Structural design optimization method, device and equipment for cylindrical heat absorption tower

Abstract

The invention provides a structural design optimization method, device and equipment for a cylindrical heat absorption tower, and relates to the technical field of data processing. The method comprises the following steps: determining optimization variables and constraint conditions of structural characteristics of the heat absorption tower according to structural design requirements of the cylindrical heat absorption tower; according to a genetic algorithm, initial population individuals are randomly generated within the limitation range of the optimization variables, constraint condition verification is carried out on the optimization variables corresponding to the initial population individuals, and qualified individuals are screened out; based on a crossover and variation rule, optimization variables corresponding to the qualified individuals are adjusted, and new individuals are generated through parameter recombination and random variation; and calculating a fitness value of a new individual according to an optimization objective function of the heat absorption tower, dynamically adjusting control parameters of a crossover and variation rule, and repeatedly iteratively optimizing a population until optimization variables are converged to an optimal solution meeting design requirements. According to the technical scheme, the efficiency and comprehensiveness of structural design optimization of the cylindrical heat absorption tower can be improved.

Description

Background Art

[0002] As an important building in a tower solar power station, the cylindrical heat absorption tower has a special structural characteristic of being heavy on top and light on the bottom. Its cross-sectional height-width ratio is large, and its mass and stiffness are unevenly distributed. When subjected to wind loads and earthquakes, its mechanical performance is significantly different from that of conventional high-rise structures, and the design calculation is more complex. At present, in engineering design, the cross-sectional dimensions and structural parameters of the heat absorption tower need to be optimized for different heights to balance the mechanical performance and engineering costs. However, due to the influence of multiple factors such as geological conditions and external loads, a systematic and comprehensive design optimization method has not yet been formed in the existing technology.

[0003] Existing technologies usually rely on finite element models to optimize the structural design of cylindrical heat absorption towers through step-by-step trial calculations and adjustments. This method manually adjusts parameters after each trial calculation, which is inefficient and is subject to the locality of the optimization process, making it difficult to achieve global optimization of the overall performance of the structure. In addition, the optimization results often fail to balance design requirements and economic efficiency, and are only applicable within certain specific parameter ranges, making it difficult to adapt to the trend of large-scale development of solar thermal power stations. In particular, with the continuous increase in the capacity and power of solar power stations, the design complexity of heat absorption towers in height and scale has become increasingly prominent, placing higher demands on existing optimization methods. Therefore, there is still room for improvement in the efficiency and comprehensiveness of the structural design optimization of cylindrical heat absorption towers. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a structural design optimization method for a cylindrical heat absorption tower, a structural design optimization device for a cylindrical heat absorption tower, an electronic device and a computer-readable storage medium, thereby improving the efficiency and comprehensiveness of the structural design optimization of the cylindrical heat absorption tower at least to a certain extent.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to a first aspect of an embodiment of the present disclosure, a method for optimizing the structural design of a cylindrical heat absorption tower is provided, the method comprising: determining optimization variables and constraints of structural characteristics of the cylindrical heat absorption tower according to structural design requirements of the cylindrical heat absorption tower; randomly generating initial population individuals within the restricted range of the optimization variables according to a genetic algorithm, performing constraint check on the optimization variables corresponding to the initial population individuals, and screening out qualified individuals; adjusting the optimization variables corresponding to the qualified individuals based on crossover and mutation rules, and generating new individuals through parameter recombination and random mutation; calculating the fitness value of the new individual according to the optimization objective function of the heat absorption tower, dynamically adjusting control parameters of the crossover and mutation rules, and repeatedly iterating the optimization population until the optimization variables converge to an optimal solution that meets the design requirements.

[0007] According to a second aspect of an embodiment of the present disclosure, a structural design optimization device for a cylindrical heat absorption tower is provided, the device comprising: an optimization variable definition module, for determining the optimization variables and constraints of the structural characteristics of the cylindrical heat absorption tower according to the structural design requirements of the cylindrical heat absorption tower; a population generation verification module, for randomly generating initial population individuals within the restriction range of the optimization variables according to a genetic algorithm, performing constraint verification on the optimization variables corresponding to the initial population individuals, and screening out qualified individuals; a crossover and mutation module, for adjusting the optimization variables corresponding to the qualified individuals based on crossover and mutation rules, and generating new individuals through parameter recombination and random mutation; a fitness iteration module, for calculating the fitness value of the new individual according to the optimization objective function of the heat absorption tower, dynamically adjusting the control parameters of the crossover and mutation rules, and repeatedly iterating the optimization population until the optimization variables converge to the optimal solution that meets the design requirements.

[0008] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the structural design optimization method of the cylindrical heat absorption tower is implemented.

[0009] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the structural design optimization method of the cylindrical heat absorption tower described above.

[0010] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The structural design optimization method of the cylindrical heat absorbing tower in the exemplary embodiment of the present disclosure, on the one hand, by determining the optimization variables and constraints according to the structural design requirements of the cylindrical heat absorbing tower, can limit the design range to a reasonable parameter space, avoid the waste of computing resources due to the introduction of invalid solutions, and ensure the pertinence of the optimization target. On the basis of the optimization variables, the genetic algorithm is used to randomly generate the initial population individuals within the restricted range, and the qualified individuals are screened out through the constraint condition verification, so that the optimization process starts from the high-quality population, laying the foundation for the subsequent iterative optimization; on the other hand, on the basis of the qualified individuals, the optimization variables are adjusted based on the crossover and mutation rules, and new individuals are generated through parameter reorganization and random mutation, which can expand the search space while maintaining the diversity of the optimized population, thereby improving the coverage of the optimized solution. The fitness value of the new individual is calculated in combination with the optimization objective function, and the control parameters of the crossover and mutation are dynamically adjusted, so that the optimization process can adapt to complex design scenarios and enhance the convergence ability to the global optimal solution; on the other hand, through multiple rounds of iterative optimization of the population, the optimization variables are gradually approached to the optimal solution that meets the design requirements, which can effectively solve the problems of low efficiency and one-sided results in the existing design methods. The above-mentioned structural design optimization method of the cylindrical heat absorption tower realizes the comprehensive control of the design process, improves the efficiency and comprehensiveness of the structural design optimization of the cylindrical heat absorption tower, and provides scientific and reasonable data support for the engineering design of the heat absorption tower.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for optimizing the structural design of a cylindrical heat absorption tower according to some embodiments of the present disclosure is schematically shown.

[0014] Figure 2 A schematic diagram of optimization variables according to some embodiments of the present disclosure is schematically shown.

[0015] Figure 3 A schematic diagram of a framework of a subject optimization strategy according to some embodiments of the present disclosure is schematically shown.

[0016] Figure 4The figure schematically shows a flow chart of generating initial population individuals according to some embodiments of the present disclosure.

[0017] Figure 5 A flowchart of checking constraint conditions according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 6 The schematic diagram schematically shows a framework of crossover and mutation policy rules according to some embodiments of the present disclosure.

[0019] Figure 7 A reference schematic diagram schematically illustrates the change in the outer radius of the tower before and after optimization according to some embodiments of the present disclosure.

[0020] Figure 8 A reference schematic diagram schematically illustrates the change in tower wall thickness before and after optimization according to some embodiments of the present disclosure.

[0021] Fig. 9 A reference schematic diagram schematically illustrates changes in tower reinforcement area before and after optimization according to some embodiments of the present disclosure.

[0022] Fig.10 A block diagram of a structural design optimization device for a cylindrical heat absorption tower according to some embodiments of the present disclosure is schematically shown.

[0023] Fig.11 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0024] Fig.12 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0025] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0027] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0028] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0030] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0031] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] In an exemplary embodiment of the present disclosure, a structural design optimization method for a cylindrical heat absorption tower is first proposed. Figure 1 The schematic diagram shows a flow chart of a method for optimizing the structural design of a cylindrical heat absorption tower according to some embodiments of the present disclosure. Figure 1 As shown, the structural design optimization method of the cylindrical heat absorption tower may include the following steps: Step S110, determining the optimization variables and constraints of the structural characteristics of the heat absorption tower according to the structural design requirements of the cylindrical heat absorption tower; Step S120, randomly generating initial population individuals within the restricted range of the optimization variables according to the genetic algorithm, performing constraint condition verification on the optimization variables corresponding to the initial population individuals, and screening out qualified individuals; Step S130, based on the crossover and mutation rules, the optimization variables corresponding to the qualified individuals are adjusted, and new individuals are generated through parameter recombination and random mutation; Step S140, calculate the fitness value of the new individual according to the optimization objective function of the heat absorption tower, dynamically adjust the control parameters of the crossover and mutation rules, and repeatedly iterate and optimize the population until the optimization variables converge to the optimal solution that meets the design requirements.

[0033] The structural design optimization method of the cylindrical heat absorption tower described above first determines the optimization variables and constraints of the structural characteristics of the heat absorption tower according to the structural design requirements of the cylindrical heat absorption tower. By defining the optimization variable range and setting the constraints, the optimization algorithm is provided with clear design boundaries and constraint rules, thereby ensuring the pertinence and rationality of the optimization process. On this basis, the initial population individuals are randomly generated within the restricted range of the optimization variables through the genetic algorithm, and the constraint condition check is performed on each individual one by one, and the individuals that meet the constraint conditions are screened as qualified individuals to improve the quality of the initial population and reduce the invalid calculation and resource waste in the subsequent optimization process. For the screened qualified individuals, the optimization variables are adjusted based on the crossover and mutation rules, the diversity of the population is increased through parameter reorganization operations, and new search directions are introduced through random mutations to further expand the distribution range of the optimization solution, providing support for global optimization. After the new individuals are generated, the fitness values ​​of the new individuals are calculated by optimizing the objective function, and the control parameters of the crossover and mutation rules are dynamically adjusted according to the fitness values, and the population is optimized for multiple rounds of iterations. As the population gradually converges, the optimization variables eventually reach the optimal solution that meets the design requirements, thereby achieving comprehensive optimization of the cylindrical heat absorption tower structure design and improving the optimization efficiency and reliability of the results.

[0034] Next, the structural design optimization method of the cylindrical heat absorption tower in the above exemplary embodiment will be further described.

[0035] In step S110, based on the structural design requirements of the cylindrical heat absorption tower, the optimization variables and constraints of the structural characteristics of the heat absorption tower are determined.

[0036] Among them, the cylindrical heat absorption tower is the core building in the tower solar power station. It adopts a concrete cylindrical structure, and the overall height is usually more than 200 meters. The absorber and related equipment layers are arranged on the top, and high-speed elevators and functional facilities are arranged in the tower body. Its structure has the characteristics of concentrated mass distribution at the top and uneven stiffness. It needs to withstand the complex mechanical conditions of wind load and earthquake action. The design process needs to strike a balance between structural performance and economy. The optimization variable can represent the design parameters describing the structural characteristics of the cylindrical heat absorption tower, which is used to characterize the geometric shape and mechanical properties of the tower body at different heights. The optimization variable may include cross-sectional size, wall thickness, reinforcement ratio and contour slope. In addition, in other embodiments of the present disclosure, the optimization variable may also include other suitable parameters such as the tower body height segment ratio, cross-sectional gradient rate and center of gravity height position. By adjusting the optimization variable, the force optimization of the tower structure and the improvement of material utilization efficiency can be achieved. The constraint condition may represent the restriction rules of the optimization variable value range, which is used to ensure that the heat absorption tower structure design meets the performance requirements and design specifications. For example, the constraints may include: mechanical property constraints, such as axial force bearing capacity, moment bearing capacity and tower wall stress must not exceed the set limit; material strength constraints, such as concrete compressive strength and tensile strength must meet the design standards; geometric size constraints, such as tower diameter, wall thickness and inner diameter must meet design requirements and functional requirements; overall stability constraints, such as tower top horizontal displacement and cross-sectional stiffness distribution must ensure structural stability. By determining the optimization variables and setting the constraints, a clear parameter range and limiting conditions can be provided for subsequent optimization to ensure the rationality of the design results and engineering feasibility.

[0037] In some embodiments, based on the structural design requirements of the cylindrical heat absorption tower, the optimization variables and constraints of the structural characteristics of the heat absorption tower are determined, specifically including the following technical steps: the heat absorption tower is divided into multiple height segments, and the cross-sectional size, wall thickness and reinforcement ratio of each height segment are used as optimization variables; based on the structural design requirements of the heat absorption tower, constraints covering mechanical properties, material strength and geometric dimensions are established; the initial value range of the optimization variables is associated with the constraints, and an optimization model for constraint verification and optimization iteration is generated.

[0038] Specifically, first, the tower of the heat absorption tower is divided into multiple height segments, so that the load, strength, material stress and structural displacement can be calculated more efficiently during the optimization design. Due to the arrangement of door openings at the bottom of the tower, its height is 5 meters and its width is 4 meters. The cross section of this part is different from other cross sections due to weakening and is divided into an independent first height segment. From the bottom to the top, every 10 meters is regarded as a height segment, and the remaining height segments are numbered in increasing order. The cross-sectional dimensions, wall thickness and reinforcement ratio of each height segment are determined as optimization variables, where the wall thickness of each height segment remains unchanged within its range to reduce the number of parameters and improve calculation efficiency. The cross-sectional dimensions include the slope of the side profile of each height segment. To simplify the calculation, the slope is represented by the cotangent value of the profile inclination. The range of wall thickness is set based on engineering conditions and design requirements to meet the requirements of bearing capacity and structural stability. The reinforcement ratio is set as a control variable according to the structural strength requirements to ensure the reasonable distribution of material strength and mechanical properties. Reference Figure 2 As shown, Indicates i The wall thickness of the cross section at each height segment, Indicates i The profile inclination of each height segment, represents the diameter of the bottom section, its value is 4000 cm, Indicates the wall thickness of the cross section of the first height segment, Indicates the profile inclination of the first height segment. For example, the limit range of the optimization variable can be set as: the diameter of the bottom section The limit range is [12,25] meters, and the cross-sectional size That is, the slope limit range is [0,0.04], and the wall thickness of the section The limit range is [220,800] mm, and the reinforcement ratio The restricted range is [0.0025,0.01].

[0039] Combined with the optimization variables, constraints that meet the design requirements of the heat absorption tower are established, covering three aspects: mechanical properties, material strength and geometric dimensions. Mechanical performance constraints include limitations on axial force bearing capacity, moment bearing capacity and shear strength to ensure that the structure is safe and reliable under the ultimate bearing capacity state. Material strength constraints cover the limit values ​​of concrete compressive strength and tensile strength to avoid overloading of material performance. Geometric size constraints specify the reasonable range of cross-sectional diameter, wall thickness and reinforcement amount to ensure the engineering feasibility and overall stability of geometric parameters. In addition, displacement limits are also included in the constraints to ensure that the structural deformation meets the design specifications under normal use. The initial value range of the optimization variables is associated with the constraints through a mathematical model to generate an optimization model suitable for verification and optimization iteration. The optimization model constructs an association expression between variables based on the design parameters of each height segment and the set constraints to verify the rationality of the variables and dynamically adjust the parameter values.

[0040] In some embodiments, according to the structural design requirements of the heat absorption tower, constraints covering mechanical properties, material strength and geometric dimensions are established, specifically including: According to the bearing capacity, geometric shape and stability requirements of the heat absorption tower, the constraints shown in the following inequality group are established: in, i Indicates the section number. , , and Respectively represent i Axial force, axial force bearing capacity, bending moment and bending moment bearing capacity of each section, , and Respectively represent i The windward side concrete compressive stress, leeward side concrete compressive stress and concrete tensile stress of each section are and They represent the standard value of concrete compressive strength and the standard value of concrete tensile strength, respectively. and They represent the horizontal displacement of the tower top and the horizontal displacement limit, Indicates i The outer diameter of the cross section, Indicates i The wall thickness of each section, Indicates the minimum allowable inner diameter, Indicates the maximum allowable outer diameter, and They represent the wall thickness of the concrete tower and the minimum limit of the wall thickness respectively. and Respectively represent i Slope and slope limit for each height segment, and Represent the diameter of the bottom section and the minimum diameter limit respectively.

[0041] Furthermore, in the above inequality group, each inequality represents from top to bottom: axial force bearing capacity limit of concrete tower section (bearing capacity limit state); moment bearing capacity limit of concrete tower section (bearing capacity limit state); compressive stress limit of windward side of concrete tower (normal use limit state); compressive stress limit of leeward side of concrete tower (normal use limit state); tensile stress limit of concrete tower (normal use limit state); horizontal displacement limit of top (normal use limit state); structural shape limit; minimum wall thickness limit; minimum inner diameter limit; maximum outer diameter limit; tower section slope limit; minimum bottom outer diameter limit. Exemplarily, the boundary constraints can be set as: horizontal displacement limit of tower top Can be H / 300, H Indicates the overall height of the heat absorption tower and the minimum limit of the concrete tower wall thickness It can be 100+0.01 d , d Indicates the minimum outer diameter and maximum allowable outer diameter of the tower section Can be 25 meters, slope limit It can be 4.4%, the minimum limit of the diameter of the bottom section It can be 12 meters.

[0042] In step S120, initial population individuals are randomly generated within the restricted range of the optimization variables according to the genetic algorithm, and constraint condition verification is performed on the optimization variables corresponding to the initial population individuals to screen out qualified individuals.

[0043] Genetic algorithm can represent a global optimization algorithm based on biological evolution mechanism. By simulating natural selection, crossover and mutation processes, it finds the optimal solution in multivariate optimization problems. The algorithm calculates multiple solutions in parallel in the form of population, evaluates the pros and cons of each individual according to the fitness value, and gradually optimizes the population performance through genetic operation. It is suitable for heat absorption tower structure design optimization problems containing complex constraints and nonlinear relationships. The initial population individuals can represent the basic solution composed of the combination of optimization variables in the genetic algorithm, which is used to represent the initial values ​​of design parameters such as the cross-sectional size, wall thickness, and reinforcement ratio of different height sections of the heat absorption tower. When generated, each individual is randomly generated within the restricted range of the optimization variables and meets the basic geometric and engineering conditions. Constraint verification is the process of checking the rationality and normativeness of the optimization variables of the initial population individuals, which is used to screen out individuals that do not meet the design requirements. The verification content includes the mechanical property limit, material strength limit, geometric size limit and overall stability limit of the tower section to ensure that the qualified individuals meet the engineering design and specification requirements. In step S120, according to the genetic algorithm, the initial population individuals are randomly generated within the restricted range of the optimization variables. The cross-sectional dimensions, wall thickness and reinforcement ratio of each individual in the population are checked for constraints, including mechanical properties, material strength, geometric dimensions and stability requirements. Individuals that meet the constraints are selected as qualified populations for subsequent optimization operations.

[0044] After determining the optimization variables and constraints of the heat absorption tower structural characteristics, the optimization variables can be iteratively optimized based on the genetic algorithm. Figure 3 The schematic diagram of the framework of the subject optimization strategy according to some embodiments of the present disclosure is schematically shown. Figure 3As shown in the figure, the initialization phase of the optimization calculation begins by randomly generating individuals in the population that meet the initial conditions. The target strategy variables of each individual in the initial population are randomly generated within the limits of the optimization variables, and are checked one by one through the constraints to screen out individuals that meet the basic design rules. In this way, it is ensured that the initial population has sufficient diversity and quality, providing a reliable foundation for subsequent iterative operations.

[0045] After entering the iteration process, the fitness value of each individual in the current population is first calculated. The fitness value is calculated based on the optimization objective function and is used to evaluate the performance of the individual in the current search space. The fitness value is used to determine whether the population meets the termination condition. The termination condition includes the number of iterations reaching the preset upper limit or the fitness value of the population converging to the target range. When the termination condition is met, the optimization process ends and the optimization result is output; if not, the next step is entered.

[0046] When the termination condition is not met, individuals with high fitness are extracted through fitness sorting to generate population A. This process retains individuals with excellent performance in the current population, which can improve the overall quality of the population and improve the efficiency of optimization. Subsequently, individual pairs are randomly selected from population A for crossover operation, and the target strategy variables are reorganized according to the set crossover ratio parameters to generate population B. The crossover operation broadens the search space by recombining variable values, which helps to explore more potential solutions.

[0047] After completing the crossover operation, individuals are randomly selected from population B to perform the mutation operation. The target strategy variables are adjusted according to the set mutation ratio to generate population C. The mutation operation introduces a new search direction, increases the diversity of the population, and combines the verification of constraint conditions to ensure that the generated mutant individuals still meet the design rules. After the mutation is completed, the new population C is used as the input for the next round of iteration, and the number of loop iterations is increased by one. During multiple rounds of iterations, the population performance is continuously optimized, and the target strategy variables are gradually evolved, so that the fitness of the population gradually approaches the global optimal region. When the termination condition is reached, the optimization process ends and the final result is output. In this way, the genetic algorithm realizes the gradual evolution and optimization of the target strategy variables, ensuring that the final solution has high quality and high fitness, and provides a reliable optimization solution for the engineering design or application of cylindrical heat absorption towers.

[0048] In some embodiments, initial population individuals are randomly generated within the restricted range of the optimization variables, specifically including the following technical steps: within the restricted range of the optimization variables, population individuals are randomly generated according to the uniform distribution principle; optimization variable values ​​of cross-sectional size, wall thickness and reinforcement ratio are assigned to each population individual to form an initial population individual.

[0049] Among them, the uniform distribution principle can represent a method of randomly generating numerical values. Within a given range of values, each possible value point has the same generation probability. When this principle is used to randomly generate optimization variables, it can ensure that the generated population individuals are evenly distributed in the design space to avoid excessive concentration in local areas. The optimized variable values ​​can represent the key parameters that describe the structural characteristics of the initial population individuals, including the values ​​of cross-sectional dimensions, wall thickness, and reinforcement ratio. These optimized variable values ​​are randomly determined according to the uniform distribution principle during initial generation, and their value range is determined by the constraints of the optimized variables to ensure that the design specifications and engineering requirements are met.

[0050] Specifically, when generating individuals of the initial population, first, the range of decision variables of each individual is defined according to the definition of the optimization variable to ensure that the initial population can be reasonably distributed in the design space. When generating individuals of the population, the variable values ​​are randomly sampled according to the principle of uniform distribution. Specifically, each variable generates values ​​with equal probability within the upper and lower limits defined by it, and the generated variable values ​​are combined to form an individual. To ensure that each individual meets the design constraints, the generated individuals are checked for constraints one by one. The constraint check includes checking the geometric parameters, mechanical properties and material strength of the individuals. The main check contents are whether the cross-sectional dimensions meet the geometric design requirements, whether the wall thickness meets the bearing capacity limit, whether the reinforcement ratio meets the resistance requirements and whether the variable value falls within the optimization range. If an individual fails to pass the check, the individual is discarded and regenerated until the number of individuals generated reaches the preset value. When the individual passes the constraint check, a specific optimization variable value is assigned to it. The cross-sectional dimensions, wall thickness and reinforcement ratio values ​​of each individual are determined by the randomly generated variable values ​​to form a complete initial population.

[0051] The number of individuals in the entire population is set according to the complexity of the optimization task and the size of the search space. In this embodiment, the initial population size is set to 100 to ensure a balance between population diversity and computational efficiency. Figure 4 The flowchart of generating initial population individuals satisfying constraint conditions according to some embodiments of the present disclosure is schematically shown. Figure 4 As shown, first determine the number of the currently generated individual, and start from the first individual, and gradually generate population individuals in sequence according to the number. During the generation process, the height distribution range of the concrete tower section is clarified. This range is defined according to the design requirements and segmentation principles of the tower section to ensure that the upper and lower limits of the tower geometric parameters such as cross-sectional size, wall thickness and reinforcement ratio are clear and definite, providing basic conditions for subsequent random generation.

[0052] After determining the height distribution range, the variable values ​​of the individuals are randomly generated. Random generation is based on the principle of uniform distribution to ensure that the variable values ​​are evenly distributed within the optimized variable range, thereby increasing the diversity of the population. Each generated individual is composed of specific values ​​of cross-sectional dimensions, wall thickness and reinforcement ratio, and these variable values ​​directly characterize the structural characteristics of the individual. Randomly generated individuals need to undergo constraint testing. The test content includes aspects such as geometric dimensions, mechanical properties and material strength. For individuals that fail the inspection, they are discarded and regenerated until the individual meets all constraints. When the generated individual meets the constraints, the genetic information of the individual, including the values ​​of the optimized variables and related parameters, is recorded and used as part of the initial population. The process continues to increment the individual number i +1, repeat the above steps until the number of individuals in the initial population reaches the preset target value.

[0053] In some embodiments, constraints are checked on the optimization variables corresponding to the individuals in the initial population to screen out qualified individuals, which specifically includes the following technical steps: verifying and calculating the optimization variables of the individuals in the initial population to determine the verification results corresponding to the mechanical properties, material strength and geometric dimensions; comparing the verification results with the constraints, and screening the individuals in the population that meet the constraints as qualified individuals.

[0054] Specifically, first, the mechanical properties, material strength and geometric dimensions of the initial population individuals are verified and calculated. Mechanical property verification includes the calculation of axial force bearing capacity and bending moment bearing capacity, material strength verification involves the verification of compressive strength and tensile strength, and geometric dimension verification checks the rationality of cross-sectional dimensions, wall thickness and reinforcement ratio. The verification results are compared with the constraints one by one to determine whether the individuals meet the requirements of mechanical, material and geometric constraints. Individuals that meet all constraints are screened as qualified individuals, and their optimized variable values ​​and related parameters are recorded. Figure 5 The following schematically shows a flow chart of checking constraint conditions according to some embodiments of the present disclosure. Figure 5 As shown in the figure, firstly, the cross-sectional data is generated. According to the set range of the optimization variables and the variable values ​​of the initial population individuals, the cross-sectional parameters of each individual are calculated one by one, including key indicators such as cross-sectional size, wall thickness and reinforcement ratio. The generated cross-sectional data is used as the input of the verification process to provide a basis for subsequent geometric and mechanical calculations. After the cross-sectional data is generated, the dimensions are checked to ensure that the cross-sectional dimensions meet the design specifications and engineering requirements. The dimension check includes the verification of the value range of the cross-sectional width, thickness and reinforcement ratio, focusing on checking whether the variables are within the preset limit range and judging the rationality of the geometric shape. The results of the dimension check are used to screen out individuals that do not meet the conditions to prevent invalid solutions from entering the subsequent calculation links.

[0055] For the individuals that pass the dimensional inspection, the load and internal force are further calculated. The internal force calculation is based on the mechanical analysis model, combined with the cross-sectional data and external load conditions to determine the distribution of the axial force, bending moment and shear force of the individual under various working conditions. On the basis of the internal force calculation, the bearing capacity under the ultimate limit state and the bearing capacity under the normal operating limit state are calculated respectively. The ultimate limit state analysis is used to verify the structural stability and safety of the individual under the maximum load, and the normal operating limit state analysis evaluates whether the material stress and deformation of the individual under long-term use conditions meet the requirements of the specification. Through the bearing capacity calculation of the two states, the structural performance of the individual is comprehensively checked.

[0056] In step S130, based on the crossover and mutation rules, the optimization variables corresponding to the qualified individuals are adjusted, and new individuals are generated through parameter recombination and random mutation.

[0057] Among them, the crossover rule can represent an operation method based on genetic algorithms, which generates individuals with new characteristics by exchanging and reorganizing the optimization variables of two or more qualified individuals. The mutation rule can represent an operation method of genetic algorithms with strong randomness, which generates new variable values ​​by making small random adjustments to the optimization variables of qualified individuals, forming individuals with new characteristics.

[0058] In some embodiments, based on the crossover and mutation rules, the optimization variables corresponding to the qualified individuals are adjusted, and new individuals are generated through parameter reorganization and random mutation, which specifically includes the following technical steps: randomly selecting individual pairs from the qualified individuals, combining the parameters of the optimization variables of the individual pairs according to the crossover rules, and generating crossover individuals; randomly adjusting the optimization variables of the crossover individuals according to a preset mutation ratio to generate mutated new individuals.

[0059] Specifically, the optimization variables of qualified individuals are adjusted based on the crossover and mutation rules, and new individuals are generated through parameter reorganization and random mutation to improve the diversity and optimization ability of the population. First, individual pairs are randomly selected from qualified individuals. The selection of individual pairs is based on the principle of randomness to ensure that the generated crossover individuals can inherit the characteristics from different individuals, thereby covering a wider search space. The selected individual pairs are composed of their optimized variables as the input of the subsequent crossover operation. After the individual pairs are randomly selected, the parameters of the optimized variables of the individual pairs are combined according to the crossover rules. The crossover rule divides the optimized variables of the individual pairs into multiple segments according to the preset crossover points, and reorganizes the variable segments according to the rules. The crossover operation can integrate the characteristics of different individuals and generate crossover individuals with new combined characteristics. Through the crossover operation, the crossover individuals not only retain the excellent characteristics of the parent individuals, but also have new variable combinations, introducing more possibilities for the evolution of the population.

[0060] For the generated crossover individuals, their optimization variables are randomly adjusted according to the preset mutation ratio. The mutation operation introduces an appropriate amount of randomness and uncertainty by randomly perturbing the optimization variables. The mutation operation is adjusted within the allowed range of the variables to ensure that the mutated new individuals still meet the design requirements and constraints. Random mutation can effectively prevent the population from falling into the local optimal solution, enhance the global search capability, and further improve the optimization effect. Through the above steps, the generated mutated new individuals combine the inheritance characteristics of the crossover operation with the random characteristics of the mutation operation, which not only retains the excellent characteristics of the parent generation, but also introduces a new variable adjustment direction.

[0061] Figure 6 The schematic diagram schematically shows the framework of the crossover and mutation strategy rules according to some embodiments of the present disclosure. Figure 6 As shown in the figure, in the framework of crossover and mutation rules, the average fitness value of all individuals in the population and the fitness of each individual are first calculated to evaluate the performance of the population and provide a basis for subsequent crossover and mutation operations. Through the analysis of fitness, excellent individuals in the population can be identified, providing high-quality parent selection for crossover operations and improving the generation quality of new individuals. According to the analysis results, the crossover and mutation ratio parameters and the crossover number requirements are set to specify specific operation rules and ranges. The crossover ratio parameter controls the degree of recombination of the parent individual variables, while the mutation ratio parameter determines the intensity of the mutation operation. The crossover number requirement ensures a sufficient number of operations to ensure the diversity of the population.

[0062] After the rules are set, the intersection individuals are randomly selected from the population and the constraints are checked. The selected individuals must meet the given constraints, including mechanical properties, material strength, and geometric dimensions, to ensure that the generated new individuals have design rationality during the optimization process. If the intersection individual does not meet the constraints, a new intersection individual is randomly selected and the check is repeated until it meets the requirements.

[0063] Next, the crossover operation is performed on the intersection individuals that meet the constraints. The crossover process combines the variables according to the set crossover rules to generate new individuals after crossover. After the new individual is generated, a constraint check is required to confirm whether it meets the design requirements. If the new individual does not meet the constraints, the intersection individual is reselected and a new crossover operation is performed according to the rules. For the crossover individuals that pass the constraint check, the variables are further randomly adjusted according to the preset mutation ratio requirements, and the mutation operation is performed. The mutation operation is used to introduce new variable values ​​to increase the diversity of the population and prevent the population from falling into a local optimum. Exemplarily, the crossover ratio parameter can be set to 0.5 and the mutation ratio parameter can be set to 0.1. The generated mutant individuals need to be checked for constraints to confirm whether their variable values ​​are within the set range and meet the basic design specifications. If the mutated new individual meets the constraints, it is recorded as a new population member after crossover and mutation; if not, the crossover and mutation individuals are randomly selected and re-operated.

[0064] In step S140, the fitness value of the new individual is calculated according to the optimization objective function of the heat absorption tower, the control parameters of the crossover and mutation rules are dynamically adjusted, and the population is optimized repeatedly until the optimization variables converge to the optimal solution that meets the design requirements.

[0065] Among them, the fitness value can represent a numerical indicator that measures the pros and cons of an individual in the optimization objective function, and directly reflects the performance of the individual in the current optimization environment. In the heat sink optimization objective function, the fitness value is associated with the structural performance of the heat sink, the material utilization efficiency, and the degree of compliance with the design specifications. The control parameter can represent the key variable that affects the intensity and frequency of crossover and mutation operations in the genetic algorithm, and is used to dynamically adjust the search range and population diversity of the optimization process.

[0066] Further, in step S140, first, the fitness value of each new individual is calculated, and the fitness value is the objective function It is a quantitative representation of the individual's performance in the optimization objective. For ease of calculation, the fitness value Defined as the inverse of the objective function ,in The smaller the individual's fitness value is, The higher the value, the more conducive it is to the realization of the optimization goal. After the fitness value calculation is completed, the fitness values ​​of all individuals in the population are normalized. By calculating the sum of fitness and the relative fitness value of each individual , clarify the relative order of individuals in the population. Based on the relative fitness value, a random number between 0.01 and 1 is randomly generated, and combined with the distribution range of the random number and the fitness value, individuals with higher fitness values ​​are selected as the basis for subsequent crossover and mutation. This selection mechanism ensures that individuals with high fitness in the population participate in the evolution process first, while retaining a certain degree of randomness to enhance population diversity.

[0067] After selecting individuals with higher fitness, their parameters are adjusted according to the crossover and mutation rules. The crossover operation generates new individuals by combining the decision variables of different individuals (such as cross-sectional diameter, wall thickness, slope and reinforcement ratio), and checks the constraints of the new individuals to ensure that they meet the requirements of geometry, mechanics and material strength. If the new individual fails the check, the crossover parameters are regenerated and adjusted. For individuals that pass the crossover check, their decision variables are further adjusted according to the mutation ratio, and small random perturbations are introduced to expand the search space and avoid falling into the local optimum. The mutated new individuals also need to be checked for constraints to ensure their rationality. Through the above crossover and mutation operations, the performance of the generated new population in the optimization target space is continuously optimized. After each round of iteration, the control parameters of crossover and mutation are dynamically adjusted according to the overall performance of the population fitness. For example, in the early stage of optimization, the mutation ratio is appropriately increased to expand the search range, while in the later stage, the mutation ratio is reduced to accelerate the convergence process. Through multiple rounds of iterative optimization, the population gradually approaches the global optimal solution, and finally achieves the convergence of the optimization variables to meet the various requirements of the heat absorption tower design.

[0068] In some embodiments, the fitness value of the new individual is calculated according to the optimization objective function of the heat absorption tower, the control parameters of the crossover and mutation rules are dynamically adjusted, and the optimization population is repeatedly iterated until the optimization variables converge to the optimal solution that meets the design requirements. The following technical steps are specifically included: the fitness of the optimization variables corresponding to the new individuals is calculated according to the optimization objective function to determine the fitness value corresponding to each new individual; the crossover ratio and mutation ratio are dynamically adjusted according to the fitness value to update the control parameters of the crossover and mutation rules; the fitness calculation, parameter adjustment and population update operations are repeated until the optimization variables converge to the optimal solution that meets the design requirements after multiple iterations.

[0069] Specifically, first, the fitness of the optimization variables of the new individuals is calculated according to the optimization objective function. The fitness value is used to quantify the performance of the individual on the objective function. The fitness value of each new individual is determined by the degree of fit between its variables and the objective function. Through fitness calculation, the pros and cons of the individual in the current population are evaluated to provide a basis for subsequent optimization operations. After determining the fitness value, the crossover ratio and mutation ratio are dynamically adjusted. The crossover ratio determines the intensity of the parent variable recombination, and the mutation ratio controls the amplitude of the random perturbation of the variable. According to the distribution of fitness values, the crossover ratio is higher in the initial stage of the optimization process to expand the search space, and the mutation ratio is higher to introduce diversity; in the later stage of optimization, the mutation ratio is gradually reduced to accelerate the convergence process, thereby enhancing the optimization efficiency. The adjusted crossover and mutation parameters further update the control rules to guide subsequent optimization operations. Based on the updated control parameters, the fitness calculation, parameter adjustment and population update operations are repeated. In each iteration, the optimization variables are gradually adjusted through crossover and mutation, and the fitness of the population continues to improve with the increase in the number of iterations. High-quality individuals are selected through fitness calculation, and the search space is expanded in combination with dynamic parameter adjustment. The optimization variables gradually converge after multiple iterations. Finally, the convergence of the optimization variables makes the individuals in the population meet the design requirements of the heat absorption tower.

[0070] In some embodiments, performing fitness calculation on the optimization variables corresponding to the new individuals according to the optimization objective function to determine the fitness value corresponding to each new individual includes: According to the following optimization objective function, the fitness of the optimization variables corresponding to the new individual is calculated to obtain the objective function value: in, represents the optimization objective function, represents the volume of concrete, Indicates the comprehensive unit price of concrete, Represents the volume of steel bars, Indicates the comprehensive unit price of steel bars. , Indicates the tower bottom diameter, , and Respectively represent i The cross-sectional dimensions, wall thickness and reinforcement ratio of each cross-section; according to the objective function value, the fitness value corresponding to each new individual is determined ,in, Indicates i The fitness value corresponding to the new individual is Indicates iThe objective function value corresponding to each new individual. This optimization objective function achieves a quantitative evaluation of the comprehensive cost of the design scheme by weighted summation of the cost of concrete and steel materials, and determines the optimal solution with the most economical material use and performance that meets the design requirements by minimizing the objective function value.

[0071] For example, take a cylindrical heat absorption tower of a large solar thermal project as an example. The cross section of the heat absorption tower changes linearly along the height. The tower height is 215m, the bottom circular ring diameter is 25m, the bottom section wall thickness is 0.6m, the inner radius is 11.9m, the top diameter is 15.1m, the top section wall thickness is 0.4m, and the corresponding inner radius is 7.15m. The heat absorber is 1980 tons according to the manufacturer, and the concrete is C35, which corresponds to the standard value of concrete compressive strength of 23.4MPa and the standard value of concrete tensile strength of 2.20MPa. Based on the genetic algorithm, the cylindrical heat absorption tower is optimized using the structural design optimization of the cylindrical heat absorption tower in the embodiment of the present disclosure. The comparison of the main design parameters before and after the optimization can be shown as follows. Figure 7 , Figure 8 and Fig. 9 shown.

[0072] in, Figure 7 The figure shows the change of the tower outer radius before and after optimization. The figure takes the tower height as the vertical axis and the tower outer diameter as the horizontal axis. Before the optimization, the tower outer diameter changed greatly, the outer diameter distribution of each height section was uneven, and there was a problem of low material utilization efficiency. After the optimization, the tower outer diameter showed a smoother distribution, and the outer diameter gradually decreased from the bottom of the tower to the top of the tower, indicating that the outer diameter parameters were reasonably adjusted through optimization, which reduced unnecessary material waste and ensured the overall performance of the tower structure. Figure 8 The figure shows the change of tower wall thickness before and after optimization. The figure takes tower height as the vertical axis and wall thickness as the horizontal axis. Before optimization, the wall thickness distribution is irregular, with some height sections having too large wall thickness and other sections having too small wall thickness, which may lead to uneven stress on the structure. After optimization, the wall thickness distribution is more in line with the laws of mechanics, gradually becoming thinner from bottom to top. The optimized wall thickness design significantly reduces the amount of material used while ensuring the bearing capacity, reflecting the economy and rationality of the structural design. Fig. 9 The figure shows the change of the tower reinforcement area before and after optimization. The figure takes the tower height as the vertical axis and the reinforcement area as the horizontal axis. The distribution of the reinforcement area before optimization is relatively discrete. Some areas have too much reinforcement, which is wasteful, while other areas have insufficient reinforcement, which may affect the safety of the structure. After optimization, the change of the reinforcement area with the tower height is smoother, and it is more consistent with the force demand. The optimized reinforcement design significantly improves the utilization efficiency of materials and enhances the mechanical properties of the structure.

[0073] The structural design method of the cylindrical heat absorption tower in the disclosed embodiment is based on the natural evolution process of the genetic algorithm. Through comprehensive constraint condition checks and reasonable control parameter adjustments, the optimization variables are limited to a good search area to ensure the scientificity and efficiency of the optimization process. Through step-by-step iteration, this method can achieve rapid convergence of the optimization variables, and finally obtain the optimal distribution ratio of the heat absorption tower type, effectively reduce the project cost, and meet the economic and actual engineering needs. The optimization method is highly targeted in the design of the objective function, and balances the mechanical properties, material utilization efficiency and economic costs through the precise calculation of the fitness value. Compared with the traditional manual trial calculation method, this method does not require manual intervention and has a high degree of automation. It not only significantly improves the optimization efficiency, but also makes the optimization target clearer. The dynamically adjusted crossover and mutation control parameters enable the algorithm to flexibly adapt to different optimization stages, further improving the scientificity and engineering applicability of the optimization results.

[0074] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0075] Next, in the embodiment of the present disclosure, a structural design optimization device for a cylindrical heat absorption tower is also provided. Fig.10 As shown in , the structural design optimization device 1000 of the cylindrical heat absorption tower can be composed of an optimization variable definition module 1001, a population generation verification module 1002, a crossover and mutation module 1003, and a fitness iteration module 1004. Among them: the optimization variable definition module 1001 can be used to determine the optimization variables and constraints of the structural characteristics of the heat absorption tower according to the structural design requirements of the cylindrical heat absorption tower; the population generation verification module 1002 can be used to randomly generate initial population individuals within the restriction range of the optimization variables according to the genetic algorithm, perform constraint condition verification on the optimization variables corresponding to the initial population individuals, and screen out qualified individuals; the crossover and mutation module 1003 can be used to adjust the optimization variables corresponding to the qualified individuals based on the crossover and mutation rules, and generate new individuals through parameter recombination and random mutation; the fitness iteration module 1004 can be used to calculate the fitness value of the new individual according to the optimization objective function of the heat absorption tower, dynamically adjust the control parameters of the crossover and mutation rules, and repeatedly iterate the optimization population until the optimization variables converge to the optimal solution that meets the design requirements.

[0076] It should be noted that the specific details of each part of the structural design optimization device of the above-mentioned cylindrical heat absorption tower have been described in detail in the implementation method of the structural design optimization method of the cylindrical heat absorption tower. The undisclosed details can be found in the implementation method content of the method part, and will not be repeated here.

[0077] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the structural design optimization method of the cylindrical heat absorption tower is also provided.

[0078] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0079] Refer to the following Fig.11 1100 is described according to an embodiment of the present disclosure. Fig.11 The electronic device 1100 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0080] like Fig.11 As shown, the electronic device 1100 is in the form of a general computing device. The components of the electronic device 1100 may include but are not limited to: the at least one processing unit 1110, the at least one storage unit 1120, a bus 1130 connecting different system components (including the storage unit 1120 and the processing unit 1110), and a display unit 1140.

[0081] The storage unit stores program codes, which can be executed by the processing unit 1110, so that the processing unit 1110 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0082] The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 1121 and / or a cache memory unit 1122 , and may further include a read-only memory unit (ROM) 1123 .

[0083] The storage unit 1120 may also include a program / utility 1124 having a set (at least one) of program modules 1125, such program modules 1125 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0084] Bus 1130 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0085] The electronic device 1100 may also communicate with one or more external devices 1170 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1100, and / or communicate with any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1150. In addition, the electronic device 1100 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 1160. As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 through a bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0086] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0087] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0088] refer to Fig.12As shown, a program product 1200 for implementing the structural design optimization method of the cylindrical heat absorption tower according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0089] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0090] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0091] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, electromagnetic waves, etc., or any suitable combination of the foregoing.

[0092] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0093] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0094] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0095] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0096] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for optimizing the structural design of a cylindrical heat absorption tower, characterized in that: include: According to the structural design requirements of the cylindrical heat absorption tower, the optimization variables and constraints of the structural characteristics of the heat absorption tower are determined; According to the genetic algorithm, initial population individuals are randomly generated within the restricted range of the optimization variables, constraint condition verification is performed on the optimization variables corresponding to the initial population individuals, and qualified individuals are screened out; Based on the crossover and mutation rules, the optimization variables corresponding to the qualified individuals are adjusted, and new individuals are generated through parameter recombination and random mutation; The fitness value of the new individual is calculated according to the optimization objective function of the heat absorption tower, the control parameters of the crossover and mutation rules are dynamically adjusted, and the population is optimized repeatedly until the optimization variable converges to the optimal solution that meets the design requirements.

2. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: According to the structural design requirements of the cylindrical heat absorption tower, the optimization variables and constraints of the structural characteristics of the heat absorption tower are determined, including: Dividing the heat absorption tower into a plurality of height sections, and taking the cross-sectional size, wall thickness and reinforcement ratio of each height section as the optimization variables; According to the structural design requirements of the heat absorption tower, the constraint conditions covering mechanical properties, material strength and geometric dimensions are established; The initial value range of the optimization variable is associated with the constraint condition to generate an optimization model for constraint verification and optimization iteration.

3. The structural design optimization method of the cylindrical heat absorption tower according to claim 2 is characterized in that: According to the structural design requirements of the heat absorption tower, the constraint conditions covering mechanical properties, material strength and geometric dimensions are established, including: According to the bearing capacity, geometric shape and stability requirements of the heat absorption tower, the constraints are established: in, i Indicates the section number. , , and Respectively represent i Axial force, axial force bearing capacity, bending moment and bending moment bearing capacity of each section, , and Respectively represent i The windward side concrete compressive stress, leeward side concrete compressive stress and concrete tensile stress of each section are and They represent the standard value of concrete compressive strength and the standard value of concrete tensile strength, respectively. and They represent the horizontal displacement of the tower top and the horizontal displacement limit, Indicates i The outer diameter of the cross section, Indicates i The wall thickness of each section, Indicates the minimum allowable inner diameter, Indicates the maximum allowable outer diameter, and They represent the wall thickness of the concrete tower and the minimum limit of the wall thickness respectively. and Respectively represent i Slope and slope limit for each height segment, and Represent the diameter of the bottom section and the minimum diameter limit respectively.

4. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The randomly generating initial population individuals within the restricted range of the optimization variables comprises: Within the limited range of the optimization variables, randomly generate population individuals according to the uniform distribution principle; The optimized variable values ​​of cross-sectional size, wall thickness and reinforcement ratio are allocated to each of the population individuals to form the initial population individuals.

5. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The step of checking the constraints of the optimization variables corresponding to the individuals in the initial population to select qualified individuals includes: Performing calibration calculations on the optimization variables of the initial population individuals to determine calibration results corresponding to mechanical properties, material strength and geometric dimensions; The verification result is compared with the constraint condition, and the population individuals satisfying the constraint condition are screened as the qualified individuals.

6. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The method of adjusting the optimization variables corresponding to the qualified individuals based on the crossover and mutation rules and generating new individuals through parameter recombination and random mutation includes: Randomly select individual pairs from the qualified individuals, perform parameter combination on the optimized variables of the individual pairs according to the crossover rule, and generate crossover individuals; The optimized variables of the crossover individuals are randomly adjusted according to a preset mutation ratio to generate new individuals after mutation.

7. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The fitness value of the new individual is calculated according to the optimization objective function of the heat absorption tower, the control parameters of the crossover and mutation rules are dynamically adjusted, and the population is optimized repeatedly until the optimization variable converges to the optimal solution that meets the design requirements, including: Performing fitness calculation on the optimization variables corresponding to the new individuals according to the optimization objective function, and determining the fitness value corresponding to each of the new individuals; Dynamically adjusting the crossover ratio and the mutation ratio according to the fitness value to update the control parameters of the crossover and mutation rules; The fitness calculation, parameter adjustment and population update operations are repeated until the optimization variables converge to an optimal solution that meets the design requirements after multiple iterations.

8. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The step of performing fitness calculation on the optimization variables corresponding to the new individuals according to the optimization objective function to determine the fitness value corresponding to each of the new individuals includes: According to the optimization objective function: The fitness of the optimization variables corresponding to the new individual is calculated to obtain the objective function value, where: represents the optimization objective function, represents the volume of concrete, Indicates the comprehensive unit price of concrete, Represents the volume of steel bars, Indicates the comprehensive unit price of steel bars. , represents the diameter of the bottom section, , and Respectively represent i The slope, wall thickness and reinforcement ratio of each section; According to the objective function value, determine the fitness value corresponding to each new individual ,in, Indicates i The fitness value corresponding to the new individual is Indicates i The objective function value corresponding to the new individual.

9. A structural design optimization device for a cylindrical heat absorption tower, characterized in that: include: An optimization variable definition module, used to determine the optimization variables and constraint conditions of the structural characteristics of the cylindrical heat absorption tower according to the structural design requirements of the cylindrical heat absorption tower; A population generation verification module is used to randomly generate initial population individuals within the restriction range of the optimization variables according to the genetic algorithm, perform constraint condition verification on the optimization variables corresponding to the initial population individuals, and screen out qualified individuals; A crossover and mutation module, used to adjust the optimization variables corresponding to the qualified individuals based on the crossover and mutation rules, and generate new individuals through parameter recombination and random mutation; The fitness iteration module is used to calculate the fitness value of the new individual according to the optimization objective function of the heat absorption tower, dynamically adjust the control parameters of the crossover and mutation rules, and repeatedly iterate the optimization population until the optimization variables converge to the optimal solution that meets the design requirements.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the structural design optimization method of the cylindrical heat absorption tower described in any one of claims 1-8 by executing the executable instructions.

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