Flow, Turbulence and Combustion最新文献

{"title":"Discovery of a Physically Interpretable Data-Driven Wind-Turbine Wake Model","authors":"Kherlen Jigjid,&nbsp;Ali Eidi,&nbsp;Nguyen Anh Khoa Doan,&nbsp;Richard P. Dwight","doi":"10.1007/s10494-025-00679-y","DOIUrl":"10.1007/s10494-025-00679-y","url":null,"abstract":"<div><p>This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard <span>(k)</span>-<span>(varepsilon)</span> formulation. Several candidate models were discovered using the symbolic regression framework Sparse Regression of Turbulent Stress Anisotropy (SpaRTA), trained on a single Large Eddy Simulation (LES) dataset of a standalone wind turbine. The leading model was selected by prioritizing simplicity while maintaining reasonable accuracy, resulting in a novel linear eddy viscosity model. This selected leading model reduces eddy viscosity in high-shear regions—particularly in the wake—to limit turbulence mixing and delay wake recovery. This addresses a common shortcoming of the standard <span>(k)</span>-<span>(varepsilon)</span> model, which tends to overpredict mixing, leading to unrealistically fast wake recovery. Moreover, the formulation of the leading model closely resembles that of the established <span>(k)</span>-<span>(varepsilon)</span>-<span>(f_P)</span> model. Consistent with this resemblance, the leading and <span>(k)</span>-<span>(varepsilon)</span>-<span>(f_P)</span> models show nearly identical performance in predicting velocity fields and power output, but they differ in their predictions of turbulent kinetic energy. In addition, the generalization capability of the leading model was assessed using three unseen six-turbine configurations with varying spacing and alignment. Despite being trained solely on a standalone turbine case, the model produced results comparable to LES data. These findings demonstrate that data-driven methods can yield interpretable, physically consistent RANS models that are competitive with traditional modeling approaches while maintaining simplicity and achieving generalizability.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 :","pages":"1181 - 1207"},"PeriodicalIF":2.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10494-025-00679-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A Multistep Reinforcement Learning Control of Shear Flows in Minimal Input–Output Plants Under Large Time-delays","authors":"Amine Saibi,&nbsp;Lionel Mathelin,&nbsp;Onofrio Semeraro","doi":"10.1007/s10494-025-00697-w","DOIUrl":"10.1007/s10494-025-00697-w","url":null,"abstract":"<div><p>Flow control has attracted research for its potential role in reducing drag, suppressing turbulence, and enhancing mixing in fluid systems. The emergence of data-driven modeling and machine learning techniques has sparked new interest in designing control strategies that can adapt in real time to complex, high-dimensional flow environments. However, fluid systems remain particularly challenging testbeds for control design due to their nonlinear and convective nature, which introduces large time delays. In active control, additional difficulties arise from practical constraints, such as the use of localized sensors in limited number. In this work, we investigate a reinforcement learning framework based on a suitable actor–critic algorithm designed to address these challenges. Two test cases representative of transitional shear flows are considered: a linearized version of the Kuramoto–Sivashinsky equation and the control of instabilities in a two-dimensional boundary-layer flow over a flat plate, using a minimal but realistic sensor–actuator configuration. This choice reflects our focus on the limitations that arise from plants of experimental interest. Time delays are identified during a pretraining stage, while the control algorithm employs multistep returns during value iteration. This approach improves both the convergence rate and stability of learning. Furthermore, we show that the look-ahead in the multistep formulation provides a non-trivial beneficial effect in plants where the control task is characterized by a severe credit-assignment issue.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 :","pages":"1379 - 1402"},"PeriodicalIF":2.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on Machine Learning for Fluids","authors":"Paola Cinnella,&nbsp;Ricardo Vinuesa","doi":"10.1007/s10494-025-00695-y","DOIUrl":"10.1007/s10494-025-00695-y","url":null,"abstract":"","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 :","pages":"989 - 989"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Reynolds-Number Flows Over Two Equal In-Line Rounded Square-Section Prisms at Incidence","authors":"Nils Paul van Hinsberg","doi":"10.1007/s10494-025-00674-3","DOIUrl":"10.1007/s10494-025-00674-3","url":null,"abstract":"<div><p>This paper investigates the time-averaged and fluctuating aerodynamics of two slightly rough square-section prisms with rounded lateral edges of <i>r/D</i> = 0.16, positioned in-line at a centre-to-centre distance <i>S/D</i> = 4.0. For that purpose, distributions of the time-dependent surface pressures along both prisms’ mid-span cross-sections, the derived mean sectional pressure drag, lift, and pitch moment coefficients, as well as spanwise-integrated fluctuating fluid loads on the downstream prism and the frequency of the eddy shedding in its wake were measured simultaneously for Reynolds numbers between 100,000 and 7 million. Evaluation of the data and comparison with the results of an identical single prism revealed substantial changes of the flow over both prisms with Reynolds number for all studied incidence angles between <span>({0^ circ })</span> and <span>({45^ circ })</span> in the form of mutual aerodynamic influences due to <i>proximity</i> and <i>wake-interference</i> effects. For most studied flow parameters, a good agreement of the trends of the aerodynamic coefficients with incidence angle between the upstream and reference prism are obtained. <i>Proximity</i> effects are nevertheless clearly visible in the surface pressures, particularly at <span>(alpha = 25.5{^ circ} )</span>. Contrarily, <i>wake-interference</i> effects lead to a much lower and even negative drag on the downstream prism. The impingement of the shear layers coming from the upstream prism or of the eddies, formed in the gap between both prisms, dominates the aerodynamics of the downstream prism. This leads not only to transitions between the adjacent <i>separation</i> and <i>wedge</i> flow regimes, as well as between the <i>co-shedding</i> and <i>reattachment</i> flow states, but also triggers the vortex shedding processes between both prisms.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"705 - 738"},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10494-025-00674-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Insights into Carbon Black Nanoparticle Formation within Flame Spray Pyrolysis Reactors by Numerical Modeling and Simulation","authors":"Fabio Henrique Bastiani,&nbsp;Pedro Bianchi Neto,&nbsp;Lizoel Buss,&nbsp;Udo Fritsching,&nbsp;Dirceu Noriler","doi":"10.1007/s10494-025-00675-2","DOIUrl":"10.1007/s10494-025-00675-2","url":null,"abstract":"<div><p>The Flame Spray Pyrolysis (FSP) process is a versatile and scalable method for controlled nanoparticle synthesis, with applications across various industrial sectors. FSP enables precise manipulation of nanoparticle properties, crucial for diverse applications. Carbon black (CB), important in emerging energy technologies like batteries and fuel cells, can be efficiently synthesized via FSP due to its controlled environment. Understanding CB formation is essential, given its impact on material properties. Computational Fluid Dynamics (CFD) simulations provide insights into nanoparticle formation and growth dynamics within FSP reactors, aiding in understanding process variables’ influence. This study models and analyzes CB nanoparticle formation within a specific enclosed FSP reactor with controlled coflow. The modeling approach is validated through a benchmarking ethylene sooting flame, and results are compared with existing experiments and previous models. The model accurately describes soot formation in the benchmarking case, providing reliable predictions of temperature, soot, and mean particle size. After validation, the model is extended to the FSP case. Two- and three-equation models describe soot and CB formation, with particle dynamics thoroughly discussed. The semi-empirical models assume spherical primary particles, and in the three-equation model, a population balance transport equation is solved for primary particle number density. Our investigation includes parametric sensitivity analysis, highlighting the significance of reliable model parameters, including the radiative effects of carbon particles. This work advances the understanding and predictive modeling of CB synthesis via FSP, promoting simpler alternative models compared to intricate quadrature-solved population balance approaches in the literature.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"955 - 987"},"PeriodicalIF":2.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Extension to the Grid-Induced Machine Learning CFD Framework for Turbulent Flows","authors":"Chin Yik Lee,&nbsp;Vân Anh Huynh-Thu ,&nbsp;Stewart Cant","doi":"10.1007/s10494-025-00667-2","DOIUrl":"10.1007/s10494-025-00667-2","url":null,"abstract":"<div><p>High-fidelity computational fluid dynamics (CFD) is widely used to understand turbulence and guide engineering design. While effective in predicting complex flow phenomena, CFD simulations at high Reynolds numbers require fine grids, resulting in prohibitive computational costs for parametric studies. To address this, we proposed a framework that uses machine learning (ML) to predict fine-grid results from coarse-grid simulations in a previous work. Coarsening the grid increases grid-induced error and affects turbulence prediction, necessitating a data-driven surrogate model to predict and correct these errors. A Random Forest (RF) regression was used to construct the surrogate model. The proposed framework was tested using a turbulent flow configuration consisting of an enclosed duct with a triangular bluff body acting as a blockage to the incoming flow. The chosen input features (IFs) were shown to be critical in predicting the turbulent flow field. In the current paper, we introduce further enhancements to the framework to allow it to be more robust in its prediction and application. These extensions also serve to reduce the computational cost of the approach without compromising on the accuracy. The proposed extensions include (i) adoption of Multivariate Random Forest (MRF) to replace the RF approach; (ii) identification and reduction of the IFs required for training and prediction using Variable IMportance Prediction (VIMP); (iii) predictions of flow field with changes in the bluff body configurations. The present paper aims to investigate the capability of the proposed extensions within the framework. We show that (i) the MRF allows for the accurate prediction of multiple outputs within one training instance but with a reduced computational cost relative to the RF approach. (ii) the impact of the IFs on the training can be understood via VIMP, and applying the MRF model with reduced IFs selected through VIMP does not cause any detriment to the accuracy of the prediction (iii) the extended framework trained with different bluff body configurations could be robustly applied to predict the flow field in an unseen configuration that is different from those trained. The predictive capability of the approach with these proposed extensions is demonstrated.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"523 - 565"},"PeriodicalIF":2.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale Resolving Methods for Aeronautical Flows toward the Era of “Industrial LES”","authors":"Kozo Fujii,&nbsp;Soshi Kawai,&nbsp;Datta Gaitonde","doi":"10.1007/s10494-025-00659-2","DOIUrl":"10.1007/s10494-025-00659-2","url":null,"abstract":"<div><p>Scale-resolving simulations possess considerable benefits over modeled approaches because of their ability to access the underlying nonlinear fluid dynamics, and thus to predict not only the correct phenomenology, but also to generate insights on strategies to mitigate or eliminate undesirable features. The expense of resolving all pertinent turbulent scales becomes prohibitive however, as the size of the problem, typically measured by the Reynolds number based on a suitable set of reference parameters, becomes large, as is the case with flows of industrial interest such as full aircraft or their complex subsystems. This paper provides an assessment of scale-resolving methods, including some of the main benefits as well as barriers for use on large problems, together with a perspective on historical and recent trends that appear promising in the quest for routine industrial use. The factors that constitute the biggest hurdles to achieving acceptable wall-clock times and costs include meshing of complicated geometries, numerical schemes that are robust as well as accurate, suitable initial and boundary conditions, economical yet appropriate representation of near-wall turbulence, code parallelism, scalability and portability, and post-processing of the resulting big datasets. Considerations for these interrelated aspects are highlighted in the context of several 3D problems of increasing complexity, from wing sections without and with sweep, to aircraft wakes, propulsion subsystems, scramjet flowpaths and finally, full aircraft including empennages. Collectively, these examples feature the benefits of scale-resolving simulations. An illustrative approach that has reached a relatively high level of maturity using automatic mesh generation, a non-dissipative yet robust scheme, wall-modeling of turbulence, superior scalability and requiring little user intervention beyond providing the surface model, is used to demonstrate the potential of scale-resolving simulations for industry, achievable at modest cost and in reasonable wall-clock time.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"405 - 446"},"PeriodicalIF":2.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10494-025-00659-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Large Eddy Simulation of Reactive Flow in a Lab-Scale Liquid Rocket Engine Using a Diffuse Interface Method","authors":"Thibault Gioud,&nbsp;Thomas Schmitt,&nbsp;Bénédicte Cuenot,&nbsp;Nicolas Odier","doi":"10.1007/s10494-025-00673-4","DOIUrl":"10.1007/s10494-025-00673-4","url":null,"abstract":"<div><p>Modeling the combustion of liquid oxygen (LOx) and methane (CH4) under subcritical conditions remains challenging due to the complex interactions between two-phase flow, atomization, and combustion processes. This study uses Large Eddy Simulation (LES) with a diffuse interface method to investigate the behavior of a LOx/GCH4 single-injector rocket combustor. The proposed multifluid approach captures phase transition phenomena while maintaining computational efficiency. Numerical results are compared against experimental data, highlighting the model ability to predict flow features, such as the wall pressure distribution and wall heat fluxes. This study emphasizes the importance of accounting for the liquid core, or the dense phase, within the Eulerian framework, rather than relying on Lagrangian injection models, resulting in enhanced predictions of flame topology and heat flux distributions. Although the model exhibits good agreement with experimental measurements, it underestimates heat flux by approximately 10% at the end of the domain, likely due to limitations in the chemical kinetics model. These results show that the diffuse interface method is a promising tool for the simulation of subcritical liquid rocket combustion.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"677 - 703"},"PeriodicalIF":2.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Turbulence Modelling for Magnetohydrodynamic Flows in Annular Pipes","authors":"Alejandro Montoya Santamaría,&nbsp;Tyler Buchanan,&nbsp;Francesco Fico,&nbsp;Ivan Langella,&nbsp;Richard P. Dwight,&nbsp;Nguyen Anh Khoa Doan","doi":"10.1007/s10494-025-00668-1","DOIUrl":"10.1007/s10494-025-00668-1","url":null,"abstract":"<div><p>We present a data-driven approach to Reynolds-averaged Navier-Stokes (RANS) turbulence closure modelling in magnetohydrodynamic (MHD) flows. In these flows the magnetic field interacting with the conductive fluid induces unconventional turbulence states such as quasi two-dimensional (2D) turbulence, and turbulence suppression, which are poorly represented by standard Boussinesq models. Our data-driven approach uses time-averaged Large Eddy Simulation (LES) data of annular pipe flows, at different Hartmann numbers, to derive corrections for the <span>(k)</span>-<span>(omega)</span> SST model. Correction fields are obtained by injecting time averaged LES fields into the MHD RANS equations, and examining the remaining residuals. The correction to the Reynolds-stress anisotropy is approximated with a modified Tensor Basis Neural Network (TBNN). We extend the generalised eddy hypothesis with a traceless antisymmetric tensor representation of the Lorentz force to obtain MHD flow features, thus keeping Galilean and frame invariance while including MHD effects in the turbulence model. The resulting data-driven models are shown to reduce errors in the mean flow, and to generalise to annular flow cases with different Hartmann numbers from those of the training cases.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"567 - 602"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10494-025-00668-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Effect of coolant on wall heat flux in premixed turbulent combustion","authors":"Chenlin Guo,&nbsp;Kunlin Li,&nbsp;Lipo Wang","doi":"10.1007/s10494-025-00672-5","DOIUrl":"10.1007/s10494-025-00672-5","url":null,"abstract":"<div><p>Inside the engine combustor, addition of the coolant from the wall makes the physics of flame-wall interaction (FWI) even more complex. Considering the application relevance, wall heat flux is analyzed and modeled. Under various flow conditions, the model predictions satisfactorily match the direct numerical simulation (DNS) results. The effects of coolant on the entrained flame and head-on flame are clearly different. Statistics of the near-wall flame orientation and curvature are sensitive to the coolant blowing ratio (BR). The entrained flame is more likely to be swept away, while the head-on flame is more stable. Both the model and simulation indicate consistently that an increase in BR, although quantitatively small, will greatly reduce the wall heat flux induced by the head-on flame. In contrast, the change of wall heat flux induced by the entrained flame is much smaller. Since most of the near-wall flame is head-on, the BR effect is significant. Additionally, in an a priori large eddy simulation (LES) study, the model predictions show better consistency with DNS, in comparison with the most commonly used turbulence sub-grid models.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 2","pages":"927 - 953"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}