90 제 1 발표장 (2 일금 ) 조용재 1, 서인덕 1, 이신형 2* ESTIMATION OF MANUEVERING COEFFICIENTS OF ONR TUMBLEHOME MODEL BY VIRTUAL CAPTIVE MODEL TESTS USING SNUFOAM Y. J. Cho, I. D. Suh, and S. H. Rhee 1.,,..,.,., (Computational Fluid Dynamics, CFD). ONR Tumblehome CFD.,. 2. 2.1 ONR Tumblehome,,. 1/48.9.,. 0.429, 2.925. Froude number 2. 3, 5. Fig. 1 STAR-CCM+. Corresponding author E-mail: shr@snu.ac.kr Fig 1 Grids of intact ship used for CFD analysis
(2 일금 ) 제 1 발표장 91 2.2 Opensource CFD toolkit OpenFOAM, SNUFOAM. (1) (2).,,,,. (3) (1), (2) RANS (Reynolds-averaged Navier-Stokes) SST. - PIMPLE (merged PISO-SIMPLE) algorithm. VOF (Volume of Fluid) (3).. 2.3 FIg 4 Force and moment for static drift test Fig 4. Fig 5 Pure sway test force of intact vessel Moment time series Fig 2 Boundary condition of domain Fig 5 PMM case.. 3. ONR Tumblehome / CFD,. Fig 3 PMM behavior implementation algorithm Fig 3 PMM, PMM PMM solver. [1] Hamid Sadat-Hosseni & Motoki Araki (2011). CFD, system-based and EFD preliminary investiation of ONR Tumblehome instability and capsize with evaluation of the mathematical model.
92 제 1 발표장 (2 일금 ) 이지수 1, 김정우 2* STUDY ON CFD UNCERTAINTY OF TURBULENT NATURAL CONVECTION FLOWS G. S. Lee and J. Kim 1. CFD. CFD Reynolds Averaged Navier-Stokes (RANS). RANS,. CFD CFD. RANS RANS., Roache (1) Richardson GCI. Polynomial Chaos Expansion. RANS GCI PCE. Residue,. Corresponding author E-mail: kimjw@seoultech.ac.kr Fig. 1 Computational details of Barakos et al. (2). 2. 2.1. ANSYS Fluent. Barakos et al. (2) ( 1). 80 80, 160 160, 320 320, 640 640 4. Barakos et al. (2) 80 80... Presto, 2 nd upwind, 1 st upwind.
(2 일금 ) 제 1 발표장 93 Fig. 2 Influence of grid resolution. Fig. 4 Influence of grid resolution.,. 3. Fig. 3 Influence of residue 2.2 2 Barakos et al. (2).. 80 80 residue, resolution. 80 80 solution control residue 3. 3 residue 0.1 0.03, residue 0.03 80 80 residue. residue 4. 4 0.03 0.015. 4,. residue residue,... This work was supported by the Nuclear Safety Research Center Program of the KORSAFe grant (Grant Code 1305011) funded by Nuclear Safety and Security Commission of the Korean government and the NRF program (NRF-2018M2B2A9065988). [1] 1994, Roache, P. J., Perspective: A method for uniform reporting of grid refinement studies, J. Fluids Eng., Vol. 116, p. 405. [2] 1994, Barakos, G., Mitsoulis, E. and Assimacopoulos, D., "Natural convection in a square cavity revisited: laminar and turbulent models with wall functions," Int. J. Numer. Meth. Fluids, Vol. 18, p. 695.
94 제 1 발표장 (2 일금 ) 윤수진 1, 칭롱린 2, 최상헌 3* A 1-D COMPUTATIONAL FLUID DYNAMICS MODEL ANALYZING AIRFLOW TO DIAGNOSE ASTHMA S.J. Yoon, C.L. Lin and S. Choi 1.. CT (CFD). Tawhai [1] branching, CT [2]. CFD[4] [3] 1-D. 2. 1. Uns Vis Kin Kin Uns P, 1, 2. [Uns], [Vis], [Kin].,, *. Acinar (1) Model A. TLC(Total Lung Capacity) FRC(Functional Residual Capacity) compliance pleural Model B [5]. compliance( ) compliance( ). (2),,. (1) compliance. Uns Vis Kin Kin Uns Model B Model A Model A Model B. 3.1 3. (3)
(2 일금 ) 제 1 발표장 95 3.2 2 Model B, 3. 4. Fig. 1 A: Fractional air-volume change of dynamic CT images vs. of 1D CFD model using two different boundary conditions neglecting kinetic energy effect, B: asthmatic subjects of dynamic CT images vs. of 1D CFD model (model B) between healthy subjects and 1 1 CFD 4-D CT.. 2018 () (NRF-2017R1D1A1B03034157) Fig. 2 Pressure distributions for A: peak inspiration, and B: peak expiration of a healthy subject; Pressure distributions for C: peak expiration, and D: peak expiration of an asthmatic subject 10 5, 5. Model A B end inspiration end expiration (, 1-D CFD) 4-DCT (, 4-DCT). 1-A Compliance Model B Model A. 1-B Model B. [1] 2000 Tawhai MH, Pullan AJ, Hunter PJ, Generation of an anatomically based three-dimensional model of the conducting airways, Annals of biomedical engineering Vol.28, p.793-802 [2] 2015 Choi S, Hoffman EA, Wenzel SE, Castro M, Fain SB, Jarjour NN, Schiebler ML, Chen K, Lin CL, Quantitative assessment of multiscale structural and functional alterations in asthmatic populations, Journal of applied physiology Vol.118, p.1286-1298 [3] 2018 Choi S, Miyawaki S, Lin CL, A Feasible Computational Fluid Dynamics Study for Relationships of Structural and Functional Alterations with Particle Depositions in Severe Asthmatic Lungs, Computational and Mathematical Methods in Medicine Vol.2018, p.1-12 [4] 2013 Choi S, Hoffman EA, Wenzel SE, Tawhai MH, Yin Y, Castro M, Lin CL, Registration-based Assessment of Regional Lung Function via Volumetric CT Images of Normal Subjects vs. Severe Asthmatics, Journal of applied physiology Vol.115, p.730-742 [5] 2013 Wongviriyawong C, Harris RS, Greenblatt E, Winkler T, Venegas JG Peripheral resistance: a link between global airflow obstruction and regional ventilation distribution, Journal of applied physiology Vol.114, p.504-514
96 제 1 발표장 (2 일금 ) γ θ 웬호아이탄 1, 장경식 2* COMPARISON OF POINT-COLLOCATION NIPC AND NON-INTRUSIVE SPECTRAL PROJECTION FOR γ-re θ TRANSITION MODEL H.T. Nguyen and K. Chang 1. ω Corresponding author E-mail: kschang76@ulsan.ac.kr 2. 2.1 Langtry and Menter Transition model Langtry and Menter(LM) SST (intermittency).. 2.2 Non-Intrusive Polynomial Chaos (NIPC) Hosder and Walters[1] Pont-Collocation NIPC Gregorio[4] Non-Intrusive Spectral Projection LM. Point-Collocation NIPC (p) (n) (1) (P+1). NISP (p) (n) (2).
(2 일금 ) 제 1 발표장 97 (2) 3,. Point- Collocation NIPC NISP oversampling 2. Table. 1 Necessary number of sampling α α LM 3 ±10% /. (2 ) Table. 2 Coefficients and min/max value 2.3 (Deterministic ) (Deterministic) OpenFoam (Re L =1.0 10 6 ). 230, 100 0.004. 2.3 Point-Collocation NIPC vs NISP Fig. 1. Point-Collocation NIPC NISP 0.5%. 3, 3 6%. 3. γ-re θ Point-Collocation NIPC NISP.. Fig. 1 Mean and STD w.r.t. the polynomial order 2016 (). (No. 2016-R1D1A1B03934121) [1] 2010, Hosder, S. Walters R.W. and Balch M., Point-collocation nonintrusive polynomial chaos method for stochastic computational fluid dynamics, AIAA J. Vol. 48, No. 12, pp. 2721-2730 [2] 2017, Wu, X., Zhang, W., and Song, S., Uncertainty Quantification and Sensitivity Analysis of Transonic Aerodynamics with Geometric Uncertainty, International Journal of Aerospace Engineering, 16 pages. [3] 2016, Schaefer, J., West, T., Hosder, S., Rumsey, C., Carlson, J., and Kleb, W., Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows, AIAA Journal, Vol. 55, No. 1, pp. 195-213. [4] 2013, Polynomial Chaos Expansion with applications to PDEs, Ph.D. Thesis, University of Verona. [5] 2006, Langtry, R.B., and Menter, F.R., Correlation-Based Transition Modeling for Unstructured Parallelized Computational Code, AIAA J., Vol. 47, No. 12, pp. 2894-2906 [6] 2017, www.openfoam.com
98 제 1 발표장 (2 일금 ) 석우찬 1, 서정화 2, 이신형 1,2* APPLICATION OF ICE-FLUID INTERACTION ANALYSIS CONSIDERING EXTERNAL FORCE TERM FOR 6DOF MOTION OF AN ICEBREAKER 1. W.-C. Seok, J.H. Seo and S.H. Rhee 2.. 10 sea ice, snow storm, ice fog,.. 6. OpenFOAM SNUFOAM 6,. one-way coupling., SNUFOAM. 7, 3%. 6, FLUENT SNUFOAM. 2.1 ARAON SNUFOAM. Fig. 1 ARAON. (Lpp) 5.09m 1/18.667. Fig. 1 Lines and specification of ARAON Table 1 7 case (KRISO). Table. 1 Simulation conditions of ARAON in calm sea Fr 0.067 0.134 0.168 0.202 0.236 0.253 0.270 Vs (knots) 4 8 10 12 14 15 16 Vm (m/s) 0.476 0.953 1.191 1.429 1.667 1.786 1.905 Corresponding author E-mail: shr@snu.ac.kr -2.36L < X < 2.36L, 0 < Y < 1.2L, -1.3L < Z < 0.52L. snappyhexmesh 130
(2 일금 ) 제 1 발표장 99. Inlet, Hull Dirichlet, Outlet Neumann, symmetry. [1] 2 nd order scheme. 2.2 Fig. 2 ARAON. 3% SNUFOAM. Fig. 4 Comparison of analysis of SNUFOAM and FLUENT Fig. 4 SNUFOAM FLUNET pitch, surge, heave. Fig. 2 Drag coefficient in calm sea 3. SNUFOAM ARAON. 3%., 6 FLUENT SNUFOAM. 10060329, ). Fig. 3 Time series data of Ice impact load Fig. 3,. SNUFOAM FLUENT. [1] 2015, Lee, H. B., A study of computational schemes for six degree-of-freedom motion of a ship in waves" Ph. D. Thesis, Seoul National University, Seoul, Korea