A simulation evaluation optimization method for a heavy-duty gas turbine combustor coannular design

Abstract

The application discloses a simulation evaluation optimization method for a heavy-duty gas turbine combustion chamber combined flame tube design, comprising the following steps: S1. Based on the chemical equilibrium and thermodynamic principles, the main combustion chamber state is calculated, and the gas mixing, flow, ignition, combustion and equilibrium processes in the main combustion chamber are simulated; S2. For the simulation results, the aerodynamic boundary conditions are input, and the geometric boundary conditions are confirmed by constructing the objective function and the constraint conditions, and then the global optimization in the continuous space of the swarm intelligence is realized by using the differential evolution algorithm through the differential information between the individuals in the swarm; the penalty mechanism is used to balance the negative influence of the specified parameters in the global optimization process. The ignition success probability parameter is constructed, the simplicity of the single parameter evaluation is met, the combined flame tube geometry is optimized according to the operation conditions at the time of ignition, the high and low of the ignition success probability after optimization is used to determine whether the operation condition ignition can make the whole machine flame, and the evaluation simulation optimization is finally realized.

Description

Technical Field

[0001] This invention relates to the field of gas turbine combustor joint flame tube design technology, and specifically to a simulation evaluation and optimization method for the design of heavy-duty gas turbine combustor joint flame tubes. Background Technology

[0002] Currently, in order to reduce testing costs and increase maintenance possibilities, the combustion chamber of heavy-duty gas turbines is mostly in the form of a flame tube. Two flame tubes are connected by a connecting tube, so that after the igniter ignites the adjacent flame tubes, the high-temperature expanding gas can ignite the two adjacent flame tubes through the connecting tube and continue to spread until all the flame tubes are ignited. The connecting tube also plays the role of balancing the pressure of a ring of flame tubes.

[0003] Therefore, the main purpose of the combined flame tube design is to enable the combustion gases in the flame tube to be transferred to the adjacent flame tube after ignition. However, in the current forward design of heavy-duty gas turbines and aircraft, the combined flame tube is mostly based on structural design rather than performance design, and even directly uses early (1940s-1950s) design drawings. However, this type of design still has the following technical problems in the actual combined flame tube design of lean premixed gas turbines:

[0004] (1) The conventional design of the flame tube in the existing technology focuses on the improvement of the flame tube structure, such as increasing the cooling capacity or adding a damper. It is not a forward design starting from 0, but only an improvement idea based on the existing technology.

[0005] (2) The conventional flame tube design used in the existing technology cannot achieve the goal of multi-parameter parallel optimization in the actual simulation of focused combustion.

[0006] Therefore, in the design of the combined flame tube of the lean premixed engine, it is still necessary to re-examine the entire design process, carry out steady-state design according to performance requirements, and be able to identify the success rate of the combined flame tube ignition.

[0007] To address this issue, this application proposes a simulation evaluation and optimization method for the design of the combustor coupling tube in a heavy-duty gas turbine. By introducing a differential evolution algorithm, combined with a physical model and a custom penalty mechanism, the method aims to solve the aforementioned technical problems and improve optimization efficiency and design reliability. Summary of the Invention

[0008] The main objective of this invention is to provide a simulation evaluation and optimization method for the design of the combustor coupling tube in a heavy-duty gas turbine, so as to solve the technical problems mentioned in the background art.

[0009] The present invention solves the above-mentioned technical problems by adopting the following technical solutions:

[0010] A simulation evaluation and optimization method for the design of the combustor co-fiber tube in a heavy-duty gas turbine involves performing the following steps using computer equipment:

[0011] Step S1. Based on the principles of chemical equilibrium and thermodynamics, calculate the state of the main combustion chamber and simulate the gas mixing, flow, ignition, combustion and equilibrium processes within the main combustion chamber;

[0012] Step S2. For the simulation results, input the aerodynamic edge strip, and confirm the geometric edge strip by constructing the objective function and constraint conditions. Then, through the differential information between individuals in the swarm, the differential evolution algorithm is used to achieve global optimization in the continuous space of swarm intelligence.

[0013] The global optimization process employs a set of penalty mechanisms to balance the negative impact of specified parameters.

[0014] Preferably, in step S1, during the simulation of the main combustion chamber state, there is an ignition energy balance, the specific balance formula of which is:

[0015]

[0016] in, The total energy provided to the unburned adjacent flame tubes through the flame tubes. The energy required to activate the fuel-air mixture The energy required to heat the mixture to its ignition point. For heat loss;

[0017] At this point, the energy balance of the entire combined flame process exists as follows: ;

[0018] and These are the inlet and outlet enthalpies of the combined flame process. and These are the inlet and outlet mass flow rates of the combined flame process, respectively.

[0019] The mass flow equation at this point is:

[0020]

[0021] in, For flow coefficient, The cross-sectional area of ​​the flame tube is... For flow rate, For gas density, The pressure difference is used as the driving force for the flow within the flame tube, and the airflow rate is used to ensure this. Sufficient flow rate improves the success rate of flame coupling. However, the flow rate through the flame coupling tube consumes the pressure difference, resulting in increased pressure loss between the two flame tubes and reduced performance of the combustion system. The flow rate is mainly determined by the area of ​​the flame coupling tube.

[0022] The main combustion chamber simulation process also includes:

[0023] Based on a turbulent combustion simulation model with fully mixed turbulence and the flame stability parameter K, the specific formula of the model is as follows:

[0024]

[0025] in, The length of the flame tube. The turbulent velocity in the flame tube is... For laminar flame thickness, The propagation speed of laminar flame. Represented by the Damköhler number, when Da >> 1, combustion is controlled by mixing; when Da << 1, it is controlled by chemical kinetics, at which point the turbulent combustion rate is determined. for:

[0026]

[0027] Among them, To preset the experimental correction coefficient, This refers to the velocity fluctuation.

[0028] The mathematical definition of the flame stability parameter is:

[0029]

[0030] in, Density of unburned gas The diameter of the flame tube is [missing information]. For thermal diffusivity, To delay ignition time, The velocity of the laminar flame.

[0031] Preferably, in step S1, the simulation process uses Cantera software to simulate the gas mixing, combustion, and equilibrium processes, including:

[0032] Taking methane fuel as an example, equivalence ratio ;

[0033] The flame temperature was calculated based on isenthalpic and isobaric equilibrium, and the heat loss of the flame tube was limited to within 6%. Similarly, the density, viscosity, and thermal conductivity of the gas were calculated.

[0034] Flow analysis is based on fluid mechanics and heat transfer, and iteratively solves for mass flow rate to match the driving pressure difference;

[0035] The pressure loss model for the flame tube is constructed as follows: Total pressure loss = Friction pressure loss + Bending pressure loss + Local pressure loss;

[0036] The formula for the frictional pressure loss model is as follows:

[0037]

[0038] The coefficient of friction Calculations based on Reynolds number for stratified / turbulent flow;

[0039] The pressure loss in bends is a resistance coefficient corrected using the Dean number;

[0040] The inlet / outlet coefficients used for local pressure loss are 0.3 and 0.8, respectively, which are related to the dynamic pressure head;

[0041] The convective heat transfer coefficient calculated using the Nuseelt number in the heat transfer model: Considering the flow time is on the order of milliseconds, the heat transfer efficiency is... Take the smaller value compared to the fixed value (40%). Consider constant pressure for temperature drop. ;

[0042] Limit the combined flame tube to within 8% of the total flow rate;

[0043] Using Cantera's OD reactor to simulate blending and chemical reactions, the blending temperature is obtained:

[0044]

[0045] The temperature rise threshold (150 K) for ignition is simulated using a Cantera isobaric reactor, or by empirical formula:

[0046]

[0047] Laminar flame velocity:

[0048]

[0049] Preferably, in step S2, the pneumatic edge strip consists of the compressor outlet pressure and temperature, and two sets of flame tube distribution inputs, wherein the flame tube distribution inputs include:

[0050] The flame tube, which has been ignited by an electric spark, has an inlet temperature higher than the compressor outlet pressure temperature. It only provides the temperature flow rate of air and the temperature flow rate of fuel, and the composition and temperature of the combustion gas are calculated by Cantera.

[0051] The airflow and gas flow rate of the flame tube to be ignited are the same as those of the flame tube that has already been ignited by an electric spark, and the composition, temperature, and inlet temperature of the gas after combustion are all lower than the compressor outlet pressure and temperature.

[0052] Preferably, the objective function of the geometric edge strip is determined in step S2 as follows:

[0053]

[0054] in These represent the diameter, length, bending radius, and bending angle of the flame tube, respectively. , , These represent the ignition delay time. Pressure difference Maximum ignition temperature The influence coefficient.

[0055] Preferably, after the simulation process is executed in step S2, a differential evolution algorithm and physical model calculation are introduced to achieve geometric optimization of the combined flame tube. The calculation process of the differential evolution algorithm is as follows:

[0056] L1. Randomly generate an initial population, in which N D-dimensional individuals are randomly generated, each corresponding to one of the D variables in the optimization problem;

[0057] L2. Mutation is generated over time using a random seed in each generation to ensure that the seed is different each time. Crossover and selection updates are performed, where for each group of target individuals... Three different individuals were randomly selected from each group. , , The generated mutation vectors are: , F is a scaling factor used to control the influence of the difference vector (the smaller F is, the more local the search; the larger F is, the stronger the exploration capability).

[0058] L3. Transformation vector With target individuals Generate trial vectors by crossover with specified probabilities. And select the next generation of individuals according to the greedy criterion: if the experimental vector If the fitness (objective function value) is better, then Otherwise, retain the original individual. ;

[0059] The objective function value is calculated independently for each candidate solution and supports parallel evaluation. The objective function is: ,in For performance weighted sum, Diameter balance including constraints and weight, heat loss, and flow rate.

[0060] Preferably, the fitness (objective function value) is evaluated for each candidate geometric edge using a multi-threaded parallel approach, and the evaluation method includes:

[0061] The main state of the combustion chamber is calculated using the Cantera tool or a specified combustion simulation method.

[0062] Cross states are obtained by iteratively solving a specified flow problem (including pressure loss and heat transfer);

[0063] Ignition parameters were obtained through ignition simulation calculations. The ignition metrics specifically include ignition delay and ignition probability;

[0064] Calculate penalty items The calculation formula is as follows:

[0065]

[0066] The diameter of the flame tube is [missing information]. This refers to the jet velocity of the combined flame tube;

[0067] Computational performance metrics The calculation formula is as follows:

[0068]

[0069] For the loss ratio, For other specified indicators;

[0070] Finally, the sum of the performance metrics and penalty items is returned and compared with a preset threshold.

[0071] Preferably, the overall optimization process of the differential evolution algorithm for global optimization includes:

[0072] Initialization settings include operating parameters such as pressure, flow rate, and temperature, and specifying constraints, boundary conditions, and the optimization objectives and ranges for the final output.

[0073] Construct a specified objective function and population conditions, and use the differential evolution algorithm to perform iterative optimization calculations until the target population is obtained;

[0074] For each set of candidate geometric parameters, an objective function evaluation operation is performed, and the penalty calculation during the objective function evaluation process also includes a penalty for the equilibrium diameter;

[0075] Finally, the optimal geometry, combustion chamber state, flow characteristics, ignition performance, target achievement check, and comprehensive rating under the best iteration are obtained. Reasonableness verification conditions are added for diagnosis and screening. After the diagnosis and screening are passed, corresponding attached figures are generated for optimization convergence history, ignition success probability history, pressure loss history, and design space exploration.

[0076] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0077] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0078] As can be seen from the above technical solution, the present invention provides a simulation evaluation and optimization method for the design of the combustor coupling tube in a heavy-duty gas turbine. Compared with the prior art, the present invention has the following advantages:

[0079] 1. This invention constructs ignition success probability parameters based on the differences in ignition of different machines and whether the aircraft ignition is accompanied by auxiliary machines, thus satisfying the simplicity of single parameter evaluation. At the same time, it also performs geometric optimization of the flame tube based on the operating conditions during ignition. By using the optimized ignition success probability, it determines whether the ignition under the operating conditions can achieve flame connection of the whole machine, which facilitates the modification of the ignition point and ultimately realizes evaluation simulation optimization.

[0080] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0081] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0082] Figure 1 This is a schematic diagram of the overall operation process of the present invention;

[0083] Figure 2 This is a schematic diagram of the optimization convergence history results of an embodiment of the present invention, where the horizontal and vertical axes represent the number of iterations and the objective function value, respectively.

[0084] Figure 3This is a schematic diagram of the historical results of ignition success probability in an embodiment of the present invention, where the horizontal and vertical axes represent the number of iterations and the ignition success probability, respectively.

[0085] Figure 4 This is a schematic diagram of the pressure loss history results according to an embodiment of the present invention, where the horizontal and vertical axes represent the number of iterations and the pressure loss ratio, respectively.

[0086] Figure 5 This is a schematic diagram of the design space exploration results of an embodiment of the present invention, where the horizontal and vertical axes represent the ignition success probability and the pressure loss ratio, respectively. Detailed Implementation

[0087] 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 a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. 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.

[0088] For details in the embodiments, please refer to Figures 1 to 5 .

[0089] Since the combined flame ignition of heavy-duty gas turbines is a complex multiphysics coupling process, the transfer of ignition energy during the flow within the combined flame tube is significantly affected by the fluid velocity (V) and density (ρ). These two factors are determined by the ignition state. Therefore, when designing the combined flame tube, the ignition state (ignition limit determined by chemical combustion, ignition residence time, pressure, etc.) must be confirmed first.

[0090] Therefore, the simulation evaluation and optimization method for the design of the combustor flame tube of a heavy-duty gas turbine proposed in this embodiment of the invention can obtain results in a short time through chemical formulas and can provide iterative optimization results on-site, such as... Figure 1 As shown, the specific steps include:

[0091] Step S1. Input system parameters (pressure, temperature, flow rate, etc.), initialize the optimizer, set constraints and boundaries, calculate the main combustion chamber state based on chemical equilibrium and thermodynamic principles, and simulate the gas mixing, flow, ignition, combustion and equilibrium processes in the main combustion chamber.

[0092] The simulation of the main combustion chamber state involves an ignition energy balance, the specific balance formula of which is:

[0093]

[0094] in, The total energy provided to the unburned adjacent flame tubes through the flame tubes. The energy required to activate the fuel-air mixture The energy required to heat the mixture to its ignition point. For heat loss;

[0095] At this point, the energy balance of the entire combined flame process exists as follows: ;

[0096] and These are the inlet and outlet enthalpies of the combined flame process. and These are the inlet and outlet mass flow rates of the combined flame process, respectively.

[0097] The mass flow equation at this point is:

[0098]

[0099] in, For flow coefficient, The cross-sectional area of ​​the flame tube is... For flow rate, For gas density, The pressure difference is used as the driving force for the flow within the flame tube, and the airflow rate is used to ensure this. Sufficient flow rate improves the success rate of flame coupling. However, the flow rate through the flame coupling tube consumes the pressure difference, resulting in increased pressure loss between the two flame tubes and reduced performance of the combustion system. The flow rate is mainly determined by the area of ​​the flame coupling tube.

[0100] Furthermore, the interaction between flame propagation velocity and flow velocity determines whether energy can be effectively transferred to adjacent flame tubes. Therefore, in the initial stage of ignition, due to the pressure difference... A larger flow rate and higher mass flow rate help to quickly deliver sufficient heat to overcome the activation energy barrier. However, as the pressure approaches equilibrium, the decrease in flow rate may lead to a reduction in penetration depth and heat loss. In summary, this application optimizes the cross-sectional area of ​​the flame-coil tube. With flow coefficient It can ensure that a sufficient energy transfer rate is maintained during the critical ignition stage, avoiding heat loss. Excessive heat can lead to ignition failure. In other words, ignition requires a small cross-sectional area to maintain a large penetration depth, while after ignition, a large cross-sectional area is needed to minimize heat loss. Through optimized design, we ensure that the flame tube provides efficient energy transfer in the initial stages of ignition.

[0101] The main combustion chamber simulation process also includes:

[0102] Based on a turbulent combustion simulation model with fully mixed turbulence and the flame stability parameter K, the specific formula of the model is as follows:

[0103]

[0104] in, The length of the flame tube. The turbulent velocity in the flame tube is... For laminar flame thickness, The propagation speed of laminar flame. Represented by the Damköhler number, when Da >> 1, combustion is controlled by mixing; when Da << 1, it is controlled by chemical kinetics, at which point the turbulent combustion rate is determined. for:

[0105]

[0106] in, To preset the experimental correction coefficient, This refers to the velocity fluctuation.

[0107] The flame stability parameter is mathematically defined as:

[0108]

[0109] in, Density of unburned gas The diameter of the flame tube is [missing information]. For thermal diffusivity, To delay ignition time, The velocity of the laminar flame.

[0110] In a confined geometric space (such as a combined flame tube in a gas turbine combustor), flame stability depends on the balance between the energy of flame propagation and the energy loss due to thermal diffusion and chemical reaction timescales.

[0111] Specifically, the above flame stability parameters can be explained in principle as follows:

[0112] A key concept in combustion theory is the chemical timescale. It represents the time required for a chemical reaction to occur. For premixed flames, it can usually be approximated as: This approximation stems from the thickness of the flame. and reaction time In the context of ignition delay, Can be used as The amount of substitution, especially in high-pressure gas turbine environments, can be affected by spontaneous combustion. This represents the dynamic flame energy scaled by the pipe diameter. Here, Similar to the dynamic pressure or energy density of a flame, D quantifies the kinetic energy associated with flame propagation, while D introduces the geometric scale of the confined space.

[0113] denominator This includes heat diffusion losses over the ignition timescale. Coefficient 4 is a correction term defined based on the cylindrical pipe in the heat transfer model, similar to the conditions used in quenching analysis.

[0114] Will Substituting, we get:

[0115] This equation shows the relationship between K and the square of the flame velocity (scaled by density and diameter) and normalized by diffusion loss. Physically, K > 1 indicates that propagation energy dominates in diffusion and delay effects, thus ensuring flame stability; the empirically determined threshold K > 10 is used to explain the turbulence enhancement effect in gas turbine crossfire tubes.

[0116] For physical process characteristic time, The characteristic time of a chemical reaction.

[0117] This parameter extends the traditional Da and Pe numbers by explicitly incorporating ignition delay; therefore, it is used in this application for co-firing tube designs, where rapid ignition between combustion chambers is crucial. In population optimization, K is included as a constraint to ensure robust flame stability.

[0118] In a further specific embodiment, using Cantera software to simulate the gas mixing, combustion, and equilibrium processes, the following are included:

[0119] Taking methane fuel as an example, equivalence ratio , Expressed as the stoichiometric air-fuel ratio, This refers to the actual fuel air-fuel ratio;

[0120] The flame temperature was calculated based on isenthalpic and isobaric equilibrium, and the heat loss of the flame tube was limited to within 6%. Similarly, the density, viscosity, and thermal conductivity of the gas were calculated.

[0121] Flow analysis is based on fluid mechanics and heat transfer, and iteratively solves for mass flow rate to match the driving pressure difference;

[0122] The pressure loss model for the flame tube is constructed as follows: Total pressure loss = Friction pressure loss + Bending pressure loss + Local pressure loss;

[0123] The formula for the frictional pressure loss model is as follows:

[0124]

[0125] The coefficient of friction Calculations based on Reynolds number for stratified / turbulent flow;

[0126] The pressure loss in bends is a resistance coefficient corrected using the Dean number;

[0127] The inlet / outlet coefficients used for local pressure loss are 0.3 and 0.8, respectively, which are related to the dynamic pressure head;

[0128] The heat transfer model uses the Nuseelt number ( ) Calculated convective heat transfer coefficient ,for: Considering the flow time is on the order of milliseconds, the heat transfer efficiency is... Take the smaller value compared to the fixed value (40%). Consider constant pressure for temperature drop. , To reduce the heat of reaction, The mass flow rate or mass of the fluid in the flame tube. The specific heat capacity at constant pressure of the fluid;

[0129] Limit the combined flame tube to within 8% of the total flow rate;

[0130] Using Cantera's OD reactor to simulate blending and chemical reactions, the blending temperature is obtained. The calculation formula is:

[0131]

[0132] in, This refers to the mass flow rate or mass of the heat transfer fluid in the flame tube. The isobaric specific heat capacity of the heat transfer fluid in the flame tube. The temperature of the heat transfer fluid in the flame tube. The mass flow rate or mass of the cold fluid in the flame tube. The isobaric specific heat capacity of the cold fluid in the flame tube. The temperature of the cold fluid in the flame tube;

[0133] Using a Cantera isobaric reactor to simulate the temperature rise threshold (150 K) for ignition, an empirical formula exists:

[0134]

[0135] Laminar flame velocity:

[0136]

[0137] The equivalence ratio is known.

[0138] Step S2. For the simulation results, input the aerodynamic edge strip, and confirm the geometric edge strip by constructing the objective function and constraint conditions. Then, through the differential information between individuals in the swarm, the differential evolution algorithm is used to achieve global optimization in the continuous space of swarm intelligence, iteratively optimize the geometric parameters, calculate the combustion state, flow characteristics, and ignition index in each iteration, and apply the penalty mechanism. Finally, the optimal design is output and the results are analyzed.

[0139] The pneumatic edge strip consists of the compressor outlet pressure and temperature, and two sets of flame tube distributed inputs, wherein the flame tube distributed inputs include:

[0140] The flame tube, which has been ignited by an electric spark, has an inlet temperature higher than the compressor outlet pressure temperature. It only provides the temperature flow rate of air and the temperature flow rate of fuel, and the composition and temperature of the combustion gas are calculated by Cantera.

[0141] The air flow and gas flow of the flame tube to be ignited are the same as those of the flame tube that has already been ignited by the electric spark. Moreover, the composition, temperature and inlet temperature of the gas after combustion are all lower than the compressor outlet pressure and temperature (considering that the gas from the connecting tube only ignites a portion of the flow of the second flame tube, the actual value given will be a portion of the first flame tube).

[0142] At this point, the inlet temperature of the first flame tube, which has been ignited by the electric spark (the air can be higher than the other group because it has undergone reverse cooling of the flame tube), only provides the temperature flow rate of the air and the temperature flow rate of the fuel. The composition and temperature of the combustion gas are then calculated by Cantera.

[0143] At this point, after the simulation process is executed, the differential evolution algorithm and physical model calculation are introduced to achieve geometric optimization of the combined flame tube. The calculation flow with the differential evolution algorithm is as follows:

[0144] L1. Randomly generate an initial population, in which N D-dimensional individuals are randomly generated, each corresponding to one of the D variables in the optimization problem;

[0145] L2. Mutation is generated over time using a random seed in each generation to ensure that the seed is different each time. Crossover and selection updates are performed, where for each group of target individuals... Three different individuals were randomly selected from each group. , , The generated mutation vectors are: , F is a scaling factor used to control the influence of the difference vector (the smaller F is, the more local the search; the larger F is, the stronger the exploration capability).

[0146] L3. Transformation vector With target individuals Generate trial vectors by crossover with specified probabilities. And select the next generation of individuals according to the greedy criterion: if the experimental vector If the fitness (objective function value) is better, then Otherwise, retain the original individual. ;

[0147] The objective function value is calculated independently for each candidate solution and supports parallel evaluation. The objective function is: ,in For performance weighted sum, Diameter balance including constraints and weight, heat loss, and flow rate.

[0148] Specifically, the objective function for the geometric edge strip is determined as follows:

[0149]

[0150] At this point, the constraints are as follows:

[0151]

[0152]

[0153]

[0154] in These represent the diameter, length, bending radius, and bending angle of the flame tube, respectively. , , These represent the ignition delay time. Pressure difference Maximum ignition temperature Influence coefficient, This refers to the ignition dwell time. This is a heat attribute value. These are the allowed values ​​for the heat attribute.

[0155] It can be further explained that during the global optimization process, a set of penalty mechanisms is set to balance the negative impact of specified parameters.

[0156] In summary, the fitness (objective function value) is further evaluated for each candidate geometric edge using a multi-threaded parallel approach. The evaluation methods include:

[0157] The main state of the combustion chamber is calculated using the Cantera tool or a specified combustion simulation method.

[0158] Cross states are obtained by iteratively solving a specified flow problem (including pressure loss and heat transfer);

[0159] Ignition parameters were obtained through ignition simulation calculations. The ignition metrics specifically include ignition delay and ignition probability;

[0160] Calculate penalty items The calculation formula is as follows:

[0161]

[0162] The diameter of the flame tube is [missing information]. This refers to the jet velocity of the combined flame tube;

[0163] Computational performance metrics The calculation formula is as follows:

[0164]

[0165] For the loss ratio, For other specified indicators;

[0166] Finally, the sum of the performance metrics and penalty items is returned and compared with a preset threshold.

[0167] The overall optimization process of the differential evolution algorithm for global optimization at this time includes:

[0168] Initialization settings include operating parameters such as pressure, flow rate, and temperature, and specifying constraints, boundary conditions, and the optimization objectives and ranges for the final output.

[0169] Construct a specified objective function and population conditions, and use the differential evolution algorithm to perform iterative optimization calculations until the target population is obtained;

[0170] For each set of candidate geometric parameters, an objective function evaluation operation is performed, and the penalty calculation during the objective function evaluation process also includes a penalty for the equilibrium diameter;

[0171] Finally, the optimal geometry, combustion chamber state, flow characteristics, ignition performance, target achievement check, and comprehensive rating under the best iteration are obtained. Reasonableness verification conditions are then added for diagnostic screening. After successful diagnostic screening, corresponding attached figures are generated, including optimization convergence history, ignition success probability history, pressure loss history, and design space exploration. Figures 2 to 5 As shown.

[0172] according to Figure 2 ,exist Figure 2The optimization convergence history graph is used to show how the objective function changes with the number of iterations. The horizontal axis represents the number of iterations, and the vertical axis represents the objective function value. The smaller the vertical axis, the better, indicating whether the optimization algorithm has converged to the optimal solution. If the optimization is successful, a clear downward trend should be seen, and ideally, the curve should gradually decrease and tend to stabilize. At this point, the optimal solution (red asterisk) should be near the lowest point of the curve.

[0173] according to Figure 3 ,exist Figure 3 The ignition probability optimization plot in the diagram can track the improvement process of ignition reliability. Its horizontal axis represents the number of iterations, and the vertical axis represents the ignition probability (between 0 and 1, the closer to 1 the better). It is used to show how the ignition reliability of the combined flame tube design improves with the optimization process. Its goal is to exceed 70% of the preset value of the red dotted line, that is, ideally the final solution should be above the dotted line.

[0174] according to Figure 4 ,exist Figure 4 The pressure loss optimization plot in the figure can monitor the control of flow resistance and show how the pressure loss of the combined flame tube design is optimized. The horizontal axis represents the number of iterations, and the vertical axis represents the pressure loss ratio (between 0 and 1, the smaller the better). Since excessive pressure loss will affect the efficiency of the gas turbine, a 12% green dashed line limit is set in the figure to avoid the pressure loss being too high and reducing the overall efficiency of the gas turbine.

[0175] according to Figure 5 ,exist Figure 5 The design space exploration in this application is the core output of the optimized method. The horizontal axis represents the pressure loss ratio (the smaller the better), and the vertical axis represents the ignition probability (the larger the better). Its function is to construct a set of two-dimensional design space diagrams to show the trade-off relationship between the two mutually restrictive key indicators. The ideal solution should be in the upper right corner to indicate low pressure loss and high ignition probability. The color in the diagram represents the iteration order. The color from dark to light shows the search trajectory of the algorithm, which shows how the algorithm searches in the design space, thus making it easier to see if the algorithm gets trapped in a local optimum.

[0176] Based on the above operating method, if it is still difficult to guarantee successful ignition based on the final optimization results, the ignition design point can be modified again using this method. This process will save a lot of costs compared to using experiments, and will also save a lot of time compared to using combustion numerical simulation methods such as CFD.

[0177] In addition, similar to conventional CFD combustion numerical simulation experiments, the method used in this application starts directly from the physical equations. However, this invention also uses the differential algorithm in the population algorithm to avoid complex experiments and large numerical simulations, thereby shortening the entire design cycle and reducing R&D costs.

[0178] In summary, this method constructs ignition success probability parameters based on the differences in ignition of different machines and whether the aircraft ignition involves auxiliary machines, thus satisfying the simplicity of single-parameter evaluation. At the same time, it also performs geometric optimization of the flame tube based on the operating conditions during ignition. By using the optimized ignition success probability, it determines whether the ignition under the operating conditions can achieve flame connection of the entire aircraft, which facilitates the modification of the ignition point and ultimately achieves evaluation, simulation and optimization.

[0179] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the simulation evaluation and optimization method for the design of the combustor co-fiber tube of any of the above embodiments.

[0180] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0181] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus.

[0182] Memory, used to store computer programs;

[0183] The processor, when executing the program stored in the memory, implements the above-mentioned simulation evaluation and optimization method for the design of the combined flame tube of the combustion chamber of a heavy-duty gas turbine.

[0184] The communication bus mentioned in the above-mentioned electronic devices can be a standard bus for interconnecting peripheral components or an extended industrial standard structure bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0185] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0186] The memory may include random access memory or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0187] The processors mentioned above can be general-purpose processors, including central processing units, network processors, etc.; they can also be digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0188] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0189] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0190] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A simulation evaluation and optimization method for the design of the combustor co-firing tube in a heavy-duty gas turbine, characterized in that, include: Step S1. Input system parameters, including pressure, temperature, and flow rate, and initialize the optimizer. Set specified constraints and boundary conditions, as well as the final output optimization target and range. Based on chemical equilibrium and thermodynamic principles, calculate the state of the main combustion chamber and simulate the gas mixing, flow, ignition, combustion, and equilibrium processes in the main combustion chamber. Step S2. For the simulation results, input the aerodynamic edge strip, and confirm the geometric edge strip by constructing the objective function and constraint conditions. Then, through the differential information between individuals in the swarm, the differential evolution algorithm is used to achieve global optimization in the continuous space of swarm intelligence. The global optimization process employs a set of penalty mechanisms to balance the negative impact of specified parameters; After the simulation process is executed in step S2, the differential evolution algorithm and physical model calculation are introduced to achieve geometric optimization of the combined flame tube. The calculation process of the differential evolution algorithm is as follows: L1. Randomly generate an initial population, in which N D-dimensional individuals are randomly generated, each corresponding to one of the D variables in the optimization problem; L2. Mutation is generated over time using random seeds in each generation, followed by crossover and selection updates, where for each group of target individuals... Three different individuals were randomly selected from each group. , , The generated mutation vectors are: , This is a scaling factor used to control the influence of the difference vector; L3. Transformation vector With target individuals Generate trial vectors by crossover with specified probabilities. And select the next generation of individuals according to the greedy criterion: if the experimental vector If the adaptability is better, then Otherwise, retain the original individual. ; The fitness is evaluated for each candidate geometric edge using a multi-threaded parallel approach. The evaluation method includes: The main state of the combustion chamber is calculated using the Cantera tool or a specified combustion simulation method. The crossover state is obtained by iteratively solving a specified flow problem. Ignition parameters were obtained through ignition simulation calculations. ; Calculate penalty items The calculation formula is as follows: The jet velocity of the combined flame tube. Indicates the diameter of the flame tube; Computational performance metrics The calculation formula is as follows: For the loss ratio, For other specified indicators; Finally, the sum of the performance metrics and penalty items is returned and compared with a preset threshold.

2. The simulation evaluation and optimization method for the design of the combustor co-fiber tube in a heavy-duty gas turbine as described in claim 1, characterized in that, In step S1, during the simulation of the main combustion chamber state, there is an ignition energy balance, the specific balance formula of which is: in, The total energy provided to the unburned adjacent flame tubes through the flame tubes. The energy required to activate the fuel-air mixture The energy required to heat the mixture to its ignition point. For heat loss; The mass flow equation at this point is: in, For flow coefficient, The cross-sectional area of ​​the flame tube is... For flow rate, For gas density, The pressure difference is used as the driving force for flow within the flame tube; The main combustion chamber simulation process also includes: Based on a turbulent combustion simulation model with fully mixed turbulence and the flame stability parameter K, the specific formula of the model is as follows: in, The length of the flame tube. The turbulent velocity in the flame tube is... For laminar flame thickness, The propagation speed of laminar flame. Represented by the Damköhler number, when Da >> 1, combustion is controlled by mixing; when Da << 1, it is controlled by chemical kinetics, at which point the turbulent combustion rate is determined. for: in, To preset the experimental correction coefficient, This refers to the velocity fluctuation. The mathematical definition of the flame stability parameter is: in, Density of unburned gas The diameter of the flame tube is [missing information]. For thermal diffusivity, This is the ignition delay time.

3. The simulation evaluation and optimization method for the design of the combustor co-firing tube of a heavy-duty gas turbine as described in claim 2, characterized in that, The simulation process in step S1 uses Cantera software to simulate gas mixing, combustion, and equilibrium processes, including: The flame temperature is calculated based on isenthalpic and isobaric equilibrium, and the density, viscosity, and thermal conductivity of the fuel gas are calculated by limiting the heat loss of the combined flame tube. Flow analysis is based on fluid mechanics and heat transfer, and iteratively solves for mass flow rate to match the driving pressure difference; The pressure loss model for the flame tube is constructed as follows: Total pressure loss = Friction pressure loss + Bending pressure loss + Local pressure loss; The formula for the frictional pressure loss model is as follows: The coefficient of friction Calculations based on Reynolds number for stratified / turbulent flow; The pressure loss in bends is a resistance coefficient corrected using the Dean number; The inlet / outlet coefficients used for local pressure loss are 0.3 and 0.8, respectively, to limit the mixing and chemical reactions of the flame tubes during the simulation process using Cantera's OD reactor under total flow conditions.

4. The simulation evaluation and optimization method for the design of the combustor co-fiber tube in a heavy-duty gas turbine as described in claim 3, characterized in that, In step S2, the pneumatic slats consist of compressor outlet pressure, temperature, and two sets of flame tube distribution inputs, wherein the flame tube distribution inputs include: The flame tube, which has been ignited by an electric spark, has an inlet temperature higher than the compressor outlet pressure temperature. It only provides the temperature flow rate of air and the temperature flow rate of fuel, and the composition and temperature of the combustion gas are calculated by Cantera. The airflow and gas flow rate of the flame tube to be ignited are the same as those of the flame tube that has already been ignited by an electric spark, and the composition, temperature, and inlet temperature of the gas after combustion are all lower than the compressor outlet pressure and temperature.

5. The simulation evaluation and optimization method for the design of the combustor co-firing tube of a heavy-duty gas turbine as described in claim 4, characterized in that, In step S2, the objective function of the geometric edge strip is determined to be: in These represent the diameter, length, bending radius, and bending angle of the flame tube, respectively. , , These represent the ignition delay time. Pressure difference Maximum ignition temperature The influence coefficient.

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