Article
Mechanics
Jiahan Wang, Joern Sesterhenn, Wolf-Christian Mueller
Summary: This study compares different structure detection schemes in two-dimensional turbulence and examines their effects on the inverse cascade. The results show that coherent structures contribute less to the cross-scale flux of energy, but play a significant role in deforming the energy spectrum at large scales.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Mechanics
Brendan Keith, Ustim Khristenko, Barbara Wohlmuth
Summary: This paper introduces a class of turbulence models based on fractional partial differential equations with stochastic loads, where solutions are incompressible velocity fields with Gaussian distributions. Interaction between turbulence and solid walls is achieved through various boundary conditions, allowing flexibility in simulating near-wall statistics. Two simple physical applications are emphasized, including the reproduction of fully developed shear-free and uniform shear boundary layer turbulence, with the former validated using experimental data. Additionally, the paper discusses the generation of inhomogeneous synthetic turbulence inlet boundary conditions, inspired by contemporary numerical wind tunnel simulations, as well as calibration of model parameters and efficient numerical methods.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Mechanics
Rafaello D. Luciano, X. B. Chen, D. J. Bergstrom
Summary: This study aims to investigate the characteristics of flow through a pipe with a sudden expansion at low Reynolds numbers, and the possible sources of perturbation in numerical simulations and their effect on transition to turbulence. The results suggest that perturbations need to be added in refined simulations in order to produce turbulence, and even without intentional perturbations, there can still be significant sources of numerical perturbation and error that trigger turbulence in simulations.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
S. Rezaeiravesh, T. Mukha, P. Schlatter
Summary: Multifidelity models (MFMs) are used to construct predictive models for flow quantities of interest (QoIs) over uncertain/design parameters, for the purpose of uncertainty quantification, data fusion, and optimization. The hierarchical MFM strategy allows for simultaneous calibration of fidelity-specific parameters in a Bayesian framework, combining lower and higher-fidelity data in an optimal way to provide improved prediction and confidence intervals for QoIs.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Mechanics
David Dritschel, Matthias Frey
Summary: We present a family of exact inviscid three-dimensional Beltrami flows with varying horizontal vorticity on each boundary. Direct numerical simulations show that the largest-scale member of the family is unstable and transitions into anisotropic turbulence, characterized by large horizontal vorticity and surface frontal features. The study highlights the role of free-slip boundaries in constraining vortex line deformation and explores the role of boundary horizontal vorticity in an inviscid context.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Mechanics
Renato Miotto, William Wolf, Datta Gaitonde, Miguel Visbal
Summary: The onset and evolution of the dynamic stall vortex are analyzed using large eddy simulations. The results show that the Kelvin-Helmholtz instability interacts with the shear layer at the leading edge, triggering flow separation. Comparisons between different Mach numbers reveal that increased compressibility leads to weaker and more diffuse vortices. The chord-normal shear layer height is found to be a more robust criterion for characterizing the onset of the dynamic stall vortex. Modal decomposition methods are used to extract physically meaningful flow structures related to the stall onset.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
J. A. K. Horwitz, G. Iaccarino, J. K. Eaton, A. Mani
Summary: A methodology for simulating two-way coupled particle-laden flows is outlined in this study, which accurately models the interaction between fluid and particles at low particle Reynolds numbers and can be extended to other physics like heat transfer and electromagnetism. By verifying and extending the discrete Green's function method, accurate results for particle-laden flows under different conditions can be obtained.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Mechanics
Xander M. de Wit, Adrian van Kan, Alexandros Alexakis
Summary: In this study, direct numerical simulations of thin-layer flow were used to investigate whether the bistable range survives as the domain size and turbulence intensity are increased. The research found that the bistable range grows as the box size and/or Reynolds number Re are increased.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
Yangwei Liu, Weibo Zhong, Yumeng Tang
Summary: The paper establishes theoretical relationships between Q series vortex criteria, eigenvalue-based vortex criteria, and the Rortex method based on LT criterion, and visually analyzes their physical meanings and interrelations on the LT-plane. The LTcri-based method effectively reflects local swirling patterns, provides new interpretations of vortex criteria, and shows potential in analyzing vortex dynamics and distinguishing swirling patterns of complex vortices.
Article
Computer Science, Interdisciplinary Applications
A. C. W. Creech, A. Jackson
Summary: This paper introduces a hybrid approach for explicitly-filtered Large Eddy Simulation using a Discontinous Galerkin discretisation for velocity, which incorporates information from a Continuous Galerkin version of the velocity field to improve computational performance while maintaining stability and accuracy.
COMPUTER PHYSICS COMMUNICATIONS
(2021)
Article
Physics, Fluids & Plasmas
B. Ripperda, J. F. Mahlmann, A. Chernoglazov, J. M. TenBarge, E. R. Most, J. Juno, Y. Yuan, A. A. Philippov, A. Bhattacharjee
Summary: Excited Alfven waves in systems such as black holes and neutron stars are the foundation of turbulence. Current sheets form as a result of nonlinear interactions between counter-propagating Alfven waves, acting as locations where magnetic energy dissipates.
JOURNAL OF PLASMA PHYSICS
(2021)
Article
Engineering, Aerospace
Yufang Wang, Nannan Wang
Summary: This study numerically investigates the effects of projectile rotation on the internal and external flow fields of a supersonic fluidic element using the sliding grid technique and RNG k-epsilon turbulence model. The study examines the influence of rotating speed on flow fields, switching time, and output characteristics. The results indicate that the external flow field shows evident asymmetry at an angular velocity of 20 r/s due to the Coriolis force, while the internal flow field is less affected. The switching time decreases with increasing rotational speed, and the deviation from the non-rotating condition is within 5%. The thrust distribution remains relatively unaffected at low rotational speeds but increases in the middle part of the right nozzle at a rotational speed of 50 r/s by approximately 20 N.
Review
Chemistry, Physical
Alhadji Malloum, Kayode A. Adegoke, Joshua O. Ighalo, Jeanet Conradie, Chinemerem R. Ohoro, James F. Amaku, Kabir O. Oyedotun, Nobanathi W. Maxakato, Kovo G. Akpomie, Emmanuel S. Okeke, Chijioke Olisah
Summary: Computation methods play a crucial role in wastewater treatment by rapidly identifying, understanding, and predicting the adsorption capacity of materials. Density functional theory is widely used due to its affordability, while molecular dynamics, Monte Carlo simulations, and machine learning-based approaches show potential in this field.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Review
Polymer Science
Zhimin Liu, Zhigang Xu, Dan Wang, Yuming Yang, Yunli Duan, Liping Ma, Tao Lin, Hongcheng Liu
Summary: Molecularly imprinted polymers (MIPs) are obtained through initiating the polymerization of functional monomers around a template molecule in the presence of crosslinkers and porogens. Theoretical calculation methods, including computational simulations and intermolecular forces, have been proven to be effective in optimizing the preparation of MIPs. The progress in research and application of molecularly imprinted polymers prepared by computational simulations and software in the past two decades is reviewed, highlighting the universal applicability of computer molecular simulation methods in MIP-based materials and exploring the new role of computational simulation in the future development of molecular imprinting technology.
Article
Mechanics
Miguel P. Encinar, Javier Jimenez
Summary: The algorithm introduced by Jimenez (J. Fluid Mech., vol. 854, 2018, R1) is used to identify the flow patterns of causal significance in three-dimensional isotropic turbulence. The study finds that the dimensions of the perturbations introduced in the flow are controlled by the kinetic energy content and the enstrophy and dissipation, and affect their significance in the flow. Strain is found to be more efficient than vorticity in propagating the perturbation contents to other regions of the flow. The findings suggest that manipulating strain-dominated vortex clusters is more effective in controlling turbulent flows.
JOURNAL OF FLUID MECHANICS
(2023)
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
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
Mechanics
Calum S. Skene, Chi-An Yeh, Peter J. Schmid, Kunihiko Taira
Summary: This paper considers the use of sparsity-promoting norms to obtain localized forcing structures from resolvent analysis. By formulating the optimal forcing problem as a Riemannian optimization, the authors are able to maximize cost functionals while maintaining a unit-energy forcing. They demonstrate this optimization procedure on two different flow cases and show that it produces sparse forcing modes that maintain important features of the structures arising from an SVD. The results highlight the benefits of utilizing a sparsity-promoting resolvent formulation in flow control applications.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
Anton Burtsev, Wei He, Kai Zhang, Vassilios Theofilis, Kunihiko Taira, Michael Amitay
Summary: This paper investigates the linear modal instabilities of flow over untapered wings with aspect ratios AR = 4 and 8. Multiple unstable linear global modes have been identified and their dependence on geometric parameters has been examined. It is found that tip vortex effects dominate on the AR = 4 wing, while amplification of the leading mode is caused by wing tip recirculation on the AR = 8 wing. These findings are important for understanding flow stability and wake instability.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Mechanics
Jean Helder Marques Ribeiro, Chi-An Yeh, Kai Zhang, Kunihiko Taira
Summary: The study reveals that wing sweep attenuates spanwise fluctuations and influences wake dynamics in terms of stability and spanwise fluctuations, especially in the development of three-dimensional wakes. Global resolvent analysis uncovers oblique modes with high disturbance amplification and shows that for flows at high sweep angles, the optimal convection speed of the response modes is faster than the optimal wavemakers speed, providing insights into the mechanism for the attenuation of perturbations in separated flows at higher Reynolds numbers.
JOURNAL OF FLUID MECHANICS
(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
Mechanics
J. H. Marques Ribeiro, Chi-An Yeh, Kunihiko Taira
JOURNAL OF FLUID MECHANICS
(2023)
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
Mechanics
Yonghong Zhong, Kai Fukami, Byungjin An, Kunihiko Taira
Summary: We developed machine learning methods to reconstruct unsteady vortical flow fields from limited sensor measurements, using only a small amount of training data. The machine learning models accurately reconstructed aerodynamic force coefficients, pressure distributions, and vorticity fields for various cases.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
(2023)
Article
Mechanics
Vishal Anatharaman, Jason Feldkamp, Kai Fukami, Kunihiko Taira
Summary: We investigate the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The effectiveness of spatial compression techniques (including JPEG and JP2) and spatiotemporal video compression techniques (namely H.264, H.265, and AV1) in minimizing compression artifacts and preserving underlying flow physics are examined for different flow scenarios. These compression techniques achieve significant data compression while maintaining dominant flow features with minimal error. AV1 and H.265 compressions demonstrate superior performance across various canonical flow regimes, outperforming traditional techniques like proper orthogonal decomposition in some cases. These image and video compression algorithms are flexible, scalable, and widely applicable in fluid dynamics for data storage and transfer.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
(2023)
Article
Mechanics
Kai Fukami, Koji Fukagata, Kunihiko Taira
Summary: This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flowfields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based superresolution analysis for fluid flow applications. The insights gained from this study can be leveraged for superresolution analysis of numerical and experimental flow data.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
(2023)
Article
Engineering, Aerospace
Dashuai Chen, Frieder Kaiser, Jiacheng Hu, David E. Rival, Kai Fukami, Kunihiko Taira
Summary: This study explores the feasibility of using multilayer perceptron (MLP) for estimating aerodynamic loads in complex gusty environments. The results show that the MLP model is able to accurately estimate the relationship between surface pressure and aerodynamic loads, and reveal the importance of sensors located near the leading edge and nose of the wing.
Article
Multidisciplinary Sciences
Kai Fukami, Kunihiko Taira
Summary: As extreme weather conditions become more frequent, it is crucial for small air vehicles to achieve stable flight in the presence of atmospheric disturbances. However, there is a lack of theoretical understanding of the influence of extreme vortical gusts on wings. In this study, machine learning is used to reveal a low-dimensional manifold that captures the extreme aerodynamics of gust-airfoil interactions, enabling real-time reconstruction, modeling, and control of unsteady gusty flows. These findings provide support for the stable flight of next-generation small air vehicles in adverse weather conditions.
NATURE COMMUNICATIONS
(2023)