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

Abstract

The present disclosure provides a method, device and equipment for optimizing the structural design of a cylindrical heat absorbing tower, and relates to the field of data processing technology. The method comprises: determining the optimization variables and constraints of the structural characteristics of the heat absorbing tower according to the structural design requirements of the cylindrical heat absorbing tower; randomly generating individuals of an initial population within the restricted range of the optimization variables according to a genetic algorithm, performing constraint verification on the optimization variables corresponding to the individuals of the initial population, 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 individuals according to the optimization objective function of the heat absorbing 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. The technical solution disclosed in the present disclosure can improve the efficiency and comprehensiveness of the structural design optimization of the cylindrical heat absorbing tower.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, device and equipment for optimizing the structural design of a cylindrical heat absorption tower. Background Art

[0002] As a key component of tower-type solar power plants, cylindrical heat absorption towers, with their unique top-heavy, bottom-light structure, have a large height-to-width ratio, resulting in uneven mass and stiffness distribution. Their mechanical properties under wind loads and earthquakes differ significantly from those of conventional tall structures, making their design and calculations more complex. Currently, in engineering design, the cross-sectional dimensions and structural parameters of heat absorption towers must be optimized for different heights to balance mechanical performance and project 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 established in the existing technology.

[0003] Existing technologies typically 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 involves manually adjusting parameters after each trial calculation, which is inefficient and limited by the local nature 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 terms of 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 practice of the present disclosure.

[0006] According to a first aspect of an embodiment of the present disclosure, a structural design optimization method for a cylindrical heat absorption tower is provided, the method comprising: determining 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; randomly generating individuals of an initial population within the restricted range of the optimization variables according to a genetic algorithm, performing constraint verification on the optimization variables corresponding to the individuals of the initial population, 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 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.

[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, which includes: 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 and verification module for randomly generating initial population individuals within the restricted 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 reorganization 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, an electronic device is provided, 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, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the structural design optimization method of the cylindrical heat absorption tower is implemented.

[0010] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0011] The structural design optimization method for a cylindrical heat absorbing tower in an exemplary embodiment of the present disclosure, on the one hand, determines optimization variables and constraints based on the structural design requirements of the cylindrical heat absorbing tower, thereby limiting the design scope to a reasonable parameter space, avoiding the waste of computing resources caused by the introduction of invalid solutions, and ensuring the pertinence of the optimization objectives. Based on the optimization variables, a genetic algorithm is used to randomly generate initial population individuals within the restricted range, and qualified individuals are screened through constraint verification. This allows the optimization process to start from a high-quality population, laying the foundation for subsequent iterative optimization. On the other hand, based on the qualified individuals, the optimization variables are adjusted based on crossover and mutation rules, and new individuals are generated through parameter recombination and random mutation. This can expand the search space while maintaining the diversity of the optimized population, thereby improving the coverage of the optimized solution. The fitness values ​​of the new individuals are calculated in conjunction with the optimization objective function, and the control parameters of crossover and mutation are dynamically adjusted. This makes the optimization process adaptive to complex design scenarios and enhances the ability to converge 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, effectively solving the problems of low efficiency and one-sided results in existing design methods. The above-mentioned structural design optimization method of the cylindrical heat absorption tower realizes 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.

[0012] 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 disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are incorporated into 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 drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0014] Figure 1 A flow chart of a structural design optimization method for a cylindrical heat absorption tower according to some embodiments of the present disclosure is schematically shown.

[0015] Figure 2 A schematic diagram schematically illustrates optimization variables according to some embodiments of the present disclosure.

[0016] Figure 3 A schematic diagram of a framework of a subject optimization strategy according to some embodiments of the present disclosure is schematically shown.

[0017] Figure 4The figure schematically shows a flow chart of generating initial population individuals according to some embodiments of the present disclosure.

[0018] Figure 5 A flowchart of checking constraint conditions according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 6 The following schematically illustrates a framework diagram of crossover and mutation policy rules according to some embodiments of the present disclosure.

[0020] Figure 7 A reference schematic diagram schematically illustrates the change in tower outer radius before and after optimization according to some embodiments of the present disclosure.

[0021] 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.

[0022] Figure 9 A reference schematic diagram schematically illustrates changes in tower reinforcement area before and after optimization according to some embodiments of the present disclosure.

[0023] Figure 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.

[0024] Figure 11 A schematic structural diagram of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0025] Figure 12 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0026] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of this specification, as detailed in the appended claims.

[0028] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many 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 thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0031] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. 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 can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0032] Furthermore, the drawings are schematic illustrations only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entities. In other words, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] 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 following schematically illustrates a flow chart of a method for optimizing the structural design of a cylindrical heat absorbing 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:

[0034] Step S110, determining the optimization variables and constraints of the structural characteristics of the cylindrical heat absorbing tower according to the structural design requirements of the cylindrical heat absorbing tower;

[0035] Step S120: randomly generate initial population individuals within the restricted range of the optimization variables according to the genetic algorithm, perform constraint check on the optimization variables corresponding to the initial population individuals, and screen out qualified individuals;

[0036] 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;

[0037] 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.

[0038] The structural design optimization method for a cylindrical heat absorption tower, based on the structural design requirements of the cylindrical heat absorption tower, first determines the optimization variables and constraints for the tower's structural characteristics. By defining the optimization variable range and setting the constraints, the optimization algorithm provides clear design boundaries and constraint rules, thereby ensuring the targeted and rational optimization process. Furthermore, a genetic algorithm is used to randomly generate individuals in the initial population within the restricted range of the optimization variables. Constraints are then checked for each individual, and those that meet the constraints are selected as qualified individuals. This improves the quality of the initial population and reduces inefficient computation and resource waste in the subsequent optimization process. For the qualified individuals selected, the optimization variables are adjusted based on crossover and mutation rules. Parameter reshuffling is used to increase population diversity. Random mutation is used to introduce new search directions, further expanding the distribution range of optimal solutions and supporting global optimization. After generating new individuals, their fitness values ​​are calculated by optimizing the objective function. The control parameters of the crossover and mutation rules are dynamically adjusted based on the fitness values, and the population is optimized over multiple rounds. 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.

[0039] Next, the structural design optimization method of the cylindrical heat absorption tower in the above exemplary embodiment will be further described.

[0040] In step S110 , based on the structural design requirements of the cylindrical heat absorbing tower, the optimization variables and constraints of the structural characteristics of the heat absorbing tower are determined.

[0041] Among them, the cylindrical heat absorption tower is the core building in the tower solar power station. It adopts a concrete cylindrical structure and is generally more than 200 meters high. The heat absorber and related equipment layers are arranged on the top, and high-speed elevators and functional facilities are installed inside the tower. Its structure has the characteristics of concentrated mass distribution at the top and uneven stiffness. It must withstand the complex mechanical conditions of wind loads and seismic effects. The design process must strike a balance between structural performance and economic efficiency. The optimization variables can represent the design parameters that describe the structural characteristics of the cylindrical heat absorption tower and are used to characterize the geometric shape and mechanical properties at different heights of the tower body. The optimization variables can include cross-sectional dimensions, wall thickness, reinforcement ratio, and profile slope. In addition, in other embodiments of the present disclosure, the optimization variables can also include other appropriate parameters such as the tower height segment ratio, cross-sectional gradient rate, and center of gravity height position. By adjusting the optimization variables, the force optimization of the tower structure and the improvement of material utilization efficiency can be achieved. The constraint conditions can represent the restriction rules of the optimization variable value range to ensure that the heat absorption tower structure design meets performance requirements and design specifications. For example, constraints may include: mechanical performance constraints, such as axial force bearing capacity, bending moment bearing capacity, and tower wall stress must not exceed set limits; material strength constraints, such as concrete compressive and tensile strength must meet design standards; geometric size constraints, such as tower diameter, wall thickness, and inner diameter must meet design and functional requirements; and overall stability constraints, such as tower top horizontal displacement and cross-sectional stiffness distribution must ensure structural stability. By determining optimization variables and setting constraints, clear parameter ranges and limiting conditions can be provided for subsequent optimization, ensuring the rationality of the design results and the feasibility of the project.

[0042] 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: dividing the heat absorption tower into multiple height segments, and using the cross-sectional dimensions, wall thickness and reinforcement ratio of each height segment as optimization variables; establishing constraints covering mechanical properties, material strength and geometric dimensions based on the structural design requirements of the heat absorption tower; associating the initial value range of the optimization variables with the constraints to generate an optimization model for constraint verification and optimization iteration.

[0043] 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 the door opening 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 up, 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 angle. 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 the i The wall thickness of the cross section of each height segment, Indicates the i The profile inclination of each height segment, represents the diameter of the bottom section, which 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 restricted 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].

[0044] By combining optimization variables, constraints are established to meet the design requirements of the heat absorption tower, covering three major aspects: mechanical properties, material strength, and geometric dimensions. Mechanical property constraints include limits on axial force bearing capacity, bending moment bearing capacity, and shear strength, ensuring the safety and reliability of the structure under the ultimate bearing capacity state. Material strength constraints include limit values ​​for concrete compressive strength and tensile strength to avoid overloading of material properties. Geometric size constraints specify reasonable ranges for cross-sectional diameter, wall thickness, and reinforcement quantity to ensure the engineering feasibility and overall stability of geometric parameters. In addition, displacement limits are also included in the constraints to ensure that structural deformation meets 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 iterations. Based on the design parameters of each height segment and the set constraints, the optimization model constructs an association expression between variables to verify the rationality of the variables and dynamically adjust the parameter values.

[0045] In some embodiments, based on the structural design requirements of the heat absorption tower, constraints covering mechanical properties, material strength, and geometric dimensions are established, specifically including:

[0046] According to the load-bearing capacity, geometry and stability requirements of the heat absorption tower, the constraints shown in the following inequality group are established:

[0047]

[0048] 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 concrete tensile strength respectively. and Respectively represent the horizontal displacement of the tower top and the horizontal displacement limit, Indicates the i The outer diameter of the cross section, Indicates the i The wall thickness of each section, Indicates the minimum allowable inner diameter, Indicates the maximum allowable outer diameter, and Respectively represent the wall thickness of the concrete tower and the minimum limit of the wall thickness, 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.

[0049] Furthermore, in the above inequality group, each inequality represents from top to bottom: concrete tower section axial force bearing capacity limit (bearing capacity limit state); concrete tower section bending moment bearing capacity limit (bearing capacity limit state); concrete tower windward side compressive stress limit (normal use limit state); concrete tower leeward side compressive stress limit (normal use limit state); concrete tower tensile stress limit (normal use limit state); top horizontal displacement limit (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. For example, the boundary constraint conditions can be set as: tower top horizontal displacement limit Can be H / 300, H Indicates the overall height of the heat absorption tower and the minimum thickness of the concrete tower wall 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 diameter limit of the bottom section It can be 12 meters.

[0050] 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.

[0051] A genetic algorithm (GA) is a global optimization algorithm based on biological evolution. It simulates processes such as natural selection, crossover, and mutation to find the optimal solution to a multivariable optimization problem. The algorithm computes multiple solutions in parallel as a population, evaluates the performance of each individual based on its fitness, and gradually optimizes the population's performance through genetic operations. This algorithm is suitable for heat sink structural optimization problems involving complex constraints and nonlinear relationships. The initial population individuals represent the basic solutions consisting of combinations of optimization variables in the GA. These individuals represent the initial values ​​of design parameters such as cross-sectional dimensions, wall thickness, and reinforcement ratio at different heights of the heat sink. During generation, each individual is randomly generated within the constraints of the optimization variables and meets basic geometric and engineering requirements. Constraint verification is the process of verifying the rationality and compliance of the optimization variables in the initial population individuals, thereby eliminating individuals that do not meet design requirements. Verification includes constraints on the mechanical properties of the tower section, material strength, geometric dimensions, and overall stability to ensure that qualified individuals meet engineering design and regulatory requirements. In step S120, the initial population individuals are randomly generated within the constraints of the optimization variables using the GA. The cross-sectional dimensions, wall thickness and reinforcement ratio of each individual in the population are checked for constraints. The verification content includes mechanical properties, material strength, geometric dimensions and stability requirements. Individuals that meet the constraints are selected as qualified populations for subsequent optimization operations.

[0052] 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 following schematically illustrates a framework diagram of the subject optimization strategy according to some embodiments of the present disclosure. Figure 3 As shown, the optimization calculation begins its initialization phase by randomly generating individuals from a population that meets the initial conditions. The target policy variable for each individual in the initial population is randomly generated within the constraints of the optimization variable. Each individual is then individually checked against the constraints to select those that meet the basic design rules. This ensures sufficient diversity and quality in the initial population, providing a reliable foundation for subsequent iterations.

[0053] After entering the iterative process, the fitness value of each individual in the current population is calculated. This value is calculated based on the optimization objective function and is used to evaluate the individual's performance in the current search space. The fitness value is then used to determine whether the population meets the termination criteria. Termination criteria include the number of iterations reaching a preset upper limit or the population's fitness value converging to the target range. If the termination criteria are met, the optimization process ends and the optimization results are output; if not, the next step is performed.

[0054] If the termination condition is not met, individuals with high fitness are sorted by fitness to generate Population A. This process retains individuals with outstanding performance within the current population, improving the overall quality of the population and enhancing optimization efficiency. Subsequently, a crossover operation is performed on randomly selected pairs of individuals from Population A. The target policy variables are recombined according to the set crossover ratio parameter to generate Population B. The crossover operation broadens the search space by recombining variable values, helping to explore more potential solutions.

[0055] After the crossover operation is completed, 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 new search directions, increases the diversity of the population, and combines the verification of constraint conditions to ensure that the generated mutated 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 gradually evolve, so that the population fitness gradually approaches the global optimal region. When the termination condition is reached, the optimization process ends and the final result is output. Through this method, 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, providing a reliable optimization solution for the engineering design or application of cylindrical heat absorption towers.

[0056] 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; and optimization variable values ​​of cross-sectional size, wall thickness and reinforcement ratio are assigned to each population individual to form an initial population individual.

[0057] The uniform distribution principle represents a method for randomly generating numerical values, where each possible value within a given range has the same probability of generation. This principle, when applied to the random generation of optimization variables, ensures that the generated population individuals are evenly distributed within the design space, avoiding excessive concentration in local areas. The optimized variable values ​​represent key parameters that describe the structural characteristics of the initial population individuals, specifically cross-sectional dimensions, wall thickness, and reinforcement ratio. These optimized variable values ​​are randomly determined during initial generation based on the uniform distribution principle, and their range of values ​​is determined by the constraints of the optimization variables to ensure that they meet design specifications and engineering requirements.

[0058] Specifically, when generating individuals in the initial population, the decision variable value range for each individual is first defined based on the definition of the optimization variables to ensure a reasonable distribution of the initial population within the design space. When generating individuals in the population, variable values ​​are randomly sampled according to the principle of uniform distribution. Specifically, each variable is generated with equal probability within its defined upper and lower limits, and the generated variable values ​​are combined to form an individual. To ensure that each individual meets the design constraints, each generated individual is subjected to constraint verification. This constraint verification includes checking the individual's geometric parameters, mechanical properties, and material strength. The main verification criteria are whether the cross-sectional dimensions meet the geometric design requirements, whether the wall thickness meets the load-bearing capacity requirements, whether the reinforcement ratio meets the resistance requirements, and whether the variable values ​​fall within the optimization range. If an individual fails the verification, it is discarded and regenerated until the number of generated individuals reaches the preset value. Once an individual passes the constraint verification, it is assigned specific optimization variable values. The cross-sectional dimensions, wall thickness, and reinforcement ratio values ​​of each individual are determined by the randomly generated variable values, thus forming a complete initial population.

[0059] 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 following schematically illustrates a flow chart of generating initial population individuals that meet the constraints according to some embodiments of the present disclosure. Figure 4 As shown, the current individual is first numbered. Starting with the first individual, population individuals are generated sequentially by number. During the generation process, the height distribution range of the concrete tower segments is clearly defined. This range is defined based on the tower segment design requirements and segmentation principles. This ensures clear upper and lower limits for tower geometric parameters such as cross-sectional dimensions, wall thickness, and reinforcement ratio, providing the foundation for subsequent random generation.

[0060] 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 consists 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 will be 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.

[0061] In some embodiments, the optimization variables corresponding to the initial population individuals are checked for constraints to screen out qualified individuals, specifically including the following technical steps: performing calibration calculations on the optimization variables of the initial population individuals to determine the calibration results corresponding to the mechanical properties, material strength, and geometric dimensions; comparing the calibration results with the constraints, and screening the population individuals that meet the constraints as qualified individuals.

[0062] Specifically, first, the mechanical properties, material strength, and geometric dimensions of the initial population individuals are verified. 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 illustrates a flow chart of checking constraint conditions according to some embodiments of the present disclosure. Figure 5 As shown, cross-sectional data is first generated. Based on the set range of 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 serves as the input of the verification process and provides the basis for subsequent geometric and mechanical calculations. After the cross-sectional data is generated, a dimensional check is performed to ensure that the cross-sectional dimensions meet the design specifications and engineering requirements. The dimensional check includes 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 dimensional check are used to screen out individuals that do not meet the conditions to prevent invalid solutions from entering the subsequent calculation links.

[0063] For 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. Based on 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 is used to evaluate 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.

[0064] 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.

[0065] The crossover rule represents a genetic algorithm-based operation method that generates individuals with new characteristics by exchanging and recombining the optimized variables of two or more qualified individuals. The mutation rule represents a genetic algorithm operation method with a strong randomness, which generates new variable values ​​by making small random adjustments to the optimized variables of qualified individuals, thus forming individuals with completely new characteristics.

[0066] 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 the preset mutation ratio to generate new individuals after mutation.

[0067] Specifically, the optimization variables of qualified individuals are adjusted based on crossover and mutation rules, and new individuals are generated through parameter recombination and random mutation to enhance the diversity and optimization capability of the population. First, pairs of individuals are randomly selected from qualified individuals. This selection is based on the principle of randomness, ensuring that the resulting crossover individuals inherit characteristics from different individuals, thereby covering a wider search space. The selected pairs are composed of their optimized variables, which serve as the input for the subsequent crossover operation. After the random selection of the individual pairs, the parameters of the optimized variables of the individual pairs are combined according to the crossover rules. The crossover rules divide the optimized variables of the individual pairs into multiple segments based on preset crossover points and recombine the variable segments according to the rules. The crossover operation integrates the characteristics of different individuals to generate crossover individuals with newly combined characteristics. Through the crossover operation, the crossover individuals retain the excellent characteristics of the parent individuals while also possessing new variable combinations, introducing more possibilities for population evolution.

[0068] For the generated crossover individuals, their optimization variables are randomly adjusted according to a preset mutation ratio. The mutation operation introduces a moderate amount of randomness and uncertainty by randomly perturbing the optimization variables. Mutation operates within the permitted range of the variables to ensure that the mutated individuals still meet the design requirements and constraints. Random mutation effectively prevents the population from being trapped in local optimal solutions, enhances global search capabilities, and thus further improves optimization results. Through these steps, the generated mutated individuals combine the inherited properties of the crossover operation with the randomness of the mutation operation, retaining the excellent characteristics of the parent generation while introducing new variable adjustment directions.

[0069] Figure 6 The schematic diagram of the framework of the crossover and mutation strategy rules according to some embodiments of the present disclosure is shown schematically. Figure 6As shown in the figure, within the framework of the crossover and mutation rules, the average fitness of all individuals in the population and the fitness of each individual are first calculated. This is used to evaluate population performance and provide a basis for subsequent crossover and mutation operations. This fitness analysis can identify high-quality individuals within the population, providing high-quality parent selection for crossover operations and improving the quality of new individuals generated. Based on the analysis results, crossover and mutation ratio parameters and crossover number requirements are set to define the specific operation rules and ranges. The crossover ratio parameter controls the degree of recombination of 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 maintain population diversity.

[0070] After the rules are set, intersection individuals are randomly selected from the population and their constraints are checked. The selected individuals must meet the established 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 verification is repeated until it meets the requirements.

[0071] Next, a crossover operation is performed on the intersection individuals that meet the constraints. The crossover process combines variables according to the set crossover rules to generate new individuals after crossover. After the new individuals are generated, a constraint check is performed to confirm whether they meet the design requirements. If the new individuals do not meet the constraints, a new intersection individual is selected and a new crossover operation is performed according to the rules. For crossover individuals that pass the constraint check, the variables are further randomly adjusted according to the preset mutation ratio 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. For example, 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 are subject to a constraint check to confirm that their variable values ​​are within the set range and meet the basic design specifications. If the mutated new individuals meet the constraints, they are recorded as members of the new population after crossover and mutation. If they do not meet the constraints, the crossover and mutation individuals are randomly selected and the operation is repeated.

[0072] 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 iteratively optimized until the optimization variables converge to the optimal solution that meets the design requirements.

[0073] The fitness value represents a numerical indicator that measures the performance of an individual in the optimization objective function, directly reflecting its performance within the current optimization environment. In the heat sink optimization objective function, the fitness value is correlated with the tower's structural performance, material efficiency, and compliance with design specifications. Control parameters represent key variables influencing the intensity and frequency of crossover and mutation operations in a genetic algorithm, and are used to dynamically adjust the search scope and population diversity during the optimization process.

[0074] Furthermore, in step S140, first, the fitness value of each new individual is calculated. The fitness value is the objective function The quantitative representation of is used to measure the degree of individual 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 total fitness and the relative fitness value of each individual , clarifying the relative merits and demerits of individuals within a population. Based on relative fitness, a random number between 0.01 and 1 is generated. Combining this random number with the fitness distribution, individuals with higher fitness values ​​are selected as the basis for subsequent crossover and mutation. This selection mechanism ensures that individuals with high fitness participate in the evolutionary process first, while retaining a certain degree of randomness to enhance population diversity.

[0075] After selecting individuals with high fitness, their parameters are adjusted according to crossover and mutation rules. The crossover operation generates new individuals by combining the decision variables (such as cross-sectional diameter, wall thickness, slope, and reinforcement ratio) of different individuals. These individuals are then subjected to constraint checks to ensure they meet geometric, mechanical, and material strength requirements. If a new individual fails these checks, it is regenerated and its crossover parameters adjusted. For individuals that pass the crossover check, their decision variables are further adjusted according to the mutation ratio, introducing small random perturbations to expand the search space and avoid falling into local optima. The mutated individuals are also subject to constraint checks to ensure their rationality. Through these crossover and mutation operations, the generated population continuously optimizes its performance in the optimization target space. After each round of iteration, the control parameters for crossover and mutation are dynamically adjusted based on the overall fitness of the population. For example, the mutation ratio can be appropriately increased in the early stages of optimization to expand the search range, while it can be reduced in the later stages to accelerate convergence. Through multiple rounds of iterative optimization, the population gradually approaches the global optimal solution, ultimately achieving convergence of the optimization variables and meeting all requirements for the heat sink design.

[0076] In some embodiments, the fitness value of the new individual is calculated based on 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 iteratively optimized until the optimization variables converge to the optimal solution that meets the design requirements. The specific technical steps include: performing fitness calculation on the optimization variables corresponding to the new individual according to the optimization objective function to determine the fitness value corresponding to each new individual; dynamically adjusting the crossover ratio and mutation ratio according to the fitness value to update the control parameters of the crossover and mutation rules; repeating the fitness calculation, parameter adjustment and population update operations until the optimization variables converge to the optimal solution that meets the design requirements after multiple iterations.

[0077] Specifically, the fitness of the optimization variables of each new individual is first calculated based on the optimization objective function. The fitness value quantifies the individual's performance 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. This fitness calculation assesses the individual's performance within the current population, providing 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 generation's variable recombination, while the mutation ratio controls the magnitude of the random perturbation of the variables. Based on the fitness distribution, the crossover ratio is initially high to expand the search space, while the mutation ratio is high to introduce diversity. Later in the optimization process, the mutation ratio is gradually reduced to accelerate convergence and enhance optimization efficiency. The adjusted crossover and mutation parameters are then used to 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 population fitness continuously improves with each iteration. Fitness calculation selects high-quality individuals, and combined with dynamic parameter adjustment to expand the search space, the optimization variables gradually converge after multiple iterations. Ultimately, the convergence of the optimization variables makes the individuals in the population meet the design requirements of the heat absorption tower.

[0078] 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:

[0079] 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:

[0080]

[0081] 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 bottom diameter of the tower. 、 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 the i The fitness value corresponding to each new individual is Indicates the i The objective function value corresponding to each new individual is calculated. This optimization objective function quantitatively evaluates the comprehensive cost of the design scheme by taking the weighted sum of the costs of concrete and steel materials. By minimizing the objective function value, the optimal solution with the most economical material use and performance that meets the design requirements is determined.

[0082] For example, taking the 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 optimization can be shown as follows. Figure 7 、 Figure 8 and Figure 9 shown.

[0083] in, Figure 7 The figure shows the changes in the outer radius of the tower before and after optimization. The figure takes the tower height as the vertical axis and the tower outer diameter as the horizontal axis. Before optimization, the outer diameter of the tower changed greatly, the outer diameter distribution of each height section was uneven, and there was a problem of low material utilization efficiency; after optimization, the outer diameter of the tower 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 graph shows the change in tower wall thickness before and after optimization. The graph uses tower height as the vertical axis and wall thickness as the horizontal axis. Before optimization, the wall thickness distribution was irregular, with some height sections showing excessive thickness while others showing less thickness, potentially leading to uneven stress on the structure. After optimization, the wall thickness distribution more closely conforms to the laws of mechanics, gradually becoming thinner from bottom to top. The optimized wall thickness design significantly reduces material usage while maintaining load-bearing capacity, demonstrating the economy and rationality of the structural design. Figure 9The figure shows the change in the tower's reinforcement area before and after optimization. The figure plots tower height on the vertical axis and reinforcement area on the horizontal axis. Before optimization, the distribution of reinforcement area was relatively discrete, with some areas experiencing excessive reinforcement and waste, while others were underreinforced, potentially compromising structural safety. After optimization, the reinforcement area changes more smoothly with tower height, more closely matching the load requirements. The optimized reinforcement design significantly improves material utilization efficiency and enhances the mechanical properties of the structure.

[0084] The structural design method of the cylindrical heat absorption tower in the embodiment of the present disclosure 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, ensuring 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 reducing the project cost and meeting 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 human 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 variation control parameters enable the algorithm to flexibly adapt to different optimization stages, further improving the scientificity and engineering applicability of the optimization results.

[0085] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one, and / or one step may be decomposed into multiple steps.

[0086] Next, in the embodiment of the present disclosure, a structural design optimization device for a cylindrical heat absorption tower is also provided. Figure 10As shown in , the structural design optimization device 1000 for a cylindrical heat absorbing tower can be composed of an optimization variable definition module 1001, a population generation and verification module 1002, a crossover and mutation module 1003, and a fitness iteration module 1004. Specifically, the optimization variable definition module 1001 can be used to determine the optimization variables and constraints of the structural characteristics of the cylindrical heat absorbing tower according to the structural design requirements of the cylindrical heat absorbing tower; the population generation and verification module 1002 can be used to randomly generate initial population individuals within the restricted range of the optimization variables according to a genetic algorithm, perform constraint 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 individuals according to the optimization objective function of the heat absorbing 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.

[0087] 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.

[0088] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned structural design optimization method of the cylindrical heat absorption tower is also provided.

[0089] 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 implemented in the following forms: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0090] Refer to the following Figure 11 1100 according to an embodiment of the present disclosure is described. Figure 11 The electronic device 1100 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0091] like Figure 11As shown, electronic device 1100 is implemented as a general-purpose computing device. Components of electronic device 1100 may include, but are not limited to, the aforementioned at least one processing unit 1110, the aforementioned at least one storage unit 1120, a bus 1130 connecting various system components (including storage unit 1120 and processing unit 1110), and a display unit 1140.

[0092] 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.

[0093] The storage unit 1120 may include a readable medium in the form of a volatile memory 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 .

[0094] 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.

[0095] The bus 1130 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0096] Electronic device 1100 can also communicate with one or more external devices 1170 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1100, and / or any device that enables electronic device 1100 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via input / output (I / O) interface 1150. Furthermore, electronic device 1100 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via network adapter 1160. As shown, network adapter 1160 communicates with other modules of electronic device 1100 via bus 1130. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with 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.

[0097] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the exemplary embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes instructions for enabling a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0098] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section of this specification.

[0099] refer to Figure 12 As shown, a program product 1200 for implementing the structural design optimization method for a cylindrical heat absorption tower according to an embodiment of the present disclosure is described. This program product 1200 can be implemented in a portable compact disc read-only memory (CD-ROM) and include program code, and can be executed on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The program product may utilize 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, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having 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.

[0101] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program code contained on the readable medium can 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.

[0103] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone 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 can be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0104] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0105] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions to enable a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present disclosure.

[0106] Other embodiments of the present disclosure will readily occur to those skilled in the art 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 from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0107] 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 can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A structural design optimization method for 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; Randomly select individual pairs from the qualified individuals, combine the optimization variables of the individual pairs according to the crossover rule to generate crossover individuals; randomly adjust the optimization variables of the crossover individuals according to a preset mutation ratio to generate new individuals after mutation; Performing fitness calculation on the optimization variables corresponding to the new individual according to the optimization objective function to obtain the objective function value; Determining the fitness value corresponding to each of the new individuals according to the objective function value; 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; Repeating the fitness calculation, parameter adjustment, and population update operations until the optimization variables converge to an optimal solution that meets the design requirements after multiple iterations; Wherein, the optimization variables and constraints of the structural characteristics of the heat absorption tower are determined according to the structural design requirements of the cylindrical heat absorption tower, including: Dividing the heat absorption tower into a plurality of height segments, and using the cross-sectional size, wall thickness and reinforcement ratio of each height segment as the optimization variables; According to the load-bearing capacity, geometry 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 concrete tensile strength respectively. and Respectively represent the horizontal displacement of the tower top and the horizontal displacement limit, Indicates the i The outer diameter of the cross section, Indicates the i The wall thickness of each section, Indicates the minimum allowable inner diameter, Indicates the maximum allowable outer diameter, and Respectively represent the wall thickness of the concrete tower and the minimum limit of the wall thickness, and Respectively represent i Slope and slope limit for each height segment, and Respectively represent the diameter of the bottom section and the minimum diameter limit; 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.

2. 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 includes: Within the limited range of the optimization variables, randomly generate population individuals according to the uniform distribution principle; 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.

3. The structural design optimization method of a cylindrical heat absorption tower according to claim 1, characterized in that: The constraint condition checking of the optimization variables corresponding to the individuals in the initial population to screen out 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 that meet the constraint condition are screened as the qualified individuals.

4. A structural design optimization device for a cylindrical heat absorbing tower, used to implement the structural design optimization method for a cylindrical heat absorbing tower according to any one of claims 1 to 3, characterized in that: include: An optimization variable definition module is 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 and verification module is used to randomly generate initial population individuals within the restricted 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 is 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; Fitness iteration module, used for 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, the fitness value corresponding to each new individual is determined ,in, Indicates the i The fitness value corresponding to each new individual is Indicates the i The objective function value corresponding to the new individual; by calculating the sum of fitness and the relative fitness value of each individual , clarify the relative order of individuals in the population, randomly generate a random number between 0.01 and 1 based on the relative fitness value, and combine the random number with the distribution range of the fitness value to select individuals with high fitness values ​​as the basis for subsequent crossover and mutation; 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.

5. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the structural design optimization method of the cylindrical heat absorption tower according to any one of claims 1 to 3 by executing the executable instructions.

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