Film hole component is one of the most important cooling components in pipe-net calculation for blade design. However, owing to a large number of gas film holes, manual parameter configuration for film hole components will affect the efficiency of pipe-net calculation. Extracting parameters from the geometric structure of film holes automatically could significantly shorten the time consumption of pipe-net calculation. In this paper, numeric analysis parameter and group data required by film hole components were summarized and analyzed, based on which a parameter configuration strategy was proposed. Aiming at the extraction of group data, a film hole grouping method was presented. By converting the grouping requirements in fluid-heat analysis into a geometric row pattern recognition problem, it further redefines the geometric row as a linear distribution by a projection method and a parametric mapping method and finally solves the grouping problem by a linear search algorithm. A numeric parameter extraction method was put forward to redefine analysis parameters with geometric properties and identify certain geometry primitives. The proposed algorithm and method are verified with instances. The results illustrate that our method could shorten parameter configuration process of film hole component into several minutes from several hours in manual, with more reliable accuracy.
MaffulliRHeL. Impact of wall temperature on heat transfer coefficient and aerodynamics for three-dimensional turbine blade passage. J Therm Sci Eng Appl2017, pp. 9.
2.
SchollSVerstraeteTDuchaineF, et al.Conjugate heat transfer of a rib-roughened internal turbine blade cooling channel using large eddy simulation. Int J Heat Fluid Flow2016; 61: 650–664.
3.
Han JC, Dutta S and Ekkad S. Gas turbine heat transfer and cooling technology. Boca Raton, FL: CRC Press, 2012.
4.
KoffBL. Gas turbine technology evolution: a designer's perspective. J Propul Power2004; 20: 577–595.
5.
YanPGShiLWangXF, et al.Retrofit design of composite cooling structure of a turbine blade by fluid networks and conjugate heat transfer methods. Sci China-Technol Sci2013; 56: 3104–3114.
6.
Li C and Liu J-J. A one-dimensional analytical method for turbine blade preliminary cooling design. In: ASME turbo expo 2016: turbomachinery technical conference and exposition, GT 2016, Seoul, South Korea, 13–17 June 2016. New York: International Gas Turbine Institute/American Society of Mechanical Engineers (ASME).
7.
ShiLSunYHanW, et al.Application of network method in the design of double wall cooling structure. J Harbin Inst Technol2017; 49: 78–85.
8.
Alizadeh M, Izadi A, Fathi A, et al. Flow and heat transfer analysis of turbine blade cooling passages using network method. In: ASME 2012 gas turbine India conference, GTINDIA 2012, Mumbai, Maharashtra, India, 1 December 2012, pp.523–531. New York: American Society of Mechanical Engineers.
9.
Rezazadeh ReyhaniMAlizadehMFathiA, et al.Turbine blade temperature calculation and life estimation - a sensitivity analysis. Propul Power Res2013; 2: 148–161.
10.
Bavassano F, Mantero M, Gasnier T, et al. Analysis of heavy duty gas turbine stator-rotor cavity through 3D CFD-1D fluid network? Field measurements combined approach. In: ASME turbo expo 2016: turbomachinery technical conference and exposition, 2016, p. V05AT15A029. New York: ASME.
11.
Andreini A, Bonini A, Da Soghe R, et al. Conjugate heat transfer calculations on GT rotor blade for industrial applications. Part II: Improvement of external flow modeling. In: ASME Turbo Expo 2012: turbine technical conference and exposition, GT 2012, Copenhagen, Denmark, 11–15 June 2012, pp.681–692. New York: American Society of Mechanical Engineers (ASME).
12.
Bonini A, Andreini A, Carcasci C, et al. Conjugate heat transfer calculations on GT rotor blade for industrial applications. Part i: Equivalent internal fluid network setup and procedure description. In: ASME turbo expo 2012: turbine technical conference and exposition, GT 2012, Copenhagen, Denmark, 11–15 June 2012, pp.669–679. New York: American Society of Mechanical Engineers (ASME).
13.
SciubbaE. Air-cooled gas turbine cycles – Part 1: an analytical method for the preliminary assessment of blade cooling flow rates. Energy2015; 83: 104–114.
14.
Chi Z, Wang S, Ren J, et al. Multi-dimensional platform for cooling design of air-cooled turbine blades. In: ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, Copenhagen, Denmark, 11-15 June 2012, paper no. GT 2012, pp. 207–218. New York: American Society of Mechanical Engineers (ASME).
15.
YanPGCuiYShiL, et al.Application of conjugate heat transfer and fluid network analysis to improvement design of turbine blade with integrated cooling structures. Proc IMechE, Part G: J Aerospace Engineering2014; 228: 2286–2299.
16.
ShiLYanPXieM, et al.Coupling network computing applications in air-cooled turbine blades optimization. Int J Turbo Jet-Engines2018; 35: 121–136.
17.
Li C, Liu JJ, An BT, et al. A two-dimensional analytical method for turbine blade cooling design. In: ASME turbo expo: turbine technical conference and exposition, GT 2017, Charlotte, NC, USA, 26–30 June 2017. New York: International Gas Turbine Institute/American Society of Mechanical Engineers (ASME).
18.
WangS-TChiZ-RWenF-B, et al.Cooling structure design for turbine blades I: parameterized design. J Eng Thermophys2011; 32: 581–584.
19.
ChiZ-RWenF-BWangS-T, et al.Cooling structure design for turbine blades II: pipe-net calculation. J Eng Thermophys2011; 32: 933–936.
20.
ChiZ-RWenF-BWangS-T, et al.Cooling structure design for turbine blades III: conjugate heat transfer analysis. J Eng Thermophys2011; 32: 1485–1488.
21.
LuoLSundenBWangST. Optimization of the blade profile and cooling structure in a gas turbine stage considering both the aerodynamics and heat transfer. Heat Transf Res2015; 46: 599–629.
22.
ThakurABanerjeeAGGuptaSK. A survey of CAD model simplification techniques for physics-based simulation applications. Comput-Aid Des2009; 41: 65–80.
23.
MunDKimBC. Extended progressive simplification of feature-based CAD models. Int J Adv Manuf Technol2017; 93: 915–932.
24.
ChenRXiP. A digraph-based hexahedral meshing method for coupled quasi-polycubes. Comput Methods Appl Mech Eng2014; 268: 18–39.
25.
ChenRXiP. A quadrilateral mesh generating method using mean value interpolation. J Mech Sci Technol2014; 28: 637–643.
26.
LiuHZhangPChienE, et al.Singularity-constrained octahedral fields for hexahedral meshing. ACM Trans Graph2018; 37: 1–17.
27.
MaSHTianL. Analysis feature recognition and mixed-dimensional model reconstruction from finite element analysis mesh model. Proc IMechE, Part C: J Mechanical Engineering Science2014; 228: 1197–1207.
28.
XiaZWangQLiuQ, et al.A novel approach for automatic reconstruction of boundary condition in structure analysis. Adv Eng Softw2016; 96: 38–57.
29.
TengSHanJCPoinsattePE. Effect of film-hole shape on turbine-blade heat-transfer coefficient distribution. J Thermophys Heat Transf2001; 15: 249–256.
30.
AlexiouAMathioudakisK. Secondary air system component modeling for engine performance simulations. J Eng Gas Turbines Power-Trans ASME2009; 131: 9.
31.
HeL. Block-spectral approach to film-cooling modeling. J Turbomach2012, pp. 134.
32.
YangJHanS. Repairing CAD model errors based on the design history. Comput-Aid Des2006; 38: 627–640.
33.
Chen R and Ping X. Analysis-oriented parametric modeling technology. In: 2nd international conference on computer science and network technology, ICCSNT 2012, Changchun, China, 29–31 December 2012, pp.1208–1211. New York: IEEE Computer Society.
34.
RajabKPieglLASmarodzinavaV. CAD model repair using knowledge-guided NURBS. Eng Comput2013; 29: 477–486.
35.
Gonzalez-LluchCCompanyPConteroM, et al.A survey on 3D CAD model quality assurance and testing tools. CAD Comput Aid Des2017; 83: 64–79.
36.
BabicBNesicNMiljkovicZ. A review of automated feature recognition with rule-based pattern recognition. Comput Ind2008; 59: 321–337.
37.
KlosowskiJTHeldMMitchellJSB, et al.Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Trans Visual Comput Graph1998; 4: 21–36.
38.
ZhangHShefferACohen-OrD, et al.Deformation-driven shape correspondence. Comput Graph Forum2008; 27: 1431–1439.
39.
LipmanYFunkhouserT. Mobius voting for surface correspondence. ACM Trans Graphics2009; 28: 12.
40.
LiJXiP. Rapid modeling of impingement pipe in turbine blade. J Beijing Univ Aeronaut Astronaut2016; 42: 1149–1155.
41.
CambaJConteroMJohnsonM, et al.Extended 3D annotations as a new mechanism to explicitly communicate geometric design intent and increase CAD model reusability. Comput-Aid Des2014; 57: 61–73.
42.
CambaJDConteroM. Assessing the impact of geometric design intent annotations on parametric model alteration activities. Comput Ind2015; 71: 35–45.
