International Journal of Numerical Methods for Heat & Fluid Flow最新文献

{"title":"Reinforcement learning for cooling rate control during quenching","authors":"Elie Hachem, Abhijeet Vishwasrao, Maxime Renault, Jonathan Viquerat, P. Meliga","doi":"10.1108/hff-11-2023-0713","DOIUrl":"https://doi.org/10.1108/hff-11-2023-0713","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The premise of this research is that the coupling of reinforcement learning algorithms and computational dynamics can be used to design efficient control strategies and to improve the cooling of hot components by quenching, a process that is classically carried out based on professional experience and trial-error methods. Feasibility and relevance are assessed on various 2-D numerical experiments involving boiling problems simulated by a phase change model. The purpose of this study is then to integrate reinforcement learning with boiling modeling involving phase change to optimize the cooling process during quenching.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The proposed approach couples two state-of-the-art in-house models: a single-step proximal policy optimization (PPO) deep reinforcement learning (DRL) algorithm (for data-driven selection of control parameters) and an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method and multi-component anisotropic mesh adaptation (to compute the numerical reward used by the DRL agent to learn), that simulates boiling after a phase change model formulated after pseudo-compressible Navier–Stokes and heat equations.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Relevance of the proposed methodology is illustrated by controlling natural convection in a closed cavity with aspect ratio 4:1, for which DRL alleviates the flow-induced enhancement of heat transfer by approximately 20%. Regarding quenching applications, the DRL algorithm finds optimal insertion angles that adequately homogenize the temperature distribution in both simple and complex 2-D workpiece geometries, and improve over simpler trial-and-error strategies classically used in the quenching industry.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>To the best of the authors’ knowledge, this constitutes the first attempt to achieve DRL-based control of complex heat and mass transfer processes involving boiling. The obtained results have important implications for the quenching cooling flows widely used to achieve the desired microstructure and material properties of steel, and for which differential cooling in various zones of the quenched component will yield irregular residual stresses that can affect the serviceability of critical machinery in sensitive industries.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"17 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042428","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 wall modeling for LES involving non-equilibrium boundary layer effects","authors":"Sarath Radhakrishnan, Joan Calafell, Arnau Miró, Bernat Font, Oriol Lehmkuhl","doi":"10.1108/hff-11-2023-0710","DOIUrl":"https://doi.org/10.1108/hff-11-2023-0710","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>A precursor to this methodology has already been tested in Radhakrishnan <em>et al.</em> (2021) for equilibrium flows using LES of channel flow data. In the current methodology, the high-fidelity data chosen for training includes direct numerical simulation of a double diffuser that has strong non-equilibrium flow regions, and LES of a channel flow. The ultimate purpose of the model is to distinguish between equilibrium and non-equilibrium regions, and to provide the appropriate wall shear stress. The ML system used for this study is gradient-boosted regression trees.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The authors show that the model can be trained to make accurate predictions for both equilibrium and non-equilibrium boundary layers. In example, the authors find that the model is very effective for corner flows and flows that involve relaminarization, while performing rather ineffectively at recirculation regions.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>Data from relaminarization regions help the model to better understand such phenomenon and to provide an appropriate boundary condition based on that. This motivates the authors to continue the research in this direction by adding more non-equilibrium phenomena to the training data to capture recirculation as well.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042429","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":"Heat transfer and entropy generation in viscous-joule heating MHD microchannels flow under asymmetric heating","authors":"Antar Tahiri, Haroun Ragueb, Mustafa Moussaoui, Kacem Mansouri, Djemaa Guerraiche, Khelifa Guerraiche","doi":"10.1108/hff-05-2024-0380","DOIUrl":"https://doi.org/10.1108/hff-05-2024-0380","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This paper aims to present a numerical investigation into heat transfer and entropy generation resulting from magnetohydrodynamic laminar flow through a microchannel under asymmetric boundary conditions. Furthermore, the authors consider the effects of viscous dissipation and Joule heating.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The finite difference method is used to obtain the numerical solution. Simulations are conducted across a broad range of Hartmann (<em>Ha</em> = 0 ∼ 40) and Brinkman (<em>Br</em> = 0.01 ∼ 1) numbers, along with various asymmetric isothermal boundaries characterized by a heating ratio denoted as <em>ϕ</em>.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The findings indicate a significant increase in the Nusselt number with increasing Hartmann number, regardless of whether <em>Br</em> equals zero or not. In addition, it is demonstrated that temperature differences between the microchannel walls can lead to substantial distortions in fluid temperature distribution and heat transfer. The results reveal that the maximum entropy generation occurs at the highest values of Ha and <em>η</em> (a dimensionless parameter emerging from the formulation) obtained for <em>ϕ</em> = −1. Moreover, it is observed that local entropy generation rates are highest near the channel wall at the entrance region.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The study provides valuable insights into the complex interactions between magnetic fields, viscous dissipation and Joule heating in microchannel flows, particularly under asymmetric heating conditions. This contributes to a better understanding of heat transfer and entropy generation in advanced microfluidic systems, which is essential for optimizing their design and performance.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042402","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":"Solution of the Poisson equation by the boundary integral method","authors":"Sandipan Kumar Das","doi":"10.1108/hff-04-2024-0251","DOIUrl":"https://doi.org/10.1108/hff-04-2024-0251","url":null,"abstract":"&lt;h3&gt;Purpose&lt;/h3&gt;\u0000&lt;p&gt;The boundary integral method (BIM) is very attractive to practicing engineers as it reduces the dimensionality of the problem by one, thereby making the procedure computationally inexpensive compared to its peers. The principal feature of this technique is the limitation of all its computations to only the boundaries of the domain. Although the procedure is well developed for the Laplace equation, the Poisson equation offers some computational challenges. Nevertheless, the literature provides a couple of solution methods. This paper revisits an alternate approach that has not gained much traction within the community. The purpose of this paper is to address the main bottleneck of that approach in an effort to popularize it and critically evaluate the errors introduced into the solution by that method.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Design/methodology/approach&lt;/h3&gt;\u0000&lt;p&gt;The primary intent in the paper is to work on the particular solution of the Poisson equation by representing the source term through a Fourier series. The evaluation of the Fourier coefficients requires a rectangular domain even though the original domain can be of any arbitrary shape. The boundary conditions for the homogeneous solution gets modified by the projection of the particular solution on the original boundaries. The paper also develops a new Gauss quadrature procedure to compute the integrals appearing in the Fourier coefficients in case they cannot be analytically evaluated.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Findings&lt;/h3&gt;\u0000&lt;p&gt;The current endeavor has developed two different representations of the source terms. A comprehensive set of benchmark exercises has successfully demonstrated the effectiveness of both the methods, especially the second one. A subsequent detailed analysis has identified the errors emanating from an inadequate number of boundary nodes and Fourier modes, a high difference in sizes between the particular solution and the original domains and the used Gauss quadrature integration procedures. Adequate mitigation procedures were successful in suppressing each of the above errors and in improving the solution accuracy to any desired level. A comparative study with the finite difference method revealed that the BIM was as accurate as the FDM but was computationally more efficient for problems of real-life scale. A later exercise minutely analyzed the heat transfer physics for a fin after validating the simulation results with the analytical solution that was separately derived. The final set of simulations demonstrated the applicability of the method to complicated geometries.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Originality/value&lt;/h3&gt;\u0000&lt;p&gt;First, the newly developed Gauss quadrature integration procedure can efficiently compute the integrals during evaluation of the Fourier coefficients; the current literature lacks such a tool, thereby deterring researchers to adopt this category of methods. Second, to the best of the author’s knowledge, suc","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"49 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007339","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":"Buoyancy-driven heat transfer and entropy analysis of a hydromagnetic GO-Fe3O4/H2O hybrid nanofluid in an energy storage enclosure partially filled with non-Darcy porous medium under an oblique magnetic field","authors":"H. Thameem Basha, Hyunju Kim, Bongsoo Jang","doi":"10.1108/hff-03-2024-0193","DOIUrl":"https://doi.org/10.1108/hff-03-2024-0193","url":null,"abstract":"&lt;h3&gt;Purpose&lt;/h3&gt;\u0000&lt;p&gt;Thermal energy storage systems use thermal energy to elevate the temperature of a storage substance, enabling the release of energy during a discharge cycle. The storage or retrieval of energy occurs through the heating or cooling of either a liquid or a solid, without undergoing a phase change, within a sensible heat storage system. In a sensible packed bed thermal energy storage system, the structure comprises porous media that form the packed solid material, while fluid occupies the voids. Thus, a cavity, partially filled with a fluid layer and partially with a saturated porous layer, has become important in the investigation of natural convection heat transfer, carrying significant relevance within thermal energy storage systems. Motivated by these insights, the current investigation delves into the convection heat transfer driven by buoyancy and entropy generation within a partially porous cavity that is differentially heated, vertically layered and filled with a hybrid nanofluid.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Design/methodology/approach&lt;/h3&gt;\u0000&lt;p&gt;The investigation encompasses two distinct scenarios. In the first instance, the porous layer is positioned next to the heated wall, while the opposite region consists of a fluid layer. In the second case, the layers switch places, with the fluid layer adjacent to the heated wall. The system of equations for fluid and porous media, along with appropriate initial and boundary conditions, is addressed using the finite difference method. The Tiwari–Das model is used in this investigation, and the viscosity and thermal conductivity are determined using correlations specific to spherical nanoparticles.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Findings&lt;/h3&gt;\u0000&lt;p&gt;Comprehensive numerical simulations have been performed, considering controlling factors such as the Darcy number, nanoparticle volume fraction, Rayleigh number, bottom slit position and Hartmann number. The visual representation of the numerical findings includes streamlines, isotherms and entropy lines, as well as plots illustrating average entropy generation and the average Nusselt number. These representations aim to provide insight into the influence of these parameters across a spectrum of scenarios.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Originality/value&lt;/h3&gt;\u0000&lt;p&gt;The computational outcomes indicate that with an increase in the Darcy number, the addition of 2.5% magnetite nanoparticles to the GO nanofluid results in an enhanced heat transfer rate, showing increases of 0.567% in Case 1 and 3.894% in Case 2. Compared with Case 2, Case 1 exhibits a 59.90% enhancement in heat transfer within the enclosure. Positioning the porous layer next to the partially cooled wall significantly boosts the average total entropy production, showing a substantial increase of 11.36% at an elevated Rayleigh number value. Positioning the hot slit near the bottom wall leads to a reduction in total entropy generation by 33.20% compared to its placement at the ","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"9 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007338","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":"MLFV: a novel machine learning feature vector method to predict characteristics of turbulent heat and fluid flow","authors":"Iman Bashtani, Javad Abolfazli Esfahani","doi":"10.1108/hff-04-2024-0282","DOIUrl":"https://doi.org/10.1108/hff-04-2024-0282","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with <em>R</em>² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.</p><!--/ Abstract__block -->\u0000<h3>Practical implications</h3>\u0000<p>The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"43 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042400","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":"Comparative study of Yamada-Ota and Xue models for MHD hybrid nanofluid flow past a rotating stretchable disk: stability analysis","authors":"Muhammad Yousuf Rafiq, Ayesha Sabeen, Aqeel ur Rehman, Zaheer Abbas","doi":"10.1108/hff-01-2024-0060","DOIUrl":"https://doi.org/10.1108/hff-01-2024-0060","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The hybrid nanofluid flow due to a rotating disk has numerous applications, including centrifugal pumps, paper production, polymers dying, air filtration systems, automobile cooling and solar collectors. This study aims to investigate the convective heat transport and magnetohydrodynamics (MHD) hybrid nanofluid flow past a stretchable rotating surface using the Yamada-Ota and Xue models with the impacts of heat generation and thermal radiation.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The carbon nanotubes such as single-wall carbon nanotubes and multi-wall carbon nanotubes are suspended in a base fluid like water to make the hybrid nanofluid. The problem’s governing partial differential equations are transformed into a system of ordinary differential equations using similarity transformations. Then, the numerical solutions are found with a bvp4c function in MATLAB software. The impacts of pertinent parameters on the flow and temperature fields are depicted in tables and graphs.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Two solution branches are discovered in a certain range of unsteadiness parameters. The fluid temperature and the rate of heat transport are enhanced when the thermal radiation and heat generation effects are increased. The Yamada-Ota model has a higher temperature than the Xue model. Furthermore, it is observed that only the first solution remains stable when the stability analysis is implemented.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>To the best of the authors’ knowledge, the results stated are original and new with the investigation of MHD hybrid nanofluid flow with convective heat transfer using the extended version of Yamada-Ota and Xue models. Moreover, the novelty of the present study is improved by taking the impacts of heat generation and thermal radiation.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007388","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":"Exploring stability of Jeffrey fluids in anisotropic porous media: incorporating Soret effects and microbial systems","authors":"S. Sridhar, M. Muthtamilselvan","doi":"10.1108/hff-02-2024-0145","DOIUrl":"https://doi.org/10.1108/hff-02-2024-0145","url":null,"abstract":"&lt;h3&gt;Purpose&lt;/h3&gt;\u0000&lt;p&gt;This paper aims to present a study on stability analysis of Jeffrey fluids in the presence of emergent chemical gradients within microbial systems of anisotropic porous media.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Design/methodology/approach&lt;/h3&gt;\u0000&lt;p&gt;This study uses an effective method that combines non-dimensionalization, normal mode analysis and linear stability analysis to examine the stability of Jeffrey fluids in the presence of emergent chemical gradients inside microbial systems in anisotropic porous media. The study focuses on determining critical values and understanding how temperature gradients, concentration gradients and chemical reactions influence the onset of bioconvection patterns. Mathematical transformations and analytical approaches are used to investigate the system’s complicated dynamics and the interaction of numerous characteristics that influence stability.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Findings&lt;/h3&gt;\u0000&lt;p&gt;The analysis is performed using the Jeffrey-Darcy type and Boussinesq estimation. The process involves using non-dimensionalization, using the normal mode approach and conducting linear stability analysis to convert the field equations into ordinary differential equations. The conventional thermal Rayleigh Darcy number &lt;span&gt;\u0000&lt;mml:math display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;R&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;D&lt;/mml:mi&gt;&lt;mml:mi&gt;a&lt;/mml:mi&gt;&lt;mml:mo&gt;,&lt;/mml:mo&gt;&lt;mml:mi&gt;c&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;/span&gt; is derived as a comprehensive function of various parameters, and it remains unaffected by the bio convection Lewis number &lt;span&gt;\u0000&lt;mml:math display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;Ł&lt;/mml:mi&gt;&lt;mml:mi&gt;e&lt;/mml:mi&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;/span&gt;. Indeed, elevating the values of &lt;em&gt;ζ&lt;/em&gt; and &lt;span&gt;\u0000&lt;mml:math display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;γ&lt;/mml:mi&gt;&lt;mml:mo&gt;′&lt;/mml:mo&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;/span&gt; in the interval of 0 to 1 has been noted to expedite the formation of bioconvection patterns while concurrently expanding the dimensions of convective cells. The purpose of this investigation is to learn how the temperature gradient affects the concentration gradient and, in turn, the stability and initiation of bioconvection by taking the Soret effect into the equation. The results provide insightful understandings of the intricate dynamics of fluid systems affected by chemical and biological elements, providing possibilities for possible industrial and biological process applications. The findings illustrate that augmenting both microbe concentration and the bioconvection Péclet number results in an unstable system. In this study, the experimental Rayleigh number &lt;span&gt;\u0000&lt;mml:math display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;R&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;D&lt;/mml:mi&gt;&lt;mml:mi&gt;a&lt;/mml:mi","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002780","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":"Artificial intelligence-based droplet size prediction for microfluidic system","authors":"Sameer Dubey, Pradeep Vishwakarma, TVS Ramarao, Satish Kumar Dubey, Sanket Goel, Arshad Javed","doi":"10.1108/hff-07-2023-0361","DOIUrl":"https://doi.org/10.1108/hff-07-2023-0361","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and <em>R</em>² = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"21 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986297","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":"Flow control by a hybrid use of machine learning and control theory","authors":"Takeru Ishize, Hiroshi Omichi, Koji Fukagata","doi":"10.1108/hff-10-2023-0659","DOIUrl":"https://doi.org/10.1108/hff-10-2023-0659","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid flows poses challenges in designing efficient control laws using the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The authors propose a partially nonlinear linear system extraction autoencoder (pn-LEAE), which consists of convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to extract low-dimensional latent dynamics from fluid velocity field data. This pn-LEAE is designed to extract a linear dynamical system so that the modern control theory can easily be applied, while a nonlinear compression is done with the autoencoder (AE) part so that the latent dynamics conform to that linear system. The key technique is to train this pn-LEAE with the ground truths at two consecutive time instants, whereby the AE part retains its capability as the AE, and the weights in the linear dynamical system are trained simultaneously.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The authors demonstrate the effectiveness of the linear system extracted by the pn-LEAE, as well as the designed control law’s effectiveness for a flow around a circular cylinder at the Reynolds number of Re<em>_D</em> = 100. When the control law derived in the latent space was applied to the direct numerical simulation, the lift fluctuations were suppressed over 50%.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>To the best of the authors’ knowledge, this is the first attempt using CNN-AE for linearization of fluid flows involving transient development to design a feedback control law.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"13 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973982","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}