Steam exhaust optimization system and method for industrial steam turbine

Abstract

The invention provides an industrial steam turbine exhaust optimization system and method, and belongs to the technical field of steam turbine design, and the method comprises the steps: obtaining a flow field data set representing the full working condition operation of a steam turbine; based on the flow field data set, processing last-stage blade outlet section flow field information so as to determine rotational flow strength factors corresponding to all the operation working conditions; converting the three-dimensional solid geometry of the exhaust hood diffuser into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters; coupling the candidate three-dimensional geometric configuration parameterized model with the corresponding rotational flow intensity factor according to each operation condition, and determining a pressure recovery coefficient and a flow stability coefficient through computational fluid dynamics simulation; and in combination with the preset performance weight coefficient and the preset working condition weight, the pressure recovery coefficients and the flow stability coefficients of all the operation working conditions are subjected to weighted calculation, so that the design points of the unit and the comprehensive operation efficiency, the full-working-condition adaptability and the operation safety under the variable working conditions are remarkably improved, and the design period is greatly shortened.

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 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 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 strong rotation under variable operating 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 operating condition of the steam turbine; 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 operating 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 operating condition, coupling the candidate three-dimensional geometric configuration parameterization model with the corresponding rotation intensity factor, and determining the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation; Combining the preset performance weight coefficient and the preset operating condition weight, and performing 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 parameterization model, and outputting the optimal geometric configuration.

[0005] Preferably, the swirl intensity factor is a dimensionless parameter for quantifying the degree of tangential flow swirl at the exhaust casing inlet, calculated based on the average steam flow angle at the outlet of the last stage blade.

[0006] 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.

[0007] Preferably, 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 by computational fluid dynamics simulation, where the wall shear stress is negative; determining the total area of the inner wall surface of the diffuser based on the candidate three-dimensional geometric parameterization 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.

[0008] Preferably, the generation of the global performance indicator comprises: for each operating condition, multiplying the corresponding pressure recovery coefficient and the flow stability coefficient by the preset performance weight coefficient and summing them to obtain a condition comprehensive performance value; multiplying the condition comprehensive performance value by the corresponding preset condition weight; accumulating the weighted results of all operating conditions to generate the global performance indicator.

[0009] Preferably, the global optimization algorithm is used for iterative solution, comprising: 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 indicator; generating a new offspring population by selection, crossover and mutation operations according to the global performance indicator; repeating the steps of generating the global performance indicator and generating the new population until the preset convergence condition is met.

[0010] Preferably, the data modeling unit is used to obtain a flow field data set representing the full operating condition of the steam turbine, and to determine the swirl intensity factor corresponding to each operating condition based on the flow field data set; The geometric configuration unit is used to convert the three-dimensional entity geometry of the exhaust casing diffuser into a candidate three-dimensional geometric parameterization model controlled by the preset geometric parameters; The performance evaluation unit is used to couple the candidate three-dimensional geometric parameterization 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 the global performance indicator in combination with the preset weight; The optimization solving unit is configured to solve the candidate three-dimensional geometric configuration parameterized model based on the global performance index by using a global optimization algorithm to output an optimal geometric configuration.

[0011] Preferably, the performance evaluation unit comprises: The performance coefficient calculator is configured to determine the pressure recovery coefficient and the flow stability coefficient by calculating the fluid dynamics simulation for each operating condition. The global index synthesizer is configured 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 the global performance index.

[0012] 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: First, the present application establishes an optimization premise that can accurately reflect the real operating environment; it obtains the flow field data set representing the full operating condition of the steam turbine, and based on this, extracts the swirl intensity factor quantifying the flow tangential swirl degree at the inlet of the exhaust cylinder, 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 deal with the swirl that has a severe impact on system performance and stability at low load, ensuring the practical guiding significance of the optimization results and the full operating condition adaptability of the final product; Second, the present application constructs a multi-objective evaluation system that takes into account aerodynamic efficiency and flow stability; it not only evaluates the pressure recovery coefficient that determines 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 surface of the diffuser to the total area of the inner wall surface, 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 that are prone to induce large-scale flow separation, thereby improving the operating stability and safety of the unit from the source, surpassing the single perspective of traditional design that only focuses on the pressure recovery efficiency; Third, the present application proposes a global performance index that comprehensively evaluates the overall performance of the candidate configuration in the whole life cycle; the index combines 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 under all operating conditions; this processing method unifies the efficiency and stability, two mutually restrictive objectives, as well as the performance under the design point and multiple variable operating conditions into a single evaluation system, enabling the optimization algorithm to intelligently balance between multiple objectives and multiple operating conditions to find a truly global optimal solution. Fourthly, the present application realizes 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 parameterized method gives the three-dimensional flow channel great freedom of design, and the use of the global optimization algorithm can efficiently search in a complex design space and avoid being trapped in a local optimum, thus being able to find innovative geometric configurations with excellent performance far beyond traditional design methods and human intuition; In summary, the present application can find the exhaust cylinder geometric configuration with the optimal comprehensive performance in the entire actual operating range of the steam turbine by establishing a comprehensive optimization framework coupling the dynamic inflow in the entire operating range, three-dimensional geometric configuration, aerodynamic efficiency and flow stability, significantly improving the comprehensive operating efficiency, full operating range adaptability and operating safety of the unit at the design point and in the variable operating range, and greatly shortening the design cycle. BRIEF DESCRIPTION OF DRAWINGS

[0013] The present application will be further explained in connection with the accompanying drawings and embodiments: Figure 1 is a flow chart of the system of the present application. DETAILED DESCRIPTION

[0014] In order to make the object, technical scheme and advantages of the present application more clear, the present application will be further explained in connection with specific embodiments.

[0015] Example 1 Please refer to Figure 1 The present application provides an industrial steam turbine exhaust optimization system and method technical scheme, comprising: acquiring a flow field data set representing the full operating range of the steam turbine; based on the flow field data set, processing the flow field information at the outlet section of the last stage blade to determine the swirl intensity factor corresponding to each operating condition; converting the three-dimensional entity geometry of the exhaust cylinder diffuser 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, and determining the pressure recovery coefficient and flow stability coefficient through computational fluid dynamics simulation; combining the preset performance weight coefficient and the preset operating condition weight, and performing weighted calculation on the pressure recovery coefficient and 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, and outputting the optimal geometric configuration; The embodiment discloses a specific implementation of an industrial steam turbine exhaust optimization method; the method is deployed in a computer workstation or a high-performance computing environment to realize automatic and global optimization of a three-dimensional geometric configuration of an exhaust system; The implementation of the method starts from a step of obtaining a flow field data set representing full-load operation of a steam turbine; the purpose of the step is to establish a boundary condition database capable of reflecting dynamic changes of upstream flow in an entire operation range of the steam turbine from low load to full load; in the embodiment, a flow field data set D is generated, which contains detailed flow field information of an outlet section of a last-stage blade of the steam turbine under N discrete operation condition points , wherein The information is obtained by simulating upstream blade stages by computational fluid dynamics (CFD) or directly measured by experimental testing, and provides a data basis for subsequent dynamic characteristic modeling; Based on the foregoing data set, a step of processing the flow field information of the outlet section of the last-stage blade based on the flow field data set is performed to determine a swirl intensity factor corresponding to each operation condition; the purpose of the step is to extract a key dimensionless parameter capable of quantifying the swirl degree from complex three-dimensional flow field information; the swirl intensity factor serves as a bridge connecting external operation conditions and internal performance evaluation, and specific definition and calculation of the swirl intensity factor will be described in the implementation of Actual Example 2; Parallel to the flow field characteristic modeling, a step of converting three-dimensional entity geometry of an exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters is performed; the purpose of the step is to accurately describe a complex, free-form three-dimensional entity by a set of limited, continuously variable mathematical parameters, thereby providing an operational design variable for an optimization algorithm; specific composition of the preset geometric parameters will be described in the implementation of Embodiment 3; The subsequent core performance prediction link is based on the logic that for each operation condition, the candidate three-dimensional geometric configuration parameterized model is coupled with the corresponding swirl intensity factor to determine a pressure recovery coefficient and a flow stability coefficient by computational fluid dynamics simulation; for any candidate geometric configuration generated by an optimization algorithm and each operation condition (represented by the corresponding swirl intensity factor) to be concerned, the system automatically establishes a CFD simulation model coupled with the geometric configuration and the inlet condition of the operation condition; by solving the model, two key performance indicators, a pressure recovery coefficient and a flow stability coefficient, can be obtained; the pressure recovery coefficient quantifies the aerodynamic efficiency of the exhaust system, and the flow stability coefficient quantifies the stability degree of the internal flow field; the determination method of the flow stability coefficient will be described in the implementation of Embodiment 4; After obtaining the performance coefficients under single operating condition, the pressure recovery coefficient and the flow stability coefficient of all operating conditions are weighted calculated by combining the preset performance weight coefficient and the preset operating condition weight to generate the global performance index. 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 operating conditions. The specific generation logic will be expanded in the implementation of embodiment 5. The preset performance weight coefficient refers to the weight value artificially set to balance different performance targets (such as efficiency and stability), which is determined according to the engineering design target of the specific project and the analysis of historical unit performance data. The preset operating condition weight refers to the weight set to reflect the importance or running time proportion of different operating conditions, which is obtained by quantitative analysis of the expected operation strategy of the steam turbine, such as setting through statistical analysis of the expected annual operation log; As the final step of the optimization process, based on the global performance index, a global optimization algorithm is used to iteratively solve the candidate three-dimensional geometric configuration parameterized model, and the optimal geometric configuration is output. This step uses the global performance index generated in the previous step as the objective function, and uses a global optimization algorithm (such as genetic algorithm) to search in a multi-dimensional design space composed of geometric parameters. Through repeated iteration to generate new candidate configurations and evaluate their global performance, the algorithm finally converges and outputs a set of optimal geometric parameters that maximize the global performance index. This set of parameters defines the optimal exhaust cylinder diffuser three-dimensional geometric configuration. This iterative solving process will be described in more detail in the implementation of embodiment 6. 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 the dynamic inflow characteristics, three-dimensional geometric configuration, aerodynamic efficiency, and flow stability under all operating conditions, the optimal exhaust cylinder geometric configuration with the best overall performance in the entire actual operating range can be found. This not only improves the average operating efficiency of the steam turbine at the design point and under variable operating conditions, but also significantly enhances the operating stability and safety of the unit by actively suppressing flow separation.

[0016] Embodiment 2 The swirl intensity factor is a dimensionless parameter calculated based on the average flow angle of the steam at the outlet of the last stage blade, which is used to quantify the degree of tangential flow at the inlet of the exhaust cylinder. In this embodiment, the swirl intensity factor is explicitly defined as a core dimensionless parameter for quantifying the strength of the tangential component of the flow at the inlet of the exhaust cylinder. The purpose is to convert the changes in external operating load of the steam turbine into specific mathematical inputs that can be directly processed by the optimization model. The calculation method of this factor is ; Wherein For this working condition, the mass flow weighted average flow angle of steam at the outlet section of the last stage blade, with the unit of degree (°); in the calculation, the angle value needs to be converted into radian to carry out the trigonometric function operation, the mass flow weighted average flow angle is obtained by integral calculation on the velocity field distribution at the outlet section of the last stage blade, and the mathematical expression is , wherein is the steam density, and are the tangential component and the axial component of the velocity respectively, is the outlet section area of the last stage blade, which refers to the included angle between the actual steam flow direction and the center axis direction of the steam turbine; The value of is not preset, but is obtained by integral calculation on the sectional velocity field distribution corresponding to the working condition in the flow field data set D in embodiment 1, and is selected Because it is proportional to the ratio of the tangential velocity and the axial velocity of the flow, the swirl intensity can be directly reflected; The gain technical effect is that by introducing and defining the swirl intensity factor, the present 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 "non-swirl", but can truly reflect and respond to the strong swirl generated under different loads (especially at low load), when the steam turbine operates at the design point, the flow angle of 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 non-swirl ideal boundary condition 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.

[0017] Embodiment 3 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; In this embodiment, the set of preset geometric parameters is used to convert a complex exhaust cylinder diffuser geometric entity composed of a free curved surface 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: ; 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; The axial length representing the main diffuser section defines the length of the main region where the flow deceleration and pressure recovery take place; 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 control point coordinates or weight factors in the definition of a non-uniform rational B-spline (NURBS) surface, 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 before the optimization process is started according to the general principles of the exhaust cylinder design and specific constraint conditions, which together constitute a bounded and explicit design space; The gain technical effect is that this parameterization method has a significant advantage over traditional two-dimensional simplification or design based on a few simple variables; it can give the exhaust cylinder design great freedom while 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 terms of pressure recovery and flow control.

[0018] Embodiment 4 The determination of the flow stability coefficient includes: 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 non-positive; The total area of the inner wall surface of the diffuser is determined based on the parameterized model of the candidate three-dimensional geometry; 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; In this embodiment, the determination 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; The generation of this coefficient first requires determining 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 non-positive; in the field, the wall shear stress is a key physical quantity for judging the state of wall flow, The region is recognized as the region where flow separation or backflow occurs; for a candidate geometry, the CFD simulation can accurately calculate the distribution of the wall shear stress at each point on the inner wall under a certain swirl intensity, and then integrate to obtain the total area of the separation region ; At the same time, the total area of the inner wall surface of the diffuser is determined based on the parameterized model of the candidate three-dimensional geometry ; this total area is uniquely determined by the geometry model G itself; Finally, based on the ratio of the total area of flow separation region to the total area of diffuser inner wall surface, the flow stability coefficient is generated ; wherein is the flow stability coefficient, dimensionless; is the total area of flow separation region, dimensionless, which is obtained through CFD simulation; is the total area of diffuser inner wall surface, dimensionless, which is calculated from the geometry parameter G; this 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 flow separation region increases, the stability coefficient will rapidly decrease, thus this formula can impose stronger "penalty" on the design with large flow separation region, so as to preferentially select the design with stable flow in the optimization process; 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, aiming 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; 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 design that is prone to induce large-scale flow separation, so as to actively seek the geometry configuration that can suppress separation and stabilize the flow field in the optimization process, fundamentally solving the defect of traditional optimization methods that only focus on pressure recovery while ignoring the internal flow quality, and directly contributing to improving the safety of the unit.

[0019] Example 5 The generation of global performance index includes: 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 condition comprehensive performance value; The condition comprehensive performance value is multiplied by the corresponding preset condition weight; The weighted results of all operating conditions are accumulated to generate a global performance index; In this embodiment, the purpose of the generation process of the global performance index is to integrate the evaluation results scattered in multiple operating 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; 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 through analyzing the load-time distribution curve of the turbine in a typical operating period, and then multiplying the comprehensive performance value of the operating condition by the preset operating condition weight corresponding to the operating condition to reflect the importance difference of different operating points in the entire life cycle of the turbine; finally, the weighted results of all N operating conditions are accumulated to generate a global performance index ; its discretization calculation formula is ; is the global performance index; N is the total number of discrete operating points; and are the pressure recovery coefficient and the flow stability coefficient of the ith operating condition, respectively, both of which are derived from the CFD simulation results for the specific operating condition; is the performance weight; is the operating condition weight, which usually satisfies ; when focusing on efficiency, it can be ; when focusing on stability, it can be ; The technical effect of the gain is the construction of the global performance index, which is the core embodiment of the "full working condition-coupling" design idea of the application. Through a mathematical model, the two performance goals of efficiency and stability, which are mutually restricted, and the performance under multiple operating conditions of the design point and variable working conditions are unified into a single evaluation system. This makes the optimization process no longer local or single objective, but can intelligently balance between multiple objectives and multiple working conditions to find a truly global optimal solution, significantly improving the comprehensive performance and working condition adaptability of the final design.

[0020] Embodiment 6 The global optimization algorithm is used for iterative solution, including: Randomly generating an initial population containing multiple candidate geometric configurations; For each candidate geometric configuration in the population, the corresponding global performance index is generated by calling the step; According to the global performance index, a new population of offspring is generated through selection, crossover and mutation operations; Repeat the steps of generating the global performance index and generating the new population until the preset convergence condition is met; In this embodiment, a global optimization technique represented by a genetic algorithm (GA) is used for iterative solution. The implementation details of the algorithm are as follows: The population size is set to 50 candidate geometric configurations; The selection operation is to use the tournament selection method, randomly select 5 individuals from the population each time, and select the one with the best fitness to enter the next generation; The crossover operation is to use the single-point crossover method, and the crossover probability is set to 0.8; 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; The convergence condition is to reach the maximum iteration number of 100 generations, or the average growth rate of the optimal global performance index of the population in the last 20 generations is less than 0.1%; The purpose is to efficiently search for the optimal solution in a high-dimensional and complex design space defined by the preset geometric parameters, and avoid falling into a local optimum; The process starts with randomly generating an initial population containing multiple candidate geometric configurations; each "individual" is a candidate geometric configuration defined by a set of specific geometric parameter vectors G; the population size is a parameter preset according to the complexity of the problem; Then, for each candidate geometric configuration in the population, the corresponding global performance index is generated by calling the aforementioned step; that is, for each individual in the population, the processes in embodiments 1, 4 and 5 are completely executed, and through CFD simulation and weighted calculation, the final global performance index is obtained This index value is used as the "fitness" of an individual here, to measure its pros and cons; Based on the fitness value, new offspring population is generated through selection operation, crossover operation and mutation operation; selection operation will determine the parent individuals according to the fitness level in a probabilistic manner, so that the individuals with high fitness have more opportunities to be selected; crossover operation exchanges part of the parameters of two parent individuals to combine new geometric configurations; mutation operation randomly changes a parameter of an individual with a small probability to introduce new genetic diversity; Finally, the steps of fitness evaluation and new population generation are repeated until the preset convergence condition is met; this cycle constitutes the evolution process of the algorithm; the preset convergence condition can be that the maximum number of iterations is reached, or the growth rate of the optimal fitness of the population in the last several generations is lower 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; The technical effect of the gain is that the global optimization algorithm, especially the heuristic search method such as genetic algorithm, enables the present application to handle complex optimization problems with nonlinearity and multiple peaks that traditional optimization methods cannot handle; it does not depend on gradient information and has strong robustness, and can find a global optimal solution with high probability; this solving method, combined with the parameterized geometric model and the global performance index, forms the basis of the automated and intelligent design capability of the present application, and can find innovative geometric configurations with excellent performance far beyond the intuition of human designers.

[0021] Example 7 The data modeling unit is used to obtain a flow field data set representing the full operating condition of the steam turbine, and to determine the swirl intensity factor corresponding to each operating condition based on the flow field data set; 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; 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, to determine the pressure recovery coefficient and the flow stability coefficient, and to generate a global performance index in combination with the preset weight; The optimization solving unit is used to use a global optimization algorithm to iteratively solve the candidate three-dimensional geometric configuration parameterized model based on the global performance index, to output the optimal geometric configuration; The present embodiment discloses the logical structure of an industrial steam turbine exhaust optimization system, which materializes the aforementioned optimization method into a set of automated software modules that work together; The data modeling unit provides dynamic and realistic inlet boundary conditions for the entire optimization process. This unit performs the functions described in Examples 1 and 2, namely, acquiring or generating a dataset D of the final-stage blade outlet flow field covering all operating conditions, processing it, and calculating the corresponding swirl intensity factor for each operating point. ; The geometric configuration unit, whose function is to establish the design variable space for the optimization problem, is responsible for performing the function in Example 3, transforming the three-dimensional CAD entity of the exhaust cylinder diffuser into a system composed of a series of preset geometric parameters. The parameterized model of control allows optimization algorithms to drive changes in geometry by modifying these parameters; The performance evaluation unit provides the objective function value for the optimization algorithm. Its core task is to evaluate the comprehensive performance of any candidate geometry under all operating conditions. It is responsible for executing the core computational tasks in Examples 1, 4, and 5, namely, for a given geometry G, evaluating the performance of each operating condition i (given by...). (Definition) Run CFD simulation to determine and Then, based on the preset weights By performing a weighted summation, the global performance index of this configuration can be calculated. ; The optimization and solution unit serves as the core control module for the entire optimization process; this unit is responsible for executing the global optimization algorithm in Example 6; it calls the geometric configuration unit to generate the population and the performance evaluation unit to calculate the fitness of each individual. The algorithm iterates through the population according to the rules of selection, crossover, and mutation until the optimal solution is found, and finally outputs a set of parameters that define the optimal geometric configuration. ; The technical effect of this system is that it decomposes a complex, multidisciplinary design task (fluid mechanics, geometric modeling, optimization algorithms) into four modular units with clear functions and well-defined interfaces. This systematic approach transforms the process, which originally relied on extensive manual trial and error and experience-based judgment by designers, into a highly automated, repeatable, and efficient workflow. It not only significantly shortens the design cycle but also, through systematic global optimization, can obtain design solutions with superior performance and higher reliability than traditional methods.

[0022] Example 8 The performance evaluation unit includes: The performance coefficient calculator is used to determine the pressure recovery coefficient and the flow stability coefficient for various operating conditions through computational fluid dynamics simulation. 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, to generate a global performance index; In the embodiment, the internal structure of the performance evaluation unit in embodiment 7 is further refined; The unit is divided into two logical sub-modules, a performance coefficient calculator and a global index synthesizer; 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 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 under this specific operating condition, the pressure recovery coefficient and the flow stability coefficient ; The function of the global index synthesizer is to perform a fast mathematical synthesis task; 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 index , and returns it to the optimization solving unit as the final fitness score of the candidate configuration; The technical effect of the gain is to divide 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 to calculate the performance of a geometric configuration under different operating conditions, or to perform parallel calculation of the performance of different individuals in the population, and the calculation results are finally aggregated by the unique global index synthesizer, thereby significantly shortening the time consumption of the entire optimization process; The present application overcomes the inherent limitations of existing technology in the design of the exhaust system of an industrial steam turbine, which is treated as an isolated component and optimized at a single point under idealized, static inlet boundary conditions, achieving significant technical progress; 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 ignores the strong swirl generated under variable working conditions, resulting in that the designed exhaust structure only performs well at a specific design point, and the comprehensive performance and operation stability in the entire operating range are insufficient.

[0023] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for optimizing exhaust steam from an industrial steam turbine, characterized in that, include: Obtain a flow field dataset characterizing the full operating conditions of the steam turbine; Based on the flow field dataset, the flow field information of the last stage blade outlet section is processed to determine the swirl intensity factor corresponding to each operating condition. The three-dimensional solid geometry of the exhaust cylinder diffuser is transformed into a parameterized model of the candidate three-dimensional geometric configuration controlled by preset geometric parameters; For each operating condition, the candidate three-dimensional geometric configuration parameterized model is coupled with the corresponding swirl intensity factor, and the pressure recovery coefficient and flow stability coefficient are determined through computational fluid dynamics simulation. By combining preset performance weighting coefficients and preset operating condition weights, the pressure recovery coefficient and flow stability coefficient of all operating conditions are weighted and calculated to generate a global performance index. Based on global performance metrics, a global optimization algorithm is used to iteratively solve the parameterized model of the candidate 3D geometric configuration and output the optimal geometric configuration.

2. The method for optimizing exhaust steam from an industrial steam turbine according to claim 1, characterized in that, The swirl intensity factor is a dimensionless parameter used to quantify the degree of tangential swirl in the exhaust cylinder inlet flow, calculated based on the average airflow angle of the steam at the outlet of the last stage blade.

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

4. The method for optimizing exhaust steam from an industrial steam turbine according to claim 1, characterized in that, The determination of the flow stability coefficient includes: The total area of ​​the flow separation region on the inner wall of the diffuser where the wall shear stress is non-positive was determined by computational fluid dynamics simulation. Based on the parameterized model of candidate three-dimensional geometric configuration, the total area of ​​the diffuser inner wall is determined; 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 diffuser inner wall.

5. The method for optimizing exhaust steam from an industrial steam turbine according to claim 1, characterized in that, The generation of global performance metrics includes: For each operating condition, the corresponding pressure recovery coefficient and flow stability coefficient are multiplied by a preset performance weighting coefficient and then summed to obtain the comprehensive performance value of the operating condition. Multiply the overall performance value of the operating condition by the corresponding preset operating condition weight; The weighted results of all operating conditions are summed to generate a global performance index.

6. The method for optimizing exhaust steam from an industrial steam turbine according to claim 1, characterized in that, Iterative solutions are obtained using a global optimization algorithm, including: Randomly generate an initial population containing multiple candidate geometric configurations; For each candidate geometric configuration in the population, the steps are called to generate the corresponding global performance metrics. Based on global performance metrics, new offspring populations are generated through selection, crossover, and mutation operations. Repeat the steps of generating global performance metrics and creating a new population until the preset convergence condition is met.

7. An industrial steam turbine exhaust optimization system, applied to the industrial steam turbine exhaust optimization method according to any one of claims 1-6, characterized in that, include: The data modeling unit is used to acquire the flow field dataset characterizing the full operating conditions of the steam turbine, and to determine the swirl intensity factor corresponding to each operating condition based on the flow field dataset. The geometric configuration unit is used to transform the three-dimensional solid geometry of the exhaust cylinder diffuser into a candidate three-dimensional geometric configuration parameterized model controlled by preset geometric parameters; The performance evaluation unit is used to determine the pressure recovery coefficient and flow stability coefficient by coupling the candidate three-dimensional geometric configuration parameterized model with the corresponding swirl intensity factor for each operating condition, and to generate global performance indicators by combining preset weights. The optimization solution unit is used to iteratively solve the parameterized model of the candidate 3D geometric configuration based on global performance indicators and a global optimization algorithm to output the optimal geometric configuration.

8. The industrial steam turbine exhaust optimization system according to claim 7, characterized in that, The performance evaluation unit includes: The performance coefficient calculator is used to determine the pressure recovery coefficient and the flow stability coefficient for various operating conditions through computational fluid dynamics simulation. The global index synthesizer combines preset performance weighting coefficients and preset operating condition weights to perform weighted calculations on the pressure recovery coefficients and flow stability coefficients of all operating conditions output by the performance coefficient calculator, in order to generate global performance indices.

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