CN114282323A - Flow distribution prediction method for turbine blade laminate cooling structure - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及航空发动机设计技术领域，具体涉及一种涡轮叶片层板冷却结构的流量分配预测方法The invention relates to the technical field of aero-engine design, in particular to a flow distribution prediction method of a turbine blade layer plate cooling structure
背景技术Background technique
在航空发动机设计过程中，提高涡轮进口燃气温度是改善航空发动机性能的重要途径。相同发动机尺寸下，涡轮进口燃气温度每提高55℃，推力就能提高10％左右。目前，世界先进军用航空发动机涡轮进口燃气温度可以达到1970K，这样的温度是叶片材料无法承受的。且涡轮进口燃气温度的增长速度远远高于材料耐温程度的增长速度。因此，我们需要设计先进的冷却结构，来适应不断提高的涡轮前温度。In the design process of aero-engine, increasing the temperature of turbine inlet gas is an important way to improve the performance of aero-engine. Under the same engine size, the thrust can be increased by about 10% for every 55°C increase in the turbine inlet gas temperature. At present, the inlet gas temperature of the world's advanced military aero-engine turbines can reach 1970K, which is unbearable for blade materials. And the growth rate of turbine inlet gas temperature is much higher than the growth rate of material temperature resistance. Therefore, we need to design an advanced cooling structure to adapt to the increasing temperature before the turbine.
目前，国内外对涡轮冷却结构的研究现状和发展趋势主要是针对层板型涡轮冷却叶片进行设计。根据计算，使用双层壁涡轮叶片可以将冷却效率提高20％～30％，涡轮前温度提高222～333℃。我们要发展高性能的五代机叶片，乃至更先进的下一代叶片，双层壁涡轮叶片都是具有重大潜力的基础结构。At present, the research status and development trend of turbine cooling structure at home and abroad are mainly for the design of laminar turbine cooling blades. According to calculations, the use of double-walled turbine blades can increase the cooling efficiency by 20% to 30%, and the temperature in front of the turbine by 222 to 333°C. We want to develop high-performance fifth-generation machine blades, and even more advanced next-generation blades, double-wall turbine blades are basic structures with significant potential.
冷气流量的多少是影响层板结构冷却效果好坏的重要因素。而目前，使用数值方法对双层壁涡轮叶片进行流量评估的一般方法可以分为CFD计算和流体网络法两种。双层壁涡轮叶片一般结构较为复杂，使用CFD计算方法进行研究需要耗费大量的时间；流体网络法一般计算较快，但内部排布复杂的层板结构往往难以简化为低维网络结构。另一方面，人工智能算法已经广泛应用于航空发动机设计领域，并已取得了丰硕的成果。为此，本发明使用人工智能算法建立了大面积层板冷却结构的流量分配预测模型。以解决层板结构流量分配快速预测问题。The amount of cold air flow is an important factor affecting the cooling effect of the laminate structure. At present, the general methods for evaluating the flow of double-walled turbine blades using numerical methods can be divided into two types: CFD calculation and fluid network method. The general structure of double-wall turbine blades is relatively complex, and it takes a lot of time to study using the CFD calculation method; the fluid network method is generally faster to calculate, but the layer structure with complex internal arrangement is often difficult to simplify into a low-dimensional network structure. On the other hand, artificial intelligence algorithms have been widely used in the field of aero-engine design and have achieved fruitful results. To this end, the present invention establishes a flow distribution prediction model of a large-area laminate cooling structure by using an artificial intelligence algorithm. In order to solve the problem of rapid prediction of flow distribution in the laminate structure.
发明内容SUMMARY OF THE INVENTION
本发明为解决现有层板结构流量评估方法计算复杂、耗费时间且难以简化为低维网络结构的问题，提供一种涡轮叶片层板冷却结构的流量分配预测方法。The present invention provides a flow distribution prediction method for a turbine blade layer cooling structure in order to solve the problems of complicated calculation, time-consuming and difficult to simplify into a low-dimensional network structure in the existing layer structure flow evaluation method.
一种涡轮叶片层板冷却结构的流量分配预测方法，针对双层壁叶片层板冷却结构，将多排气膜孔、冲击孔的流量分配预测问题拆解为局部流量预测和整体流量修正；该预测方法通过局部流量预测模块和整体流量修正模块实现；具体实现步骤如下：A flow distribution prediction method for a turbine blade laminate cooling structure. For a double-wall blade laminate cooling structure, the flow distribution prediction problem of multiple exhaust membrane holes and impingement holes is disassembled into local flow prediction and overall flow correction; The prediction method is realized by a local flow prediction module and an overall flow correction module; the specific implementation steps are as follows:
步骤一、将涡轮叶片主流压力场、叶片分腔/分区方案、冷却结构几何参数作为所述局部流量预测模块的输入；Step 1. Use the mainstream pressure field of the turbine blade, the blade cavity/partition scheme, and the geometric parameters of the cooling structure as the input of the local flow prediction module;
步骤二、设定层板结构冷气夹层内无横流，所有气膜/冲击孔的流量取决于局部冷却结构几何参数和层板局部内外压差；Step 2: There is no cross flow in the cold air interlayer of the laminate structure, and the flow rate of all air films/impact holes depends on the geometric parameters of the local cooling structure and the local internal and external pressure difference of the laminate;
所述局部流量预测模块将层板结构拆分为若干个局部单元，对于每个局部单元，将气膜孔直径、层板结构内外压差作为所述局部流量预测模块输入，将无横流气膜/冲击孔的流量作为局部流量预测模块的输出；The local flow prediction module divides the laminate structure into several local units. For each local unit, the diameter of the gas membrane hole and the pressure difference between the inside and outside of the laminate structure are input to the local flow prediction module. / The flow of the impact hole is used as the output of the local flow prediction module;
训练BP神经网络，建立局部流量预测模块输入与输出间的关联；并将输出的无横流气膜/冲击孔的流量输入到整体流量修正模块；Train the BP neural network to establish the correlation between the input and output of the local flow prediction module; and input the output flow without cross-flow air film/impingement hole to the overall flow correction module;
步骤三、所述局部流量预测模块将所述涡轮叶片主流压力场、叶片分腔/分区方案、冷却结构几何参数以及无横流气膜/冲击孔的流量作为所述整体流量修正模块的输入；Step 3: The local flow prediction module uses the mainstream pressure field of the turbine blade, the blade cavity/partition scheme, the geometric parameters of the cooling structure, and the flow without cross-flow air film/impingement hole as the input of the overall flow correction module;
步骤四、所述整体流量修正模块对步骤三输入的数据进行分析，建立层板结构冷气夹层内的流量输运模型，并将所述流量输运模型分为自由横流层和分段修正层；
在所述自由横流层内将冲击孔流量的一部分数据提取出来，根据冲击孔直径进行加权平均，分配到各个冲击孔；A part of the data of the impact hole flow is extracted from the free cross-flow layer, and the weighted average is carried out according to the diameter of the impact hole, and distributed to each impact hole;
在所述分段修正层内根据横流强度和冷气的流动特性对各孔排气膜/冲击孔流量进行进一步修正，输出气膜孔排冷气流量、冲击孔排冷气流量以及层板内不同区域冷气流动方向和强度。In the segmented correction layer, the flow rate of each hole exhaust film/impact hole is further corrected according to the cross-flow intensity and the flow characteristics of the cold air, and the flow rate of the cold air discharged from the air film hole, the cold air flow rate of the impact hole and the cold air flow of different areas in the laminate are output. Flow direction and strength.
本发明的有益效果：本发明将涡轮叶片主流场、分腔/分区方案、冷却结构参数作为输入，几秒内即可完成对结构的流量评估。输出结果为各排气膜孔流量、冲击孔流量、层板内横流情况。Beneficial effects of the present invention: The present invention takes the main flow field of the turbine blade, the sub-cavity/partition scheme, and the cooling structure parameters as input, and can complete the flow evaluation of the structure within a few seconds. The output results are the flow rate of each exhaust membrane hole, the flow rate of the impingement hole, and the cross flow in the laminate.
本发明相比于目前预测涡轮叶片层板冷却结构冷气分配的CFD计算方法，主要具有以下优点：Compared with the current CFD calculation method for predicting the cold air distribution of the cooling structure of the turbine blade layer, the present invention mainly has the following advantages:
(1)本发明利用平板模型进行测试，流量的平均相对误差控制在10％以内，取得了非常优秀的预测效果。(1) The present invention uses a flat plate model for testing, and the average relative error of the flow rate is controlled within 10%, and a very good prediction effect is achieved.
(2)本发明预测层板流量的效率非常高，得到流量分配预测结果仅需几秒钟。(2) The present invention has a very high efficiency in predicting the flow rate of the laminate, and it takes only a few seconds to obtain the flow distribution prediction result.
附图说明Description of drawings
图1为本发明所述的一种涡轮叶片层板冷却结构的流量分配预测方法原理图；1 is a schematic diagram of a flow distribution prediction method for a turbine blade laminate cooling structure according to the present invention;
图2为冷气夹层的结构示意图；Fig. 2 is the structural representation of cold air interlayer;
图3为层板结构外部压力条件不均匀时的效果图；Figure 3 is an effect diagram when the external pressure conditions of the laminate structure are uneven;
图4为冷气输运模型拓扑结构图。Figure 4 is a topology diagram of the cold air transport model.
具体实施方式Detailed ways
具体实施方式一、结合图1至图4说明本实施方式，一种涡轮叶片层板冷却结构的流量分配预测方法，该方法针对双层壁叶片层板冷却结构，将多排气膜孔、冲击孔的流量分配预测问题拆解为局部流量预测和整体流量修正两部分。具体步骤如下：DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 1. The present embodiment will be described with reference to FIGS. 1 to 4 , a method for predicting the flow distribution of the cooling structure of the turbine blade laminate. The problem of flow distribution prediction of orifices is disassembled into two parts: local flow prediction and overall flow correction. Specific steps are as follows:
步骤一、将涡轮叶片主流压力场、叶片分腔/分区方案、冷却结构几何参数作为所述局部流量预测模块的输入。其中涡轮叶片的主流压力场可以通过对无冷却结构实心叶片进行仿真计算得到，而叶片分腔/分区方案由叶片设计人员提供。一般来说，叶片分腔/分区方案需要考虑叶片强度、叶片外表面压力分布等多方面因素，较为复杂。而叶片分腔/分区的具体方案对本发明所述流量分配预测无影响，因此在这里直接作为已知量输入，不作过多讨论。Step 1: Use the mainstream pressure field of the turbine blade, the blade cavity/partition scheme, and the geometric parameters of the cooling structure as the input of the local flow prediction module. The mainstream pressure field of the turbine blade can be obtained by simulating the solid blade without cooling structure, and the blade cavity/partition scheme is provided by the blade designer. Generally speaking, the blade cavity/partition scheme needs to consider various factors such as blade strength and pressure distribution on the outer surface of the blade, which is relatively complicated. However, the specific scheme of the blade cavity/partition has no influence on the flow distribution prediction of the present invention, so it is directly input as a known quantity here, and will not be discussed too much.
步骤二、所述局部流量预测模块假设层板的冷气夹层内不存在横流，所有气膜/冲击孔的流量只取决于局部冷却结构几何参数和层板局部内外压差。将气膜孔直径、层板结构内外压差等变量作为输入，不考虑层板结构冷气夹层内横流时的气膜/冲击孔的流量作为输出，训练BP神经网络，建立局部流量预测模块输入与输出间的关联；并将输出的无横流气膜/冲击孔的流量输入到整体流量修正模块；
步骤三、将整体的输入的涡轮叶片主流压力场、叶片分腔/分区方案、冷却结构几何参数与局部流量预测模块的输出的无横流情况下气膜/冲击孔的流量合并，作为整体流量修正模块的输入。Step 3. Combine the overall input turbine blade mainstream pressure field, blade cavity/partition scheme, cooling structure geometric parameters and the output of the local flow prediction module with the flow rate of the gas film/impingement hole without cross flow as the overall flow correction module input.
步骤四、所述整体流量修正模块根据接收的数据进行操作。根据理论分析结果，将整体层板结构冷气夹层内的流量输运模型拆分成自由横流层和分段修正层两部分。在自由横流层内将冲击孔流量的一部分提取出来，根据冲击孔直径进行加权平均；在分段修正层内根据横流强度和冷气的流动特性对各孔排气膜/冲击孔流量进行进一步修正。Step 4: The overall flow correction module operates according to the received data. According to the theoretical analysis results, the flow transport model in the cold air interlayer of the overall laminate structure is divided into two parts: the free cross-flow layer and the segmented correction layer. A part of the impact hole flow is extracted in the free cross-flow layer, and the weighted average is carried out according to the diameter of the impact hole; in the segmented correction layer, the exhaust film/impingement hole flow rate of each hole is further corrected according to the cross-flow strength and the flow characteristics of the cold air.
当横流在层板间达到的最大强度与当地冲击孔流量大小相当时，对各孔排流量进行进一步修正；具体过程为：When the maximum strength of the cross flow between the layers is equal to the local impact hole flow, the discharge flow of each hole is further corrected; the specific process is as follows:
设定自由横流层内流阻等于0，且自由横流层不影响气膜孔流量；自由横流层的总流量由各排冲击孔流入，流入量取决于当地气膜孔流量和直径比；The flow resistance in the free cross-flow layer is set equal to 0, and the free cross-flow layer does not affect the flow rate of the gas membrane pores; the total flow of the free cross-flow layer flows in from each row of impact holes, and the inflow depends on the local gas membrane hole flow rate and diameter ratio;
各孔排的流入量累加后获得自由横流层的总流量，设定所述总流量经过各排冲击孔均匀流入，按照当地冲击孔剩余面积均匀分配自由横流层总流量，则实现对自由横流层的修正；根据所述自由横流层输出结果，找到层板内的横流最大位置，并确定分段修正层不同区域的范围。After the inflow of each hole row is accumulated, the total flow of the free cross-flow layer is obtained, and the total flow of the free cross-flow layer is set to flow uniformly through each row of impact holes, and the total flow of the free cross-flow layer is evenly distributed according to the remaining area of the local impact holes. According to the output result of the free lateral flow layer, find the maximum position of the lateral flow in the layer plate, and determine the range of different regions of the subsection correction layer.
本实施方式中，在所述自由横流层内将冲击孔流量的0～90％提取出来，具体为：提取的比例取决于当地冲击孔直径与气膜孔直径的比例k；In this embodiment, 0-90% of the flow rate of the impingement hole is extracted in the free cross-flow layer, specifically: the extraction ratio depends on the ratio k of the diameter of the local impingement hole and the diameter of the gas film hole;
当1≤k≤2时，提取该冲击孔90％×(k-1)0.5的流量；When 1≤k≤2, extract the flow rate of the impact hole 90%×(k-1) 0.5 ;
当k>2时，提取该冲击孔90％的流量。When k>2, 90% of the flow of the impingement hole is extracted.
具体实施方式二、结合图1至图4说明本实施方式，本实施方式为具体实施方式一所述的一种涡轮叶片层板冷却结构的流量分配预测方法的实施例：本实施例中：要实现层板结构流量分配的预测，首先要确定的是程序的输入和输出。对于本发明的应用场景，程序的输入为计算模型的几何结构参数和边界条件，输出为层板整体的流量分配结果。整个系统的组成如附图1所示。1 to 4, this embodiment is an example of the flow distribution prediction method of the cooling structure of a turbine blade layer plate described in the specific embodiment 1: In this embodiment: To realize the prediction of the flow distribution of the laminate structure, the first thing to determine is the input and output of the program. For the application scenario of the present invention, the input of the program is the geometric structure parameters and boundary conditions of the calculation model, and the output is the flow distribution result of the entire laminate. The composition of the whole system is shown in Figure 1.
1、局部流量预测模块；1. Local flow prediction module;
通过数值计算验证不同几何参数对流量的影响。初步结果可以证明以下结论：The effects of different geometric parameters on the flow are verified by numerical calculation. Preliminary results can support the following conclusions:
(1)气膜孔角度对局部流量的影响可以忽略不计；(1) The influence of the air film hole angle on the local flow can be ignored;
(2)冲击孔直径大于气膜孔时，气膜孔直径是决定局部流量的主要因素，冲击孔直径是次要因素。(2) When the diameter of the impact hole is larger than the gas film hole, the gas film hole diameter is the main factor determining the local flow, and the impact hole diameter is a secondary factor.
(3)层板结构内外压差是影响局部结构流量的最终决定性因素。(3) The pressure difference inside and outside the laminate structure is the final decisive factor affecting the flow rate of the local structure.
根据上述结论，初步选取局部结构内外压差、气膜孔直径、冲击孔与气膜孔的直径比这三个变量构建BP神经网络的训练数据集。根据变量重要程度，变量不同的取值数量从16到3不等，共计448组数据(目前已计算并统计其中384组)。训练数据集的具体方案如下表所示。According to the above conclusions, three variables are initially selected: the pressure difference between the internal and external structures of the local structure, the diameter of the gas film hole, and the ratio of the diameter of the impact hole to the gas film hole to construct the training data set of the BP neural network. According to the importance of the variable, the number of different values of the variable ranges from 16 to 3, with a total of 448 groups of data (384 groups of which have been calculated and counted). The specific scheme of the training data set is shown in the following table.
统计个数据集的流量，建立数据集。之后训练神经网络模型，建立内外压差、气膜孔直径等数据集变量到局部流量的映射。Count the traffic of each dataset and create a dataset. After that, the neural network model is trained, and the mapping of data set variables such as internal and external pressure difference and air membrane hole diameter to local flow is established.
2、整体流量修正模块2. Overall flow correction module
将程序整体的输入和局部流量预测模块的输出合并，作为整体流量修正模块的输入。本模块针对大面积层板的孔排结构进行了大量流动换热分析。最终根据流动形式的不同，将层板内流动分为两层：冲击靶面附近的A区，进气版附近的B区，如图2所示。The overall input of the program and the output of the local flow prediction module are combined as the input of the overall flow correction module. This module performs a large number of flow heat transfer analysis for the hole row structure of large area laminates. Finally, according to the different flow forms, the flow in the laminate is divided into two layers: the area A near the impact target surface, and the area B near the intake plate, as shown in Figure 2.
当层板结构外部压力条件不均匀时，层板内不同区域间会出现横向流动。当层板结构外部压力单调变化时，分析A区和B区的横向流动，如附图3所示。根据层板内冷却气体输运形式，将A区划分成发展段、稳定段和回流段。When the external pressure conditions of the laminate structure are not uniform, lateral flow occurs between different areas in the laminate. When the external pressure of the laminate structure changes monotonically, the lateral flow in the A and B regions is analyzed, as shown in Fig. 3. According to the transport form of cooling gas in the laminate, the A zone is divided into a development section, a stabilization section and a recirculation section.
根据上述分析建立层板内冷气输运模型，将层流结构的B区抽象为流量输运模型的自由横流层，将A区抽象为分段修正层，其拓扑结构如附图4所示。According to the above analysis, a cold air transport model in the laminate is established, the B area of the laminar flow structure is abstracted as the free cross-flow layer of the flow transport model, and the A area is abstracted as a segmented correction layer, and its topology is shown in Figure 4.
之后以此流量输运模型为基础结合物理分析，对各孔排流量进行修正，得到最终结果。具体修正方法为：Then, based on this flow transport model, combined with physical analysis, the discharge flow of each hole is corrected to obtain the final result. The specific correction method is:
(1)根据物理和数值分析结果，横流几乎不影响气膜孔流量，而使冲击孔流量分配均匀，且层板间的流阻远小于气膜孔和冲击孔。(1) According to the results of physical and numerical analysis, the cross flow hardly affects the flow rate of the air film holes, and the flow distribution of the impingement holes is uniform, and the flow resistance between the layers is much smaller than that of the air film holes and the impingement holes.
因此假设自由横流层内流阻等于0，且自由横流层不影响气膜孔流量。自由横流层的总流量由各排冲击孔流入，流入量取决于当地气膜孔流量和直径比k。各孔排的流入量累加后便得到自由横流层的总流量，假设这些流量经过各排冲击孔均匀流入。按照当地冲击孔剩余面积(当地冲击孔面积-当地气膜孔面积)均匀分配自由横流层总流量，便得到了自由横流层的修正结果。Therefore, it is assumed that the flow resistance in the free lateral flow layer is equal to 0, and the free lateral flow layer does not affect the pore flow of the gas film. The total flow of the free cross-flow layer flows into each row of impingement holes, and the inflow depends on the local gas membrane hole flow and the diameter ratio k. The total flow of the free cross-flow layer is obtained by summing the inflows of each row of holes, assuming that these flows flow uniformly through each row of impingement holes. According to the remaining area of the local impact hole (the area of the local impact hole - the area of the local gas film hole), the total flow of the free cross-flow layer is evenly distributed, and the corrected result of the free cross-flow layer is obtained.
(2)当横流在层板间达到的最大强度与当地冲击孔流量大小相当时，需要对各孔排流量进行进一步修正。根据自由横流层输出结果，可以找到层板内的横流最大位置，由此确定分段修正层不同区域的范围。(2) When the maximum strength achieved by the cross flow between the laminates is equivalent to the local impact hole flow, it is necessary to further correct the discharge flow of each hole. According to the output result of the free lateral flow layer, the maximum position of the lateral flow in the layer can be found, and thus the range of different regions of the segmented correction layer can be determined.
横流最大位置之前的区域为发展段，横向流动逐渐增强；横流最大位置之后的区域为衰退段，横向流动逐渐减弱；衰退段又可等分为稳定段和回流段。在三个区域分别提取物理特征量，对气膜孔和冲击孔的流量进行修正。具体修正方法为：The area before the maximum cross-flow position is the development section, and the lateral flow gradually increases; the area after the maximum cross-flow position is the recession section, and the lateral flow gradually weakens; the recession section can be divided into a stable section and a recirculation section. The physical feature quantities are extracted from the three regions respectively, and the flow rates of the air film holes and the impact holes are corrected. The specific correction method is:
对于发展段的冲击/气膜流量，冲击孔的流量参照自由横流层的方法进一步加权平均，内外压差最低的气膜孔的流量减半；衰退段横流经气膜孔均匀流出，取最大横流强度的三分之一，按照气膜孔直径加权平均，分配到各个气膜孔；For the impingement/air film flow rate in the developing section, the flow rate of the impingement hole is further weighted and averaged according to the method of the free cross-flow layer, and the flow rate of the air film hole with the lowest internal and external pressure difference is halved; One-third of the intensity, weighted and averaged according to the diameter of the air film holes, and distributed to each air film hole;
稳定段冲击孔流量不进行修正。回流段冲击孔流量下降，且越靠近层板结构的末端，冲击孔流量减少的比例越大(二次分布)。回流区冲击孔减少的流量之和等于最大横流强度的三分之一。The flow rate of the shock hole in the stable section is not corrected. The flow rate of the impingement holes in the recirculation section decreases, and the closer to the end of the laminate structure, the greater the reduction ratio of the flow rate of the impingement holes (secondary distribution). The sum of the flow reductions from the impingement holes in the recirculation zone is equal to one third of the maximum cross-flow intensity.
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity, all possible combinations of the technical features in the above-described embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, All should be regarded as the scope described in this specification.
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。The above-mentioned embodiments only represent several embodiments of the present invention, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can also be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676533A (en) * | 2022-05-26 | 2022-06-28 | 中国航发四川燃气涡轮研究院 | Turbine moving blade cooling sensitivity analysis method coupled with air system |
| CN115470587A (en) * | 2022-09-16 | 2022-12-13 | 西北工业大学 | Method for predicting and evaluating forward design parameters of composite cooling structure of turbine blade leading edge |
| CN115470587B (en) * | 2022-09-16 | 2025-04-15 | 西北工业大学 | Forward design parameter prediction and evaluation method for composite cooling structure of turbine blade leading edge |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004353550A (en) * | 2003-05-29 | 2004-12-16 | Ishikawajima Harima Heavy Ind Co Ltd | Prediction method of cooling performance of impingement / pin fin combined cooling structure |
| CN101126324A (en) * | 2007-07-13 | 2008-02-20 | 北京航空航天大学 | Combination Cooling Structure of Inclined Impingement Air-entrained Film in the Middle of Turbine Blade |
| CN109751972A (en) * | 2019-03-01 | 2019-05-14 | 北京金轮坤天特种机械有限公司 | The cooling air film hole detection platform of high-pressure turbine working blade and test method |
| CN113358336A (en) * | 2021-04-25 | 2021-09-07 | 西北工业大学 | Laminate cooling structure test simulation piece, design method thereof and fatigue test device |
-
2021
- 2021-12-27 CN CN202111616270.1A patent/CN114282323B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004353550A (en) * | 2003-05-29 | 2004-12-16 | Ishikawajima Harima Heavy Ind Co Ltd | Prediction method of cooling performance of impingement / pin fin combined cooling structure |
| CN101126324A (en) * | 2007-07-13 | 2008-02-20 | 北京航空航天大学 | Combination Cooling Structure of Inclined Impingement Air-entrained Film in the Middle of Turbine Blade |
| CN109751972A (en) * | 2019-03-01 | 2019-05-14 | 北京金轮坤天特种机械有限公司 | The cooling air film hole detection platform of high-pressure turbine working blade and test method |
| CN113358336A (en) * | 2021-04-25 | 2021-09-07 | 西北工业大学 | Laminate cooling structure test simulation piece, design method thereof and fatigue test device |
Non-Patent Citations (1)
| Title |
|---|
| 向安定, 刘松龄, 朱惠人: "涡轮叶片型面气膜冷却效率的计算模型", 推进技术, vol. 25, no. 4, 25 August 2004 (2004-08-25), pages 303 - 306 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676533A (en) * | 2022-05-26 | 2022-06-28 | 中国航发四川燃气涡轮研究院 | Turbine moving blade cooling sensitivity analysis method coupled with air system |
| CN114676533B (en) * | 2022-05-26 | 2022-08-02 | 中国航发四川燃气涡轮研究院 | Turbine moving blade cooling sensitivity analysis method coupled with air system |
| CN115470587A (en) * | 2022-09-16 | 2022-12-13 | 西北工业大学 | Method for predicting and evaluating forward design parameters of composite cooling structure of turbine blade leading edge |
| CN115470587B (en) * | 2022-09-16 | 2025-04-15 | 西北工业大学 | Forward design parameter prediction and evaluation method for composite cooling structure of turbine blade leading edge |
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