Industrial steam turbine exhaust steam optimization system and method

Abstract

The application provides an industrial steam turbine exhaust optimization system and method thereof, and belongs to the technical field of steam turbine design. The system comprises obtaining a flow field data set representing the full working condition operation of a steam turbine; based on the flow field data set, processing the flow field information of the outlet section of the last stage blade to determine the swirl intensity factor corresponding to each working condition; converting the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterization model controlled by preset geometric parameters; for each working condition, coupling the candidate three-dimensional geometric configuration parameterization model with the corresponding swirl intensity factor, and determining the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation; and combining the preset performance weight coefficient and the preset working condition weight to perform weighted calculation on the pressure recovery coefficient and flow stability coefficient of all working conditions, thereby significantly improving the comprehensive operation efficiency, full working condition adaptability and operation safety of the unit at the design point and under variable working conditions, and greatly shortening the design cycle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steam turbine design, and particularly relates to an industrial steam turbine exhaust optimization system and method. BACKGROUND

[0002] In the prior art, the optimization design of the exhaust system of an industrial steam turbine is dominated by a simplified model based on a two-dimensional plane assumption. This method simplifies the complex three-dimensional exhaust cylinder structure into a two-dimensional diffuser passage and takes maximizing the axial flow pressure recovery as the optimization objective. The establishment of this design method relies on an implicit premise that the gas flow entering the exhaust system is assumed to be uniform and irrotational ideal axial flow. However, in the actual operation of the steam turbine, especially under off-design conditions such as low load conditions, the flow field at the outlet of the upstream last stage blade will change dramatically. At this time, the decrease in exhaust steam flow rate causes the blade angle to increase, thereby forming strong, unsteady tangential rotation at the inlet of the exhaust cylinder. The strong rotation flow interacts with the inner wall of the diffuser, which is prone to induce large-scale flow separation, not only causing energy loss, but also causing pressure pulsation, which threatens the safety of the unit. The traditional design idea regards the exhaust system as an isolated, static component and sets a static, idealized inlet boundary condition for it. This technical framework cannot solve the sharp contradiction between maximizing the pressure recovery and suppressing the flow separation induced by the strong rotation flow under variable working conditions. SUMMARY

[0003] The present application aims to provide an industrial steam turbine exhaust optimization system and method to solve the problems raised in the background.

[0004] The technical solution of the present application comprises obtaining a flow field data set representing the full working condition operation of the steam turbine;

[0005] Based on the flow field data set, processing the flow field information at the outlet cross section of the last stage blade to determine the rotation intensity factor corresponding to each working condition;

[0006] Converting the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometry parameterization model controlled by preset geometric parameters;

[0007] For each working condition, coupling the candidate three-dimensional geometry parameterization model with the corresponding rotation intensity factor to determine the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation;

[0008] Combining the preset performance weight coefficient and the preset working condition weight to perform weighted calculation on the pressure recovery coefficient and the flow stability coefficient of all working conditions to generate a global performance index;

[0009] Based on the global performance index, a global optimization algorithm is used to iteratively solve the candidate three-dimensional geometric configuration parameterized model, and output the optimal geometric configuration.

[0010] Preferably, the swirl intensity factor is a dimensionless parameter used to quantify the degree of tangential swirling flow at the inlet of the exhaust casing, calculated based on the average flow angle of the steam at the outlet of the last stage blade.

[0011] Preferably, the preset geometric parameters define the equivalent expansion half-angle and axial length of the main diffuser, and define a series of control parameters for describing the three-dimensional surface morphology.

[0012] Preferably, the determination of the flow stability coefficient includes:

[0013] Through computational fluid dynamics simulation, the total area of the flow separation region where the wall shear stress on the inner wall surface of the diffuser is non-positive is determined;

[0014] Based on the candidate three-dimensional geometric configuration parameterized model, the total area of the inner wall surface of the diffuser is determined;

[0015] Based on the ratio of the total area of the flow separation region to the total area of the diffuser inner wall surface, the flow stability coefficient is generated.

[0016] Preferably, the generation of the global performance index includes:

[0017] For each operating condition, the corresponding pressure recovery coefficient and flow stability coefficient are multiplied by the preset performance weight coefficient respectively and summed to obtain the condition comprehensive performance value;

[0018] The condition comprehensive performance value is multiplied by the corresponding preset condition weight;

[0019] The weighted results of all operating conditions are accumulated to generate the global performance index.

[0020] Preferably, the global optimization algorithm is used for iterative solution, including:

[0021] Randomly generate an initial population containing multiple candidate geometric configurations;

[0022] For each candidate geometric configuration in the population, the corresponding global performance index is generated by calling the step;

[0023] According to the global performance index, a new offspring population is generated through selection operation, crossover operation and mutation operation;

[0024] Repeat the steps of generating the global performance index and generating the new population until the preset convergence condition is met.

[0025] Preferably, the data modeling unit is configured to obtain a flow field data set representing full-load operation of the steam turbine, and determine a swirl intensity factor corresponding to each operating condition based on the flow field data set;

[0026] The geometry configuration unit is configured to convert the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometry configuration parameterized model controlled by preset geometry parameters;

[0027] The performance evaluation unit is configured to couple the candidate three-dimensional geometry configuration parameterized model with the corresponding swirl intensity factor for each operating condition, determine a pressure recovery coefficient and a flow stability coefficient, and generate a global performance index in combination with a preset weight;

[0028] The optimization solving unit is configured to iteratively solve the candidate three-dimensional geometry configuration parameterized model based on the global performance index using a global optimization algorithm to output an optimal geometry configuration.

[0029] Preferably, the performance evaluation unit comprises:

[0030] The performance coefficient calculator is configured to determine the pressure recovery coefficient and the flow stability coefficient by performing computational fluid dynamics simulation for each operating condition;

[0031] The global index synthesizer is configured to perform weighted calculation on the pressure recovery coefficient and the flow stability coefficient of all operating conditions output by the performance coefficient calculator in combination with a preset performance weight coefficient and a preset operating condition weight to generate a global performance index.

[0032] The present application provides an industrial steam turbine exhaust optimization system and method, which has the following improvements and advantages compared with the prior art:

[0033] Firstly, the present application establishes an optimization premise that can accurately reflect the real operating environment; it obtains a flow field data set representing full-load operation of the steam turbine, and extracts a swirl intensity factor quantifying the tangential swirl degree of the exhaust cylinder inlet flow based on the data set, thereby fundamentally solving the distortion problem of the ideal inlet condition in the prior art; this method enables the optimization process to directly respond to the real inflow dynamics under different loads, especially to capture and cope with the swirl that has a dramatic impact on system performance and stability at low load, ensuring the practical guiding significance of the optimization results and the full-load adaptability of the final product;

[0034] Secondly, the scheme constructs a multi-objective evaluation system considering aerodynamic efficiency and flow stability; it not only evaluates the pressure recovery coefficient determining the aerodynamic efficiency, but also innovatively introduces the flow stability coefficient; the coefficient is generated by determining the ratio of the total area of the flow separation region with non-positive wall shear stress on the inner wall of the diffuser to the total area of the inner wall, which converts the complex flow separation phenomenon into an explicit and quantifiable optimization index; this enables the optimization process to actively suppress and punish designs prone to large-scale flow separation, thereby improving the operation stability and safety of the unit from the source, and surpassing the traditional design which only focuses on the single perspective of pressure recovery efficiency;

[0035] Thirdly, the scheme proposes a global performance index to comprehensively evaluate the overall performance of the candidate configuration in the whole life cycle; the index calculates the pressure recovery coefficient and the flow stability coefficient in all operating conditions by combining the preset performance weight coefficient and the preset operating condition weight; this processing method unifies the efficiency and stability of the two mutually restrictive targets, as well as the performance in the design point and multiple variable conditions, into a single evaluation system, enabling the optimization algorithm to intelligently balance between multiple targets and multiple conditions, and find a truly global optimal solution;

[0036] Fourthly, the scheme realizes the high automation and intelligence of the design process by converting the three-dimensional entity geometry of the exhaust cylinder diffuser into a parameterized model controlled by preset geometric parameters, and using a global optimization algorithm for iterative solution; the parameterization method gives the three-dimensional flow channel great freedom in design, and the use of the global optimization algorithm can efficiently search in the complex design space, avoiding local optimization, and thus has the ability to find innovative geometric configurations with excellent performance far beyond traditional design methods and human intuition;

[0037] In summary, the present application can find the exhaust cylinder geometry with the optimal comprehensive performance in the entire actual operating range of the steam turbine by establishing a comprehensive optimization framework coupling the full-condition dynamic flow, three-dimensional geometric configuration, aerodynamic efficiency and flow stability, which significantly improves the comprehensive operating efficiency, full-condition adaptability and operating safety of the unit in the design point and variable conditions, and greatly shortens the design cycle. BRIEF DESCRIPTION OF DRAWINGS

[0038] The present application will be further explained in conjunction with the accompanying drawings and examples:

[0039] Figure 1 is a flow chart of the system of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further explained in detail in conjunction with specific examples.

[0041] Embodiment 1

[0042] Referring to Figure 1 The present application provides an industrial steam turbine exhaust optimization system and method technical solutions, comprising:

[0043] Obtain a flow field data set representing the full operating condition of the steam turbine;

[0044] Based on the flow field data set, process the flow field information at the outlet section of the last stage blade to determine the swirl intensity factor corresponding to each operating condition;

[0045] Convert the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterization model controlled by preset geometric parameters;

[0046] For each operating condition, couple the candidate three-dimensional geometric configuration parameterization model with the corresponding swirl intensity factor, and determine the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation;

[0047] Combine the preset performance weight coefficient and the preset operating condition weight to perform weighted calculation on the pressure recovery coefficient and flow stability coefficient of all operating conditions to generate a global performance index;

[0048] Based on the global performance index, use a global optimization algorithm to iteratively solve the candidate three-dimensional geometric configuration parameterization model, and output the optimal geometric configuration;

[0049] The embodiment discloses a specific implementation of an industrial steam turbine exhaust optimization method; the method is deployed in a computer workstation or high-performance computing environment to realize automatic and global optimization of the three-dimensional geometric configuration of the exhaust system;

[0050] The implementation of the method starts with the step of obtaining a flow field data set representing the full operating condition of the steam turbine; the purpose of this step is to establish a boundary condition database that can reflect the dynamic changes of the upstream flow in the entire operating range of the steam turbine from low load to full load; in this embodiment, a flow field data set D is generated, which contains detailed flow field information at the outlet section of the last stage blade of the steam turbine under N discrete operating condition points , wherein These information is obtained through computational fluid dynamics (CFD) simulation of the upstream blade cascade, or directly measured through experimental test, providing a data basis for subsequent dynamic characteristic modeling;

[0051] Based on the aforementioned data set, a flow field data set is then processed to determine the swirl intensity factor corresponding to each operating condition at the outlet section of the last stage blade; the purpose of this step is to extract the key dimensionless parameter that quantifies the swirl level from the complex three-dimensional flow field information; this swirl intensity factor serves as a bridge connecting the external operating conditions and internal performance evaluation, and its specific definition and calculation will be described in detail in the embodiment of Actual Example 2;

[0052] In parallel with the flow field feature modeling, a step of converting the three-dimensional entity geometry of the exhaust cylinder diffuser into a parameterized model of the candidate three-dimensional geometric configuration controlled by preset geometric parameters is performed; the purpose of this step is to accurately describe a complex, free-form three-dimensional entity with a set of limited, continuously variable mathematical parameters, thereby providing the optimization algorithm with operable design variables; the specific composition of these preset geometric parameters will be described in the embodiment of Example 3;

[0053] The subsequent core performance prediction link, the internal logic of which is to couple the parameterized model of the candidate three-dimensional geometric configuration with the corresponding swirl intensity factor for each operating condition, and determine the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation; for any candidate geometric configuration generated by the optimization algorithm and each operating condition of interest (represented by its corresponding swirl intensity factor), the system will automatically establish a CFD simulation model coupling the geometry and the inlet conditions of the operating condition; by solving the model, two key performance indicators can be obtained: the pressure recovery coefficient, which quantifies the aerodynamic efficiency of the exhaust system, and the flow stability coefficient, which quantifies the stability of the internal flow field; the determination method of the flow stability coefficient will be described in detail in the embodiment of Example 4;

[0054] After obtaining the performance coefficients under a single condition, the step of combining the preset performance weight coefficient and the preset condition weight to perform weighted calculation on the pressure recovery coefficients and flow stability coefficients of all operating conditions to generate a global performance indicator is entered; this step aims to construct a single objective function that can comprehensively and comprehensively evaluate the overall performance of the candidate geometric configuration under all conditions; the specific generation logic will be expanded in the embodiment of Example 5; the preset performance weight coefficient refers to the weight value artificially set to balance different performance goals (such as efficiency and stability), which is determined according to the specific project design goals and analysis of historical unit performance data; the preset condition weight refers to the weight set to reflect the importance or running time proportion of different operating conditions, which is derived from the quantitative analysis of the expected operation strategy of the steam turbine, for example, by setting through the statistical analysis of the expected annual operation log;

[0055] As the final step of the optimization procedure, a global optimization algorithm is employed to iteratively solve the candidate 3D geometric configuration parameterized model based on the global performance indicator, and output the optimal geometric configuration; this step takes the global performance indicator generated in the previous step as the objective function, and utilizes a global optimization algorithm (e.g. genetic algorithm) to search in a multi-dimensional design space composed of geometric parameters; through repeated iterations, new candidate configurations are generated and evaluated for their global performance, and the algorithm eventually converges and outputs a set of optimal geometric parameters that maximize the global performance indicator, which defines the optimal 3D geometric configuration of the exhaust cylinder diffuser; this iterative solving process will be described in more detail in the implementation of Example 6;

[0056] The technical effect is that the method overcomes the limitations of existing technologies that treat the exhaust system as an isolated component and use static, idealized inlet boundary conditions; by establishing a comprehensive optimization framework that couples dynamic inflow characteristics, 3D geometric configuration, aerodynamic efficiency, and flow stability across all operating conditions, the optimal exhaust cylinder geometric configuration with the best overall performance can be found within the entire actual operating range; this not only improves the average operating efficiency of the steam turbine at the design point and under varying operating conditions, but also significantly enhances the operating stability and safety of the unit by actively suppressing flow separation.

[0057] Example 2

[0058] Swirl intensity factor, a dimensionless parameter calculated based on the average flow angle of the steam at the outlet of the last stage blade, used to quantify the degree of tangential swirl of the exhaust cylinder inlet flow;

[0059] In this example, the swirl intensity factor is explicitly defined as a core dimensionless parameter for quantifying the strength of the tangential component of the exhaust cylinder inlet flow; the purpose is to convert changes in external operating load of the steam turbine into specific mathematical inputs that the optimization model can directly handle; the calculation method of this factor is ; where is the mass flow weighted average flow angle of the steam at the outlet cross-section of the last stage blade, with units of degrees (°); in the calculation, the angle value needs to be converted to radians for trigonometric function operations, and the mass flow weighted average flow angle is calculated by integrating the velocity field distribution at the outlet cross-section of the last stage blade, and its mathematical expression is , where is the steam density, and are the tangential and axial components of the velocity, is the outlet cross-sectional area of the last stage blade, which refers to the angle between the actual flow direction of the steam and the central axis of the steam turbine; The value of the number is not preset, but is obtained by integral calculation of the cross-section velocity field distribution corresponding to the working condition in the flow field data set D in embodiment 1, and the selection Because it is proportional to the ratio of the tangential velocity and the axial velocity of the airflow, the swirl intensity can intuitively reflect the swirl intensity;

[0060] The technical effect of the gain is that by introducing and defining the swirl intensity factor, the application first provides a physical quantity that can accurately and quantitatively describe the influence of the upstream variable working condition for the optimization design of the exhaust system; this makes the optimization process no longer rely on the idealized assumption of "no swirl", but can truly reflect and respond to the strong swirl generated under different loads (especially at low loads), when the steam turbine operates at the design point, the flow angle at the outlet section of the last stage blade is close to 0, at this time, the swirl intensity factor is close to 0, the model can automatically degenerate to the ideal boundary condition of "no swirl" close to the traditional design method, thereby verifying the universality of the model, so that the optimization result can effectively cope with the real and harsh operating environment, and significantly improve the real guiding significance of the design and the working condition adaptability of the final product.

[0061] Embodiment 3

[0062] The preset geometric parameters define the equivalent expansion half-angle and the axial length of the main diffuser, and define a series of control parameters for describing the three-dimensional curved surface form;

[0063] In this embodiment, the set of preset geometric parameters is used to convert a complex exhaust cylinder diffuser geometric entity composed of free curved surfaces into a parameterized model controlled by a limited number of optimization variables; this enables the optimization algorithm to systematically explore and generate various different three-dimensional geometric shapes by adjusting these variables; the parameterized model G can be represented by a parameter vector: ;

[0064] Wherein represents the equivalent expansion half-angle of the main diffuser, which is a key macroscopic parameter for controlling the overall expansion rate of the diffuser passage; represents the axial length of the main diffuser, which defines the length of the main action area of flow deceleration and pressure recovery; these two parameters mainly control the basic profile and size of the diffuser; are a series of micro-control parameters used to describe and control the local shape of the complex three-dimensional curved surface; in this embodiment, these parameters can be the control point coordinates or weight factors in the non-uniform rational B-spline (NURBS) surface definition, which together determine the smooth transition shape of the diffuser passage from the circular inlet to the rectangular outlet and the curvature variation of the inner wall; the preset meaning is that the type, number and influence range of these parameters are determined by the designer according to the general principles of the exhaust cylinder design and specific constraint conditions before the optimization process is started, which together constitute a bounded and explicit design space;

[0065] The technical effect of the gain is that this parameterization method has a significant advantage over the traditional two-dimensional simplification or design based on a few simple variables; it can give the exhaust cylinder design great freedom under the premise of maintaining the continuity and smoothness of the geometric topology; through the coordinated optimization of macro and micro parameters, the algorithm can find a more refined and efficient three-dimensional flow passage configuration that is difficult to conceive by traditional design methods, thereby further tapping the performance potential of the exhaust system in pressure recovery and flow control.

[0066] Embodiment 4

[0067] The determination of the flow stability coefficient includes:

[0068] The total area of the flow separation region on the inner wall surface of the diffuser is determined by computational fluid dynamics simulation, in which the wall shear stress is a non-positive value;

[0069] The total area of the inner wall surface of the diffuser is determined based on the parameterized model of the candidate three-dimensional geometric configuration;

[0070] The flow stability coefficient is generated based on the ratio of the total area of the flow separation region to the total area of the inner wall surface of the diffuser;

[0071] In this embodiment, the determination process of the flow stability coefficient aims to provide an index that can quantify the severity of flow separation for the optimization target; flow separation is the root cause of energy loss and pressure pulsation, so it is crucial to quantitatively control it;

[0072] The generation of this coefficient first requires the determination of the total area of the flow separation region on the inner wall surface of the diffuser by computational fluid dynamics simulation, in which the wall shear stress is a non-positive value; in the field, the wall shear stress is a key physical quantity for judging the state of wall-attached flow, regions with a negative value are recognized as regions where flow separation or backflow occurs; for a candidate geometric configuration, under a certain swirl intensity, CFD simulation can accurately calculate the distribution of the wall shear stress at each point on the inner wall, and then integrate to obtain the total area of the separation region ;

[0073] At the same time, based on the candidate three-dimensional geometric configuration parameterized model, the total area of the diffuser inner wall surface is determined ; this total area is uniquely determined by the geometric model G itself;

[0074] Finally, based on the ratio of the total area of the flow separation region to the total area of the diffuser inner wall surface, the flow stability coefficient is generated ; its mathematical expression is defined as ;

[0075] Wherein is the flow stability coefficient, dimensionless; is the total area of the flow separation region, dimensionless, which is obtained by CFD simulation; is the total area of the diffuser inner wall surface, dimensionless, which is calculated by the geometric parameter G; the exponential function maps the area ratio in the range of to the stability coefficient in the range of ; since the exponential function monotonically decreases with the increase of x, when the total area of the flow separation region increases, the stability coefficient will rapidly decrease, so this formula can impose stronger 'penalty' on the configuration with large flow separation region, so as to preferentially select the stable flow design in the optimization process;

[0076] When the flow is completely without separation, at this time, the flow stability coefficient , indicating that the flow state is the most stable, which conforms to the physical intuition; when the flow separation covers the entire diffuser inner wall surface, at this time ; this value is not 0, which aims to express that although the flow is extremely unstable, the system has not completely collapsed, and still has assessable performance, while strongly 'penalizing' this extreme case through its smaller value;

[0077] The gain technical effect is that this method innovatively proposes the flow stability coefficient , which converts the complex fluid mechanics phenomenon of flow separation into an explicit, calculable and optimizable performance index; this enables the optimization algorithm to directly'see' and penalize the designs that are prone to induce large-scale flow separation, so as to actively seek the geometric configuration that can suppress separation and stabilize the flow field in the optimization process, fundamentally solving the defect of the traditional optimization method that only focuses on pressure recovery and ignores the internal flow quality, and directly contributing to improving the unit safety.

[0078] Example 5

[0079] The generation of the global performance index includes:

[0080] For each operating condition, the corresponding pressure recovery coefficient and flow stability coefficient are multiplied by the preset performance weight coefficient respectively, and then summed to obtain the operating condition comprehensive performance value;

[0081] The operating condition comprehensive performance value is multiplied by the corresponding preset operating condition weight;

[0082] The weighted results of all operating conditions are accumulated to generate the global performance index;

[0083] In this embodiment, the purpose of the generation process of the global performance index is to integrate the evaluation results scattered in multiple condition points and involving multiple performance dimensions into a single scalar value that can represent the overall advantages and disadvantages of the candidate configuration, as the objective function of the optimization algorithm;

[0084] The generation logic is as follows: first, for the ith operating condition, the corresponding pressure recovery coefficient and the flow stability coefficient are multiplied by the preset performance weight coefficients and respectively, and then summed to obtain the comprehensive performance value of the operating condition; here and are used to reflect the designer's preference degree for pressure recovery efficiency and flow stability, for example, the value of can be increased in applications requiring high safety; usually they are normalized, such as ; the preset performance weight coefficients and can be determined by the analytic hierarchy process (AHP) or expert scoring method combined with the performance requirements and design priorities of the specific unit. The preset operating condition weight can be obtained by normalizing the proportion of operating time under different loads by analyzing the load-time distribution curve of the turbine in a typical operating period, and then multiplying the operating condition comprehensive performance value by the preset operating condition weight corresponding to the operating condition to reflect the importance difference of different condition points in the entire life cycle of the turbine; finally, the weighted results of all N operating conditions are accumulated to generate the global performance index ; the discretization calculation formula is ;

[0085] is the global performance index;

[0086] N is the total number of discrete condition points;

[0087] and respectively, are the pressure recovery coefficient and the flow stability coefficient of the i-th operating condition, both of which are derived from the CFD simulation results for this specific operating condition;

[0088] is the performance weight;

[0089] is the operating condition weight, which generally satisfies ;

[0090] is preferable when focusing on efficiency ; is preferable when focusing on stability ;

[0091] The construction of the global performance index is the core embodiment of the "full operating condition-coupling" design idea of the present application; it unifies the two performance objectives of efficiency and stability, which are mutually restrictive, and the performance under the design point and multiple operating conditions into a single evaluation system through a mathematical model; this makes the optimization process no longer local or single objective, but can intelligently trade off between multiple objectives and operating conditions to find a truly global optimal solution, significantly improving the comprehensive performance and operating condition adaptability of the final design.

[0092] Embodiment 6

[0093] The global optimization algorithm is used for iterative solution, including:

[0094] An initial population containing multiple candidate geometric configurations is randomly generated;

[0095] For each candidate geometric configuration in the population, the corresponding global performance index is generated by calling step

[0096] According to the global performance index, a new population of offspring is generated through selection, crossover and mutation operations;

[0097] The steps of generating the global performance index and generating the new population are repeated until the preset convergence condition is met;

[0098] In this embodiment, a global optimization technique represented by a genetic algorithm (GA) is selected for the process of iterative solution by the global optimization algorithm; the implementation details of the algorithm are as follows:

[0099] The population size is preset to 50 candidate geometric configurations;

[0100] The selection operation is to randomly select 5 individuals from the population each time, and select the one with the best fitness to enter the next generation;

[0101] The crossover operation is to use the single-point crossover method with a crossover probability of 0.8;

[0102] The mutation operation is to randomly modify one or more parameters in the geometric parameter vector of each individual in the new population with a mutation probability of 0.05;

[0103] The convergence condition is to reach a maximum iteration number of 100 generations or the average growth rate of the optimal global performance index of the population in 20 consecutive generations is less than 0.1%;

[0104] The purpose is to efficiently search for an optimal solution in a high-dimensional and complex design space defined by preset geometric parameters, and avoid falling into a local optimum;

[0105] The process starts with randomly generating an initial population containing a plurality of candidate geometric configurations; each "individual" is a candidate geometric configuration, which is defined by a specific geometric parameter vector G; the population size is a parameter preset according to the complexity of the problem;

[0106] Then, for each candidate geometric configuration in the population, the foregoing steps are called to generate its corresponding global performance index; that is, for each individual in the population, the processes in Embodiments 1, 4 and 5 are completely executed to obtain its final global performance index through CFD simulation and weighted calculation ; This index value is used as the "fitness" of the individual here to measure its pros and cons;

[0107] Based on the fitness value, a new offspring population is generated through selection, crossover and mutation operations; the selection operation will determine the parent individuals in a probabilistic manner according to the fitness, so that individuals with high fitness have more opportunities to be selected; the crossover operation exchanges some parameters of two parent individuals to combine new geometric configurations; the mutation operation randomly changes a parameter of an individual with a small probability to introduce new genetic diversity;

[0108] Finally, the steps of fitness evaluation and new population generation are repeatedly executed until the preset convergence condition is met; this cycle constitutes the evolution process of the algorithm; the preset convergence condition can be to reach a preset maximum iteration number or the growth rate of the optimal fitness of the population in a plurality of consecutive generations is less than a set threshold; once the condition is met, the iteration is terminated, and the geometric parameters corresponding to the individual with the highest fitness in the current population are output as the final optimization result;

[0109] The technical effect is that the global optimization algorithm, especially the heuristic search method such as the genetic algorithm, enables the present application to handle complex optimization problems with nonlinearity and multiple peaks that the traditional optimization method cannot cope with; it does not depend on gradient information and has strong robustness, and can find a global optimal solution with a high probability; this solving method, combined with the parameterized geometric model and the global performance index, constitutes the basis of the automatic and intelligent design capability of the present application, and can find innovative geometric configurations with excellent performance far beyond the intuition of human designers.

[0110] Embodiment 7

[0111] The data modeling unit is used to obtain a flow field data set representing the full operating condition of the steam turbine, and determine a swirl intensity factor corresponding to each operating condition based on the flow field data set;

[0112] The geometric configuration unit is used to convert the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters;

[0113] The performance evaluation unit is used to couple the candidate three-dimensional geometric configuration parameterized model with the corresponding swirl intensity factor for each operating condition, determine the pressure recovery coefficient and the flow stability coefficient, and generate a global performance index in combination with a preset weight;

[0114] The optimization solving unit is used to iteratively solve the candidate three-dimensional geometric configuration parameterized model based on the global performance index using a global optimization algorithm to output an optimal geometric configuration;

[0115] The embodiment discloses a logic structure of an industrial steam turbine exhaust optimization system, which materializes the foregoing optimization method into a set of automated software modules working cooperatively;

[0116] The data modeling unit is used to obtain a flow field data set representing the full operating condition of the steam turbine, and determine a swirl intensity factor corresponding to each operating condition based on the flow field data set; ;

[0117] The geometric configuration unit is used to convert the three-dimensional entity geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters , so that the optimization algorithm can drive the change of the geometric shape by modifying these parameters;

[0118] The performance evaluation unit is used to couple the candidate three-dimensional geometric configuration parameterized model with the corresponding swirl intensity factor for each operating condition, determine the pressure recovery coefficient and the flow stability coefficient, and generate a global performance index in combination with a preset weight; ; and , and then perform weighted summation according to the preset weight to finally calculate the global performance index of the configuration ;

[0119] An optimization solving unit, which functions as a core control module of the whole optimization process; this unit is responsible for executing the global optimization algorithm in embodiment 6; it calls the geometric configuration unit to generate a population, calls the performance evaluation unit to calculate the fitness of each individual (F ), and iterates the population according to the algorithm rules (selection, crossover, mutation) until the optimal solution is found, finally outputs a set of parameters defining the optimal geometric configuration ;

[0120] The technical effect is that the system divides a complex, multi-disciplinary (fluid mechanics, geometric modeling, optimization algorithm) design task into four modular units with clear functions and explicit interfaces; this systematic implementation changes the process that originally relies on a large amount of manual trial and error and experience judgment into a highly automated, repeatable, and efficient process; it not only greatly shortens the design cycle, but also, through systematic global optimization, can obtain a design scheme with better performance and higher reliability than traditional means.

[0121] Embodiment 8

[0122] The performance evaluation unit includes:

[0123] The performance coefficient calculator is used to determine the pressure recovery coefficient and the flow stability coefficient by calculating the computational fluid dynamics simulation for each operating condition;

[0124] The global index synthesizer is used to combine the preset performance weight coefficient and the preset operating condition weight to perform weighted calculation on the pressure recovery coefficient and the flow stability coefficient of all operating conditions output by the performance coefficient calculator, to generate a global performance index;

[0125] In this embodiment, the internal structure of the performance evaluation unit in embodiment 7 is further refined;

[0126] The unit is divided into two logical sub-modules, a performance coefficient calculator and a global index synthesizer;

[0127] The function of the performance coefficient calculator is to perform a computationally intensive physical simulation task; it receives the candidate geometric configuration G from the optimization solving unit and the operating condition swirl intensity factor from the data modeling unit as input; for each combination of input (G, ), it is responsible for establishing and solving the corresponding computational fluid dynamics model, thereby outputting two basic performance indicators, the pressure recovery coefficient and the flow stability coefficient under this specific operating condition; ;

[0128] The function of the global indicator synthesizer is to perform fast mathematical synthesis tasks; it receives the performance coefficient pairs of all (N) operating conditions output by the performance coefficient calculator , ); then, it calls the preset performance weight ( ) and operating condition weight ( ), and applies the weighted summation formula defined in Embodiment 5 to synthesize these scattered coefficient values into a single global performance indicator , and returns it to the optimization solver as the final fitness score of the candidate configuration;

[0129] The technical effect of the gain is to decompose the performance evaluation unit into a calculator and a synthesizer, which has clear engineering advantages; it realizes the logical separation of the calculation task, decouples the time-consuming CFD simulation (calculator) and the fast algebraic operation (synthesizer); this modular design not only makes the system architecture clearer, easy to develop and maintain, but also provides the possibility to improve the calculation efficiency; for example, in a high-performance computing environment, multiple performance coefficient calculator instances can be deployed in parallel, and the performance of a geometric configuration under different operating conditions or the performance of different individuals in the population can be calculated in parallel, and the calculation results are finally summarized by the unique global indicator synthesizer, thereby significantly shortening the time-consuming of the entire optimization process.

[0130] The present application overcomes the inherent limitations of the prior art in the design of the exhaust system of industrial steam turbines, which treats it as an isolated component and only performs single-point optimization under idealized, static inlet boundary conditions, achieving significant technical progress.

[0131] The prior art fails to fully consider the complex and dynamic upstream flow characteristics caused by load changes in actual operation of the steam turbine, especially ignoring the strong swirl generated under variable operating conditions, resulting in an exhaust configuration that only performs well at a specific design point, but lacks comprehensive performance and operational stability within the entire operating range.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application.

Claims

1. A method of industrial steam turbine exhaust steam optimization, characterized by, The method comprises the following steps: obtaining a flow field data set representing the full operating condition of a steam turbine; processing the flow field information at the outlet section of the last stage blade based on the flow field data set to determine the swirl intensity factor corresponding to each operating condition; converting the three-dimensional entity geometry of the diffuser of the exhaust casing into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters; for each operating condition, coupling the candidate three-dimensional geometric configuration parameterized model with the corresponding swirl intensity factor to determine the pressure recovery coefficient and the flow stability coefficient through computational fluid dynamics simulation; combining the preset performance weight coefficient and the preset operating condition weight to perform weighted calculation on the pressure recovery coefficient and the flow stability coefficient of all operating conditions to generate a global performance index; based on the global performance index, using a global optimization algorithm to iteratively solve the candidate three-dimensional geometric configuration parameterized model to output an optimal geometric configuration; A swirl intensity factor, which is a non-dimensional parameter used to quantify the degree of tangential flow swirl at the exhaust casing inlet calculated based on the average flow angle of the steam at the outlet of the last stage blade; the calculation method of the swirl intensity factor is ; wherein is the mass flow weighted average flow angle of the steam at the outlet section of the last stage blade under this working condition, and the unit is degree. the determination of the flow stability coefficient comprises: determining the total area of the flow separation region on the inner wall surface of the diffuser through computational fluid dynamics simulation, wherein the wall shear stress is a non-positive value; determining the total area of the inner wall surface of the diffuser based on the candidate three-dimensional geometric configuration parameterized model; generating the flow stability coefficient based on the ratio of the total area of the flow separation region to the total area of the inner wall surface of the diffuser.

2. A method of exhaust steam optimization for an industrial steam turbine according to claim 1, characterized in that, The preset geometric parameters define the equivalent expansion half-angle and the axial length of the main diffuser section, and define a series of control parameters for describing the three-dimensional surface morphology.

3. The method of claim 1, wherein, The generation of the global performance index comprises: for each operating condition, multiplying the corresponding pressure recovery coefficient and the flow stability coefficient by the preset performance weight coefficient and then summing them to obtain a condition comprehensive performance value; multiplying the condition comprehensive performance value by the corresponding preset operating condition weight; accumulating the weighted results of all operating conditions to generate the global performance index.

4. The method of claim 1, wherein, The iterative solution using the global optimization algorithm comprises: randomly generating an initial population containing multiple candidate geometric configurations; for each candidate geometric configuration in the population, calling the step to generate the corresponding global performance index; generating a new offspring population through selection, crossover and mutation operations according to the global performance index; repeating the steps of generating the global performance index and generating the new population until the preset convergence condition is met.

5. An industrial steam turbine exhaust steam optimization system for use in an industrial steam turbine exhaust steam optimization method according to any one of claims 1 to 4, characterized in that, The method comprises the following steps: a data modeling unit is used to obtain a flow field data set representing the full operating condition of a steam turbine, and to determine the swirl intensity factor corresponding to each operating condition based on the flow field data set; a geometric configuration unit is used to convert the three-dimensional entity geometry of the diffuser of the exhaust casing into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters; a performance evaluation unit is used to couple the candidate three-dimensional geometric configuration parameterized model with the corresponding swirl intensity factor for each operating condition to determine the pressure recovery coefficient and the flow stability coefficient, and to generate a global performance index in combination with the preset weight; an optimization solving unit is used to iteratively solve the candidate three-dimensional geometric configuration parameterized model based on the global performance index using a global optimization algorithm to output an optimal geometric configuration.

6. An industrial steam turbine exhaust optimization system according to claim 5, wherein, The performance evaluation unit comprises: a performance coefficient calculator is used to determine the pressure recovery coefficient and the flow stability coefficient through computational fluid dynamics simulation for each operating condition; The global index synthesizer is used for weighting calculation of the pressure recovery coefficient and the flow stability coefficient of all operating conditions output by the performance coefficient calculator in combination with preset performance weight coefficients and preset operating condition weights, so as to generate a global performance index.

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