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The problem of the geodynamo is simple to formulate (Why does the Earth possess a magnetic field?), yet it proves surprisingly hard to address. As with most geophysical flows, the fluid flow of molten iron in the Earth's core is strongly influenced by the Coriolis effect. Because the liquid is electrically conducting, it is also strongly influenced by the Lorentz force. The balance is unusual in that, whereas each of these effects considered separately tends to impede the flow, the magnetic field in the Earth's core relaxes the effect of the rapid rotation and allows the development of a large-scale flow in the core that in turn regenerates the field. This review covers some recent developments regarding the interplay between rotation and magnetic fields and how it affects the flow in the Earth's core.
Chemical gradients, the spatial variations in chemical concentrations and components, are omnipresent in environments ranging from biological and environmental systems to industrial processes. These thermodynamic forces often play a central role in driving transport processes taking place in such systems. This review focuses on diffusiophoresis, a phoretic transport phenomenon driven by chemical gradients. We begin by revisiting the fundamental physicochemical hydrodynamics governing the transport. Then we discuss diffusiophoresis arising in flow systems found in natural and artificial settings. By exploring various scenarios where chemical gradients are encountered and exploited, we aim to demonstrate the significance of diffusiophoresis and its state-of-the-art development in technological applications.
Raye Jean Montague (1935–2018) was a computer programmer and self-taught engineer who was at the forefront of modernizing naval architecture and naval engineering through the use of computer-aided design. In this biographical review, she is referred to as Montague, the surname she had for much of her professional life. Since she was a working engineer rather than a scholar, she did not create a publication record by which her achievements can be easily tracked, but her name appears in committee memberships, conference and working group proceedings, and other such interstices of computer-aided ship design. This key contributor to computer-aided design and manufacturing and to naval engineering is well worth getting to know.
The environmental setting of the Dead Sea combines several aspects whose interplay creates flow phenomena and transport processes that cannot be observed anywhere else on Earth. As a terminal lake with a rapidly declining surface level, the Dead Sea has a salinity that is close to saturation, so that the buoyancy-driven flows common in lakes are coupled to precipitation and dissolution, and large amounts of salt are being deposited year-round. The Dead Sea is the only hypersaline lake deep enough to form a thermohaline stratification during the summer, which gives rise to descending supersaturated dissolved-salt fingers that precipitate halite particles. In contrast, during the winter the entire supersaturated, well-mixed water column produces halite. The rapid lake level decline of O(1 m/year) exposes vast areas of newly formed beach every year, which exhibit deep incisions from streams. Taken together, these phenomena provide insight into the enigmatic salt giants observed in the Earth's geological record and offer lessons regarding the stability, erosion, and protection of arid coastlines under sea level change.
Thermoacoustic instability is a flow instability that arises due to a two-way coupling between acoustic waves and unsteady heat release rate. It can cause damaging, large-amplitude oscillations in the combustors of gas turbines, aeroengines, rocket engines, etc., and the transition to decarbonized fuels is likely to introduce new thermoacoustic instability problems. With a focus on practical thermoacoustic instability problems, especially in gas turbine combustors, this review presents the common types of combustor and burner geometry used. It discusses the relevant flow physics underpinning their acoustic and unsteady flame behaviors, including how these differ across combustor and burner types. Computational tools for predicting thermoacoustic instability can be categorized into direct computational approaches, in which a single flow simulation resolves all of the most important length scales and timescales, and coupled/hybrid approaches, which couple separate computational treatments for the acoustic waves and flame, exploiting the large disparity in length scales associated with these. Examples of successful computational prediction of thermoacoustic instability in realistic combustors are given, along with outlooks for future research in this area.
Many flows that are expected to be symmetric are actually observed to be asymmetric. The appearance of asymmetry in the face of no particular cause is a widespread although underappreciated occurrence. This rather puzzling and sometimes frustrating phenomenon can occur in wide-angle diffusers, over the forebody of axisymmetric bodies at high angles of attack, in the wake downstream of streamlined as well as bluff bodies, and in the flow over three-dimensional bumps and ramps. We review some notable examples and highlight the extreme sensitivity of many such flows to small disturbances in the body geometry or the incoming flow. Some flows appear to be permanently asymmetric, while others are bistable on timescales that are orders of magnitude longer than any convective timescale. Convective or global instabilities can occur, bistability is common, and mode interactions become important when multiple similar but distinct timescales and length scales are present. Our understanding of these phenomena is still very limited, and further research is urgently required; asymmetries in otherwise symmetric flows can have serious real-world consequences on vehicle control and performance.
Our understanding of respiratory flow phenomena has been consolidated over decades with the exploration of in vitro and in silico canonical models that underscore the multiscale fluid mechanics spanning the vast airway complex. In recent years, there has been growing recognition of the significant intersubject variability characterizing the human lung morphometry that modulates underlying canonical flows across subjects. Despite outstanding challenges in modeling and validation approaches, exemplified foremost in capturing chronic respiratory diseases, the field is swiftly moving toward hybrid in silico whole-lung simulations that combine various model classes to resolve airflow and aerosol transport spanning the entire respiratory tract over cumulative breathing cycles. In the years to come, the prospect of accessible, community-curated datasets, in conjunction with the use of machine learning tools, could pave the way for in silico population-based studies to uncover unrecognized trends at the population level and deliver new respiratory diagnostic and pulmonary drug delivery endpoints.
The merging of two fluid drops is one of the fundamental topological transitions occurring in free surface flow. Its description has many applications, for example, in the chemical industry (emulsions, sprays, etc.), in natural flows driving our climate, and for the sintering of materials. After the reconnection of two drops, strongly localized surface tension forces drive a singular flow, characterized by a connecting liquid bridge that grows according to scaling laws. We review theory, experiment, and simulation of the coalescence of two spherical drops for different parameters and in the presence of an outer fluid. We then generalize to other geometries, such as drops spreading on a substrate and in Hele–Shaw flow, and we discuss other types of mass transport, apart from viscous flow. Our focus is on times immediately after reconnection and on the limit of initially undeformed drops at rest relative to one another.
By imploding fuel of hydrogen isotopes, inertial confinement fusion (ICF) aims to create conditions that mimic those in the Sun's core. This is fluid dynamics in an extreme regime, with the ultimate goal of making nuclear fusion a viable clean energy source. The fuel must be reliably and symmetrically compressed to temperatures exceeding 100 million degrees Celsius. After the best part of a century of research, the foremost fusion milestone was reached in 2021, when ICF became the first technology to achieve an igniting fusion fuel (thermonuclear instability), and then in 2022 scientific energy breakeven was attained. A key trade-off of the ICF platform is that greater fuel compression leads to higher burn efficiency, but at the expense of amplified Rayleigh–Taylor and Richtmyer–Meshkov instabilities and kinetic-energy-wasting asymmetries. In extreme cases, these three-dimensional instabilities can completely break up the implosion. Even in the highest-yielding 2022 scientific breakeven experiment, high-atomic-number (high-Z) contaminants were unintentionally injected into the fuel. Here we review the pivotal role that fluid dynamics plays in the construction of a stable implosion and the decades of improved understanding and isolated experiments that have contributed to fusion ignition.
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s):
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s): Christian Voß, Martin Siggel, Jan Backhaus, Georgios Goinis, Andreas Pahs
Publication date: Available online 26 March 2025
Source: Computers & Fluids
Author(s): Ksenia Kozhanova, Yannick Hoarau, Eric Goncalves da Silva
Publication date: Available online 24 March 2025
Source: Computers & Fluids
Author(s): P. Olivucci, X. Shao, M. Albers, W. Schröder, R. Semaan
Publication date: Available online 21 March 2025
Source: Computers & Fluids
Author(s): Fandi D. Suprianto, Ming-Jyh Chern, Chin-Cheng Wang
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s): Krishnan Swaminathan Gopalan, Arnaud Borner, Kelly A. Stephani
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s): Jérôme Breil, Guillaume Damour, Sébastien Guisset, Arnaud Colaïtis
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s): Hoai-Thanh Nguyen, Byeong-Cheon Kim, Sang-Wook Lee, Jaiyoung Ryu, Minjae Kim, Jaemoon Yoon, Kyoungsik Chang
Publication date: 15 May 2025
Source: Computers & Fluids, Volume 293
Author(s): Jiaxian Qin, Yaming Chen, Xiaogang Deng
Publication date: 30 April 2025
Source: Computers & Fluids, Volume 292
Author(s): Omar Sallam, Mirjam Fürth
Reinforcement learning (RL) is a computational method where an agent learns to complete tasks by interacting with an unknown dynamic environment. An RL agent comprises a policy and a learning algorithm. The policy, typically a function approximator like a neural network, maps observations from the environment to actions. The Actor Network decides which actions to take grounded in the present observations, while the Critic Network assesses these actions by assessing their rewards or penalties. The learning algorithm updates the policy using the feedback from the Critic Network to optimize cumulative rewards.
This work investigates the optimization of airfoil shapes using various reinforcement learning (RL) algorithms, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Trust Region Policy Optimization (TRPO). The primary objective is to enhance the aerodynamic performance of airfoils by maximizing lift forces across different angles of attack (AoA). The study compares the optimized airfoils against the standard NACA 2412 airfoil. The DDPG-optimized airfoil demonstrated superior performance at lower and moderate AoAs, while the TRPO-optimized airfoil excelled at higher AoAs. In contrast, the TD3-optimized airfoil consistently underperformed. The results indicate that RL algorithms, particularly DDPG and TRPO, can effectively improve airfoil designs, offering substantial benefits in lift generation. This paper underscores the potential of RL techniques in aerodynamic shape optimization, presenting significant implications for aerospace and related industries.
The CIP (constrained interpolation profile)-Soroban method is an excellent adaptive method. This study proposed a modified CIP-Soroban method to handle scenarios involving severe compressible hydrodynamics with large gradients of physical values and strong nonlinearity. We took the implosion process of inertial confinement fusion as an application example (see snapshots in [a, b]), which demonstrated that the method reduced computational costs for achieving the same precision results compared to the conventional uniform grid CIP method (as shown in [c, d]).
The CIP-Soroban method is an excellent adaptive meshless method capable of solving advection problems with 3rd-order accuracy by combining the Constrained Interpolation Profile/Cubic Interpolated Pseudo-particle (CIP) method. This study proposes a modified version of the CIP-Soroban method specifically designed to address severe compressible hydrodynamic scenarios. The proposed method includes a material distinguishing approach, incorporates a modified form of monitoring functions for grid generation, utilizes a staggered grid arrangement, incorporates the Maximum and minimum Bounds method, solves non-advection terms using a finite difference method, and employs an adjusted procedure for stably solving the governing equations. We applied the modified CIP-Soroban method to simulate the implosion process in inertial confinement fusion (ICF), which is commonly modeled by compressible fluid and has the problems of large gradients of physical values and strong nonlinearity for stable and accurate numerical analysis. Implosion simulations were performed using a series of grids with increasing resolutions, ranging from coarse to fine grid settings, as one of the application examples. The results indicated that compared to the conventional uniform grid CIP method, the modified CIP-Soroban method reduced computational costs (calculation time, memory occupancy, and grid number) for obtaining the same precision results.
In nature, many complex multi-physics coupling problems exhibit significant diffusivity inhomogeneity, where one process occurs several orders of magnitude faster than others temporally. Simulating rapid diffusion alongside slower processes demands intensive computational resources due to the necessity for small time steps. To address these computational challenges, we have developed an efficient numerical solver named Finite Difference informed Random Walker (FDiRW). In this study, we propose a GPU-accelerated, mixed-precision configuration for the FDiRW solver to maximize efficiency through GPU multi-threaded parallel computation and lower precision computation. Numerical evaluation results reveal that the proposed GPU-accelerated mixed-precision FDiRW solver can achieve a 117× speedup over the CPU baseline, while an additional 1.75× speedup is achieved by employing lower precision GPU computation. Notably, for large model sizes, the GPU-accelerated mixed-precision FDiRW solver demonstrates strong scaling with the number of nodes used in simulation. When simulating radionuclide absorption processes by porous wasteform particles with a medium-sized model of 192 × 192 × 192, this approach reduces the total computational time to 10 min, enabling the simulation of larger systems with strongly inhomogeneous diffusivity.
In this article, we derive a non-hydrostatic extension to the SWE to solve bottom-generated waves along with its pressure relation. This relation is built on a linear vertical velocity assumption, leading us to a quadratic pressure profile, where we alternatively write it so that we can solve it by a projection method without ambiguity due to the involved time derivative of an unknown. Comparison with a linear and a simplified quadratic pressure relation demonstrates the accuracy of the new approach.
We formulate a depth-averaged non-hydrostatic model to solve wave equations with generation by a moving bottom. This model is built on the shallow water equations, which are widely used in tsunami wave modelling. An extension leads to two additional unknowns to be solved: vertical momentum and non-hydrostatic pressure. We show that a linear vertical velocity assumption turns out to give us a quadratic pressure relation, which is equivalent to Boussinesq-type equations, the Green-Naghdi equations specifically, making it suitable for weakly dispersive cases. However, this extension involves a time derivative of an unknown parameter, rendering the solution by a projection method ambiguous. In this study, we derive an alternative form of the elliptic system of equations to avoid such ambiguity. The new set of equations satisfies the desired solubility property, while also consistently representing the non-flat moving topography wave generation. Validations are performed using several test cases based on the previous experiments and a high-fidelity simulation. First, we show the efficiency of our model in solving a vertical movement, which represents an undersea earthquake-generated tsunami. Following that, we demonstrate the accuracy of the model for landslide-generated waves. Finally, we compare the performance of our novel set of equations with the linear and simplified quadratic pressure profiles.
An extended height function method for 3D VOF simulations applicable to the wetting phenomena on super-hydrophilic and super-hydrophobic surfaces is proposed. By implementing specific treatments of contact line identification and height function construction, reflecting the contact angle boundary condition, the proposed method ensures the first- or second-order convergence of the curvature at the contact line for a wide range of contact angles. Additionally, droplet spreading driven by surface tension on solid walls can be reproduced.
An extended height-function (HF) method that can be consistently utilized for 3D volume of fluid (VOF) simulations of wetting phenomena on super-hydrophilic and super-hydrophobic surfaces, is proposed. First, the standard HF method is briefly explained. Then, 2D and 3D HF methods that reflect the contact angles reported so far are described, with their limitations discussed. Finally, specific treatments of contact line identification and HF construction reflecting the contact angle boundary condition, required to overcome such limitations, are presented in detail. Numerical tests for a sessile droplet reveal that the contact line identification and HF construction are conducted appropriately with respect to the imposed contact angles ranging from 15∘$$ 1{5}^{\circ } $$ to 165∘$$ 16{5}^{\circ } $$ in the proposed numerical scheme. Additionally, the present method shows approximately first- or second-order convergence of the curvature at the contact line for a wide range of contact angles. Moreover, simulations of droplet spreading driven by surface tension reveal that the proposed method can reasonably reproduce the behavior of a droplet reaching an equilibrium state defined by an imposed contact angle.
A cavitation implementation algorithm is developed using a pressure-based method for incompressible flows with three-phase interactions, which involve high Reynolds number multi-phase turbulent flows interacting with moving bodies of complex geometries.
In the present study, a cavitation implementation algorithm is developed using a pressure-based method for incompressible flows with three-phase interactions. Central to this implementation algorithm is the treatment of the velocity jump due to the phase change, which is included in both the cavitation transport and pressure equations. The velocity jump, as a function of the phase change rate, is added as a source term to the pressure Poisson equation. A non-conservative form of the vapor transport equation is derived, and the velocity divergence is replaced by a term related to the mass phase change rate. An algorithm for the three-phase (air, water, and vapor) interactions is also developed. The VOF method is modified and used to identify the ‘dry’ (air) phase and the ‘wet’ (water/vapor mixture) phase, since the cavitation can only occur inside the water phase. The liquid volume fraction is used to distinguish water and vapor phases. The numerical results of the 2D NACA66MOD and 3D Delft Twist 11 hydrofoils show good agreement with the experimental measurement. The forced unsteady cavitation flows are investigated using a pitching foil with the results compared with the experimental observations. Air–water interface effect on the cavitation is investigated using the NACA66MOD hydrofoil. The code is applied to simulate a surface piercing super cavitating hydrofoil with both ventilation and cavitation involved.
We present a non-dissipative, energy-conserving, arbitrary high-order Hybridizable Discontinuous Galerkin method for the incompressible Navier–Stokes equations on triangular/tetrahedral unstructured meshes. An efficient implementation of the method using orthonormal and degree-ordered basis functions is given. Numerical experiments are shown that demonstrate the various features of the proposed method.
In the inviscid limit, the energy of a velocity field satisfying the incompressible Navier–Stokes equations is conserved. Non-dissipative numerical methods that discretely mimic this energy conservation feature have been demonstrated in the literature to be extremely valuable for robust and accurate large-eddy simulations of high Reynolds number incompressible turbulent flows. For complex geometries, such numerical methods have been traditionally developed using the finite volume framework and they have been at best second-order accurate. This paper proposes a non-dissipative and energy-conserving numerical method that is arbitrary high-order accurate for triangle/tetrahedral meshes along with its efficient implementation. The proposed method is a Hybridizable Discontinuous Galerkin (HDG) method. The crucial ingredients of the numerical method that lead to the discretely non-dissipative and energy-conserving features are: (i) The tangential velocity on the interior faces, just for the convective term, is set using the non-dissipative central scheme and the normal velocity is enforced to be continuous, that is, H$$ H $$(div)-conforming. (ii) An exactly (pointwise) divergence-free basis is used in each element of the mesh for the stability of the convective discretization. (iii) The combination of velocity, pressure, and velocity gradient spaces is carefully chosen to avoid using stabilization which would introduce numerical dissipation. The implementation description details our choice of the orthonormal and degree-ordered basis for each quantity and the efficient local and global problem solution using them. Numerical experiments demonstrating the various features of the proposed method are presented. The features of this HDG method make it ideal for high-order LES of incompressible flows in complex geometries.
In this paper, we shall be concerned with the development, application, and numerical analysis of the monolithic Newton-Multigrid finite element method (FEM) to simulate thixoviscoplastic (TVP) flows. We demonstrate the importance of robustness and efficiency of Newton-Multigrid FEM solver for obtaining accurate solutions. To put our work in proper perspective w.r.t. the delicate challenge of obtaining accurate numerical solutions for TVP flow problems, we content our investigation to TVP quasi-Newtonian modeling approach with an extensive analysis on lid-driven cavity flows, and expose the impact of thixotropic scale in 4:1 contraction configuration application. fldauth.cls class file for setting papers for the International Journal for Numerical Methods in Fluids. Copyright 2010 John Wiley & Sons Ltd.
In this paper, we shall be concerned with the development, application, and numerical analysis of the monolithic Newton-Multigrid finite element method (FEM) to simulate thixoviscoplastic (TVP) flows. We demonstrate the importance of robustness and efficiency of Newton-Multigrid FEM solver for obtaining accurate solutions. To put our work in proper perspective w.r.t. the delicate challenge of obtaining accurate numerical solutions for TVP flow problems, we restrict our investigation to TVP quasi-Newtonian modeling approach and lid-driven cavity flows.
An improved single-layer smoothed particle hydrodynamics (SPH) model is proposed for water–soil two-phase flow modeling. While inheriting the higher efficiency of the single-layer SPH modeling comparing with the multilayer counterpart, the proposed method is equipped with the newly proposed soakage function, diffusion terms, and the revised particle shifting algorithm. This method shows improved accuracy and stability in recovering complex interfacial behaviors, comparing with previous versions of single-layer SPH, which is supported by abundant numerical evidence in four benchmark examples.
In coastal and offshore engineering, the intense water–soil motion poses significant challenges to the safety of buildings and structures. The smoothed particle hydrodynamics (SPH) method, as a mesh-free Lagrangian solver, has considerable advantages in the numerical resolution of such problems. SPH models for the water–soil two-phase flow can be categorized into the multilayer type and the single-layer type. Although the single-layer model envisions a simpler algorithm and higher computational efficiency, its accuracy, stability, and recovery of interfacial details are far from satisfactory. In the present work, an improved single-layer model is established to alleviate these limitations. First, the soakage function, which takes effect near the phase interface, is introduced to characterize the two-phase coupling status. Additionally, the stress diffusion term and a modified density diffusion term applicable in density discontinuity scenario are introduced to ease the numerical oscillation. Finally, to remove the unphysical voids in the interfacial region, the particle shifting technique with special treatment tailored for free-surface particles is implemented. Validations of the proposed model are carried out by a number of numerical tests, including the erodible dam-break problem, the wall-jet scouring, the flushing case, and the water jet excavation. Appealing agreements with either experimental data or published numerical results have been achieved, which verifies the accuracy, stability, and robustness of the proposed model for water–soil two-phase flows.
We present an augmented Lagrangian trust-region method to efficiently solve constrained optimization problems governed by large-scale nonlinear systems using hyperreduced models constructed on-the-fly. Our approach circumvents a traditional training phase and ensures global convergence of the augmented Lagrangian subproblem. A speedup of 12.7×$$ 12.7\times $$ relative to a standard optimization approach that does not leverage model reduction is shown for aerodynamic shape optimization.
We present an augmented Lagrangian trust-region method to efficiently solve constrained optimization problems governed by large-scale nonlinear systems with application to partial differential equation-constrained optimization. At each major augmented Lagrangian iteration, the expensive optimization subproblem involving the full nonlinear system is replaced by an empirical quadrature-based hyperreduced model constructed on-the-fly. To ensure convergence of these inexact augmented Lagrangian subproblems, we develop a bound-constrained trust-region method that allows for inexact gradient evaluations, and specialize it to our specific setting that leverages hyperreduced models. This approach circumvents a traditional training phase because the models are built on-the-fly in accordance with the requirements of the trust-region convergence theory. Two numerical experiments (constrained aerodynamic shape design) demonstrate the convergence and efficiency of the proposed work. A speedup of 12.7×$$ 12.7\times $$ (for all computational costs, even costs traditionally considered “offline” such as snapshot collection and data compression) relative to a standard optimization approach that does not leverage model reduction is shown.
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Benjamin A. Hyatt, Daniel Lecoanet, Evan H. Anders, Keaton J. Burns
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Chen Chen, Jiuyang Liang, Zhenli Xu
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Dong Min Roh, Dean Lee, Pieter Maris, Esmond Ng, James P. Vary, Chao Yang
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Yajuan Wang, Youjun Deng, Fanbo Sun, Lingzheng Kong
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Tamara A. Tambyah, David Lee, Santiago Badia
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Gnana Murugan Subramaniam, Prakash Vedula
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Hadden Kim, Tamer A. Zaki
Publication date: 15 June 2025
Source: Journal of Computational Physics, Volume 531
Author(s): Kai Yan, Chengbao Yao
Publication date: Available online 19 March 2025
Source: Journal of Computational Physics
Author(s): Jason Hicken, Ge Yan, Sharanjeet Kaur
Publication date: Available online 6 March 2025
Source: Journal of Computational Physics
Author(s): T.D. Sandnes, V.R. Eke, J.A. Kegerreis, R.J. Massey, S. Ruiz-Bonilla, M. Schaller, L.F.A. Teodoro
