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Experimental and Computational Fluid Dynamics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Fluid Science and Technology".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4052

Special Issue Editor

Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: fluid mechanics; renewable energy; fluid–structure interactions; turbulent flow; energy-efficient locomotion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fluid dynamics are relevant to many aspects of engineering applications, including wind energy, hydroelectric power, and the transport of oil/gas in pipelines, among others. Understanding the fundamental mechanisms of fluid dynamics in different engineering fields plays a key role in improving the efficiency of energy production and the lifespan of the facilities. This Special Issue entitled “Experimental and Computational Fluid Dynamics” provides an open access forum focusing on the state-of-the-art advances in different areas of fluid mechanics and their applications in engineering fields. The scope of this Special Issue includes (but is not limited to) both experimental and computational works for fluid dynamics across multiple scales, including advanced flow diagnostic tools and computational approaches, and how such advancements improve our knowledge of fluid dynamics in different engineering applications.

This Special Issue aims to provide opportunities for research scholars, scientists and engineers to share and discuss both their original research works and reviews on relevant topics. Studies on the strong connection between underlying fluid dynamic mechanisms and their applications in engineering fields are highly welcome.

Dr. Yaqing Jin
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • fluid dynamics
  • flow diagnostic tools
  • computational fluid dynamics
  • multiscale fluid dynamics
  • fluid dynamics in engineering

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Published Papers (2 papers)

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Research

31 pages, 49110 KiB  
Article
Optimizing Design and Operational Parameters for Enhanced Mixing and Hydrodynamics in Bubbling Fluidized Bed Gasifiers: An Experimental and CFD-Based Approach
by Naveed Raza, Rifat Mehdi, Muhammad Ahsan, Muhammad Taqi Mehran, Salman Raza Naqvi and Emad Uddin
Appl. Sci. 2023, 13(16), 9317; https://doi.org/10.3390/app13169317 - 16 Aug 2023
Cited by 3 | Viewed by 1960
Abstract
An experimental investigation of hydrodynamics of gas-solid flow is carried out by engaging different designs of air distributor plates. An analysis of three different plates, i.e., perforated, 45° slotted and novel hybrid plate, revealed the difference in pressure drop and minimum fluidization velocities [...] Read more.
An experimental investigation of hydrodynamics of gas-solid flow is carried out by engaging different designs of air distributor plates. An analysis of three different plates, i.e., perforated, 45° slotted and novel hybrid plate, revealed the difference in pressure drop and minimum fluidization velocities (Umf) for varying input operational variables. Umf is found to be lowest for perforated and highest for 45° slotted plate, whereas pressure drop is found to be highest for 45° slotted plate and lowest for novel hybrid distributor plate. The bubbles rise velocity ratio (Umf,b/Umf,f) is noticed minimum for 45° slotted plate due to relatively larger bubbles originating from the bigger slot openings and maximum for perforated distributor plate owing to smaller bubbles with dominant axial rise. Furthermore, the bed height rise ratio (h/L) is observed as a minimum for perforated distributor and maximum for 45° slotted plate due to larger bubbles through 45° slots rupturing the bed surface, causing more bed expansion. Furthermore, CFD analysis is also carried out to observe the insight flow dynamics using the distributor plates. The simulations use a two-fluid model (TFM) and K-Epsilon turbulence models. CFD model shows promising results in agreement with the experimental results. CFD results revealed that the lower portion enhanced lateral dispersion/mixing of solid particles due to 45° angular openings of an air inlet. In contrast, the perforated plate exhibited a straight upward motion of small air bubbles, causing no radial/lateral mixing. CFD results for the hybrid plate show the mixed axial as well as lateral mixing of solids by revealing velocity distribution; therefore, the novel hybrid plate is found to be an optimum distributor plate due to its lowest pressure drop, adequate Umf, intermediary bed height rise ratio and moderate bubble rise velocity ratio across the bed. Full article
(This article belongs to the Special Issue Experimental and Computational Fluid Dynamics)
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15 pages, 785 KiB  
Article
Classification of Red Blood Cells Using Time-Distributed Convolutional Neural Networks from Simulated Videos
by Samuel Molčan, Monika Smiešková, Hynek Bachratý, Katarína Bachratá and Peter Novotný
Appl. Sci. 2023, 13(13), 7967; https://doi.org/10.3390/app13137967 - 7 Jul 2023
Cited by 1 | Viewed by 1649
Abstract
The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health [...] Read more.
The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose a novel approach to determine RBC elasticity by analyzing video recordings and using a convolutional neural network (CNN) for classification. Due to the scarcity of available blood flow recordings, computer simulations based on a numerical model are employed to generate a substantial amount of training data. The simulation model incorporates the representation of RBCs as elastic objects within a fluid flow, allowing for a detailed understanding of their behavior. We compare the performance of different CNN architectures, including ResNet and EfficientNet, for video classification of RBC elasticity. Our results demonstrate the potential of using CNNs and simulation-based data for the accurate classification of RBC elasticity. Full article
(This article belongs to the Special Issue Experimental and Computational Fluid Dynamics)
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