Multi-disciplinary optimization design method, system and equipment for winglet blade with blade tip and medium

Abstract

The invention relates to the technical field of blade tip winglet design, and discloses a multidisciplinary optimization design method, system and device for a blade with a blade tip winglet and a medium, and the method comprises the following steps: obtaining an initial geometric model of the blade with the blade tip winglet; determining an objective function of multidisciplinary optimization of the winglet blade with the tip; and optimizing the initial geometric model based on the target function and a preset constraint condition to obtain an optimized geometric model, and generating the optimal winglet blade with the tip based on the optimized geometric model. According to the method, through combination of the multidisciplinary optimization objective function and the preset constraint condition, the multidisciplinary optimization requirement of the winglet blade with the tip is considered, and multidimensional performance collaborative optimization can be realized; and based on multidisciplinary optimization of the initial geometric model, an optimal geometric parameter combination meeting constraint conditions can be locked, so that the performance of the blade is ensured to meet design requirements, the design accuracy and reliability of the blade are improved, the optimal blade tip winglet structure can be optimized, and the optimal design of the blade with the blade tip winglet is realized.

Description

Technical Field

[0001] This invention relates to the field of blade tip winglet design technology, specifically to multidisciplinary optimization design methods, systems, equipment, and media for blades with blade tip winglets. Background Technology

[0002] The turbine is one of the core components of an aero gas turbine engine, its function being to convert the energy in the high-temperature, high-pressure gas into mechanical energy. As a key component of the turbine, the turbine blades rotate at high speeds under high temperature and pressure conditions, resulting in extremely complex airflow patterns on their surface. The quality of the blade design directly affects the performance and efficiency of the entire engine.

[0003] In actual turbine flow processes, due to the tiny gap (tip clearance) between the turbine blade tip and the inner wall of the stationary casing surrounding the blade, some high-temperature gas leaks from the pressure side to the suction side, forming tip leakage flow. This type of leakage flow is characterized by strong three-dimensionality, strong shear, and unsteadiness. It not only reduces the turbine's work capacity but also causes significant aerodynamic losses, leading to a sharp increase in the heat transfer coefficient in the tip region and even tip ablation. Studies have shown that tip leakage losses account for about one-third of the total turbine aerodynamic losses, and secondary flow losses related to leakage flow account for more than half of the endwall losses.

[0004] To suppress tip leakage flow and reduce losses, existing technologies have proposed solutions including clearance control, jet control, tip treatment, and casing treatment. Among these, tip treatment, as a passive control method, interferes with the leakage flow path by altering the geometry of the tip region, thereby suppressing leakage. Common tip treatment methods include adding ribs, grooves, or winglets to the blade tip. Tip winglet technology, in particular, has been extensively studied due to its potential in controlling leakage flow. For example, experiments have shown that full-circumference tip winglets can improve efficiency under all turbine expansion ratio conditions; related measurements indicate that tip winglets have the best control effect on leakage flow; experiments show that winglets arranged simultaneously on the suction and pressure surfaces can minimize flow losses; researchers have proposed a winglet scheme with locally recessed grooves at the blade tip, which can significantly improve turbine efficiency under high load conditions; further experiments have verified the advantages of pressure surface winglets in improving turbine efficiency.

[0005] However, existing technologies for blade tip winglet design often focus on a single discipline, particularly optimizing aerodynamic performance, or determining structural parameters solely through experience and experimentation, lacking a systematic, multidisciplinary collaborative design approach. Turbine blades involve multiple disciplines in actual operation, such as the coupled effects of aerodynamics and heat transfer. Considering only a single discipline often makes it difficult to achieve optimal overall performance, and may even lead to decreased blade reliability due to neglecting heat transfer constraints. Furthermore, traditional design schemes rely on manual trial and error and experience-based adjustments, resulting in long optimization cycles, low efficiency, and difficulty in fully realizing the design potential of blade tip winglets.

[0006] Therefore, there is an urgent need for a multidisciplinary optimization design scheme for blades with winglets that can systematically consider the multidisciplinary coupling effects of blade design and achieve automated and efficient design, in order to find the best winglet structure and thus realize the design of blades with winglets. Summary of the Invention

[0007] This invention provides a multidisciplinary optimization design method, system, device, and medium for blades with blade tips, to solve the problem that existing technologies ignore the multidisciplinary coupling effects of blade design, making it difficult to achieve the optimal design of blades with blade tips, and thus seriously affecting design efficiency.

[0008] In a first aspect, the present invention provides a multidisciplinary optimization design method for blades with blade tips and winglets, the method comprising: Obtain the initial geometric model of the blade with tip winglets; Determine the objective function for multidisciplinary optimization of blades with tip winglets; The initial geometric model is optimized based on the objective function and preset constraints to obtain the optimized geometric model, and the optimal blade with blade tip is generated based on the optimized geometric model.

[0009] This invention combines a multidisciplinary optimization objective function with preset constraints to take into account the multidisciplinary optimization needs of blades with winglets, achieving multi-dimensional performance synergy optimization. Furthermore, based on the initial geometric model, multidisciplinary optimization can lock the optimal combination of geometric parameters that satisfies the constraints, thereby ensuring that the blade performance meets the design requirements. This not only improves the accuracy and reliability of blade design but also helps to find the best winglet structure, realizing the optimal design of blades with winglets and greatly ensuring the design efficiency of the blade.

[0010] In one optional implementation, the multidisciplinary approach for the bladed winglet includes at least aerodynamics, structural strength, and heat transfer; the objective function for multidisciplinary optimization of the bladed winglet includes: Determine the optimization indicators for aerodynamics, structural strength, and heat transfer respectively; Based on various optimization indicators, the objective function for multidisciplinary optimization of blades with blade tips is determined.

[0011] This invention, by clearly identifying the three key disciplines of turbine blade design—aerodynamics, structural strength, and heat transfer—can precisely cover the core design requirements of blade performance, safety, and lifespan, thus effectively avoiding the imbalance of priorities caused by the generalization of multidisciplinary optimization. Furthermore, by first clarifying the optimization indicators for each discipline and then integrating them to form the objective function, it ensures that the requirements of each discipline are not overlooked, avoiding the dominance of a single discipline in the optimization results. This makes the objective function construction more accurate, providing data support for subsequent multidisciplinary optimization design and effectively ensuring the stability and reliability of the final design scheme.

[0012] In one optional implementation, the optimization index corresponding to aerodynamics is turbine efficiency, the optimization index corresponding to structural strength is the maximum equivalent stress at the blade root, and the optimization index corresponding to heat transfer is the average temperature in the blade tip region. Based on each optimization index, the objective function for multidisciplinary optimization of the blade with a blade tip is determined, including: Dimensionless processing was performed on turbine efficiency, maximum equivalent stress at blade root, and average temperature at blade tip region to obtain corresponding dimensionless parameters. By weighted summing of the dimensionless parameters, the objective function for multidisciplinary optimization of the blade tip winglet is obtained.

[0013] This invention eliminates the dimensional and numerical differences between turbine efficiency, stress, and temperature through dimensionless processing, allowing these three different-dimensional indicators to participate equally in the construction of the objective function. This avoids a single high-value indicator dominating the optimization result, thus ensuring that the needs of various disciplines are fully considered and achieving equal collaboration among multi-dimensional indicators. Furthermore, the objective function is constructed using a weighted summation method, which allows for flexible adjustment of the weights of each dimensionless parameter according to the blade application scenario, such as efficiency-priority aero-turbines or life-priority industrial gas turbines. This helps to achieve multi-objective synergistic optimization, making the construction of the objective function more flexible and reliable.

[0014] In one optional implementation, the initial geometric model is optimized based on the objective function and preset constraints to obtain an optimized geometric model, including: The initial geometric model is optimized using a preset optimization algorithm based on the objective function and preset constraints to obtain an optimized geometric model. The optimized geometric model includes a combination of geometric parameters of the blade with a blade tip that achieves the comprehensive optimality of the objective function under the preset constraints. The preset constraints include that the average temperature of the blade tip region of the optimized blade with a blade tip is lower than the preset reference temperature, and / or that the maximum equivalent stress at the root of the optimized blade with a blade tip is lower than the preset reference stress.

[0015] This invention relies on a pre-set optimization algorithm to carry out multi-disciplinary optimization, which can quickly identify the optimal combination of geometric parameters that meets the constraints. This helps to reduce the design cycle and human error, and ensures the scientific nature and repeatability of the optimization results. Furthermore, by setting pre-set constraints, the safety thresholds for the average tip temperature and the maximum equivalent stress at the blade root are clearly defined. While pursuing the comprehensive optimization of the objective function (improving turbine efficiency and balancing multiple disciplines), the bottom line of blade operation safety is maintained to avoid risks such as thermal damage and structural failure caused by performance optimization.

[0016] In one alternative implementation, obtaining the initial geometric model of the blade with tiplets includes: An initial geometric model of a blade with a tip winglet is generated using a pre-defined parametric modeling method. The initial geometric model contains a set of geometric parameters, which are used to adjust and control the geometry of the tip winglet.

[0017] The initial model generated by this invention using a parametric modeling method has independently adjustable geometric parameters, such as winglet span and installation angle, which can be adjusted individually. This perfectly matches the subsequent requirements for optimizing parameter combinations based on objective functions, allowing the optimization process to quickly update the model through parameter iteration without rebuilding the overall model, greatly improving the smoothness of the optimization process. At the same time, parametric design means that adjusting the geometry of the blade tip winglets only requires modifying the corresponding parameters, significantly reducing the workload of model adjustment in subsequent optimization processes, shortening the iteration cycle from the initial model to the optimized model, and helping to improve overall design efficiency.

[0018] In one alternative implementation, generating the optimal bladelet with a blade tip based on an optimized geometric model includes: The optimized geometric model was verified through simulation. After the simulation verification of the optimized geometric model is passed, the optimized geometric model is adapted to the manufacturing process based on the preset manufacturing process requirements, and the corresponding production parameters are output. Optimal blades with blade tips are generated using production parameters.

[0019] This invention verifies the actual performance of the optimized geometric model through simulation, ensuring it meets the objective function and constraints. This avoids performance discrepancies between theoretical optimization and actual operation. Furthermore, the model is adapted to pre-defined manufacturing process requirements, ensuring the optimized geometric model not only possesses theoretical performance advantages but also adapts to actual machining capabilities, such as cutting and forging processes, thereby enhancing the engineering practicality of the solution. Simultaneously, standardized production parameters, such as 3D machining data and process parameter thresholds, are output, providing a clear basis for mass production of blades, reducing human intervention and errors in the manufacturing process, ensuring geometric accuracy and performance consistency for each batch of blades, and effectively reducing production losses and design costs.

[0020] In one alternative implementation, simulation verification of the optimized geometric model includes: Joint simulations of aerodynamics, structural strength, and heat transfer were performed on the optimized geometric model, and the performance indicators of each discipline were calculated. The performance indicators for aerodynamics include at least one of aerodynamic efficiency, total pressure loss coefficient, leakage flow rate, and turbine efficiency. The performance indicators for structural strength include at least one of maximum equivalent stress at the blade root, vibration characteristics, and fatigue life. The performance indicators for heat transfer include at least one of average temperature, maximum temperature, temperature difference distribution, and heat transfer coefficient in the blade tip region. Determine whether each performance indicator meets the objective function and preset constraints; If all performance indicators meet the objective function and preset constraints, then the simulation verification of the optimized geometric model is considered successful. If any performance index fails to meet the objective function or preset constraints, the process returns to the step of determining the objective function for multidisciplinary optimization of the blade tip winglet.

[0021] This invention employs joint simulation across three disciplines: aerodynamics, structural strength, and heat transfer. Each discipline covers multiple core performance indicators, enabling multi-dimensional and comprehensive verification of the optimized design effect of bladed airfoils across multiple disciplines. This avoids the design bias caused by verification of a single indicator or discipline. At the same time, it fully considers the coupling effects between different disciplines, such as the linkage between aerodynamic load changes and structural stress and heat transfer efficiency, ensuring that the optimized combination of geometric parameters not only meets the standards in a single discipline but also achieves comprehensive optimality in a multi-disciplinary collaborative scenario.

[0022] Secondly, this invention provides a multidisciplinary optimization design system for blades with blade tips and winglets, the system comprising: The acquisition module is used to acquire the initial geometric model of the blade with the tip winglet; The determination module is used to determine the objective function for multidisciplinary optimization of blades with tiplets; The optimization module is used to optimize the initial geometric model based on the objective function and preset constraints to obtain the optimized geometric model, and to generate the optimal blade with blade tip based on the optimized geometric model.

[0023] The multidisciplinary optimization design system for bladed winglets of the present invention achieves optimal multidisciplinary performance of bladed winglets by combining multidisciplinary optimization objective functions with preset constraints. Furthermore, based on the multidisciplinary optimization of the initial geometric model, the system can lock the optimal combination of geometric parameters that satisfies the constraints, thereby ensuring that the blade performance meets the design requirements. This not only improves the accuracy and reliability of blade design but also achieves the optimal design of bladed winglets, greatly improving the design efficiency of blades.

[0024] Thirdly, the present invention provides an electronic device, which includes a controller, the controller including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform a multidisciplinary optimization design method for a bladed winglet with a blade tip as described in the first aspect or any corresponding embodiment.

[0025] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute a multidisciplinary optimization design method for a blade tip winglet according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating the multidisciplinary optimization design method for blades with blade tips according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating another multidisciplinary optimization design method for blades with blade tips according to an embodiment of the present invention; Figure 3(A) is a schematic diagram of the initial blade shape of the winglet at the blade tip; Figure 3(B) is a schematic diagram of the suction and pressure surfaces of the initial airfoil of the blade tip winglet; Figure 3(C) is a schematic diagram of the circumferential control curve of the blade tip winglet; Figure 3(D) is a schematic diagram of the initial planar curve of the blade tip winglet; Figure 3(E) is a schematic diagram of the radial control curve of the blade tip airfoil; Figure 3(F) is a schematic diagram of the side surface of the blade tip airfoil; Figure 4 This is a flowchart illustrating the non-dominated sorting genetic algorithm. Figure 5 It is a flowchart illustrating multidisciplinary optimization; Figure 6(A) is a schematic diagram of the original leaf shape of the leaf tip winglet; Figure 6(B) is a schematic diagram of the blade shape after multidisciplinary optimization of the tip winglet; Figure 7(A) is a cloud map of the stress distribution at the root of the pressure surface of the original airfoil; Figure 7(B) is a cloud map of the stress distribution at the root of the suction surface of the original blade shape; Figure 7(C) is a cloud map of the stress distribution at the root of the blade on the pressure surface of the blade profile after multidisciplinary optimization; Figure 7(D) is a cloud map of the stress distribution at the root of the suction surface of the blade after multidisciplinary optimization. Figure 8 This is a structural block diagram of a multidisciplinary optimization design system for blades with blade tips according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention provides an embodiment of a multidisciplinary optimization design method for blades with blade tips. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a multidisciplinary optimization design method for blades with blade tips and winglets. Figure 1 This is a flowchart illustrating the multidisciplinary optimization design method for blades with blade tips according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the initial geometric model of the blade with the tip winglet.

[0031] It should be noted that the initial geometric model in this embodiment refers to the complete three-dimensional geometric model of the blade, including the blade tip winglets. It consists of two parts: the main blade body and the blade tip winglet structure, connected by a smooth transition surface without obvious geometric edges. The main blade maintains the conventional streamlined airfoil of a turbine blade, while the blade tip winglets are airfoil structures extending on one or both sides. The overall shape conforms to the basic requirements of turbine aerodynamic design. Note that this initial geometric model serves as the benchmark for subsequent multidisciplinary optimization. On one hand, the model's parametric characteristics support morphological iteration by adjusting geometric parameters; on the other hand, the initial model can be directly used for the first round of aerodynamic, strength, and heat transfer simulations, providing performance benchmark data for optimization. Finally, the optimal model satisfying multiple objectives is obtained through parameter iteration optimization.

[0032] In this embodiment, the specific method for obtaining the initial geometric model can be adaptively determined according to actual needs. For example, a parametric modeling method can be used, specifically by determining core design parameters (such as blade parameters including blade height, chord length, bending and twist angle, and cross-sectional airfoil profile parameters; and blade tip winglet-specific parameters including winglet span, installation angle, bilateral offset, cross-sectional thickness, and transition surface curvature radius), and building a parametric modeling framework. This can be achieved using professional software such as UG (Unigraphics NX, which supports the entire process from conceptual design to manufacturing, and has built-in professional modules such as injection mold wizards, enabling efficient completion of complex model construction, assembly, and engineering drawing) and Pro / E (now known as Creo). Parametric (focused on product design and engineering analysis) and ANSYS DesignModeler (focused on simulation preprocessing), etc., use the main blade basic model as a carrier to associate and constrain the key parameters of the blade tip winglet with the blade tip position of the main blade, and establish parameter-driven modeling logic, such as associating the winglet span with the percentage of the main blade radial height, and the installation angle with the arc of the blade tip profile, etc., to generate an initial geometric model (such as inputting the initial values ​​of each parameter, which can refer to the empirical values ​​of similar turbine blades or the initial trial values ​​of simulation optimization, and automatically generating a complete three-dimensional geometric model of the blade including the blade tip winglet through the software's parameter-driven function; at the same time, ensuring that all key parameters in the model can be adjusted independently, providing an iterative variable basis for subsequent multidisciplinary optimization); this is only an example.

[0033] Step S102: Determine the objective function for multidisciplinary optimization of the blade with tip winglets.

[0034] In this embodiment, key considerations are given to related disciplines in turbine blade design, such as aerodynamics (which mainly studies the flow characteristics of turbine blades in high-speed airflow, including aerodynamic loads, pressure distribution, and flow losses, specifically by optimizing blade shape through aerodynamic analysis models, such as computational fluid dynamics, to improve turbine efficiency and flow capacity), structural strength (which focuses on the mechanical strength and deformation behavior of blades under high temperature and high pressure environments, involving stress analysis, vibration characteristics, and fatigue life prediction, typically using tools such as the finite element method to assess structural safety and ensure that blades do not fail under extreme conditions), and heat transfer (which focuses on the thermal load and thermal stress distribution of blades, studying the impact of cooling technology on the temperature field, specifically by optimizing cooling channel design through heat transfer analysis models, such as computational heat transfer, to control blade temperature and prevent overheating damage). These disciplines are closely integrated in a multidisciplinary design optimization framework, and through data transfer and collaborative analysis, a comprehensive improvement in the overall performance, efficiency, and reliability of turbine blade design is achieved.

[0035] It should be noted that the specific method for determining the objective function in this embodiment can be adapted to actual needs. For example, the optimization indicators can be clearly defined by discipline (such as efficiency and leakage suppression in aerodynamics, stress control in strength, and temperature control in heat transfer), and then integrated to form the objective function (such as weighted integration). This is only an example for illustration.

[0036] Step S103: Optimize the initial geometric model based on the objective function and preset constraints to obtain the optimized geometric model, and generate the optimal blade tip winglet based on the optimized geometric model.

[0037] It should be noted that the preset constraints in this embodiment refer to performance thresholds or boundary conditions that are pre-set and cannot be exceeded during the optimization process to ensure the safety, reliability, and engineering feasibility of the blade operation. These are the safety bottom line and feasible boundary of the optimization. In addition, the constraints in this embodiment are mandatory (e.g., the optimized blade geometry model must meet all constraints; otherwise, even if the overall score of the objective function is high, it is still an invalid solution), targeted (e.g., the constraints directly correspond to the multidisciplinary optimization objectives, focusing on the key risk points of the three core disciplines of aerodynamics, structural strength, and heat transfer), and engineering practical (e.g., the constraint thresholds are set based on actual operating conditions, material properties, and manufacturing capabilities, rather than theoretical limits).

[0038] Note that the specific content of the preset constraints in this embodiment is not limited again and can be adjusted adaptively according to actual needs. For example, the preset constraints may include that the average temperature of the area above 95% of the blade height after optimization is not higher than the average temperature of the area corresponding to the original blade shape, the highest local temperature at the blade tip is not higher than the material's heat resistance threshold, and the maximum equivalent stress at the root of the optimized blade (i.e., the root of the blade) is not higher than the preset reference stress (which is usually 85%-90% of the stress of the original blade shape, with a safety margin). These are only illustrative examples.

[0039] In this embodiment, this step aims to find the optimal combination of blade tip winglet geometric parameters within the feasible domain that satisfies all preset constraints. Specifically, this can be achieved by building an optimization solution framework (which can be combined with a parameterized initial geometric model to define design variables, such as key geometric parameters like the blade tip winglet's span, installation angle, and bilateral offset; the objective function, such as a weighted and integrated multidisciplinary comprehensive objective; and preset constraints, such as temperature, stress, and clearance thresholds, forming an optimized mathematical model of variables, objectives, and constraints); selecting a suitable optimization algorithm (using multi-objective optimization algorithms, such as genetic algorithms, particle swarm optimization, and response surface methodology, to iteratively optimize the design variables of the initial geometric model); and screening the optimal feasible solution (first filtering out invalid solutions, i.e., eliminating all models that do not meet the preset constraints, such as blade tip temperature exceeding the limit or blade root stress exceeding the limit; then, among the remaining feasible solutions, selecting the model with the highest comprehensive score of the objective function, whose corresponding geometric parameter combination is the optimal parameter combination; finally, updating the parameterized initial model based on the optimal parameter combination to obtain the final optimized geometric model).

[0040] The multidisciplinary optimization design method for blades with winglets in this invention combines a multidisciplinary optimization objective function with preset constraints to take into account the multidisciplinary optimization needs of blades with winglets, achieving multi-dimensional performance synergy optimization. Furthermore, based on the multidisciplinary optimization of the initial geometric model, the optimal combination of geometric parameters that satisfies the constraints can be locked, thereby ensuring that the blade performance meets the design requirements. This not only improves the accuracy and reliability of blade design but also helps to find the best winglet structure, realizing the optimal design of blades with winglets and greatly ensuring the design efficiency of the blade.

[0041] This embodiment provides a multidisciplinary optimization design method for blades with blade tips and winglets. Figure 2 This is a flowchart illustrating another multidisciplinary optimization design method for blades with tip winglets according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the initial geometric model of the blade with the tip winglet.

[0042] In this embodiment, step S201 includes: Step S2011: Generate an initial geometric model of the blade with a blade tip winglet using a preset parametric modeling method; wherein, the initial geometric model contains a set of geometric parameters, which are used to adjust and control the geometry of the blade tip winglet.

[0043] It should be noted that in the multidisciplinary optimization design scenario of blades with winglets, the pre-defined parametric modeling method is a 3D modeling technique that pre-defines rules and drives geometry with parameters. Its core is to quickly generate a flexible and iterative initial geometric model of the blade through a standardized parameter system and related logic, providing a precise and efficient foundation for subsequent optimization. For example, before modeling, the geometric control rules of the blade (including winglets) (such as curvature constraints of transition surfaces), key parameters, and the related constraints between parameters are first defined (such as setting the winglet span as a percentage of the main blade height). Then, using the parametric function of professional software (such as UG), these pre-defined rules are embedded into the modeling process, and finally, a 3D geometric model is automatically generated by inputting parameter values.

[0044] In this embodiment, the specific content of the preset parametric modeling method can be adaptively adjusted according to actual needs. Specifically, the initial model generated by the parametric modeling method has independently adjustable geometric parameters, such as winglet span and installation angle, which can be adjusted individually. This perfectly adapts to the subsequent requirements of optimizing parameter combinations based on objective functions, enabling the optimization process to quickly update the model through parameter iteration without rebuilding the overall model, greatly improving the smoothness of the optimization process. At the same time, parametric design allows the adjustment of the blade tip winglet geometry to only require modifying the corresponding parameters, significantly reducing the workload of model adjustment in subsequent optimization processes, shortening the iteration cycle from the initial model to the optimized model, and helping to improve overall design efficiency.

[0045] Step S202: Determine the objective function for multidisciplinary optimization of the blade with tip winglets.

[0046] In this embodiment, the multidisciplinary aspects of the blade with a blade tip include at least aerodynamics, structural strength, and heat transfer. Note that the relevant content of the multidisciplinary aspects can be found in the previous text and will not be repeated here.

[0047] In this embodiment, step S202 includes: Step S2021: Determine the optimization indicators corresponding to aerodynamics, structural strength and heat transfer respectively.

[0048] In this embodiment, the optimization indicators must directly serve the core requirements of suppressing leakage flow in the blade tip winglets, improving turbine performance, and ensuring the safe operation of the blades, avoiding irrelevant indicators. Furthermore, such indicators must be converted into specific values ​​(such as efficiency %, stress MPa, and temperature ℃) that can be obtained through simulation or experimental measurement.

[0049] It should be noted that the core of the aerodynamics in this embodiment is to solve how the blade tip winglet can reduce leakage losses and enhance the airflow's work-capability. The selection of indicators should focus on leakage suppression effect and overall aerodynamic performance, such as maximizing turbine efficiency (which directly reflects the turbine's ability to convert the thermal energy of the combustion gas into mechanical energy and is the core objective of blade aerodynamic design; blade tip leakage is the main source of turbine efficiency loss, accounting for 30%-50% of the total loss, and the core function of the blade tip winglet is to improve this indicator by suppressing leakage), minimizing the blade tip leakage loss coefficient (which can directly quantify the core functional effect of the blade tip winglet; the smaller the leakage loss coefficient, the stronger the winglet's ability to block the leakage flow; this indicator is directly negatively correlated with turbine efficiency and is a key intermediate indicator for aerodynamic optimization), and minimizing the total pressure loss coefficient (which can avoid over-designing the winglet to suppress blade tip leakage, such as excessive span or unreasonable installation angle leading to separation of the mainstream airflow and increasing additional pressure loss; this indicator can comprehensively reflect the aerodynamic losses in the blade passage, including leakage loss and separation loss). The core of structural strength science is to address whether blades meet strength and fatigue requirements after the addition of mass and aerodynamic loads to the blade tip winglets. The selection of indicators needs to focus on stress control and lifespan assurance, such as minimizing the maximum equivalent stress at the blade root (since the blade is a cantilever beam structure, the blade root is the most stress-concentrated part; the added mass and aerodynamic loads from the blade tip winglets will directly lead to increased stress at the blade root. If it exceeds the allowable stress of the material, it will cause fracture failure, which is the core risk point for structural safety) and maximizing the blade fatigue life (since turbine blades are subjected to alternating loads during operation, such as airflow pulsation and vibration, fatigue failure is one of the main failure modes; the presence of blade tip winglets may change the vibration characteristics of the blade, and life indicators are needed to ensure long-term service reliability) The core of heat transfer science is to address the issues of overheating and excessive temperature differences in the blade tip region after the blade tip winglets alter the local flow field. The selection of indicators should focus on temperature control and thermal stability, such as minimizing the average temperature in the blade tip region (since the blade tip is the high-temperature zone of the turbine blade, i.e., the highest gas temperature and poor heat transfer conditions, the presence of blade tip winglets may obstruct cooling airflow and alter the local heat transfer environment, leading to temperature increases; excessively high average temperatures will accelerate material oxidation and creep, shortening blade life); minimizing the local maximum temperature at the blade tip (due to local airflow stagnation at the transition between the blade tip winglets and the main blade, and at the leading edge of the winglets, a sudden temperature rise can form hot spots; excessively high hot spot temperatures can directly trigger thermal fatigue cracks; this indicator is a key risk point for thermal stability); and homogenizing the temperature difference distribution in the blade tip region (due to excessively large temperature differences in the blade tip region, such as a temperature difference exceeding 80°C between the leading and trailing edges of the winglets, thermal stress will be generated, leading to thermal fatigue under long-term effects; this indicator reflects heat transfer uniformity and avoids local overheating or undercooling), etc.; these are only examples for illustration.

[0050] Step S2022: Based on each optimization index, determine the objective function for multidisciplinary optimization of the blade with tip winglets.

[0051] In this embodiment, the optimization index corresponding to aerodynamics is turbine efficiency, the optimization index corresponding to structural strength is the maximum equivalent stress at the blade root, and the optimization index corresponding to heat transfer is the average temperature of the blade tip region.

[0052] Specifically, step S2022 above includes: Step a1 involves performing dimensionless processing on turbine efficiency, maximum equivalent stress at blade root, and average temperature at blade tip region to obtain corresponding dimensionless parameters.

[0053] In this embodiment, this step aims to eliminate the dimensional and numerical differences between turbine efficiency (%), stress (MPa), and temperature (°C), allowing these three different-dimensional indicators to participate equally in the construction of the objective function. Note that the specific method of dimensionless processing in this embodiment can be adaptively adjusted according to actual needs. For example, assuming there is no need to strictly limit the dimensionless values ​​to [0,1], and only the dimensional differences need to be eliminated, the relative rate of change method can be used. That is, define a baseline turbine efficiency (%), obtain the optimized turbine efficiency (%), and subtract the baseline turbine efficiency from the optimized turbine efficiency to obtain the dimensionless parameter of the turbine efficiency. This is only an illustrative example.

[0054] Step a2 involves weighted summation of the dimensionless parameters to obtain the objective function for multidisciplinary optimization of the blade tip winglet.

[0055] It should be noted that in this embodiment, the specific values ​​of the corresponding weight coefficients of the dimensionless parameters after dimensionless processing of turbine efficiency, maximum equivalent stress at the blade root, and average temperature in the blade tip region are not limited. They can be adaptively adjusted according to actual design requirements. For example, if performance is emphasized, the corresponding weight value of turbine efficiency is strengthened, and if thermal stability is emphasized, the corresponding weight value of average temperature is strengthened. This is only an example.

[0056] In this embodiment of the invention, dimensionless processing eliminates the dimensional and numerical differences between turbine efficiency, stress, and temperature, allowing these three different-dimensional indicators to participate equally in the construction of the objective function. This avoids a single high-value indicator dominating the optimization result, ensuring that the needs of each discipline are fully considered and achieving equal collaboration among multi-dimensional indicators. Furthermore, the objective function is constructed using a weighted summation method, which allows for flexible adjustment of the weights of each dimensionless parameter according to the blade application scenario, such as efficiency-priority aero-turbines or life-priority industrial gas turbines. This helps to achieve multi-objective synergistic optimization, making the objective function construction more flexible and reliable.

[0057] In this embodiment of the invention, by clearly identifying the three key disciplines of aerodynamics, structural strength, and heat transfer in turbine blade design, the core design requirements of performance, safety, and lifespan of the blade can be accurately covered, thereby effectively avoiding the imbalance of priorities caused by the generalization of multidisciplinary optimization. Furthermore, by first clarifying the optimization indicators for each discipline and then integrating them to form the objective function, it can be ensured that the requirements of each discipline are not overlooked, avoiding the dominance of a single discipline in the optimization results. This makes the objective function construction more accurate, providing data support for subsequent multidisciplinary optimization design and effectively ensuring the stability and reliability of the final design scheme.

[0058] Step S203: Optimize the initial geometric model based on the objective function and preset constraints to obtain the optimized geometric model, and generate the optimal blade tip winglet based on the optimized geometric model.

[0059] In this embodiment, step S205 above optimizes the initial geometric model based on the objective function and preset constraints to obtain an optimized geometric model, including: Step b1: The initial geometric model is optimized using a preset optimization algorithm based on the objective function and preset constraints to obtain an optimized geometric model. The optimized geometric model includes a combination of geometric parameters of the blade with a blade tip that satisfies the preset constraints to achieve the comprehensive optimality of the objective function. The preset constraints include that the average temperature of the blade tip region of the optimized blade with a blade tip is lower than the preset reference temperature, and / or that the maximum equivalent stress at the root of the optimized blade with a blade tip is lower than the preset reference stress.

[0060] In this embodiment, the specific content of the preset optimization algorithm can be adaptively adjusted according to actual needs. Specifically, this embodiment relies on the preset optimization algorithm to carry out multidisciplinary optimization, which can quickly lock the optimal combination of geometric parameters that meets the constraints, helping to reduce the design cycle and human error, and ensuring the scientific nature and repeatability of the optimization results. Moreover, by defining the safety thresholds of the blade tip average temperature and the blade root maximum equivalent stress through preset constraints, while pursuing the comprehensive optimization of the objective function (turbine efficiency improvement, multidisciplinary balance), the bottom line of blade operation safety is maintained to avoid risks such as thermal damage and structural failure caused by performance optimization.

[0061] In this embodiment, step S205 above, which generates the optimal bladelet with a blade tip based on the optimized geometric model, includes: Step c1: Perform simulation verification on the optimized geometric model.

[0062] In this embodiment, step c1 includes: Step c11: Perform joint simulations of aerodynamics, structural strength, and heat transfer on the optimized geometric model, and calculate the performance indicators of the corresponding disciplines. The performance indicators for aerodynamics include at least one of aerodynamic efficiency, total pressure loss coefficient, leakage flow rate, and turbine efficiency. The performance indicators for structural strength include at least one of maximum equivalent stress at the blade root, vibration characteristics, and fatigue life. The performance indicators for heat transfer include at least one of the average temperature, maximum temperature, temperature difference distribution, and heat transfer coefficient of the blade tip region.

[0063] In this embodiment, the corresponding meanings of the performance indicators involved in each discipline can be adapted to the well-known content in the field. For example, aerodynamic efficiency is a measure of the ratio of gas kinetic energy to mechanical energy, and maximum equivalent stress is used to reflect the degree to which a material can withstand loads.

[0064] Step c12: Determine whether each performance index meets the objective function and preset constraints.

[0065] Step c13: If all performance indicators meet the objective function and preset constraints, then the simulation verification of the optimized geometric model is considered successful.

[0066] Step c14: If any performance index does not meet the objective function or preset constraints, return to the step of determining the objective function for multidisciplinary optimization of the blade tip winglet.

[0067] It should be noted that this embodiment sets up a closed-loop process of backtracking and adjusting the objective function if the indicator fails to meet the standard. That is, when a certain performance indicator (such as insufficient fatigue life or excessive leakage flow) fails to meet the requirements, the objective function is re-optimized (such as adjusting weights and supplementing constraints) to iterate and optimize, so as to ensure that the final optimized geometric model fully meets the preset requirements and avoid performance shortcomings in the optimization result.

[0068] In this embodiment of the invention, aerodynamics, structural strength, and heat transfer are jointly simulated, with each discipline covering multiple core performance indicators. This enables multi-dimensional and comprehensive verification of the multi-disciplinary optimization design effect of bladed airfoils, thereby avoiding the design bias caused by verification of a single indicator or discipline. At the same time, the coupling effects between different disciplines are fully considered, such as the linkage effect of aerodynamic load changes on structural stress and heat transfer efficiency, ensuring that the optimized geometric parameter combination not only meets the standards in a single discipline, but also achieves comprehensive optimality in a multi-disciplinary collaborative scenario.

[0069] Step c2: After the simulation verification of the optimized geometric model is passed, the optimized geometric model is adapted to the manufacturing process based on the preset manufacturing process requirements, and the corresponding production parameters are output.

[0070] In this embodiment of the invention, the specific content of the preset manufacturing process requirements can be adaptively set according to actual needs. For example, the preset manufacturing process requirements may include supplementing machining tolerances and optimizing transition structures. Specifically, model adaptation is performed for the preset manufacturing process requirements, so that the optimized geometric model not only has theoretical performance advantages but can also adapt to actual machining capabilities, such as cutting and forging processes. This completely solves the industry pain point of theoretically optimal but difficult to manufacture, thereby effectively improving the engineering practicality of the solution.

[0071] Step c3: Generate the optimal blade with blade tip using production parameters.

[0072] In this embodiment of the invention, standardized production parameters (such as three-dimensional processing data and process parameter thresholds) are used to provide a clear basis for the mass production of blades, reduce human intervention and errors in the manufacturing process, and ensure the geometric accuracy and performance consistency of each batch of blades.

[0073] In this embodiment of the invention, the simulation verification process verifies in advance whether the actual performance of the optimized geometric model meets the objective function and constraints, thus avoiding substandard blade performance due to deviations between theoretical optimization and actual operation. Furthermore, the model is adapted to the preset manufacturing process requirements, ensuring that the optimized geometric model not only possesses theoretical performance advantages but also adapts to actual processing capabilities, thereby improving the engineering practicality of the solution. Simultaneously, standardized production parameters are output, providing a clear basis for the mass production of blades, thereby effectively reducing production losses and design costs.

[0074] In one specific embodiment, considering the characteristics of turbine blade tip clearance flow, a design optimization scheme based on intelligent multi-objective optimization algorithm is proposed, which combines turbine blade tip clearance control analysis and parametric modeling. Specifically, a multidisciplinary design optimization framework for turbine blades with tip airfoil structures is constructed, and the multidisciplinary design optimization technology for turbine blades with tip airfoil structures is mastered. When designing the tip airfoil structure, the mutual influence of various disciplines is considered simultaneously, fully tapping the design potential, reducing human intervention, and realizing high-performance turbine blade design.

[0075] It should be noted that the above scheme aims to optimize the multi-disciplinary coupled design of turbine blades with blade tip winglets based on neural network automatic optimization, considering aerodynamics, heat transfer, and strength. Specifically, the multi-disciplinary coupled design optimization of turbine blades with blade tip winglets using neural network automatic optimization mainly addresses the problems of complex blade tip winglet structure design and long optimization iteration cycle. In the early stage of blade structure design, multiple dimensions such as blade aerodynamics, heat transfer, and strength are considered. For different turbine configurations, the optimization algorithm automatically finds the optimal blade tip winglet structure.

[0076] In one specific embodiment, the parametric modeling of a turbine blade with a blade tip winglet includes: the parametric modeling method for the blade tip winglet first requires finding a plane of revolution in the radial direction of the blade (i.e., determined according to different turbine configurations, the blade tip winglet structure is mainly distributed in the area above 95% of the blade height) as the starting surface for generating the blade tip winglet (the starting surface of the blade tip winglet is formed by the intersection line formed by the plane of revolution and the blade); the planar airfoil on the intercepted plane of revolution is divided into a suction surface and a pressure surface based on the mid-arc line, so as to facilitate the separate control of the blade tip winglets on the suction and pressure surfaces; at the same time, by controlling the circumferential parameters of the blade tip winglet in this plane, the law of the planar curve changing along the circumferential direction is controlled, and the two planar airfoils of the pressure surface and the suction surface are partially offset in the circumferential direction to generate the planar curve of the blade tip winglet; the development law of each circumferential parameter of the blade tip winglet along the radial blade height direction is controlled by a blade tip winglet radial control curve to generate the side surface of the blade tip winglet; finally, the tip surface is completed to obtain the turbine blade with the blade tip winglet. The specific process of parametric design of the turbine blades with blade tips can be found in Figures 3(A)-3(F).

[0077] It should be noted that the initial airfoil in Figure 3(A) is the profile curve of the rotating surface; the concave surface in Figure 3(B) is the pressure surface, and the convex surface is the suction surface; points A and D in Figure 3(C) are the starting and ending points of the winglet. For example, points B and C are the starting and ending points of the effective width of the winglet, and the height of the straight line containing points B and C is the absolute width of the winglet. The airfoil curve is controlled by adjusting the circumferential parameter values ​​of the winglet at the blade tip to control the circumferential variation of the initial airfoil curve. The pressure and suction surface curves of the initial airfoil are circumferentially offset to obtain the airfoil profile profile. The initial planar curve of the blade tip winglet is shown in Figure 3(D). The radial control curve of the blade tip winglet is determined. The side surface of the blade tip winglet is generated from the aforementioned initial planar curve based on the circumferential control curve and the radial control curve. Figure 3(F) is a schematic diagram of the side surface of the blade tip winglet. The radial control curve is the numerical variation law of the blade tip winglet width along the radial direction of the blade. That is, it is a curve with the relative radial height of the blade as the abscissa and the winglet width as the ordinate. It is used to control the development law of each circumferential parameter of the blade tip winglet along the radial blade height direction. Figure 3(E) is a schematic diagram of the radial control curve of the blade tip winglet.

[0078] In this embodiment, after parametric modeling of the turbine blades with blade tips, multidisciplinary optimization design is performed. It should be noted that multidisciplinary design optimization is essentially an optimization problem, and finding its optimal solution requires the use of appropriate optimization algorithms integrated into each level of the multidisciplinary design optimization optimizer, serving as the control core of the optimization process. Excellent algorithms can enable the optimization problem to find the optimal solution faster or more directly. To achieve turbine aerodynamic-heat transfer-strength coupled three-dimensional multidisciplinary optimization, the selected optimization algorithm must meet the following requirements: 1) An optimal solution can be obtained within an acceptable timeframe; 2) Algorithms with multi-objective optimization capabilities should be given priority; 3) Prioritize algorithms that rely on less information; 4) Prioritize algorithms with strong fault tolerance and robustness.

[0079] In this embodiment, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is proposed for optimization. It should be noted that an optimization system for turbine blades with tip airfoil structures is established within a multidisciplinary feasible optimization process embedded in a single-level optimization algorithm. The NSGA-II algorithm uses real-valued vectors to represent individual variables, which is conceptually closest to the variable space and can encode parameters with arbitrary precision. Other encoding methods may not be able to fully reflect parameter values ​​with good fitness due to insufficient precision. The main features are: (1) The fast non-dominated solution classification method based on the optimal solution is adopted, which improves the calculation speed, maintains appropriate selection pressure, and avoids premature convergence of calculation.

[0080] (2) In order to maintain the diversity of the population and prevent individuals from clustering in local areas, a method for calculating virtual fitness is proposed. It characterizes the degree of local crowding between each point in the target space and its two adjacent points of the same level. This method realizes fitness sharing, can automatically adjust the niche, and has good robustness.

[0081] (3) A competition selection mechanism is used to retain superior individuals and eliminate inferior individuals, so that the optimization moves towards the optimal solution and the solution results are evenly distributed. The specific process is as follows: two individuals are randomly selected for comparison. If the non-inferior solutions are of different levels, the individual with the higher level is retained. Otherwise, if the individuals are at the same level, the individual in the sparser region is retained.

[0082] (4) An elite retention strategy that combines parent and offspring populations to select superior individuals improves the computational convergence speed of the algorithm. First, all individuals from both the parent and offspring generations are merged into a new population. Then, the new population is classified according to its non-dominated solution level, and the local crowding distance for each individual is calculated. A competitive selection mechanism is used to select individuals one by one until the total number of individuals reaches the population size, thus producing a new generation of parent population. Finally, a new round of selection, crossover, and mutation begins on this basis to form a new offspring population. For details, please refer to [link to relevant documentation]. Figure 4 Understand its optimization process.

[0083] In this embodiment, the optimization objectives of each discipline are used as the system-level objective function, and the various styling parameters of the blade tip winglet structure (which determine the geometry of the blade tip winglet) are used as system-level design variables. Through a multidisciplinary feasible method solution process, a better solution is obtained. The multidisciplinary optimization system framework constructed in this way is simple, clear, and easy to operate. The general optimization process of constructing a multidisciplinary optimization system framework for turbine blades with blade tip winglets based on the multidisciplinary feasible method is as follows: Figure 5 As shown. See also Figure 5 The optimized design system framework mainly consists of two modules: 1) the optimizer (the dotted box in the diagram); and 2) the discipline analysis module. The optimizer primarily comprises intelligent optimization algorithms and blade parameterization. The optimization algorithm adjusts design variables based on the objective function to find a better solution; parameterization mainly analyzes variables to provide the geometric model needed for the subsystem's calculations. Discipline analysis mainly performs aerodynamic, heat transfer, and strength analyses of the turbine blades, providing various results and data for multidisciplinary design optimization.

[0084] In this embodiment, the multidisciplinary optimization problem includes multiple interrelated disciplines. It is assumed that each discipline can be expressed as follows: (1) Furthermore, in solving multidisciplinary optimization problems, the Multidisciplinary Feasible Method (MDF) offers the most readily conceivable optimization solution system structure. This method includes an optimizer and a multidisciplinary analysis process. All disciplinary design variables serve as global design variables for finding the optimal solution, and all disciplinary objectives serve as the system's optimization goals. Feasible solutions to the system are obtained through the multidisciplinary analysis process. During the multidisciplinary analysis, based on the given input variables... and Solving for coupling variables Specifically, MDF can be described as follows: (2) in, It is the objective function. These are all global and local constraints. It is the total number of coupled disciplines. It is a global variable. It is a variable that couples between disciplines. These are local variables. In addition, each discipline also needs other parameters that remain constant throughout the disciplinary analysis process. .

[0085] In this embodiment, for multidisciplinary optimization problems, the result needs to simultaneously satisfy multiple conflicting objective functions, yielding only a relatively good solution; no single solution can optimize all objective functions. The selection of constraints and objective functions in this embodiment mainly includes the following process: (1) Limit the average temperature of the area above 95% of the blade height after optimization to not exceed the average temperature of the original blade shape; the blade tip winglet structure increases the blade mass and also affects the blade centroid. When limiting the maximum stress at the blade root during the optimization process, a certain amount of space should be left.

[0086] (2) For the three disciplines of blade aerodynamics, heat transfer and strength: Since aerodynamics, heat transfer and strength are the main disciplines covered in turbine blade design, it is possible to add them but it will greatly increase the optimization iteration process. Considering all factors, it is most appropriate to optimize these three disciplines.

[0087] (3) Integration in the objective function mainly includes the following three processes: a) Unify the optimization direction of the three disciplines, with the optimization goal of improving turbine efficiency and reducing blade average temperature and root stress. The results need to be adjusted accordingly during data processing. b) Based on the results of the original leaf shape, the parameters of the results extracted from the three disciplines were processed to be dimensionless. c) Add corresponding scaling factors to the result parameters of the three disciplines to control the weights between disciplines during optimization. Note that the scaling factor for each discipline depends on the actual situation. Different structural forms or different optimization objectives will affect the allocation of the scaling factor, and its specific value should be adaptively adjusted based on actual needs.

[0088] In this embodiment, after optimizing the blade tip winglet structure, following the optimization objective of a double-sided blade tip winglet structure with superior aerodynamic performance and minimal impact on blade heat transfer and strength, a bladelet airfoil that effectively improves turbine aerodynamic performance and has minimal impact on blade heat transfer and strength was selected as the optimized airfoil, as shown in Figures 6(A) and 6(B). It should be noted that, compared to the original airfoil, the multidisciplinary optimized airfoil in Figure 6(B) adds a double-sided blade tip winglet structure to the blade tip region, which can suppress tip leakage (improving aerodynamic performance), and the multidisciplinary optimization also controls the negative impact of the winglets on blade heat transfer (avoiding local overheating) and structural strength (avoiding stress overload), achieving a balance between performance improvement and risk control.

[0089] Furthermore, after multidisciplinary optimization of the blade tip airfoil structure, the tip leakage flow and total pressure loss coefficient of the moving blades were significantly reduced compared with the original airfoil, and the turbine efficiency increased by 0.74%. Table 1 is a comparison table of the performance results of the multidisciplinary optimized airfoil and the original airfoil.

[0090] Table 1

[0091] Furthermore, after multidisciplinary optimization of the blade tip winglet structure, compared with the original blade shape, the average temperature of the area above 95% of the blade height decreased by 8.59 degrees, the temperature difference increased by 5.56 degrees, and the maximum temperature and heat transfer coefficient showed relatively minor changes. Table 2 is a comparison table of heat transfer results between the blade shape after multidisciplinary optimization and the original blade shape.

[0092] Table 2

[0093] Furthermore, the maximum stress level at the blade root of the optimized winglet is basically the same as that of the original blade. Figures 7(A)-7(D) are comparisons of the root stress distribution cloud maps of turbine blades with winglets before and after multidisciplinary optimization, used to verify the influence of the multidisciplinary optimization scheme on the blade structural strength and to reflect the difference in strength results between the optimized and original blades.

[0094] It should be noted that Figures 7(A) and 7(B) show the original blade shape (blade without the optimized tip winglets), displaying stress cloud maps from different perspectives; Figures 7(C) and 7(D) show the blade shape after multidisciplinary optimization (blade with the optimized tip winglets), also displaying stress cloud maps from the corresponding perspectives; where the color of the stress cloud map represents the stress magnitude (the industry standard is: blue, green, yellow to red, corresponding to stress from low to high), and the red area in the figure is the maximum stress area at the blade root (marked "Max" in the figure).

[0095] In this embodiment, the maximum stress level at the blade root is basically the same as that of the original blade after multidisciplinary optimization. That is, although the blade tip winglet is added after optimization (which theoretically increases the load on the blade), the stress in the core stress area of ​​the blade (i.e., the blade root) does not increase due to the multidisciplinary collaborative optimization of aerodynamics, structural strength and heat transfer in this embodiment (such as adjusting the geometric parameters of the blade tip winglet and the blade profile). The structural strength level is maintained in the same way as the original blade, which effectively avoids the problem of decreased structural strength caused by increased load after adding the blade tip winglet. This achieves a multi-objective balance between performance improvement and structural reliability.

[0096] This embodiment also provides a multidisciplinary optimization design system for blades with winglets at the tip. This system is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, a "module" can be a combination of software and / or hardware that performs a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0097] This invention provides a multidisciplinary optimization design system for blades with winglets at the blade tip, such as... Figure 8 As shown, the system includes: The acquisition module 801 is used to acquire the initial geometric model of the blade with the tip winglet.

[0098] Module 802 is used to determine the objective function for multidisciplinary optimization of blades with tip winglets.

[0099] The optimization module 803 is used to optimize the initial geometric model based on the objective function and preset constraints to obtain the optimized geometric model, and to generate the optimal blade with tipped winglets based on the optimized geometric model.

[0100] In some optional implementations, the acquisition module 801 includes: an acquisition submodule, used to generate an initial geometric model of a blade with a blade tip winglet using a preset parametric modeling method; wherein the initial geometric model contains a set of geometric parameters, which are used to adjust and control the geometry of the blade tip winglet.

[0101] In some alternative implementations, the determining module 802 includes: The first determination submodule is used to determine the optimization indicators corresponding to aerodynamics, structural strength, and heat transfer respectively.

[0102] The second determination submodule is used to determine the objective function for multidisciplinary optimization of the blade tip winglet based on various optimization indicators.

[0103] In some optional implementations, the second determining submodule includes: The first determining unit is used to perform dimensionless processing on turbine efficiency, maximum equivalent stress at the blade root, and average temperature in the blade tip region, respectively, to obtain corresponding dimensionless parameters.

[0104] The second determining unit is used to perform weighted summation of each dimensionless parameter to obtain the objective function for multidisciplinary optimization of the blade tip winglet.

[0105] In some alternative implementations, the optimization module 803 includes: The first optimization submodule is used to perform multidisciplinary optimization of the initial geometric model based on the objective function and preset constraints using a preset optimization algorithm to obtain an optimized geometric model. The optimized geometric model includes a combination of geometric parameters of the blade with a blade tip that satisfies the preset constraints to achieve the comprehensive optimality of the objective function. The preset constraints include that the average temperature of the blade tip region of the optimized blade with a blade tip is lower than a preset reference temperature, and / or that the maximum equivalent stress at the root of the optimized blade with a blade tip is lower than a preset reference stress.

[0106] The second optimization submodule is used to perform simulation verification of the optimized geometric model.

[0107] The third optimization submodule is used to adapt the optimized geometric model to the manufacturing process based on the preset manufacturing process requirements after the simulation verification of the optimized geometric model has passed, and output the corresponding production parameters.

[0108] The fourth optimization submodule is used to generate the optimal blade with blade tip using production parameters.

[0109] In some optional implementations, the second optimization submodule includes: The first verification unit is used to conduct joint simulations of the optimized geometric model in the fields of aerodynamics, structural strength, and heat transfer, and to calculate the performance indicators of the corresponding disciplines. The performance indicators for aerodynamics include at least one of aerodynamic efficiency, total pressure loss coefficient, leakage flow rate, and turbine efficiency. The performance indicators for structural strength include at least one of the maximum equivalent stress at the blade root, vibration characteristics, and fatigue life. The performance indicators for heat transfer include at least one of the average temperature, maximum temperature, temperature difference distribution, and heat transfer coefficient of the blade tip region.

[0110] The second verification unit is used to determine whether each performance index meets the objective function and preset constraints.

[0111] The third verification unit is used to determine whether the simulation verification of the optimized geometric model has passed if all performance indicators meet the objective function and preset constraints.

[0112] The fourth verification unit is used to return to the step of determining the objective function for multidisciplinary optimization of the blade tip winglet if any performance index does not meet the objective function or preset constraints.

[0113] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0114] The multidisciplinary optimization design system for bladed winglets with blade tips in this embodiment is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0115] The multidisciplinary optimization design system for bladed winglets in this invention combines a multidisciplinary optimization objective function with preset constraints to achieve optimal multidisciplinary performance of bladed winglets. Furthermore, based on the initial geometric model, the multidisciplinary optimization can lock the optimal combination of geometric parameters that satisfies the constraints, thereby ensuring that the blade performance meets the design requirements. This not only improves the accuracy and reliability of blade design but also achieves the optimal design of bladed winglets, greatly improving the design efficiency of blades.

[0116] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0117] The following is a detailed reference. Figure 9 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device includes a controller, which may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 902 or a program loaded from memory 908 into a random access memory RAM 903. The RAM 903 also stores various programs and data required for the operation of the electronic device. The processor 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0118] Typically, the following devices can be connected to I / O interface 905: input devices 906 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 907 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 908 including, for example, magnetic tapes, hard disks, etc.; and communication devices 909. Communication device 909 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 9 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0119] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 909, or installed from a memory 908, or installed from a ROM 902. When the computer program is executed by the processor 901, it performs the functions defined in the in-vehicle voice testing method of the embodiments of the present invention.

[0120] Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0121] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor main control chips, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0122] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A multidisciplinary optimization design method for blades with winglets at the blade tip, characterized in that, The method includes: Obtain the initial geometric model of the blade with tip winglets; Determine the objective function for the multidisciplinary optimization of the blade with blade tip; The initial geometric model is optimized based on the objective function and preset constraints to obtain an optimized geometric model, and the optimal blade with a blade tip is generated based on the optimized geometric model.

2. The multidisciplinary optimization design method for blades with winglets at the blade tip according to claim 1, characterized in that, The multidisciplinary field of the bladed winglet includes at least aerodynamics, structural strength, and heat transfer. The objective function for determining the multidisciplinary optimization of the blade with a blade tip includes: Determine the optimization indicators for aerodynamics, structural strength, and heat transfer respectively; Based on the aforementioned optimization indices, the objective function for the multidisciplinary optimization of the blade with a blade tip is determined.

3. The multidisciplinary optimization design method for blades with winglets at the blade tip according to claim 2, characterized in that, The optimization index corresponding to the aerodynamics discipline is turbine efficiency; the optimization index corresponding to the structural strength discipline is the maximum equivalent stress at the blade root; and the optimization index corresponding to the heat transfer discipline is the average temperature of the blade tip region. The objective function for multidisciplinary optimization of the blade with a blade tip is determined based on each of the optimization indices, including: Dimensionless processing was performed on turbine efficiency, maximum equivalent stress at blade root, and average temperature at blade tip region to obtain corresponding dimensionless parameters. The objective function for the multidisciplinary optimization of the blade tip winglet is obtained by weighted summation of the dimensionless parameters.

4. The multidisciplinary optimization design method for blades with winglets at the blade tip according to any one of claims 1 to 3, characterized in that, The optimization of the initial geometric model based on the objective function and preset constraints to obtain an optimized geometric model includes: The initial geometric model is optimized using a preset optimization algorithm based on the objective function and preset constraints to obtain an optimized geometric model. The optimized geometric model includes a combination of geometric parameters for the blade with a winglet that satisfies the preset constraints to achieve the comprehensive optimality of the objective function. The preset constraints include that the average temperature of the blade tip region after optimization is lower than a preset reference temperature, and / or that the maximum equivalent stress at the root of the blade after optimization is lower than a preset reference stress.

5. The multidisciplinary optimization design method for blades with winglets at the blade tip according to claim 1, characterized in that, The process of obtaining the initial geometric model of the blade with the tip winglet includes: An initial geometric model of a blade with a tip winglet is generated using a preset parametric modeling method; wherein, the initial geometric model contains a set of geometric parameters, which are used to adjust and control the geometry of the tip winglet.

6. The multidisciplinary optimization design method for blades with winglets at the blade tip according to claim 1, characterized in that, The process of generating the optimal bladelet with a blade tip based on the optimized geometric model includes: The optimized geometric model was verified by simulation. After the optimized geometric model passes simulation verification, the optimized geometric model is adapted to the manufacturing process based on the preset manufacturing process requirements, and the corresponding production parameters are output. The optimal blade with a blade tip is generated using the aforementioned production parameters.

7. The multidisciplinary optimization design method for blades with winglets at the blade tip according to claim 6, characterized in that, The simulation verification of the optimized geometric model includes: Joint simulations of aerodynamics, structural strength, and heat transfer were performed on the optimized geometric model, and the performance indicators of the corresponding disciplines were calculated. The performance indicators corresponding to aerodynamics include at least one of aerodynamic efficiency, total pressure loss coefficient, leakage flow rate, and turbine efficiency. The performance indicators corresponding to structural strength include at least one of maximum equivalent stress at the blade root, vibration characteristics, and fatigue life. The performance indicators corresponding to heat transfer include at least one of average temperature, maximum temperature, temperature difference distribution, and heat transfer coefficient in the blade tip region. Determine whether each of the performance indicators satisfies the objective function and preset constraints; If all performance indicators satisfy the objective function and the preset constraints, then the simulation verification of the optimized geometric model is deemed successful. If any of the performance indicators fails to meet the objective function or the preset constraints, the process returns to the step of determining the objective function for the multidisciplinary optimization of the blade tip winglet.

8. A multidisciplinary optimization design system for blades with winglets at the blade tip, characterized in that, The system includes: The acquisition module is used to acquire the initial geometric model of the blade with the tip winglet; The determination module is used to determine the objective function for the multidisciplinary optimization of the blade tip winglet; The optimization module is used to optimize the initial geometric model based on the objective function and preset constraints to obtain an optimized geometric model, and to generate the optimal bladelet with blade tip based on the optimized geometric model.

9. An electronic device, characterized in that, The electronic device includes a controller, which includes a memory and a processor. The memory and the processor are communicatively connected to each other. The memory stores computer instructions. The processor executes the computer instructions to perform the multidisciplinary optimization design method for bladed winglets according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the multidisciplinary optimization design method for bladed winglets with blade tips as described in any one of claims 1 to 7.

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