Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.4
1.8
Journals
Fluids
Special Issues
Future Trends and Challenges in High Performance Computing for Turbulence
share announcement

Future Trends and Challenges in High Performance Computing for Turbulence

A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Turbulence".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 5034

Special Issue Editors

Dr. Yonghua Yan

E-Mail Website
Guest Editor
Department of Mathematics and Statistical Sciences, Jackson State University, Jackson, MS 39217, USA
Interests: computational fluid dynamics; flow control; turbulence; shock boundary layer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Yong Yang

E-Mail Website
Guest Editor
Department of Mathematics, West Texas A&M University, Canyon, TX 79016, USA
Interests: turbulence; shock/boundary layer interaction

Special Issue Information

Dear Collegues,

This Special Issue delves into the enigma of turbulent flow, a persistent conundrum in classical physics and engineering. Even in the present day, comprehending turbulence at its core and crafting precise models for turbulent flows remain formidable challenges. Nevertheless, recent breakthroughs, propelled by the synergy of high-performance computing, sophisticated numerical methods, and advanced analysis techniques, have illuminated fresh perspectives on the intricate nature of turbulence.

Within the pages of this Special Issue, we aim to capture the latest pioneering advancements propelling the field forward. We extend an invitation for both original research contributions and comprehensive review articles, elucidating the latest strides made in this domain. The scope of this Special Issue encompasses theoretical explorations, computational simulations, and experimental investigations of turbulence across a wide spectrum of flow scenarios. The topics of interest include, but are not limited to, the following:

  • High-fidelity numerical simulations that unveil novel physical insights;
  • Innovative computational techniques tailored for simulation and modeling;
  • The application of data-driven methodologies, such as machine/deep learning, for analysis and modeling;
  • Explorations into turbulence control, modeling, and engineering applications.

By amalgamating these cutting-edge accomplishments and offering insights into the challenges that endure, this Special Issue aims to showcase the dynamic nature of contemporary turbulence research. Our objective is to provide a snapshot of the current state of the art, serving as a source of inspiration for future advances in our comprehension and capabilities concerning turbulent flow. We enthusiastically welcome specialized contributions, as well as those offering a broader overview of this multifaceted field.

Dr. Yonghua Yan
Dr. Yong Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • turbulence
  • computational fluid dynamics (CFD)
  • high-performance computing (HPC)
  • boundary layer
  • flow stability

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 1209 KiB  
Article
Investigation of Nonlinear Relations Among Flow Profiles Using Artificial Neural Networks
by Shiming Yuan, Caixia Chen, Yong Yang and Yonghua Yan
Fluids 2024, 9(12), 276; https://doi.org/10.3390/fluids9120276 (registering DOI) - 23 Nov 2024
Viewed by 139
Abstract
This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated [...] Read more.
This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated through high-fidelity numerical simulations, encompassing both the one-dimensional benchmark problems and the more intricate three-dimensional boundary layer transition problem, this research convincingly demonstrates that neural networks possess a remarkable capacity to effectively capture the discontinuities and the subtle wave characteristics that occur at small scales within complex fluid flows, thereby showcasing their robustness in handling intricate fluid dynamics phenomena. Furthermore, even in the context of challenging three-dimensional problems, this study reveals that the average velocity profiles can be predicted with a high degree of accuracy, utilizing a limited number of input profiles during the training phase, which underscores the efficiency and efficacy of the model in understanding complex systems. The findings of this study significantly underscore the immense potential that artificial neural networks, along with deep learning methodologies, hold in advancing our comprehension of the fundamental physics that govern complex fluid dynamics systems, while concurrently demonstrating their applicability across a variety of flow scenarios and their capacity to yield insightful revelations regarding the nonlinear relationships that exist among diverse flow parameters, thus paving the way for future research in this critical area of study. Full article
(This article belongs to the Special Issue Future Trends and Challenges in High Performance Computing for Turbulence)
19 pages, 6416 KiB  
Article
Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations
by Yaobin Hao and Fangying Song
Fluids 2024, 9(11), 258; https://doi.org/10.3390/fluids9110258 - 6 Nov 2024
Viewed by 487
Abstract
In this paper, we used Fourier Neural Operator (FNO) networks to solve reaction–diffusion equations. The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We applied the FNO to the Surface Quasi-Geostrophic (SQG) equation, [...] Read more.
In this paper, we used Fourier Neural Operator (FNO) networks to solve reaction–diffusion equations. The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We applied the FNO to the Surface Quasi-Geostrophic (SQG) equation, and we tested the model with two significantly different initial conditions: Vortex Initial Conditions and Sinusoidal Initial Conditions. Furthermore, we explored the generalization ability of the model by evaluating its performance when trained on Vortex Initial Conditions and applied to Sinusoidal Initial Conditions. Additionally, we investigated the modes (frequency parameters) used during training, analyzing their impact on the experimental results, and we determined the most suitable modes for this study. Next, we conducted experiments on the number of convolutional layers. The results showed that the performance of the models did not differ significantly when using two, three, or four layers, with the performance of two or three layers even slightly surpassing that of four layers. However, as the number of layers increased to five, the performance improved significantly. Beyond 10 layers, overfitting became evident. Based on these observations, we selected the optimal number of layers to ensure the best model performance. Given the autoregressive nature of the FNO, we also applied it to solve the Gray–Scott (GS) model, analyzing the impact of different input time steps on the performance of the model during recursive solving. The results indicated that the FNO requires sufficient information to capture the long-term evolution of the equations. However, compared to traditional methods, the FNO offers a significant advantage by requiring almost no additional computation time when predicting with new initial conditions. Full article
(This article belongs to the Special Issue Future Trends and Challenges in High Performance Computing for Turbulence)
►▼ Show Figures

Figure 1

14 pages, 1561 KiB  
Article
Effects of Partial Premixing and Coflow Temperature on Flame Stabilization of Lifted Jet Flames of Dimethyl Ether in a Vitiated Coflow Based on Stochastic Multiple Mapping Conditioning Approach
by Sanjeev Kumar Ghai, Rajat Gupta and Santanu De
Fluids 2024, 9(6), 125; https://doi.org/10.3390/fluids9060125 - 26 May 2024
Viewed by 761
Abstract
The Reynolds-averaged Navier–Stokes (RANS)-based stochastic multiple mapping conditioning (MMC) approach has been used to study partially premixed jet flames of dimethyl ether (DME) introduced into a vitiated coflowing oxidizer stream. This study investigates DME flames with varying degrees of partial premixing within a [...] Read more.
The Reynolds-averaged Navier–Stokes (RANS)-based stochastic multiple mapping conditioning (MMC) approach has been used to study partially premixed jet flames of dimethyl ether (DME) introduced into a vitiated coflowing oxidizer stream. This study investigates DME flames with varying degrees of partial premixing within a fuel jet across different coflow temperatures, delving into the underlying flame structure and stabilization mechanisms. Employing a turbulence k-ε model with a customized set of constants, the MMC technique utilizes a mixture fraction as the primary scalar, mapped to the reference variable. Solving a set of ordinary differential equations for the evolution of Lagrangian stochastic particles’ position and composition, the molecular mixing of these particles is executed using the modified Curl’s model. The lift-off height (LOH) derived from RANS-MMC simulations are juxtaposed with experimental data for different degrees of partial premixing of fuel jets and various coflow temperatures. The RANS-MMC methodology adeptly captures LOH for pure DME jets but exhibits an underestimation of flame LOH for partially premixed jet scenarios. Notably, as the degree of premixing escalates, a conspicuous underprediction in LOH becomes apparent. Conditional scatter and contour plots of OH and CH2O unveil that the propagation of partially premixed flames emerges as the dominant mechanism at high coflow temperatures, while autoignition governs flame stabilization at lower coflow temperatures in partially premixed flames. Additionally, for pure DME flames, autoignition remains the primary flame stabilization mechanism across all coflow temperature conditions. The study underscores the importance of considering the degree of premixing in partially premixed jet flames, as it significantly impacts flame stabilization mechanisms and LOH, thereby providing crucial insights into combustion dynamics for various practical applications. Full article
(This article belongs to the Special Issue Future Trends and Challenges in High Performance Computing for Turbulence)
►▼ Show Figures

Figure 1

23 pages, 15075 KiB  
Article
Turbulent Channel Flow: Direct Numerical Simulation-Data-Driven Modeling
by Antonios Liakopoulos and Apostolos Palasis
Fluids 2024, 9(3), 62; https://doi.org/10.3390/fluids9030062 - 3 Mar 2024
Viewed by 2697
Abstract
Data obtained using direct numerical simulations (DNS) of pressure-driven turbulent channel flow are studied in the range 180 ≤Reτ≤ 10,000. Reynolds number effects on the mean velocity profile (MVP) and second order statistics are analyzed with a view of [...] Read more.
Data obtained using direct numerical simulations (DNS) of pressure-driven turbulent channel flow are studied in the range 180 ≤Reτ≤ 10,000. Reynolds number effects on the mean velocity profile (MVP) and second order statistics are analyzed with a view of finding logarithmic behavior in the overlap region or even further from the wall, well in the boundary layer’s outer region. The values of the von Kármán constant for the MVPs and the Townsend–Perry constants for the streamwise and spanwise fluctuation variances are determined for the Reynolds numbers considered. A data-driven model of the MVP, proposed and validated for zero pressure-gradient flow over a flat plate, is employed for pressure-driven channel flow by appropriately adjusting Coles’ strength of the wake function parameter, Π. There is excellent agreement between the analytic model predictions of MVP and the DNS-computed MVP as well as of the Reynolds shear stress profile. The skin friction coefficient Cf is calculated analytically. The agreement between the analytical model predictions and the DNS-based computed discrete values of Cf is excellent. Full article
(This article belongs to the Special Issue Future Trends and Challenges in High Performance Computing for Turbulence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop