating the contradictory relationship between NOx and carbon emissions that is common in diesel engines. 4. The final optimized nozzle design not only significantly improved combustion efficiency, but also achieved a synergistic reduction in NOx and soot emissions, comprehensively enhancing the performance of biodiesel / n-butanol blended fuel in diesel engines. Attached Figure Description

[0020] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Wherein: Figure 1 A flowchart illustrating a method for nozzle optimization in the combustion of a biodiesel-n-butanol blend fuel according to an embodiment of the present invention is shown. Figure 2 The present invention provides a three-dimensional model of a constant-volume combustion chamber and a nozzle structure; Figure 3 The present invention provides comparative data of experimental and simulation results; Figure 4 The orthogonal experimental design factor-level plot provided by this invention; Figure 5 A graph showing the range analysis results of PHRR versus emissions in this invention; Figure 6 The heat release rate results for different numbers of nozzles provided by this invention; Figure 7 Temperature distribution cloud maps for different numbers of nozzles provided by this invention; Figure 8 The NOx and SOOT emission results for different nozzle diameters provided by this invention are shown in the figure. Figure 9 A block diagram of an apparatus for nozzle optimization of biodiesel-n-butanol blended fuel combustion according to an embodiment of the present invention is provided; Figure 10 A schematic diagram of a device for nozzle optimization of biodiesel-n-butanol blended fuel combustion according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] According to an embodiment of the present invention, a method for nozzle optimization of biodiesel / n-butanol blended fuel combustion is proposed. By applying orthogonal experimental design to the joint optimization of nozzle geometric parameters of biodiesel / n-butanol blended fuel, and considering the interaction between NN and ND, the influence of each parameter on peak heat release rate, NOx and soot emissions is scientifically quantified through range analysis, thus overcoming the limitations of the traditional univariate method.

[0023] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0024] Figure 1 This is a schematic flowchart of a method for nozzle optimization in the combustion of a biodiesel-n-butanol blend fuel according to an embodiment of the present invention. The method includes: Step S01: Establish a three-dimensional computational fluid dynamics model of the constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; Step S02: With the goal of maximizing peak heat release rate and minimizing NOx and SOOT emissions, an orthogonal experimental design method is used to construct an experimental scheme. The orthogonal experimental factors are NN and ND. Step S03: Based on the orthogonal experimental design, perform simulation calculations using a three-dimensional CFD model; Step S04: Perform range analysis on the simulation results to determine the order of importance of each factor's influence on the optimization objective.

[0025] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0026] To provide a clearer explanation of the above-mentioned method for optimizing the nozzle for combustion of biodiesel-n-butanol blended fuel, a specific embodiment is described below. However, it is worth noting that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention.

[0027] The following specific example will further illustrate the method for nozzle optimization in the combustion of biodiesel-n-butanol blended fuels: Step S01: Establish a three-dimensional computational fluid dynamics model of the constant volume combustion chamber, including: RNGk-ε turbulence model, physical process sub-model, SOOT (smoke) emission prediction model, and laminar flame velocity calculation model.

[0028] Specifically, a three-dimensional computational fluid dynamics model of the constant-volume combustion chamber was established using CONVERGE 3.0 software, such as... Figure 2 As shown, the model couples a simplified chemical reaction mechanism for a biodiesel / n-butanol blend, which includes 210 components and 1122 reaction steps. This mechanism is based on biodiesel substitutes, primarily methyl palmitate (MHD), methyl oleate (MOD), methyl octadecanoate (MOD9D12D), methyl decanoate (MD5D), and n-decane coupled with n-butanol. The volumetric blending ratio of n-butanol is set at 20%, controlled within the range of 10%–30%, to balance combustion characteristics and emissions performance.

[0029] The governing equations in the RNGk-ε turbulence model are: , , in, For turbulent kinetic energy, For turbulent dissipation rate, For fluid velocity, For turbulent viscosity, For turbulence generation term, , , , are model constants, and =1.0, =1.3, =1.42, =1.68.

[0030] The physical process sub-models are as follows: droplet breakup uses the KH-RT model to describe the initial breakup caused by aerodynamics and the secondary breakup induced by deceleration; combustion simulation uses the SAGE detailed chemical reaction model to handle fuel oxidation and reaction kinetics; NOx emission prediction uses the extended Zeldowicz mechanism, including the following reactions: , , .

[0031] SOOT emissions prediction uses the Hiroyasu model, and the soot formation rate is expressed as: , in, =1000, =12500 kJ / mol, For carbon smoke quality, For fuel quality, For pressure, For temperature, is the gas constant.

[0032] The laminar flame velocity calculation model uses empirical formulas to accurately describe flame propagation: , in, For reference laminar flame velocity, Temperature of the unburned mixture. Reference temperature (298K). For pressure, The reference pressure is 0.1 MPa. The mass fraction of exhaust gas. and These are the temperature index and the pressure index, respectively, and are equivalent ratios. The function, and , .

[0033] In this embodiment, the model mesh is generated using adaptive mesh refinement (AMR) technology. The basic mesh size is 6 mm, and the mesh is refined in three levels in areas with large velocity and temperature gradients. The minimum mesh size is 0.5 mm, in order to balance computational accuracy and resource consumption.

[0034] Step S02: With the goal of maximizing peak heat release rate and minimizing NOx and SOOT emissions, an orthogonal experimental design method is used to construct an experimental scheme. The orthogonal experimental factors are the number of nozzles (NN) and the nozzle diameter (ND).

[0035] Specifically, the number of nozzles is set to four: 4, 5, 6, and 7 holes; the nozzle diameter is set to four: 0.15, 0.18, 0.21, and 0.24 mm. Sixteen sets of experiments are arranged using an L16(4^5) orthogonal array, each set corresponding to a different nozzle configuration, such as... Figure 3 As shown. The experimental design considered the interactions between factors to ensure comprehensive coverage of the parameter space. The selection of the orthogonal array was based on best practices in the literature, which can efficiently reduce the number of experiments while maintaining statistical reliability.

[0036] Step S03: Based on the orthogonal experimental design, perform simulation calculations using a three-dimensional CFD model.

[0037] The simulation conditions were: ambient pressure 3 MPa, ambient temperature 900 K, injection pressure 80 MPa, and injection duration 2.5 ms. Each test run simulated the following performance data: Peak heat release rate (PHRR): The maximum value extracted from the heat release rate curve, in J / s.

[0038] Nitrogen oxide emissions (NOx): In-cylinder NOx concentration at the end of the simulation, in ppm.

[0039] SOOT (Soot Emissions): In-cylinder soot concentration at the end of the simulation, in ppm.

[0040] The simulation process utilized a high-performance computing cluster, with an average computation time of approximately 4 hours per experiment, totaling 64 core hours of computation. Data extraction was automated using the CONVERGE post-processing tool and custom scripts, ensuring consistency and accuracy. Figure 4 The figure shown is a range analysis result of one of the PHRRs and emissions.

[0041] Step S04: Perform range analysis on the simulation results to determine the order of importance of each factor's influence on the optimization objective.

[0042] Specifically, the range is calculated as follows: , in, This represents the average value of the indicators (peak heat release rate, NOx emissions, SOOT emissions) at each factor level.

[0043] The analysis results show that: For peak heat release rate, the range of nozzle diameter (ND) is 12.5% ​​and the range of nozzle number (NN) is 8.3%, indicating that ND has a stronger influence.

[0044] For NOx emissions, the range for ND is 15.2%, and the range for NN is 10.1%.

[0045] For SOOT emissions, the range for ND is 18.7%, and the range for NN is 9.5%.

[0046] The optimal nozzle geometry combination is: 6 nozzles and a nozzle diameter of 0.18 mm. Under these conditions, the simulation results are: peak heat release rate: 1.89 × 10⁻⁶. 7 J / s; Nitrogen oxide emissions: 295 ppm; Soot emissions: 249 ppm, such as Figure 5-7 The figures shown are: heat release rate results for different numbers of nozzles, temperature distribution cloud map for different numbers of nozzles, and NOx and SOOT emission results for different nozzle diameters.

[0047] The combination achieved optimal performance balance at an ambient pressure of 3 MPa, an ambient temperature of 900 K, and an injection pressure of 80 MPa, with a peak heat release rate increased by 56.4% and NOx emissions reduced by 13.7% and SOOT emissions reduced by 6.4% compared to the baseline configuration (NN=4, D=0.24 mm).

[0048] Validate the model: Before implementing optimization, model validation was conducted to ensure accuracy. By comparing the simulated ignition delay and flame rise length with experimental results, the error was less than 8%. Experimental data came from constant-volume combustion chamber tests at an ambient pressure of 3 MPa and an ambient temperature of 900 K, measured using high-speed photography and pressure sensors. Figure 8 As shown, the simulation and experiment showed an error of 7.83% in the ignition delay period and 6.5% in the flame rise length, both within acceptable ranges, confirming the model's reliability. The validation process included mesh independence testing and parameter sensitivity analysis, further enhancing the model's credibility.

[0049] Comparative Case 1: Traditional Univariate Optimization Method

[0050] In the comparative simulation, a traditional univariate optimization strategy was employed. First, the nozzle diameter ND was fixed at 0.18 mm, and the number of nozzles NN (4, 5, 6, 7 holes) was varied across four simulations to find the highest PHRR under this condition, with NN=6 holes. Then, with NN=6 holes fixed, ND (0.15, 0.18, 0.21, 0.24 mm) was varied across four simulations to find the optimal ND. The final "optimal" combination was also NN=6 and ND=0.18 mm.

[0051] However, this method requires a total of 8 simulations and cannot examine the interaction between the neural network (NN) and the densitoid (ND). If the interaction is significant, the solution found by this method may not be globally optimal. In contrast, the orthogonal experimental method of this invention, although it involves 16 simulations, uses a scientifically designed table to not only find the optimal solution but also obtain information on the relative importance and interaction of each factor, resulting in more valuable data and more reliable optimization conclusions.

[0052] Comparative Case 2: System with Fixed Nozzle Parameters

[0053] The comparative example uses a combustion system with fixed nozzle parameters, a common 7-hole design, and a diameter of 0.21 mm, to burn the same biodiesel / n-butanol blend.

[0054] Under the same operating conditions, the PHRR was 1.60 × 1 The system has a combustion efficiency of 345 ppm NOx and 301 ppm Soot emissions per liter (J / s). Compared with the optimized system of this invention, the comparative system has a lower combustion efficiency (PHRR of approximately 15.3%) and higher NOx and Soot emissions of 16.2% and 16.2% respectively, resulting in significantly inferior overall performance compared to the optimized system of this invention.

[0055] Based on the same inventive concept, this invention also proposes an apparatus for optimizing the nozzle for combustion of a biodiesel-butanol blend. The implementation of this apparatus is similar to the implementation of the method described above, and repeated details will not be elaborated further. Figure 9As shown, the device 100 includes: Model building module 101: Used to build a three-dimensional computational fluid dynamics model of a constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; Scheme Construction Module 102: Used to construct experimental schemes with multiple objectives of maximizing peak heat release rate, minimizing NOx emissions and SOOT emissions, and employing orthogonal experimental design method, with orthogonal experimental factors being NN and ND; Model calculation module 103: used for simulation calculations based on orthogonal experimental schemes using a three-dimensional CFD model; Results Analysis Module 104: Used to perform range analysis on simulation results and determine the order of importance of each factor's influence on the optimization objective.

[0056] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0057] As shown in Figure 10, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0058] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0059] The processing unit executes the various methods and processes described above, such as method steps S01 to S04. For example, in some embodiments, method steps S01 to S04 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of method steps S01 to S04 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute method steps S01 to S04 by any other suitable means (e.g., by means of firmware).

[0060] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0061] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0062] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0063] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0064] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method of nozzle optimization for biodiesel n-butanol blend fuel combustion, characterized by, The method includes: Step S01: Establish a three-dimensional computational fluid dynamics model of the constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; Step S02: With the goal of maximizing peak heat release rate and minimizing NOx and SOOT emissions, an orthogonal experimental design method is used to construct an experimental scheme. The orthogonal experimental factors are NN and ND. Step S03: Based on the orthogonal experimental design, perform simulation calculations using a three-dimensional CFD model; Step S04: Perform range analysis on the simulation results to determine the order of importance of each factor's influence on the optimization objective.

2. A method of nozzle optimization for biodiesel n-butanol blended fuel combustion as claimed in claim 1 wherein, The governing equations in the RNGk-ε turbulence model described in step S01 are: ， ， wherein is the turbulent kinetic energy, is the turbulent dissipation rate, is the fluid velocity, is the turbulent viscosity, is the turbulent production term, , , , are model constants, and = 1.0, = 1.3, = 1.42, = 1.

68.

3. A method of optimizing a nozzle for biodiesel n-butanol blended fuel combustion as claimed in claim 1, wherein, The physical process sub-models described in step S01 are as follows: Droplet breakup uses the KH-RT model to describe the initial breakup caused by aerodynamics and the secondary breakup induced by deceleration; combustion simulation uses the SAGE detailed chemical reaction model to handle fuel oxidation and reaction kinetics; NOx emission prediction uses the extended Zeldowicz mechanism, including the following reactions: ， ， 。 4. A method of biodiesel n-butanol blended fuel combustion nozzle optimization as claimed in claim 1 wherein, The SOOT emission prediction in step S01 uses the Hiroyasu model, and the soot generation rate is expressed as: ， wherein, = 1000, = 12500 kJ / mol, is the soot mass, is the fuel mass, is the pressure, is the temperature, is the gas constant.

5. A method of biodiesel n-butanol blended fuel combustion nozzle optimization as claimed in claim 1 wherein, The laminar flame velocity calculation model described in step S01 uses empirical formulas to accurately describe flame propagation: ， wherein, Sref is the reference laminar flame speed, Tun is the unburnt mixture temperature, Tref is the reference temperature (298 K), P is the pressure, Pref is the reference pressure (0.1 MPa), X is the exhaust gas mass fraction, and are the temperature and pressure exponents, respectively, are functions of the equivalence ratio and , .

6. A method of biodiesel n-butanol blended fuel combustion nozzle optimization as claimed in claim 1 wherein, In step S02, four NN holes are set: 4, 5, 6, and 7, and four ND holes are set: 0.15, 0.18, 0.21, and 0.24 mm. Sixteen sets of experiments are arranged using an L16 (4^5) orthogonal array.

7. The method for nozzle optimization for combustion of biodiesel-n-butanol blended fuel according to claim 1, characterized in that, The simulation conditions described in step S03 are: ambient pressure 3 MPa, ambient temperature 900 K, injection pressure 80 MPa, and injection duration 2.5 ms. The data from the simulation calculations are as follows: Peak heat release rate: The maximum value extracted from the heat release rate curve, in J / s; Nitrogen oxide emissions: In-cylinder NOx concentration at the end of the simulation, in ppm; Carbon soot emissions: In-cylinder carbon soot concentration at the end of the simulation, in ppm.

8. An apparatus for nozzle optimization in the combustion of a biodiesel-n-butanol blend, characterized in that, The device implements the method as described in any one of claims 1 to 7, comprising: Model building module: used to build a three-dimensional computational fluid dynamics model of a constant volume combustion chamber, including: RNG k-ε turbulence model, physical process sub-model, SOOT emission prediction model, and laminar flame velocity calculation model; The scheme construction module is used to construct experimental schemes with multiple objectives, namely maximizing peak heat release rate and minimizing NOx and SOOT emissions, using orthogonal experimental design method. The orthogonal experimental factors are NN and ND. Model calculation module: used for simulation calculations based on orthogonal experimental schemes using three-dimensional CFD models; Results Analysis Module: Used to perform range analysis on simulation results and determine the order of importance of each factor's influence on the optimization objective.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

Images