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황진율 연구실
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황진율 연구실

부산대학교 기계공학부 황진율 교수

본 연구실은 기계공학 기반의 유체공학 연구실로서 벽난류와 코히런트 구조, 멀티스케일 난류 상호작용, 에너지 캐스케이드, 직접수치모사 및 데이터 기반 난류 해석을 중심으로 연구하며, 최근에는 딥러닝을 이용한 난류 구조 학습과 가스 아토마이제이션에서의 액체금속 분열 메커니즘 분석까지 확장하여 기초 난류 이론과 실제 공학 응용을 연결하는 전산유체역학 연구를 수행하고 있다.

대표 연구 분야
연구 영역 전체보기
벽난류의 코히런트 구조와 로그 속도 법칙 thumbnail
벽난류의 코히런트 구조와 로그 속도 법칙
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수

11총합

5개년 연도별 피인용 수

66총합
주요 논문
3
논문 전체보기
1
article
|
인용수 2
·
2025
Vortical structures and primary breakup of liquid metal in gas atomization
S.C. Lee, Jin-Soo Jae, Jinyul Hwang
IF 4.3
Physics of Fluids
High-pressure gas atomization (HPGA) is a widely used method for producing metal powders using high-velocity gas jets, offering high efficiency for large-scale production. Achieving small and spherical powders is critical for this process, which requires a comprehensive understanding of the primary breakup of liquid metals. However, the highly turbulent nature of gas jets complicates the breakup process, making it difficult to control. Here, we explore the influence of vortical structures on the primary breakup during atomization using large-eddy simulations for an annular-slit, close-coupled gas atomizer with molten aluminum and nitrogen gas. We extract individual droplets from the instantaneous flow field and classify them as fibers, ligaments, or spheroids based on their sphericity and aspect ratio. In the near field (z/D < 4), smaller and more spherical droplets are produced compared to the far field (z/D > 4). To analyze the effects of turbulence on the droplet breakup process, we track individual droplets to investigate how strong adjacent vortical structures influence droplet breakup, focusing on the near field. Approximately 70% of the droplets that evolve into spheroids detach far from the nozzle inlet (r/D > 1.5) and experience frequent breakups, averaging more than four times during their lifetime. The droplets undergoing breakup interact with strong vortical structures over 10 times more frequently than those that remain intact. Conditionally averaged flow fields further show that the droplets continuously interact with strong vortical structures before the breakup, generating opposing rotational forces. After the breakup, the maximum magnitude of the surface normal vorticity, which represents the rotational force acting on the droplet interface, decreases by nearly 35%. A comparison of the Weber number (We) for droplets interacting with strong and weak vortical structures indicates that droplets overlapping with strong vortical structures maintain higher We values (35 < We < 80). This range corresponds to the multimode breakup, ultimately leading to droplet breakup. Our findings provide valuable insights into improving nozzle designs from the perspective of recirculation zones and vortical structures, contributing to the production of high-quality spherical powders in HPGA.
https://doi.org/10.1063/5.0255545
Physics
Breakup
Primary (astronomy)
Liquid metal
Mechanics
Metal
Metallurgy
Astrophysics
2
article
|
인용수 3
·
2024
Evolution of wide backflow via large-scale streak collision in turbulent channel flow
I R Park, Jinyul Hwang
IF 4.3
Physics of Fluids
Backflow (BF) events, distinguished by negative wall-shear stress (τx), are rare phenomena occurring in the near-wall region of fully developed wall turbulence. Although these events manifest as small-scale patches of viscous scales, they originate from collisions between large-scale structures (LSSs). Hence, we explore the formation of BF, focusing particularly on interactions with the surrounding LSSs to elucidate the associated inner–outer interactions. We perform direct numerical simulations of turbulent channel flows at Reτ = 180 and 550, including a narrow box simulation at Reτ = 550 to restrict the LSSs. We observe the presence of wide BFs, which are absent at the lower Reynolds number and in the narrow box simulation. These wide BFs have widths significantly larger than the mean size of typical BF regions. Temporal tracking of the BFs with surrounding LSSs and vortical structures reveals that wide BFs result from symmetric collisions between streamwise-aligned high- and low-speed LSSs, whereas narrow BFs stem from asymmetric collisions. In the symmetric collisions, the upstream high-speed structure overrides the downstream low-speed structure, forming a wide shear layer and a significant velocity jump at the interface. This induces a strong prograde vortex near the wall, which elongates laterally and descends owing to the downwash motion of the high-speed structure, ultimately inducing wide BF regions. Conversely, the narrow BF regions develop from the asymmetric collisions occurring at the sides of the spanwise-aligned LSSs, forming narrow, laterally tilted shear layers. The large-scale collisions also induce extreme positive-τx events, particularly noticeable over broad streamwise extents during symmetric collisions. These insights into BF dynamics can inform the development of novel drag reduction strategies by manipulating LSS collisions.
https://doi.org/10.1063/5.0229922
Physics
Backflow
Turbulence
Streak
Mechanics
Collision
Flow (mathematics)
Scale (ratio)
Open-channel flow
Channel (broadcasting)
3
article
|
인용수 7
·
2022
Unsupervised deep learning of spatial organizations of coherent structures in a turbulent channel flow
Mohammad Javad Sayyari, Jinyul Hwang, Kyung Chun Kim
IF 4.3
Physics of Fluids
We examined the capability of an unsupervised deep learning network to capture the spatial organizations of large-scale structures in a cross-stream plane of a fully developed turbulent channel flow at Reτ≈180. For this purpose, a generative adversarial network (GAN) is trained using the instantaneous flow fields in the cross-stream plane obtained by a direct numerical simulation (DNS) to generate similar flow fields. Then, these flow fields are examined by focusing on the turbulent statistics and the spatial organizations of coherent structures. We extracted the intense regions of the streamwise velocity fluctuations (u) and the vortical structures in the cross-stream plane. Comparing the DNS and GAN flow fields, it is revealed that the network not only presents the one-point and two-point statistics quite accurately but also successfully predicts the structural characteristics hidden in the training dataset. We further explored the meandering motions of large-scale u structures by measuring their waviness in the cross-stream plane. It is shown that as the size of the u structures increases, they exhibit more aggressive waviness behavior which in turn increases the average number of vortical structures surrounding the low-momentum structures. The success of GAN in this study suggests its potential to predict similar information at a high Reynolds number and, thus, be utilized as an inflow turbulence generator to provide instantaneous boundary conditions for more complicated problems, such as turbulent boundary layers. This has the potential to greatly reduce the computational costs of DNS related to a required large computational domain at high Reynolds numbers.
https://doi.org/10.1063/5.0123555
Turbulence
Physics
Waviness
Reynolds number
Direct numerical simulation
Flow (mathematics)
Open-channel flow
Plane (geometry)
Mechanics
Statistical physics
정부 과제
2
과제 전체보기
1
2023년 2월-2027년 2월
|118,607,000
물리 공간에서의 멀티스케일 난류 구조 상호작용 분석을 통한 고 레이놀즈수 벽 난류 유동에서의 에너지 케스케이드 현상 연구
고 레이놀즈수 멀티스케일 난류 구조 상호작용 분석을 통한 energy cascade 현상 규명 및 unified scaling law 제시
벽 난류
직접수치모사
난류 구조
에너지 케스케이스
난류 이론
2
주관|
2020년 5월-2023년 2월
|50,000,000
물리 정보 기반 딥러닝을 통한 난류 경계층 생성
(1) 순환신경망(RNN) 및 LSTM 신경망을 이용한 저 레이놀즈수 유동 재건 및 예측 Ÿ RNN은 연속적인 시계열 데이터 예측에 적합한 모델로 출력을 다시 입력으로 받는 순환 뉴런으로 구성. Ÿ LSTM은 RNN의 한 종류로 은닉층에 대한 기억 기능 혹은 제거 기능이 존재하기에 RNN의 장기 의존성 문제를 해결하고 신경망 학습을 빠르게 하는 장점 존재. Ÿ TensorFlow의 RNN 알고리즘 및 시간에 대한 역전파 알고리즘을 사용하여 다음 시간 스탭에서의 난류 유동장을 예측. (2) POD 기반 난류 구조 물리 정보가 반영된 인공신경망 구축 Ÿ RNN과 POD (Proper Orthogonal Decomposition)를 결합하여 시간에 따른 난류 유동장 예측하는 인공신경망 구축. Ÿ POD는 난류 구조를 분석하는 하나의 방법으로 POD 모드의 선형적 관계를 통해 유동장을 재구축 할 수 있음. POD 상수는 시간적 정보, 모드 공간적 정보 포함하고 있음. Ÿ 특히 POD를 통해 재구축된 유동장의 경우 고 레이놀즈수 유동 내 난류 구조의 특성을 지님. Ÿ 속도장을 input으로 output을 POD 상수로 하는 신경망 모델을 개발, 시간에 따른 난류 유동장 재구축. (3) 횡방향 영역 확장을 통한 유입 난류 생성 및 고 레이놀즈수 DNS 수행 Ÿ Convolution layer 내 sampling rate을 조절하여 reshape 시 유입 난류의 횡방향 도메인 확장. Ÿ 인공신경망을 통하여 예측한 유입 난류의 검증은 독립적인 TBL DNS 데이터와 비교를 통해 평가함. Ÿ 계층 구조 특성과 함께 자기 상사성을 가지는 난류 구조의 기하학적 크기 변화에 기반하여 유동 방향 도메인 크기를 변화시키며 난류 경계층 DNS 수행. Ÿ Minimal flow unit (MFU) 내부 난류 구조 추출 및 에너지 스펙트럼 비교를 통한 난류 구조 기반 TBL MFU 현상 규명.
난류 경계층
직접수치모사
딥러닝
난류 구조

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