A Method and System for Optimizing the Jet Quality of Impact Turbines Based on the Crocodile Ambush Algorithm
The alligator ambush algorithm optimizes the jet quality of an impact turbine, solving the problem of low jet quality optimization efficiency. It achieves precise optimization of jet quality and improves energy conversion efficiency, making it suitable for automated design in complex engineering scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN UNIV OF TECH
- Filing Date
- 2026-04-21
- Publication Date
- 2026-05-26
AI Technical Summary
In the existing technology, the jet quality optimization method of impulse turbine has problems such as low efficiency of structural parameter optimization and insufficient optimization accuracy, resulting in jet quality that does not meet expectations and low energy conversion efficiency.
The alligator ambush algorithm is adopted. By determining the jet quality optimization parameters, an initial population is generated and initial energy is allocated. The movement distance of parameter combinations during iteration is calculated, the energy state is updated, the iteration direction is determined according to the probability of the objective function value, the jet quality fluctuation is monitored, the parameter position is updated by combining the guiding direction and random perturbation, the step size and perturbation coefficient are dynamically adjusted, and the global optimal solution is updated iteratively.
It achieves precise optimization of jet quality, improves the energy conversion efficiency of water turbines, reduces computing resources and time consumption, adapts to complex engineering scenarios, and the optimal parameter combination output can be directly applied to engineering design, reducing R&D and operation and maintenance costs.
Smart Images
Figure CN122087993A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent optimization and hydraulic machinery technology, specifically relating to a method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm. This invention also relates to a system for optimizing the jet quality of an impact turbine using the alligator ambush algorithm. Background Technology
[0002] Hydropower resources are a core component of clean energy systems. Impulse turbines, as the main equipment for hydropower development in high-drop basins, can efficiently convert the kinetic and potential energy of water, adapting to complex scenarios such as difficult dam construction and the absence of tailrace pipes. They also possess advantages such as minimal environmental impact and strong power supply stability. Jet quality is a core indicator determining the energy conversion efficiency of impulse turbines, directly affecting the work done by the water flow impacting the bucket and the stability of the runner operation. Optimizing jet quality design is crucial for improving unit power generation efficiency and extending the lifespan of flow components. However, jet quality is influenced by multiple structural parameters, including nozzle angle, needle angle, bifurcation angle, and the ratio of main pipe to branch pipe diameters. These parameters exhibit a highly nonlinear relationship, making it impossible to directly derive the precise mapping relationship between jet velocity distribution, energy loss, and design parameters. Traditional optimization methods rely on repeated simulations and calculations, resulting in low optimization efficiency and insufficient optimization accuracy, failing to meet the demands of efficient and precise engineering optimization.
[0003] The Crocodile Ambush Optimization Algorithm (CAOA) is a novel biomimetic intelligent optimization algorithm. This algorithm simulates the dynamic energy regulation and adaptive optimization behavior of crocodiles during their ambush predation process, possessing core advantages such as strong global optimization capability, fast convergence, and good stability. It can effectively handle complex engineering optimization problems involving multiple variables and strong nonlinearity. Currently, research on the application of this algorithm in the field of impulse turbines is relatively scarce. How to apply the Crocodile Ambush Optimization Algorithm to achieve jet quality-oriented structural parameter optimization, promote the integration of the algorithm with jet characteristic simulation analysis, and efficiently complete the precise optimization design of jet quality are key technical problems that urgently need to be solved. Summary of the Invention
[0004] The purpose of this invention is to provide a jet quality optimization method for impact turbines based on the alligator ambush algorithm, which solves the problems of low efficiency in optimizing structural parameters, insufficient optimization accuracy leading to unsatisfactory jet quality, and low energy conversion efficiency in existing jet quality optimization methods for impact turbines.
[0005] Another objective of this invention is to provide a jet quality optimization system for impact turbines based on the alligator ambush algorithm.
[0006] The technical solution adopted in this invention is a method for optimizing the jet quality of an impact turbine based on the alligator ambush algorithm, comprising:
[0007] Step 1: Determine the jet quality optimization parameters and their range, generate an initial population and allocate uniform initial energy, simulate and quantify the objective function value, and select the initial optimal individuals; Step 2: Calculate the distance the parameter combination moves during the iteration and update the energy, then perform deep development or reset according to the energy state; Step 3: Determine the direction of population iteration guidance based on the probability of the objective function value; Step 4: Monitor the quality fluctuation of the continuously iterated jet and adjust the trigger parameters according to the threshold; Step 5: Combine the guidance direction with the position of the random perturbation to update the parameters, and dynamically adjust the step size and perturbation coefficient; Step 6: Iterate and update the global optimal solution. When the termination condition is met, output the optimal combination of parameters for jet quality.
[0008] The invention is further characterized by: The jet quality optimization parameters include turbine structural parameters and operating parameters; turbine structural parameters include nozzle angle, needle angle, bifurcation angle, and the ratio of main pipe to branch pipe diameter; operating parameters include opening degree. The initial population was generated in the five-dimensional parameter space using a uniform sampling method. N The initial parameter combinations are grouped, with each parameter combination treated as an individual, as follows:
[0009] In the formula, Indicates the first i The individual j The dimension parameter can take the following values, where , ; , They represent the first j The lower and upper limits of the values that a dimension parameter can take; r A random number that follows a uniform distribution in the range [0,1].
[0010] In step 1, the jet quality objective function value is quantified through CFD simulation, and the calculation formula is as follows:
[0011] In the formula, Indicates the first i The objective function value of the jet quality for each individual. As the weight of the energy loss coefficient, As a weight for jet velocity uniformity, For the first i The jet energy loss coefficient corresponding to each individual. For the first i Uniformity of jet velocity for each individual.
[0012] Step 2 is as follows: First, the distance traveled using the Euclidean distance is calculated, expressed as:
[0013] In the formula, Indicates the first i The individual t The distance moved in the next iteration; n To optimize the parameter space dimension, ; For the first i The individual j Dimensional parameter t +1 iterations to obtain the value, For the first i The individual j Dimensional parameter t The value is obtained in the next iteration.
[0014] Secondly, based on the distance traveled using the parameter combination, the energy is updated according to the following formula:
[0015] In the formula, and and represent the first and second, respectively. i The individual in the first t +1st time and the first t Energy value at the next iteration Energy consumption coefficient; Subsequently, deep development or reset is performed based on the energy state, specifically as follows: like If so, it is determined that there is sufficient energy, and in-depth development will be carried out; like If the energy is exhausted, the energy will be reset to the initial energy, and the parameters will be randomly assigned to a new parameter region.
[0016] Step 3 specifically involves: First, calculate the number of... according to the following formula. i The probability that an individual is selected as the guiding direction:
[0017] In the formula, For the first i The probability that the guiding direction of an individual is selected. Let represent the jet quality objective function value for the i-th individual; Secondly, the probability of all individuals being selected as the guiding direction is normalized to obtain the normalized probability of each individual. Subsequently, a conformity is generated. The individual corresponding to the probability interval into which the random number falls is the guiding direction of the current iteration.
[0018] Step 4 specifically involves: First, calculate the jet mass fluctuation, expressed as:
[0019] In the formula, Indicates the first i The change in the objective function value of each individual in continuous iterations , They represent the first i The individual t +1st time and the first t The objective function value for the next iteration; Secondly, preset fluctuation threshold d According to the preset threshold d The trigger parameter is adjusted as follows: when When adjusting the parameter position: Optimize along the original direction; Optimize in the opposite direction of the original direction; when If necessary, continue exploring while maintaining the current parameter combination.
[0020] In step 5, update the parameter positions according to the following formula:
[0021] In the formula, For step size parameters, For the first t In the next iteration, the guiding direction is at the... j The values that can be taken on the dimension parameter, The disturbance coefficient is... To obey Uniformly distributed random numbers; and ; During the iterative optimization process, the step size parameter is dynamically adjusted according to the iteration stage. and disturbance coefficient Specifically: Reduce in the early stages of iteration ,improve This enhances the algorithm's global exploration capabilities and expands the parameter search range; Improvement in the later stages of iteration ,reduce This enhances the convergence ability of the algorithm and performs fine optimization on regions with high-quality parameters.
[0022] In step 6, the global optimal solution is updated according to the following formula:
[0023] In the formula, This represents the globally optimal combination of parameters. Represents the set of all parameter combinations in the population. This represents the combination of parameters that minimizes the objective function value, i.e., optimizes the jet quality. The termination condition is reaching the preset maximum number of iterations, or the improvement in the objective function value corresponding to the global optimal solution is less than a set threshold.
[0024] Another technical solution adopted in this invention is an impact turbine jet quality optimization system based on the alligator ambush algorithm, which is used to implement the impact turbine jet quality optimization method based on the alligator ambush algorithm. It includes a parameter initialization module, an energy dynamic control module, a guidance direction dynamic screening module, a search behavior adaptive adjustment module, an exploration and development balance module, and an iterative optimization and result output module that operate in sequence.
[0025] Another feature of the technical solution of the present invention is that: The parameter initialization module generates an initial population containing N individuals in the jet quality optimization parameter space. Each individual corresponds to a combination of turbine structural parameters and operating parameters and is assigned initial energy. The fitness of the individuals is evaluated by the jet quality objective function value. The energy dynamic management module uniformly allocates initial energy, quantifies the individual's movement distance using Euclidean distance, and consumes energy proportionally. When energy is sufficient, it deeply develops high-quality parameter areas; when energy is depleted, it resets energy and explores new areas. The guidance direction dynamic screening module uses the jet quality objective function value as the criterion to probabilistically select the guidance direction, thus avoiding premature convergence of the algorithm. The search behavior adaptive adjustment module quantifies the jet quality fluctuations during continuous iterations. If the threshold is exceeded, the parameter position is adjusted to accelerate convergence; otherwise, the exploration state is maintained. The exploration and development balance module combines deterministic guidance with random perturbation to update parameters, balancing fine-grained optimization with global search; The iterative optimization and result output module iterates and dynamically updates the global optimal solution, and outputs the optimal parameter combination when the termination condition is met.
[0026] The beneficial effects of this invention are: 1. The alligator ambush algorithm-based jet quality optimization method for impact turbines of this invention utilizes the algorithm's dynamic energy control mechanism, combined with Euclidean distance quantification of parameter movement costs, to efficiently balance the exploration and development of the parameter space. This avoids redundant operations of repeated simulation verification in traditional optimization, significantly reducing computational resources and time consumption. Simultaneously, relying on the algorithm's strong global optimization capability, it accurately captures the optimal parameter combination under the coupling of multiple factors such as nozzle structure and operating conditions. Combined with Flunet simulation verification, it effectively improves jet velocity uniformity, reduces energy loss, achieves precise jet quality optimization, and thus improves the turbine's energy conversion efficiency.
[0027] 2. This invention can be flexibly adapted to complex engineering scenarios such as high drop and no tailrace pipe, and has a wide range of applications; the optimization process is highly automated, and the output optimal parameter combination can be directly connected to actual engineering design, effectively reducing R&D and operation and maintenance costs, providing reliable technical support for the efficient and stable operation of impulse turbines, and has good engineering application prospects. Attached Figure Description
[0028] Figure 1 The flowchart shows the jet quality optimization method for impact turbines based on the alligator ambush algorithm of the present invention. Figure 2 This is a schematic diagram of the structure of the impact turbine jet quality optimization system based on the alligator ambush algorithm of the present invention. Detailed Implementation
[0029] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.
[0030] Example 1 This embodiment provides a method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm, such as... Figure 1 As shown, the specific steps include the following: Step 1: Determine the jet quality optimization parameters and their range, generate an initial population and allocate uniform initial energy, simulate and quantify the objective function value, and select the initial optimal individuals; Step 2: Calculate the distance the parameter combination moves during the iteration and update the energy, then perform deep development or reset according to the energy state; Step 3: Determine the direction of population iteration guidance based on the probability of the objective function value; Step 4: Monitor the quality fluctuation of the continuously iterated jet and adjust the trigger parameters according to the threshold; Step 5: Combine the guidance direction with the position of the random perturbation to update the parameters, and dynamically adjust the step size and perturbation coefficient; Step 6: Iterate and update the global optimal solution. When the termination condition is met, output the optimal combination of parameters for jet quality.
[0031] Example 2 In step 1, the jet quality optimization parameters include turbine structural parameters and operating parameters; the turbine structural parameters include nozzle angle, nozzle angle, bifurcation angle, and the ratio of main pipe diameter to branch pipe diameter; the operating parameters include the opening degree. The initial population was generated in the five-dimensional parameter space using a uniform sampling method. N The initial parameter combination (i.e., N individual crocodiles, each corresponding to a complete set of jet quality optimization parameters) is represented as follows:
[0032] In the formula, Indicates the first i The individual j The dimension parameter can take the following values, where , ; , They represent the first j The lower and upper limits of the values that a dimension parameter can take; r A random number that follows a uniform distribution in the range [0,1].
[0033] The jet quality objective function value is quantified using CFD simulation and used as the fitness of all individuals. The specific calculation formula is as follows:
[0034] In the formula, Indicates the first i The objective function value of the jet quality for each individual. As the weight of the energy loss coefficient, The weighting factor is the uniformity of the jet velocity. The weighting coefficient can be optimized and adjusted according to the actual engineering situation. For the first i The jet energy loss coefficient corresponding to each individual ( The smaller the value, the less energy is lost from the jet. For the first i Uniformity of jet velocity for each individual ( The closer it is to 1, the more uniform the jet velocity.
[0035] The initial jet quality objective function value for all parameter combinations (individual crocodiles) is calculated using the formula above, and the individual with the optimal jet quality is evaluated as the guiding direction, whose position will serve as the search benchmark for subsequent iterations.
[0036] In the method of this invention, energy dynamic simulation is the core mechanism for exploring and developing the jet mass parameter space, derived from the energy conservation and energy consumption (exploration budget consumption) characteristics of crocodiles during ambush and waiting in nature. To ensure consistent initial states in the algorithm, all parameter combinations (individual crocodiles) representing jet mass parameter combinations are assigned the same initial energy level during initialization, the mathematical expression of which is:
[0037] In the formula, This is a preset constant, representing the initial energy level of each parameter combination (individual crocodile); N This represents the total number of crocodiles in the population that represent the parameter combinations. This uniform initialization ensures that all individuals with the same parameter combinations start with the same exploration potential, allowing the algorithm to evenly distribute search resources within the parameter space optimized for jet quality, thus avoiding search bias in the initial stages.
[0038] Example 3 Step 2 is as follows: First, the distance traveled by the parameter combination (individual crocodile) is calculated using Euclidean distance, expressed as:
[0039] In the formula, Indicates the first i The individual t The distance moved in the next iteration; n To optimize the parameter space dimension, ; For the first i The individual j Dimensional parameter t +1 iterations to obtain the value, For the first i The individual j Dimensional parameter t The value is obtained in the next iteration.
[0040] The parameter combination (individual crocodile) moves within the jet quality optimization parameter space to explore and develop the target area. This movement process incurs energy consumption proportional to the distance traveled (exploration budget consumption). Therefore, based on the distance traveled by the parameter combination, the energy is updated according to the following formula:
[0041] In the formula, and and represent the first and second, respectively. i The individual in the first t +1st time and the first t Energy value at the next iteration This is the energy consumption coefficient, also known as the exploration budget consumption coefficient, used to control the energy consumed per unit distance traveled. Euclidean distance accurately reflects the overall displacement of the parameter combination individual across all dimensions, ensuring that longer travel distances result in higher energy consumption.
[0042] Subsequently, the process of performing deep development or reset based on energy state specifically involves: like If the energy is sufficient, then in-depth development will be carried out; that is, during the iteration process, the parameter combination with sufficient preset exploration budget will be finely optimized in the range of jet quality parameters that are currently performing well.
[0043] like If the energy is exhausted, the energy will be reset to the initial energy, and the parameters will be randomly assigned to a new parameter region (a region in the parameter space that has not been explored or has been explored less).
[0044] In this step, to accurately measure the adjustment range of the parameter combination during the iteration process, Euclidean distance is introduced as a quantitative indicator of the individual movement distance in the parameter space. Euclidean distance can comprehensively reflect the overall displacement of the parameter combination in all dimensions, ensuring that the longer the movement distance, the higher the energy consumption (exploration budget consumption). Through this mechanism, the algorithm can achieve a balance between the parameter range with better jet quality performance and the potential optimization region, ensuring both the diversity of the search and continuous convergence towards the optimal solution for jet quality.
[0045] Example 4 In step 3, each parameter combination (individual crocodile) representing a combination of jet quality parameters will be assigned a fitness value. Evaluating the merits of the current parameter combination—this fitness value quantifies the jet quality (e.g., energy loss, velocity uniformity) corresponding to this parameter set. A better fitness value (i.e., lower...) is preferred. ， Parameter combinations that correspond to better jet quality will have a greater probability of influencing the search direction of the population and being selected as the guiding direction, thereby guiding other parameter combinations to move towards potential areas with better jet quality. Specifically, First, calculate the number of... according to the following formula. i The probability that an individual is selected as the guiding direction:
[0046] In the formula, For the first i The probability that the guiding direction of an individual is selected. Let represent the jet quality objective function value for the i-th individual. This expression ensures that parameter combinations with lower objective function values (i.e., better jet quality) have a higher probability of being selected.
[0047] Secondly, the probability of all individuals being selected as the guiding direction is normalized to obtain the normalized probability of each individual. Subsequently, a conformity is generated. The individual corresponding to the probability interval into which the random number falls is the guiding direction of the current iteration.
[0048] The probabilistic selection mechanism in this step effectively avoids the algorithm from always focusing only on the current optimal solution, thereby maintaining the diversity of parameter search and preventing premature convergence. At the same time, it maintains the diversity of the population, ensuring that the algorithm can continuously explore the global optimal solution for jet quality.
[0049] In step 4, drawing inspiration from the crocodile's ability to dynamically adjust its hunting strategy based on environmental changes, the algorithm can adaptively adjust the search behavior of parameter combinations according to the dynamic changes in jet quality. By comparing the jet quality objective function values across consecutive iterations, the magnitude and direction of quality changes are quantified, thereby determining the optimization effect of the current parameter combination. Specifically, First, calculate the jet mass fluctuation, expressed as:
[0050] In the formula, Indicates the first i The change in the objective function value of each individual in continuous iterations , They represent the first i The individual t +1st time and the first t The objective function value for the next iteration; Secondly, preset fluctuation threshold d According to the preset threshold d The specific adjustments to the position of the trigger parameter combination are as follows: when At that time, the position of the trigger parameter combination is adjusted to accelerate convergence to a better region: Optimize along the original direction, that is, continue to update the parameter combination along the current search direction with the goal of jet quality optimization as the guide; Optimize in the opposite direction of the original direction, that is, update the parameter combination in the opposite direction of the current search direction.
[0051] when At the same time, the current parameter combination is maintained to continue the exploration, avoiding ineffective adjustments, thereby achieving a balance between rapid convergence and stable exploration, and improving optimization efficiency and stability.
[0052] Example 5 In step 5, update the parameter positions according to the following formula:
[0053] In the formula, The step size parameter controls the degree of influence of the guiding direction and determines the convergence speed of the high-quality region of the parameter combination. For the first t In the next iteration, the guiding direction is at the... j The values that can be taken on the dimension parameter; The disturbance coefficient is... To obey Uniformly distributed random numbers, and Together, they provide random exploration capabilities, preventing the algorithm from getting trapped in local optima; and ; During the iterative optimization process, the step size parameter is dynamically adjusted according to the iteration stage. and disturbance coefficient Specifically: Reduce in the early stages of iteration ,improve This enhances the algorithm's global exploration capabilities and expands the parameter search range; Improvement in the later stages of iteration ,reduce This enhances the convergence ability of the algorithm and performs fine optimization on regions with high-quality parameters.
[0054] This step combines deterministic guidance with random perturbation. This mechanism ensures that the parameter combination converges quickly to the current optimal jet quality region while maintaining the ability to explore potential high-quality regions. This achieves a dynamic balance between development and exploration, improving the algorithm's global optimization performance and stability.
[0055] Example 6 Step 6 involves a pre-set number of iterations, each iteration representing a complete cycle of fine-tuning the current high-quality parameter region and a global search for new potential parameter regions. This ensures that all parameter combinations are systematically searched in the parameter space, gradually approaching the global optimum. In each iteration, the jet quality of all parameter combinations is continuously evaluated, and the current globally optimal parameter combination is dynamically updated, ensuring that the algorithm always converges towards a higher jet quality.
[0056] Specifically, the global optimal solution is updated according to the following formula:
[0057] In the formula, This represents the globally optimal combination of parameters. Represents the set of all parameter combinations in the population. This represents the combination of parameters that minimizes the objective function value, i.e., optimizes the jet quality. When the algorithm reaches the preset maximum number of iterations, or the improvement in the jet quality objective function value corresponding to the global optimal solution is less than a set threshold (e.g. When the algorithm reaches a certain threshold, it automatically terminates and outputs the final optimal parameter combination, ensuring both optimization effectiveness and avoiding unnecessary waste of computational resources. The parameter combination output by the algorithm of this invention can be directly used to guide the engineering design of the nozzle structure of impulse turbines.
[0058] Example 7 This embodiment provides a jet quality optimization system for an impact turbine based on the alligator ambush algorithm, used to implement the aforementioned jet quality optimization method for an impact turbine based on the alligator ambush algorithm. The system structure is as follows: Figure 2 As shown, it includes a parameter initialization module, an energy dynamic management module, a guidance direction dynamic screening module, a search behavior adaptive adjustment module, an exploration and development balance module, and an iterative optimization and result output module that work in sequence and in tandem.
[0059] Specifically, the parameter initialization module generates an initial population containing N individuals in the jet quality optimization parameter space. Each individual corresponds to a combination of turbine structural parameters and operating condition parameters and is assigned initial energy. The fitness of the individuals is evaluated through the jet quality objective function value. The energy dynamic management module uniformly allocates initial energy to all individuals (parameter combinations), quantifies the individual's movement distance using Euclidean distance, and consumes energy proportionally. When energy is sufficient, it deeply develops high-quality parameter areas; when energy is depleted, it resets energy and explores new areas. The dynamic filtering module for guiding direction uses the jet quality objective function value as the criterion to probabilistically select the guiding direction, thus avoiding premature convergence of the algorithm. The adaptive adjustment module for search behavior quantifies the jet quality fluctuations during continuous iterations. If the fluctuation exceeds a threshold, the parameter position is adjusted to accelerate convergence; otherwise, the exploration state is maintained. The exploration and development balancing module combines deterministic guidance with random perturbation to update parameters, balancing fine-grained optimization with global search. The iterative optimization and result output module iterates and dynamically updates the global optimal solution, and outputs the optimal parameter combination when the termination condition is met.
[0060] Example 8 To verify the effectiveness of the method of the present invention, this embodiment selects key influencing parameters of the jet quality of the impulse turbine as optimization variables, including two main categories: turbine structural parameters and operating condition parameters, as follows: Nozzle structural parameters: nozzle angle c (Value range 68°~92°), nozzle angle e (Value range 42.5°~57.5°), bifurcation angle i (Value range 56°~84°), supervisorD With branch pipe diameter d Ratio (range 1.092~1.508); Operating condition parameters: different opening degrees 'a' (range 35~65%). See Table 1 below for details: Table 1 Variable Parameter Table
[0061] Generate within the defined five-dimensional parameter search space N =100 initial parameter combinations (i.e., 100 individual crocodiles), each individual corresponding to a complete set of jet quality optimization parameters.
[0062] Assign the same initial energy value of 100 to all parameter combinations in the initial population.
[0063] The jet quality objective function value was quantified using CFD simulation, and the formula was analyzed. , Assign values of 0.6 and 0.4 respectively.
[0064] Energy updates during the movement of parameter combinations within the parameter space are achieved by taking the energy dissipation coefficient. =0.8.
[0065] In step 4, a preset jet quality fluctuation threshold is set. δ= 0.01; In step 5, the step size parameter =0.6, disturbance coefficient =0.4; In the initial iteration (first 30 rounds), reduce the step size parameter. =0.4, increase =0.6; In the later stages of iteration (after 30 rounds), improve =0.7, decrease =0.3.
[0066] Preset maximum number of iterations T =100, follow the steps and iterate repeatedly. The threshold for the improvement of the jet quality objective function value corresponding to the global optimal solution is set to... This means that the improvement in jet quality tends to stagnate.
[0067] The algorithm of this invention outputs the final globally optimal parameter combination, which includes: Turbine structural parameters: nozzle angle 72.92°, needle angle 52.26°, bifurcation angle 61.44°, main pipe to branch pipe diameter ratio 1.267; Operating parameters: opening degree 57.59%.
[0068] Compared with the initial design parameters (see Table 1), the parameter combination optimized by the method of this invention improves jet velocity uniformity by approximately 4.1%, reduces flow energy loss by approximately 15.5%, reduces jet geometric deviation by approximately 52.3%, and improves turbine energy conversion efficiency by approximately 4.8%. It can be directly used to guide the engineering design and operational parameter debugging of impingement turbine nozzle structures.
Claims
1. A method for optimizing the jet quality of an impact turbine based on the alligator ambush algorithm, characterized in that, include: Step 1: Determine the jet quality optimization parameters and their range, generate an initial population and allocate uniform initial energy, simulate and quantify the objective function value, and select the initial optimal individuals; Step 2: Calculate the distance the parameter combination moves during the iteration and update the energy, then perform deep development or reset according to the energy state; Step 3: Determine the direction of population iteration guidance based on the probability of the objective function value; Step 4: Monitor the quality fluctuation of the continuously iterated jet and adjust the trigger parameters according to the threshold; Step 5: Combine the guidance direction with the position of the random perturbation to update the parameters, and dynamically adjust the step size and perturbation coefficient; Step 6: Iterate and update the global optimal solution. When the termination condition is met, output the optimal combination of parameters for jet quality.
2. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 1, characterized in that, The jet quality optimization parameters include turbine structural parameters and operating condition parameters; the turbine structural parameters include nozzle angle, needle angle, bifurcation angle, and the ratio of main pipe to branch pipe diameter; the operating condition parameters include opening degree. The initial population was generated in the five-dimensional parameter space using a uniform sampling method. N The initial parameter combinations are grouped, with each parameter combination treated as an individual, as follows: In the formula, Indicates the first i The individual j The dimension parameter can take the following values, where , ; , They represent the first j The lower and upper limits of the values that a dimension parameter can take; r A random number that follows a uniform distribution in [0,1].
3. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 1, characterized in that, In step 1, the jet quality objective function value is quantified through CFD simulation, and the calculation formula is as follows: In the formula, Indicates the first i The objective function value of the jet quality for each individual. As the weight of the energy loss coefficient, As a weight for jet velocity uniformity, For the first i The jet energy loss coefficient corresponding to each individual. For the first i Uniformity of jet velocity for each individual.
4. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 2, characterized in that, Step 2 is as follows: First, the distance traveled using the Euclidean distance is calculated, expressed as: In the formula, Indicates the first i The individual t The distance moved in the next iteration; n To optimize the parameter space dimension, ; For the first i The individual j Dimensional parameter t +1 iterations to obtain the value, For the first i The individual j Dimensional parameter t The value is obtained in the next iteration; Secondly, based on the distance traveled using the parameter combination, the energy is updated according to the following formula: In the formula, and and represent the first and second, respectively. i The individual in the first t +1st time and the first t Energy value at the next iteration Energy consumption coefficient; Subsequently, the process of performing deep development or reset based on energy state specifically involves: like If the energy is sufficient, then proceed with in-depth development. like If the energy is exhausted, the energy will be reset to the initial energy, and the parameters will be randomly assigned to a new parameter region.
5. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 1, characterized in that, Step 3 specifically involves: First, calculate the number of... according to the following formula. i The probability that an individual is selected as the guiding direction: In the formula, For the first i The probability that the guiding direction of an individual is selected. Let represent the jet quality objective function value for the i-th individual; Secondly, the probability of all individuals being selected as the guiding direction is normalized to obtain the normalized probability of each individual. Subsequently, a conformity is generated. The individual corresponding to the probability interval into which the random number falls is the guiding direction of the current iteration.
6. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 1, characterized in that, Step 4 is as follows: First, calculate the jet mass fluctuation, expressed as: In the formula, Indicates the first i The change in the objective function value of each individual in continuous iterations , They represent the first i The individual t +1st time and the first t The objective function value for the next iteration; Secondly, preset fluctuation threshold δ According to the preset threshold δ The trigger parameter is adjusted as follows: when When adjusting the parameter position: Optimize along the original direction; Optimize in the opposite direction of the original direction; when If necessary, continue exploring while maintaining the current parameter combination.
7. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 4, characterized in that, In step 5, update the parameter positions according to the following formula: In the formula, For step size parameters, For the first t In the next iteration, the guiding direction is at the... j The values that can be taken on the dimension parameter, The disturbance coefficient is... To obey Uniformly distributed random numbers; and ; During the iterative optimization process, the step size parameter is dynamically adjusted according to the iteration stage. and disturbance coefficient Specifically: Reduce in the early stages of iteration ,improve This enhances the algorithm's global exploration capabilities and expands the parameter search range; Improvement in the later stages of iteration ,reduce This enhances the convergence ability of the algorithm and performs fine optimization on regions with high-quality parameters.
8. The method for optimizing the jet quality of an impact turbine using the alligator ambush algorithm according to claim 4, characterized in that, In step 6, the global optimal solution is updated according to the following formula: In the formula, This represents the globally optimal combination of parameters. Represents the set of all parameter combinations in the population. This represents the combination of parameters that minimizes the objective function value, i.e., optimizes the jet quality. The termination condition is reaching the preset maximum number of iterations, or the improvement in the objective function value corresponding to the global optimal solution is less than a set threshold.
9. A jet quality optimization system for an impact turbine using the alligator ambush algorithm, used to implement the jet quality optimization method for an impact turbine using the alligator ambush algorithm as described in any one of claims 1 to 8, characterized in that, It includes a parameter initialization module, an energy dynamic management module, a guidance direction dynamic screening module, a search behavior adaptive adjustment module, an exploration and development balance module, and an iterative optimization and result output module that work in sequence.
10. The impact turbine jet quality optimization system based on the alligator ambush algorithm according to claim 9, characterized in that, The parameter initialization module generates an initial population containing N individuals in the jet quality optimization parameter space. Each individual corresponds to a combination of turbine structural parameters and operating condition parameters and is assigned initial energy. The fitness of the individuals is evaluated by the jet quality objective function value. The energy dynamic management module uniformly allocates initial energy, quantifies the individual's movement distance using Euclidean distance, and consumes energy proportionally. When energy is sufficient, it deeply develops high-quality parameter areas; when energy is depleted, it resets energy and explores new areas. The guidance direction dynamic screening module uses the jet quality objective function value as the criterion to probabilistically select the guidance direction, thereby avoiding premature convergence of the algorithm. The adaptive adjustment module for search behavior quantifies the jet quality fluctuations during continuous iterations. If the threshold is exceeded, the parameter position is adjusted to accelerate convergence; otherwise, the exploration state is maintained. The exploration and development balance module combines deterministic guidance with random perturbation to update parameters, balancing fine-grained optimization with global search; The iterative optimization and result output module iterates cyclically and dynamically updates the global optimal solution, outputting the optimal parameter combination when the termination condition is met.