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Fluids
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Future Trends and Challenges in High Performance Computing for Turbulence
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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 2103

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

Published Papers (2 papers)

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Research

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 200
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)
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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 1449
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)
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