Article
Engineering, Aerospace
Qiang Liu, Zhenbing Luo, Lin Wang, Guohua Tu, Xiong Deng, Yan Zhou
Summary: Direct numerical simulations were conducted on a spatially developing Ma 2.25 supersonic turbulent boundary layer with streamwise-striped wall blowing for turbulence drag reduction. It was found that despite weak control amplitudes, SSB can result in drag reduction effects. Analysis using compressible Renard-Deck decomposition revealed that the spatial growth term is mainly responsible for turbulence drag reduction.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Mechanics
Azadeh Jafari, Beverley J. McKeon, Maziar Arjomandi
Summary: The potential of frequency-tuned surfaces as a passive control strategy for reducing drag in wall-bounded turbulent flows is investigated using resolvent analysis. It is shown that wall impedance can suppress the modes resembling the near-wall cycle and the very-large-scale motions and the Reynolds stress contribution of these modes. Furthermore, a wall with only shear-driven impedance is found to suppress turbulent structures over a wider range in spectral space, leading to an overall turbulent drag reduction.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Multidisciplinary Sciences
Giulio Foggi Rota, Alessandro Monti, Marco E. Rosti, Maurizio Quadrio
Summary: Viscous dissipation in fluid flows causes significant energy losses. Laminar flows in ducts have minimum resistance, while turbulence increases friction and energy requirements for pumping. A novel technique of intermittently pumping the flow accelerates it to a quasi-laminar state, saving energy compared to constant pumping and reducing harmful emissions.
SCIENTIFIC REPORTS
(2023)
Article
Mechanics
Tingting Bao, Jun Hu, Can Huang, Yong Yu
Summary: This study proposes an improved SPH method that couples the γ-e turbulence model and the wall function to simulate wall-bounded turbulent flows at medium and high Reynolds numbers. By decomposing the second-order partial derivative term of the composite function containing the turbulent viscosity coefficient into two terms, the proposed method avoids numerical errors and difficulties in dealing with boundary conditions. Particle shifting technique, d-SPH method, and graphics processing unit parallel technology are used to ensure uniform particles, smooth pressure field, and high computational efficiency in the simulations, respectively.
Article
Mechanics
F. Serafini, F. Battista, P. Gualtieri, C. M. Casciola
Summary: Polymer-laden turbulent pipe flows were investigated using direct numerical simulations and Lagrangian transport. Comparison between the FENE and FENE-P models revealed quantitative and qualitative discrepancies, mainly caused by the failure of Peterlin's approximation in the latter model. A new parameter, the polymer Reynolds number Rep, was defined, showing that the dynamics only depend on the Weissenberg number Wi and not on individual polymer parameters. This finding reduces the cost of Lagrangian simulations and suggests the FENE model as an alternative to the well-established FENE-P model.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2023)
Article
Mechanics
Reza Azadi, David S. Nobes
Summary: This study explores the impact of polymer additives on hydrodynamic cavitation in semidilute solution flows. The results show that polymer additives can effectively mitigate the intensity of cloud cavitation and the growth of violent cavity structures. Polymers reduce cavitation through three main mechanisms.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
N. N. Haq, J. M. Floryan
Summary: The effect of surface vibrations on pressure-gradient-driven flows in channels was studied. It was found that waves propagating upstream always increase pressure losses. The response of the flow to waves propagating downstream changes as the flow Reynolds number varies. Supercritical waves reduce pressure losses, while subcritical waves generally increase pressure losses and can only reduce them at larger Reynolds numbers. Waves with very small amplitudes that match the natural flow frequencies produce significant pressure losses.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Mechanics
Feng Xie, Jose D. Perez-Munoz, Ning Qin, Pierre Ricco
Summary: A turbulent drag-reduction method utilizing synthetic jet sheets was investigated through direct numerical simulations. By adjusting the angle and height of the jet sheets, a drag reduction of 10% to 30% was achieved. The study also found that the global skin-friction drag reduction was a result of a finite counter flow induced by the interaction between the jet-sheet flow and the main flow.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
Yitong Fan, Marco Atzori, Ricardo Vinuesa, Davide Gatti, Philipp Schlatter, Weipeng Li
Summary: The study on the use of blowing and suction control strategies in wall-bounded turbulence shows that blowing enhances the contribution of large-scale motions and suppresses that of small scales, while suction behaves contrarily. However, the contributions related to cross-scale interactions remain almost unchanged with different control strategies.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Mechanics
Kushal U. Kempaiah, Fulvio Scarano
Summary: This study quantifies the distortions of turbulent structures induced by spanwise wall oscillations through feature analysis and compares the results with statistical analysis to understand the mechanism of drag reduction. The findings suggest that the rear region close to the wall is primarily affected by the wall motion, inhibiting hairpin auto-generation and resulting in different organizations of turbulent structures in the near-wall region. The reduction of low-speed streaks and ejections further supports the hypothesis of rapid lateral distortion being responsible for drag reduction.
Article
Physics, Multidisciplinary
F. Serafini, F. Battista, P. Gualtieri, C. M. Casciola
Summary: In DNA suspensions, the maximal drag reduction is achieved when the macromolecules are fully stretched, contrary to the assumptions of classical viscoelastic models.
PHYSICAL REVIEW LETTERS
(2022)
Article
Mechanics
J. M. F. Peter, M. J. Kloker
Summary: This article introduces the research on film cooling technology for the nozzle extension of rocket engines. By conducting basic experiments and numerical simulations, several new influencing factors have been identified, which are of great guidance for improving cooling modeling and simulation.
Article
Physics, Multidisciplinary
Fangying Song, George Em Karniadakis
Summary: Modeling wall-bounded turbulent flows using fractional calculus, a fundamentally new approach, revealed a universal form of variable fractional order for all Reynolds numbers and three different flow types. Continuous changes in turbulent diffusion rate from the wall and strong nonlocality of turbulent interactions intensifying away from the wall were discovered through the study, which utilized existing databases and experimental measurements.
Article
Physics, Fluids & Plasmas
Subharthi Chowdhuri, Tirtha Banerjee
Summary: In this study, we investigated the burstlike activities in turbulent signals from a multiscale perspective using two data sets. The burstiness index was used to describe the multiscale nature of turbulent bursting, and the effect of Reynolds number on bursting events was assessed. The results showed that large amplitude fluctuations in the turbulence signals were governed by coherent structures in the flow, irrespective of Reynolds number. However, a Reynolds number dependence was observed in the smallscale turbulence. The research provides a way to evaluate the effect of bursts on turbulence statistics at specified scales of the flow.
PHYSICAL REVIEW FLUIDS
(2023)
Article
Mechanics
Carlo Cossu
Summary: In this study, the genesis of large-scale coherent rolls in turbulent wall-bounded flows was investigated through linear stability analysis, revealing the importance of modeling turbulent Reynolds stresses for consistent predictions. The onset of large-scale convection was found to be associated with a critical friction Richardson number.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
Kai Fukami, Koji Fukagata, Kunihiko Taira
Summary: The proposed method uses supervised machine learning techniques to reconstruct high-resolution turbulent flows from coarse data, accurately reproducing flow fields and tracking temporal evolution, demonstrating strong capability and robustness.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Mechanics
Taichi Nakamura, Kai Fukami, Kazuto Hasegawa, Yusuke Nabae, Koji Fukagata
Summary: The applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows is investigated. The combination of CNN-AE and LSTM in the current ML-ROM successfully reproduces turbulent flow fields and conducts statistical analysis.
Article
Engineering, Aerospace
Masahiro Ohashi, Koji Fukagata, Naoko Tokugawa
Summary: The study investigates the sensitivities of drag and lift to body forces and blowing/suction through a continuous adjoint method. It finds that effective control can significantly improve the lift-to-drag ratio by reducing pressure drag and enhancing pressure lift. The analysis also shows that control amplitude, Reynolds number, and angle of attack can influence the sensitivities.
Article
Mechanics
Kai Fukami, Takaaki Murata, Kai Zhang, Koji Fukagata
Summary: The study focuses on sparse identification of nonlinear dynamics (SINDy) for low-dimensional complex flow phenomena. By utilizing a convolutional neural network-based autoencoder (CNN-AE) to map high-dimensional dynamics into a low-dimensional latent space, and combining it with a CNN decoder to remap the low-dimensional vector, the study successfully reproduces high-dimensional flow fields through SINDy and CNN-SINDy modeling.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Y. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata, P. Schlatter
Summary: The study demonstrates the flexibility, efficiency, and versatility of the BO-GPR approach in CFD applications, highlighting advantages such as diverse optimization problems, independence of the approach, ease of using different CFD solvers, and requiring a relatively small number of flow simulations.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Masaki Morimoto, Kai Fukami, Kai Zhang, Koji Fukagata
Summary: This paper explores techniques to promote the practical use of neural networks in fluid flow estimation, focusing on challenges such as interpretability of machine-learned results, bulking out of training data, and generalizability of neural networks. The study demonstrates methods to enhance interpretability and generalizability, as well as techniques to increase training data for fluid flow problems, indicating promising results for applications of machine learning in fluid dynamics.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Geochemistry & Geophysics
Daisuke Hiruma, Ryo Onishi, Keiko Takahashi, Koji Fukagata
Summary: By strategically using air conditioners, storms can be modulated downstream, reducing rainfall and mitigating flood disasters. The study reveals that removing a significant amount of moisture from a city can significantly reduce rainfall accumulation, highlighting the potential for coupling weather with the economy and promoting the development of a sustainable society.
ATMOSPHERIC SCIENCE LETTERS
(2022)
Article
Multidisciplinary Sciences
Taichi Nakamura, Kai Fukami, Koji Fukagata
Summary: This paper investigates the fundamental differences between neural networks and linear stochastic estimation in fluid-flow regressions. Through comparisons and analyses of two fluid-flow problems, the study demonstrates that neural networks outperform linear methods due to the presence of nonlinear activation functions.
SCIENTIFIC REPORTS
(2022)
Article
Thermodynamics
Yusuke Nabae, Koji Fukagata
Summary: Direct numerical simulation of a fully developed turbulent channel flow controlled using a streamwise traveling wave reveals that the spanwise variation of the wave affects the drag reduction effect, with larger wavelengths resulting in more significant reductions. The flow field becomes more uniform in the streamwise direction but less uniform in the spanwise direction with the wave-machine-like traveling wave compared to the spanwise-uniform traveling wave.
FLOW TURBULENCE AND COMBUSTION
(2022)
Article
Thermodynamics
Taichi Nakamura, Koji Fukagata
Summary: This study focuses on the capability of neural networks in fluid flow estimation problems, with an emphasis on robust training. Utilizing a convolutional neural network, the study investigates the practicality of the models in estimating velocity fields from sectional sensor measurements and examines the effectiveness of various training approaches for robustness against sensor limitations. The findings from this study can potentially contribute to the development of practical machine learning techniques in fluid flow modeling.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
(2022)
Article
Mathematics, Applied
Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata
Summary: The paper utilizes Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty in neural-network-based function approximation for fluid flows. With SWAG, multiple models with different combinations of weights can be created to obtain ensemble predictions. The average of the ensemble represents the mean estimation, while the standard deviation can be used to construct confidence intervals for uncertainty quantification. The method is applicable for various complex datasets and network architectures. The authors demonstrate its applicability for different types of neural networks and find that SWAG provides physically-interpretable confidence-interval estimates.
PHYSICA D-NONLINEAR PHENOMENA
(2022)
Article
Thermodynamics
Marco Atzori, Fermin Mallor, Ramon Pozuelo, Koji Fukagata, Ricardo Vinuesa, Philipp Schlatter
Summary: For adverse-pressure-gradient turbulent boundary layers, the aggregation of different skin-friction contributions still presents challenges due to the significant in-homogeneity in the flow. In this study, a new formulation of the identity derived from the convective form of the governing equations is proposed, considering wall-tangential convection and pressure gradient together. This formulation allows for the identification of different regimes and provides a more effective description of control effects.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
(2023)
Article
Computer Science, Artificial Intelligence
Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira
Summary: This text discusses the challenges of accurately reconstructing global time-evolving fields using a data-driven spatial field recovery technique and structured grid-based deep learning approach from a limited number of sensors. By leveraging Voronoi tessellation to handle sensors at arbitrary positions, it overcomes major limitations of existing reconstruction methods and allows for handling moving sensors.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Mechanics
Mehdi Badri Ghomizad, Hosnieh Kor, Koji Fukagata
Summary: A versatile and accurate structured adaptive mesh refinement (S-AMR) strategy is developed for incompressible fluid-structure interaction (FSI) simulations, featuring nested blocks of different refinement levels and novel techniques for data transfer and mass conservation. The method demonstrates robustness and accuracy in handling complex test cases and simulation-driven mesh adaptivity.
JOURNAL OF FLUID SCIENCE AND TECHNOLOGY
(2021)
Article
Mechanics
Mehdi Badri Ghomizad, Hosnieh Kor, Koji Fukagata
Summary: The proposed sharp interface direct-forcing immersed boundary method utilizes the Moving Least Square approximation for incompressible fluid flows with fixed and moving boundaries. By employing a two-step predictor-corrector method to mitigate numerical oscillation, the method is able to handle complex moving problems accurately. The approach shows potential for versatile interpolation and sharp boundary conditions, demonstrating effectiveness in fluid-structure interaction problems.
JOURNAL OF FLUID SCIENCE AND TECHNOLOGY
(2021)
