International Journal for Numerical Methods in Fluids最新文献

{"title":"Lattice Boltzmann Simulation of Droplet Impact on an Oscillating Horizontal Surface","authors":"Qing Li, Juanyong Wang, Lei Wang, Zijin Zhu","doi":"10.1002/fld.70079","DOIUrl":"https://doi.org/10.1002/fld.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>In this work, the droplet impact on a sinusoidally oscillating hydrophobic substrate is numerically simulated using the lattice Boltzmann method. We focus particularly on energy transfer and the maximum droplet spreading diameter (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_m $$</annotation>\u0000 </semantics></math>) as functions of substrate oscillation parameters, the Weber number, and the wettability of the substrate. Our analysis shows that the maximum spreading diameter scales linearly with the tangential Weber number (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ W{e}_s $$</annotation>\u0000 </semantics></math>), calculated from the substrate velocity amplitude. Notably, the spreading dynamics exhibit a nonmonotonic, resonance-like response, peaking when the spreading time coincides with half the oscillation period. Additionally, decreasing the contact angle strengthens interfacial adhesion to amplify horizontal migration, whereas superhydrophobic surfaces severely attenuate this tangential momentum exchange through inherent slip. Furthermore, the analysis of the normal Weber number (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ W{e}_n $$</annotation>\u0000 </semantics></math>) uncovers a “forgetting” mechanism, whereby the droplet ultimately converges to a consistent dynamic response across different <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ W{e}_n $$</annotation>\u0000 </semantics></math> as the initial kinetic energy dissipates. The study further elucidates the distinct regulatory effects of amplitude and period: amplitude primarily shifts the overall <span></span><math>\u0000 ","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1017-1034"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine Learning Methods to Predict the Temperature Value in Conjugate Convective Heat Transfer in a Fully Insulated Enclosure With Different Thermal Conductivity Properties","authors":"Alibek Issakhov,&nbsp;Aidana Sabyrkulova,&nbsp;Aizhan Abylkassymova","doi":"10.1002/fld.70067","DOIUrl":"https://doi.org/10.1002/fld.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines the influence of the thermophysical properties of various partition materials on heat transfer processes in a cavity filled with liquid. To achieve this goal, both direct computational fluid dynamics (CFD) and machine learning (ML) methods were applied. Direct CFD simulations were performed to obtain a detailed understanding of the temperature distribution within the cavity for different septum materials. Subsequently, machine learning methods were applied to create regression models capable of predicting changes in the temperature value inside the cavity based on different septum materials. The best results were achieved using third-degree polynomial regression, achieving a coefficient of determination (<i>R</i>\u0000 ²) of 0.816. This indicates that the model is well adapted to the data and is able to explain the variability of the target variable. Additionally, random forest regression models were examined, but their results were slightly less accurate compared to polynomial regression. The study demonstrates that integrating machine learning techniques into computational fluid dynamics can significantly improve the analysis and prediction of thermal processes.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1083-1101"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicontinuum Homogenization for Poroelasticity Model","authors":"Dmitry Ammosov,&nbsp;Mohammed Al Kobaisi,&nbsp;Yalchin Efendiev","doi":"10.1002/fld.70080","DOIUrl":"https://doi.org/10.1002/fld.70080","url":null,"abstract":"<p>In this paper, we derive multicontinuum poroelasticity models using the multicontinuum homogenization method. Poroelasticity models are widely used in many areas of science and engineering to describe coupled flow and mechanics processes in porous media. However, in many applications, the properties of poroelastic media possess high contrast, presenting serious computational challenges. It is well known that standard homogenization approaches often fail to give an accurate solution due to the lack of macroscopic parameters. Multicontinuum approaches allow us to consider such cases by defining several average states known as continua. In the field of poroelasticity, multiple-network models arising from the multiple porous media theory are representatives of these approaches. In this work, we extend previous findings by deriving the generalized multicontinuum poroelasticity model. We apply the recently developed multicontinuum homogenization method and provide a rigorous derivation of multicontinuum equations. For this purpose, we formulate coupled constraint cell problems in oversampled regions to consider different averages and gradient effects. Then, we obtain a multicontinuum expansion of the fine-scale fields and derive the multicontinuum model supposing the smoothness of macroscopic variables. We present the most general version of equations and the simplified ones based on our numerical experiments. Numerical results are presented for different heterogeneous media cases and demonstrate the high accuracy of our proposed multicontinuum models.</p>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1035-1060"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fld.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine Learning Methods to Predict the Temperature Value in Conjugate Convective Heat Transfer in a Fully Insulated Enclosure With Different Thermal Conductivity Properties","authors":"Alibek Issakhov,&nbsp;Aidana Sabyrkulova,&nbsp;Aizhan Abylkassymova","doi":"10.1002/fld.70067","DOIUrl":"https://doi.org/10.1002/fld.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines the influence of the thermophysical properties of various partition materials on heat transfer processes in a cavity filled with liquid. To achieve this goal, both direct computational fluid dynamics (CFD) and machine learning (ML) methods were applied. Direct CFD simulations were performed to obtain a detailed understanding of the temperature distribution within the cavity for different septum materials. Subsequently, machine learning methods were applied to create regression models capable of predicting changes in the temperature value inside the cavity based on different septum materials. The best results were achieved using third-degree polynomial regression, achieving a coefficient of determination (<i>R</i>\u0000 ²) of 0.816. This indicates that the model is well adapted to the data and is able to explain the variability of the target variable. Additionally, random forest regression models were examined, but their results were slightly less accurate compared to polynomial regression. The study demonstrates that integrating machine learning techniques into computational fluid dynamics can significantly improve the analysis and prediction of thermal processes.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1083-1101"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Informed Neural Networks for Modeling Near-Wall Steady Laminar Flows at High Reynolds Numbers","authors":"B. Y. Pang,&nbsp;Y. Xiao,&nbsp;L. M. Yang","doi":"10.1002/fld.70081","DOIUrl":"https://doi.org/10.1002/fld.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>In this work, a multiscale, zonal-coupled physics-informed neural network (PINN) framework, termed BL-NS-PINNs, is proposed for accurately predicting steady laminar near-wall flow fields at high Reynolds numbers without requiring near-wall data. The framework ensures physical continuity across flow regions by solving the Blasius and Navier–Stokes equations in the inner and outer zones, respectively, while enforcing velocity consistency within the matching region. Numerical experiments demonstrate that BL-NS-PINNs significantly outperform conventional PINNs trained with data supervision, accurately capturing near-wall flow features and sharp gradients in high-Reynolds-number laminar boundary layers. The method is further extended to wedge flows with self-similar solutions, offering both a theoretical foundation and a practical pathway for integrating wall functions into data-driven boundary-layer modeling. Overall, BL-NS-PINNs effectively overcome the limitations of conventional PINNs in resolving sharp near-wall velocity gradients and show strong potential for efficient, high-fidelity modeling of high-Reynolds-number flows.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1061-1082"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicontinuum Homogenization for Poroelasticity Model","authors":"Dmitry Ammosov,&nbsp;Mohammed Al Kobaisi,&nbsp;Yalchin Efendiev","doi":"10.1002/fld.70080","DOIUrl":"https://doi.org/10.1002/fld.70080","url":null,"abstract":"<p>In this paper, we derive multicontinuum poroelasticity models using the multicontinuum homogenization method. Poroelasticity models are widely used in many areas of science and engineering to describe coupled flow and mechanics processes in porous media. However, in many applications, the properties of poroelastic media possess high contrast, presenting serious computational challenges. It is well known that standard homogenization approaches often fail to give an accurate solution due to the lack of macroscopic parameters. Multicontinuum approaches allow us to consider such cases by defining several average states known as continua. In the field of poroelasticity, multiple-network models arising from the multiple porous media theory are representatives of these approaches. In this work, we extend previous findings by deriving the generalized multicontinuum poroelasticity model. We apply the recently developed multicontinuum homogenization method and provide a rigorous derivation of multicontinuum equations. For this purpose, we formulate coupled constraint cell problems in oversampled regions to consider different averages and gradient effects. Then, we obtain a multicontinuum expansion of the fine-scale fields and derive the multicontinuum model supposing the smoothness of macroscopic variables. We present the most general version of equations and the simplified ones based on our numerical experiments. Numerical results are presented for different heterogeneous media cases and demonstrate the high accuracy of our proposed multicontinuum models.</p>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 9","pages":"1035-1060"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fld.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patch Bubbles for Advection-Dominated Steady and Unsteady Problems","authors":"Eberhard Bänsch,&nbsp;Pedro Morin,&nbsp;Itatí Zocola","doi":"10.1002/fld.70077","DOIUrl":"https://doi.org/10.1002/fld.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>A novel variant of the residual-free bubble method (RFB) for advection-dominated problems is presented. Since the usual RFB still suffers from oscillations and strong under/overshoots, the bubble space is enriched by patch bubbles, giving more freedom to the bubble space. We use a recursive and efficient approach to accurately compute the bubbles. Numerical experiments clearly demonstrate the superiority of our method compared to the standard RFB. While similar in spirit to the enhanced residual-free bubble (eRFB) method by Cangiani and Süli [7, 8], our approach differs in the definition and computation of the additional bubbles. In addition, we extend the methodology to solve problems with non constant coefficients and we develop a novel bubble-based stabilization technique for time-dependent problems, which performs very accurately.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 8","pages":"1000-1015"},"PeriodicalIF":2.1,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Generalised Semi-Analytical Approach for the Multidimensional Nonlinear Collisional Fragmentation Equations","authors":"Somveer Keshav,&nbsp;Ashok Das,&nbsp;Sukhjit Singh,&nbsp;Randhir Singh,&nbsp;Mehakpreet Singh","doi":"10.1002/fld.70078","DOIUrl":"https://doi.org/10.1002/fld.70078","url":null,"abstract":"<p>The nonlinear collisional fragmentation equation is valuable for studying particle collisions and can model the evolution of raindrops, liquid-liquid dispersion, bubble columns, astrophysical planetary phenomena, and granulation processes in the pharmaceutical industry. Solving this equation analytically is highly tedious due to the complex structures of rate kernels, along with the nonlinear nature of the equation. This work aims to develop a generalised and efficient semi-analytical method that combines the Laplace decomposition method with Padé approximation to solve multidimensional nonlinear integro-partial differential equations. The Laplace decomposition method yields a series solution that represents the collisional fragmentation process over short time periods, while the combined Laplace decomposition with Padé approximation approach effectively captures the long-term dynamics. The mathematical formulation is validated through a detailed convergence analysis in a Banach space. Several examples including binary, Austin's and a gelling kernel are examined to demonstrate its accuracy and robustness. Most of the cases analysed in one-dimension, explicit (closed-form) solutions for the number density functions are derived for the first time. For the remaining multidimensional cases, accuracy is evaluated by comparison with the finite volume scheme [Das et al. (2020), <i>SIAM J. Sci. Comput.</i> <b>42</b>, B1570-B159], the homotopy perturbation method [Yadav et al. (2023), <i>Proceedings of the Royal Society A</i>, <b>479</b>(2279), 20230567] and the blues function method [Hussain et al. (2024), <i>Physics of Fluids</i>, <b>36</b>, 103359]. The new approach captures both number density functions and their integral moments with high precision. Errors in number density functions are computed using various series solutions.</p>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 8","pages":"980-999"},"PeriodicalIF":2.1,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fld.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location Effect of Free Surface on the Toroidal Vorticity of a Cell Bioreactor","authors":"Akila Meziane,&nbsp;Fadhila Lachekhab,&nbsp;Fatma Yahi","doi":"10.1002/fld.70072","DOIUrl":"https://doi.org/10.1002/fld.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>This numerical study focuses on the development of secondary recirculation zones in viscous, incompressible, rotating flows. Two main objectives are pursued: extending Karl Buhler's reference cavity and analyzing the influence of upstream boundary conditions on the evolution of reverse flow. To this end, several spherical-bottom configurations are studied using a finite-volume approach in order to characterize the behavior of fully open flows under the combined effects of the radius ratio (1.48 ≤ <i>α</i> ≤ 2) and the Reynolds number (766 ≤ <i>Re</i> ≤ 2757). The results reveal that the size and the number of the recirculation region strongly depend on the imposed thermal gradient for the concentric hemispherical annular cavity. Specifically, a positive Rayleigh number (<span></span><math>\u0000 <mrow>\u0000 <mi>Ra</mi>\u0000 <mo>&gt;</mo>\u0000 <mn>0</mn>\u0000 </mrow></math>) suppresses the reverse axial zone, while a negative Rayleigh number (<span></span><math>\u0000 <mrow>\u0000 <mi>Ra</mi>\u0000 <mo>&lt;</mo>\u0000 <mn>0</mn>\u0000 </mrow></math>) reinforces it. Beyond these findings, a refined modeling framework is proposed, capable of accurately predicting both the onset and behavior of recirculating regions. Furthermore, the study provides a comprehensive analysis of vortex breakdown dynamics, offering new insights into their formation and evolution across different flow regimes. The reliability of the proposed model is further confirmed through validation against experimental data, demonstrating its robustness and applicability to real-world scenarios. These outcomes carry important implications for fluid mechanics, as they deepen the understanding of turbulence and contribute to the design of more efficient engineering systems as well as the optimization of industrial processes. Finally, the model shows strong potential for bioengineering applications by creating an environment with minimal cavity velocity that supports cell growth, while reducing energy consumption and preserving cell viability.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 8","pages":"939-954"},"PeriodicalIF":2.1,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidimensional Free Surface Detection in Smoothed Particle Hydrodynamics","authors":"Hongshan Ma,&nbsp;Shuling Chen,&nbsp;Yijie Wang,&nbsp;Xingbo Niu,&nbsp;Haohao Song","doi":"10.1002/fld.70066","DOIUrl":"https://doi.org/10.1002/fld.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a multidimensional free-surface detection method for Smoothed Particle Hydrodynamics (SPH) simulations aimed at highly dynamic flows. The approach combines density-guided candidate selection with an efficient seed-and-expand strategy: outermost-particle seeding is followed by breadth-first search (BFS) refinement, while local surface normal estimated via principal component analysis (PCA) guide adaptive sector/cone scanning to improve robustness in high-curvature and fragmented interfaces. To accelerate neighborhood queries, the method employs dimension-dependent spatial indexing (hash grids in 2D and an octree in 3D), reducing the practical cost of neighbor search compared with exhaustive scanning. The framework is validated on benchmark cases including 2D/3D tank sloshing and a 3D dam-break scenario. Results show improved interface identification accuracy over a standard density-ratio baseline (e.g., 12.7% higher interfacial precision in our tests) and substantially faster neighborhood-query performance (up to 72% reduction in the query stage), enabling reliable near–real-time free-surface detection for large-scale SPH simulations.</p>\u0000 </div>","PeriodicalId":50348,"journal":{"name":"International Journal for Numerical Methods in Fluids","volume":"98 8","pages":"873-889"},"PeriodicalIF":2.1,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}