[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (3,122)

Search Parameters:
Keywords = centrifuge

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
15 pages, 1824 KiB  
Article
Assessment of Gravity Deportment of Gold-Bearing Ores: Gravity Recoverable Gold Test
by Oldřich Šigut, Tomáš Široký, Iva Janáková, Radek Střelecký and Vladimír Čablík
Minerals 2024, 14(12), 1279; https://doi.org/10.3390/min14121279 - 16 Dec 2024
Viewed by 238
Abstract
This study investigated the potential of low-grade gold deposits in modern mining, particularly in the context of declining high-grade resources. The primary method for processing these ores was gravity separation with the Knelson concentrator. A GRG test (gravity recoverable gold test) was conducted [...] Read more.
This study investigated the potential of low-grade gold deposits in modern mining, particularly in the context of declining high-grade resources. The primary method for processing these ores was gravity separation with the Knelson concentrator. A GRG test (gravity recoverable gold test) was conducted on two gold-bearing samples: a polymetallic Cu-Zn-Au ore from Zlaté Hory–Západ (Czech Republic) containing refractory gold and an ore with free gold from Kašperské Hory (Czech Republic). The study evaluated the effectiveness of the GRG test for gold recovery from these ores. The results showed that the Kašperské Hory sample predominantly contained relatively large gold grains, with recovery rates dropping significantly upon finer comminution. In the sample from the Zlaté Hory–Západ deposit, the greatest GRG release occurred in the first and last test stages, suggesting that larger sulfide grains with bound gold passed predominantly in the first stage, while fine gold with residual sulfides passed in the third. Both samples achieved high overall GRG recovery rates, with 64.2% for Kašperské Hory and more than 66% for Zlaté Hory–Západ, demonstrating the efficacy of centrifugal concentrators for both ores. Full article
Show Figures

Figure 1

Figure 1
<p>A procedural flow sheet for the GRG test.</p>
Full article ">Figure 2
<p>Microstructure of the input sample from Kašperské Hory (SEM and microanalysis).</p>
Full article ">Figure 3
<p>Recovery of gold by concentration stage (Kašperské Hory).</p>
Full article ">Figure 4
<p>Microstructure of the input sample from Zlaté Hory–Západ (SEM and microanalysis).</p>
Full article ">Figure 5
<p>Recovery of gold by concentration stage (Zlaté Hory–Západ).</p>
Full article ">
16 pages, 6709 KiB  
Article
Effects of Different Processing on miRNA and Protein in Small Extracellular Vesicles of Goat Dairy Products
by Yuqin Fan, Zhikang Li, Yanmei Hou, Chumin Tan, Sheng Xiong, Jinjing Zhong and Qiuling Xie
Nutrients 2024, 16(24), 4331; https://doi.org/10.3390/nu16244331 - 16 Dec 2024
Viewed by 272
Abstract
Objectives: Small extracellular vesicles (sEVs) are nanosized vesicles with biological activities that exist in milk, playing functional roles in immunity, gut balance, and the nervous system. Currently, little is known about the impact of processing on milk sEVs. Methods: In this study, sEVs [...] Read more.
Objectives: Small extracellular vesicles (sEVs) are nanosized vesicles with biological activities that exist in milk, playing functional roles in immunity, gut balance, and the nervous system. Currently, little is known about the impact of processing on milk sEVs. Methods: In this study, sEVs were collected from raw goat milk (g-sEV), pasteurized goat milk (pg-sEV), and goat milk powder (p-sEV) using a sucrose cushion centrifugation combined with qEV chromatography. Then, the sEVs were identified and compared using NTA, Western blot, and TEM. After extracting RNA and the total proteome from sEVs derived from different samples, the RNA was subjected to high-throughput sequencing, and peptide fragments were analyzed using mass spectrometry. Finally, GO and KEGG pathway analyses were performed on the results. Results: The characterization results revealed a decrease in diameter as the level of processing increased. High-throughput sequencing results showed that all three types of small extracellular vesicles were found to be rich in miRNA, and no significant differences were observed in the most abundant sEV species. Comparing with g-sEV, there were 3938 and 4645 differentially expressed miRNAs in pg-sEV and p-sEV, respectively, with the majority of them (3837 and 3635) being downregulated. These differentially expressed miRNAs were found to affect biological processes or signaling pathways such as neurodevelopment, embryonic development, and transcription. Proteomic analysis showed that there were 339 differentially expressed proteins between g-sEV and pg-sEV, with 209 proteins being downregulated. Additionally, there were 425 differentially expressed proteins between g-sEV and p-sEV, with 293 proteins being downregulated. However, no significant differences were observed in the most abundant protein species among the three types of sEVs. Enrichment analysis indicated that the differentially expressed proteins were associated with inflammation, immunity, and other related processes. Conclusions: These results indicate that extracellular vesicles have a protective effect on their cargo, while processing steps can have an impact on the size and quantity of the sEVs. Furthermore, processing can also lead to the loss of immune-related miRNA and proteins in sEVs. Full article
(This article belongs to the Section Proteins and Amino Acids)
Show Figures

Figure 1

Figure 1
<p>Comparison of exosome content and purity in different fractions of goat dairy products. (<b>A</b>) Detection of exosome marker proteins CD63 and TSG101 in different fractions of different goat dairy products by Western blot, (<b>B</b>) NTA and BCA were used to detect the content of exosomes (blue) and protein concentration (red) in each fraction of different goat milk products, and (<b>C</b>) The purity of exosomes in different fractions of different goat milk products was measured by NTA/BCA. All data are presented as mean ± SD, <span class="html-italic">p</span> &lt; 0.05 (*), or <span class="html-italic">p</span> &lt; 0.0001 (****), no significance (ns).</p>
Full article ">Figure 2
<p>Identification and comparison of milk small extracellular from different goat milk products. (<b>A</b>) TEM observation of the shape and size of small extracellular (red arrows) from different goat milk products; the bar—200 nm; (<b>B</b>) the diameter distribution of small extracellular in different goat milk products were detected by NTA. (<b>C</b>) Comparison of small extracellular content in different goat dairy products; (<b>D</b>) comparison of protein concentration in different goat dairy products; (<b>E</b>) comparison of the purity of different goat milk products, <span class="html-italic">p</span> &lt; 0.05 (*), <span class="html-italic">p</span> &lt; 0.01 (**), <span class="html-italic">p</span> &lt; 0.001 (***), no significance (ns).</p>
Full article ">Figure 3
<p>Annotation, classification, and length distribution of small RNAs in milk exosomes of different goat dairy products. (<b>A</b>) Comparison of small RNA abundance in exosomes from different dairy products; (<b>B</b>) the length distribution of various small RNAs in exosomes of different goat dairy products.</p>
Full article ">Figure 4
<p>miRNA identification and cluster analysis of differential miRNAs. (<b>A</b>) Venn diagram analysis of miRNAs identified in exosomes of different goat milk products; (<b>B</b>) heatmap of differential miRNAs in g-sEV and pg-sEV comparison; (<b>C</b>) heatmap of differential miRNAs in g-sEV and <span class="html-italic">p</span>-sEV comparison.</p>
Full article ">Figure 5
<p>GO and KEGG analysis of the target genes of the top 10 differentially expressed miRNAs. (<b>A</b>) GO analysis of target genes of top 10 differentially expressed miRNAs in g-sEV and pg-sEV comparison; (<b>B</b>) KEGG analysis of the target genes of the top 10 differentially expressed miRNAs between g-sEV and pg-sEV; (<b>C</b>) GO analysis of the target genes of the top 10 differentially expressed miRNAs between g-sEV and <span class="html-italic">p</span>-sEv; (<b>D</b>) KEGG analysis of target genes of top 10 differentially expressed miRNAs between g-sEV and <span class="html-italic">p</span>-sEV.</p>
Full article ">Figure 6
<p>Protein identification and differentially expressed protein cluster analysis. (<b>A</b>) Venn diagram was used to analyze the proteins identified in exosomes of different goat milk products. (<b>B</b>) Heat map of differentially expressed proteins in g-sEV and pg-sEV comparison; (<b>C</b>) heatmap of differentially expressed proteins in g-sEV and <span class="html-italic">p</span>-sEV comparison.</p>
Full article ">Figure 7
<p>GO and KEGG analysis of differential proteins. (<b>A</b>) GO analysis of differential proteins in g-sEV and pg-sEV comparison; (<b>B</b>) KEGG analysis of differential proteins between g-sEV and pg-sEv; (<b>C</b>) GO analysis of differential proteins in g-sEV and <span class="html-italic">p</span>-sEV comparison; (<b>D</b>) KEGG analysis of differential proteins in g-sEV and <span class="html-italic">p</span>-sEV comparison.</p>
Full article ">
25 pages, 2439 KiB  
Systematic Review
Application of Advanced Platelet-Rich Fibrin in Oral and Maxillo-Facial Surgery: A Systematic Review
by Marek Chmielewski, Andrea Pilloni and Paulina Adamska
J. Funct. Biomater. 2024, 15(12), 377; https://doi.org/10.3390/jfb15120377 - 14 Dec 2024
Viewed by 290
Abstract
Background: Advanced platelet-rich fibrin (A-PRF) is produced by centrifuging the patient’s blood in vacuum tubes for 14 min at 1500 rpm. The most important component of A-PRF is the platelets, which release growth factors from their ⍺-granules during the clotting process. This process [...] Read more.
Background: Advanced platelet-rich fibrin (A-PRF) is produced by centrifuging the patient’s blood in vacuum tubes for 14 min at 1500 rpm. The most important component of A-PRF is the platelets, which release growth factors from their ⍺-granules during the clotting process. This process is believed to be the main source of growth factors. The aim of this paper was to systematically review the literature and to summarize the role of A-PRF in oral and maxillo-facial surgery. Materials and Methods: A systematic review was carried out, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (PROSPERO: CRD42024584161). Results: Thirty-eight articles published before 11 November 2024 were included in the systematic review. The largest study group consisted of 102 patients, and the smallest study group consisted of 10 patients. A-PRF was most often analyzed compared to leukocyte-PRF (L-PRF) or blood cloth. A-PRF was correlated with lower postoperative pain. Also, A-PRF was highlighted to have a positive effect on grafting material integration. A-PRF protected areas after free gingival graft very well, promoted more efficient epithelialization of donor sites and enhanced wound healing. Conclusions: Due to its biological properties, A-PRF could be considered a reliable addition to the surgical protocols, both alone and as an additive to bio-materials, with the advantages of healing improvement, pain relief, soft tissue management and bone preservation, as well as graft integration. However, to determine the long-term clinical implications and recommendations for clinical practice, more well-designed randomized clinical trials are needed in each application, especially those with larger patient cohorts, as well as additional blinding of personnel and long follow-up periods. Full article
(This article belongs to the Special Issue Functional Biomaterials for Regenerative Dentistry)
Show Figures

Figure 1

Figure 1
<p>A-PRF clot in glass-coated plastic tubes.</p>
Full article ">Figure 2
<p>PRISMA workflow.</p>
Full article ">Figure 3
<p>The risk of bias assessment using RoS 2 [<a href="#B2-jfb-15-00377" class="html-bibr">2</a>,<a href="#B19-jfb-15-00377" class="html-bibr">19</a>,<a href="#B20-jfb-15-00377" class="html-bibr">20</a>,<a href="#B21-jfb-15-00377" class="html-bibr">21</a>,<a href="#B24-jfb-15-00377" class="html-bibr">24</a>,<a href="#B25-jfb-15-00377" class="html-bibr">25</a>,<a href="#B26-jfb-15-00377" class="html-bibr">26</a>,<a href="#B27-jfb-15-00377" class="html-bibr">27</a>,<a href="#B28-jfb-15-00377" class="html-bibr">28</a>,<a href="#B29-jfb-15-00377" class="html-bibr">29</a>,<a href="#B30-jfb-15-00377" class="html-bibr">30</a>,<a href="#B31-jfb-15-00377" class="html-bibr">31</a>,<a href="#B32-jfb-15-00377" class="html-bibr">32</a>,<a href="#B33-jfb-15-00377" class="html-bibr">33</a>,<a href="#B34-jfb-15-00377" class="html-bibr">34</a>,<a href="#B35-jfb-15-00377" class="html-bibr">35</a>,<a href="#B36-jfb-15-00377" class="html-bibr">36</a>,<a href="#B37-jfb-15-00377" class="html-bibr">37</a>,<a href="#B38-jfb-15-00377" class="html-bibr">38</a>,<a href="#B39-jfb-15-00377" class="html-bibr">39</a>,<a href="#B40-jfb-15-00377" class="html-bibr">40</a>,<a href="#B44-jfb-15-00377" class="html-bibr">44</a>,<a href="#B45-jfb-15-00377" class="html-bibr">45</a>,<a href="#B46-jfb-15-00377" class="html-bibr">46</a>,<a href="#B47-jfb-15-00377" class="html-bibr">47</a>,<a href="#B48-jfb-15-00377" class="html-bibr">48</a>,<a href="#B49-jfb-15-00377" class="html-bibr">49</a>,<a href="#B50-jfb-15-00377" class="html-bibr">50</a>,<a href="#B51-jfb-15-00377" class="html-bibr">51</a>,<a href="#B52-jfb-15-00377" class="html-bibr">52</a>,<a href="#B53-jfb-15-00377" class="html-bibr">53</a>,<a href="#B54-jfb-15-00377" class="html-bibr">54</a>,<a href="#B55-jfb-15-00377" class="html-bibr">55</a>,<a href="#B56-jfb-15-00377" class="html-bibr">56</a>,<a href="#B57-jfb-15-00377" class="html-bibr">57</a>,<a href="#B58-jfb-15-00377" class="html-bibr">58</a>,<a href="#B59-jfb-15-00377" class="html-bibr">59</a>,<a href="#B60-jfb-15-00377" class="html-bibr">60</a>].</p>
Full article ">
18 pages, 5708 KiB  
Article
Stress Distribution and Transverse Vibration of Flywheel Within Linear Elastic Range
by Desejo Filipeson Sozinando, Kgotso Koketso Leema, Vhahangwele Colleen Sigonde, Bernard Xavier Tchomeni and Alfayo Anyika Alugongo
Vibration 2024, 7(4), 1248-1265; https://doi.org/10.3390/vibration7040064 - 13 Dec 2024
Viewed by 423
Abstract
Flywheels have been largely used in rotating machine engines to save inertial energy and to limit speed fluctuations. A stress distribution problem is created due to the centrifugal forces that are formed when the flywheel is spinning around, which leads to different levels [...] Read more.
Flywheels have been largely used in rotating machine engines to save inertial energy and to limit speed fluctuations. A stress distribution problem is created due to the centrifugal forces that are formed when the flywheel is spinning around, which leads to different levels of pressure and decompression inside its structure. Lack of balance leads to high energy losses through various mechanisms, which deteriorate both the flywheel’s expectancy and their ability to rotate at high speeds. Deviation in the design of flywheels from their optimum performance can cause instability issues and even a catastrophic failure during operation. This paper aims to analytically examine the stress distribution of radial and tangential directions along the flywheel structure within a linear elastic range. The eigenvalues and eigenvectors, which are representative of free vibrational features, were extracted by applying finite element analysis (FEA). Natural frequencies and their corresponding vibrating mode shapes and mass participation factors were identified. Furthermore, Kirchhoff–Love plate theory was employed to model the transverse vibration of the system. A general solution for the radial component of the equation of flywheel motion was derived with the help of the Bessel function. The results show certain modes of vibration identified as particularly influential in specific directions. Advanced time-frequency analysis techniques, including but not limited to continuous wavelet transform (CWT) and Hilbert–Huang transform (HHT), were applied to extract transverse vibration features of the flywheel system. It was also found that using CWT, low-frequency vibrations contribute to the majority of the energy in the extracted signal spectrum, while HHT exposes the high-frequency components of vibration that may cause significant structural damage if not addressed in time. Full article
Show Figures

Figure 1

Figure 1
<p>Rotating flywheel and its element.</p>
Full article ">Figure 2
<p>Stress distributions: (<b>a</b>) radial variation; (<b>b</b>) tangential variation.</p>
Full article ">Figure 3
<p>(<b>a</b>) Variation in tangential vs. radial stresses; (<b>b</b>) displacement along flywheel radius.</p>
Full article ">Figure 4
<p>Flowchart of the methodology implied in the FEA simulation.</p>
Full article ">Figure 5
<p>First four deformation modes of vibration.</p>
Full article ">Figure 6
<p>Waterfall response of mass participation factor.</p>
Full article ">Figure 7
<p>Percentage of mass participation factor in the X, Y, and Z directions.</p>
Full article ">Figure 8
<p>Four normal vibrational modes in waveform and polar spectrum of flywheel.</p>
Full article ">Figure 9
<p>Transverse deflection: (<b>a</b>) time domain; (<b>b</b>) FFT response; (<b>c</b>) CWT spectrum.</p>
Full article ">Figure 10
<p>Transverse velocity: (<b>a</b>) time domain; (<b>b</b>) FFT response; (<b>c</b>) CWT spectrum.</p>
Full article ">Figure 11
<p>Block diagram of the IF extraction based on HHT.</p>
Full article ">Figure 12
<p>Extraction of IMFs and IF based on HHT.</p>
Full article ">
14 pages, 11591 KiB  
Article
Optimal Design of a Liquid Hydrogen Centrifugal Pump Impeller
by Catur Harsito, Jeong-Eui Yun, Joon-Young Shin and Jae-Min Kim
Energies 2024, 17(24), 6299; https://doi.org/10.3390/en17246299 - 13 Dec 2024
Viewed by 320
Abstract
Global energy consumption has continued to increase in recent years with economic development. Fossil energy sources are now being replaced with renewable energy, and hydrogen is one of such alternatives. Pumps are used for storage, transportation, and distribution. One such pump is the [...] Read more.
Global energy consumption has continued to increase in recent years with economic development. Fossil energy sources are now being replaced with renewable energy, and hydrogen is one of such alternatives. Pumps are used for storage, transportation, and distribution. One such pump is the liquefied hydrogen centrifugal pump. In this study, optimisation design of a liquefied hydrogen centrifugal pump was performed using the response surface method, which is the optimisation method of the DesignXplorer provided by ANSYS, based on the flow analysis results of the impeller of the centrifugal pump. The design variables used in the optimisation process are the outlet width b2, % of the blade thickness Su2, leading edge inclination angle α, hub inclination angle δ, wrap angle θ, and outlet blade angle β2. The optimisation analysis results obtained confirmed that all the selected design variables are semi-galactic and are sensitive to pump efficiency and head. It was confirmed that the efficiency of the centrifugal pump achieved using liquefied hydrogen as the working fluid is approximately 82.4%, which is significantly higher than that achieved by a centrifugal pump using water as the working fluid under the same operating conditions. Full article
(This article belongs to the Special Issue Hydrogen Energy Generation, Storage, Transportation and Utilization)
Show Figures

Figure 1

Figure 1
<p>Global energy use [<a href="#B13-energies-17-06299" class="html-bibr">13</a>].</p>
Full article ">Figure 2
<p>Flow chart of the optimisation process.</p>
Full article ">Figure 3
<p>Design parameters for optimisation.</p>
Full article ">Figure 4
<p>Computational mesh of the volute case.</p>
Full article ">Figure 5
<p>Computational mesh of the impeller blade.</p>
Full article ">Figure 6
<p>Mesh independence test.</p>
Full article ">Figure 7
<p>Local sensitivity of six optimisation design variables.</p>
Full article ">Figure 8
<p>Response surface showing the effect of the optimal design variables <span class="html-italic">α</span>, <span class="html-italic">δ</span> on pump efficiency and head at the optimal design point. (<b>a</b>) Efficiency. (<b>b</b>) Head.</p>
Full article ">Figure 9
<p>Response surface showing the effect of the optimal design variables <span class="html-italic">δ</span>, <span class="html-italic">θ</span> on pump efficiency and head at the optimal design point. (<b>a</b>) Efficiency. (<b>b</b>) Head.</p>
Full article ">Figure 10
<p>Response surface showing the effect of the optimal design variables <span class="html-italic">θ</span>, <span class="html-italic">β</span><sub>2</sub> on pump efficiency and head at the optimal design point. (<b>a</b>) Efficiency. (<b>b</b>) Head.</p>
Full article ">Figure 11
<p>Response surface showing the effect of the optimal design variables <span class="html-italic">β</span><sub>2</sub>, <span class="html-italic">Su</span><sub>2</sub> on pump efficiency and head at the optimal design point. (<b>a</b>) Efficiency. (<b>b</b>) Head.</p>
Full article ">Figure 12
<p>Response surface showing the effect of the optimal design variables <span class="html-italic">Su</span><sub>2</sub>, <span class="html-italic">b</span><sub>2</sub> on pump efficiency and head at the optimal design point. (<b>a</b>) Efficiency. (<b>b</b>) Head.</p>
Full article ">Figure 13
<p>The effect of the optimal design variables on pump efficiency and head at the optimal design point. (<b>a</b>) Leading edge inclination angle, <span class="html-italic">α</span>; (<b>b</b>) hub inclination angle, <span class="html-italic">δ</span>; (<b>c</b>) wrap angle, <span class="html-italic">θ</span>; (<b>d</b>) outlet blade angle, <span class="html-italic">β</span><sub>2</sub>; (<b>e</b>) % of blade thickness, <span class="html-italic">Su</span><sub>2</sub>; (<b>f</b>) outlet width, <span class="html-italic">b</span><sub>2</sub>.</p>
Full article ">Figure 13 Cont.
<p>The effect of the optimal design variables on pump efficiency and head at the optimal design point. (<b>a</b>) Leading edge inclination angle, <span class="html-italic">α</span>; (<b>b</b>) hub inclination angle, <span class="html-italic">δ</span>; (<b>c</b>) wrap angle, <span class="html-italic">θ</span>; (<b>d</b>) outlet blade angle, <span class="html-italic">β</span><sub>2</sub>; (<b>e</b>) % of blade thickness, <span class="html-italic">Su</span><sub>2</sub>; (<b>f</b>) outlet width, <span class="html-italic">b</span><sub>2</sub>.</p>
Full article ">Figure 14
<p>The effect of the outlet width b<sub>2</sub> on entropy production in the impeller span sectional plans.</p>
Full article ">Figure 15
<p>The effect of the outlet width b<sub>2</sub> on the entropy production in the meridional plans.</p>
Full article ">Figure 16
<p>The effect of the outlet width <span class="html-italic">b<sub>2</sub></span> on the eddy dissipation in the impeller span = 0.5 plan.</p>
Full article ">Figure 17
<p>Comparison of efficiency and head for flow rate change between the base and optimisation model.</p>
Full article ">Figure 18
<p>Comparison of entropy production and pressure between the base and optimisation model at design point Q/Q<sub>d</sub> = 1.</p>
Full article ">
25 pages, 14280 KiB  
Article
The Use of Chemical Flocculants and Chitosan as a Pre-Concentration Step in the Harvesting Process of Three Native Microalgae Species from the Canary Islands Cultivated Outdoors at the Pilot Scale
by Laura Figueira Garcia, Zivan Gojkovic, Marianna Venuleo, Flavio Guidi and Eduardo Portillo
Microorganisms 2024, 12(12), 2583; https://doi.org/10.3390/microorganisms12122583 - 13 Dec 2024
Viewed by 501
Abstract
Biomass harvesting represents one of the main bottlenecks in microalgae large-scale production. Solid–liquid separation of the biomass accounts for 30% of the total production costs, which can be reduced by the use of flocculants as a pre-concentration step in the downstream process. The [...] Read more.
Biomass harvesting represents one of the main bottlenecks in microalgae large-scale production. Solid–liquid separation of the biomass accounts for 30% of the total production costs, which can be reduced by the use of flocculants as a pre-concentration step in the downstream process. The natural polymer chitosan and the two chemical flocculants FeCl3 and AlCl3 were tested on freshwater Chlorella sorokiniana and two marine algae, Dunaliella tertiolecta and Tetraselmis striata. A preliminary screening at the laboratory scale was performed to detect the most suitable doses of flocculants. On the basis of these results, selected doses were tested on the pilot scale, using the flocculants for a pre-concentration step and the centrifugation as a second step to confirm the effectiveness of flocculants in a realistic operational environment. The biomass recoveries (Rpilot, %) of 100 L cultures were as follows: (1) for T. striata, Rpilot = 94.6% for 0.08 g/L AlCl3, 88.4% for 0.1 g/L FeCl3, and 68.3% for 0.04 g/L chitosan; (2) for D. tertiolecta, Rpilot = 81.7% for 0.1 g/L AlCl3, 87.9% for 0.2 g/L FeCl3, and 81.6% for 0.1 g/L chitosan; and (3) for C. sorokiniana, Rpilot = 89.6% for 0.1 g/L AlCl3, 98.6% for 0.2 g/L FeCl3, and 68.3% for 0.1 g/L chitosan. Flocculation reduced the harvesting costs by 85.9 ± 4.5% using chemical flocculants. Excesses of aluminum and iron in the biomass could be solved by decreasing the pH in the biomass combined with washing. This is the first study, to the best of our knowledge, that investigates the pilot-scale flocculation of three native Canarian microalgal strains. A pilot-scale pre-concentration step before centrifugation can improve the yield and reduce costs in the microalgae harvesting process. Full article
(This article belongs to the Section Microbial Biotechnology)
Show Figures

Figure 1

Figure 1
<p>Recovery R<sub>lab</sub> (%) of the selected species during the 180 min of flocculation. (<b>a</b>) Recovery (%) of <span class="html-italic">T. striata</span> added with AlCl<sub>3</sub> at doses of 0.2 g/L, 0.1 g/L, and 0.05 g/L. (<b>b</b>) Recovery (%) of <span class="html-italic">T. striata</span> added with FeCl<sub>3</sub> at doses of 0.24 g/L, 0.1 g/L, and 0.05 g/L. (<b>c</b>) Recovery (%) of <span class="html-italic">T. striata</span> added with chitosan at doses of 0.1 g/L, 0.06 g/L, and 0.03 g/L. (<b>d</b>) Recovery (%) of <span class="html-italic">D. tertiolecta</span> added with AlCl<sub>3</sub> at doses of 0.1 g/L, 0.05 g/L, and 0.045 g/L. (<b>e</b>) Recovery (%) of <span class="html-italic">D. tertiolecta</span> added with FeCl<sub>3</sub> at doses of 0.24 g/L, 0.1 g/L, and 0.05 g/L. (<b>f</b>) Recovery (%) of <span class="html-italic">D. tertiolecta</span> added with chitosan at doses of 0.25 g/L, 0.125g/L, and 0.05 g/L. (<b>g</b>) Recovery (%) of <span class="html-italic">C. sorokiniana</span> added with AlCl<sub>3</sub> at doses of 0.22 g/L, 0.1 g/L, and 0.045 g/L. (<b>h</b>) Recovery (%) of <span class="html-italic">C. sorokiniana</span> added with FeCl<sub>3</sub> at doses of 0.35 g/L, 0.24 g/L, and 0.1 g/L. (<b>i</b>) Recovery (%) of <span class="html-italic">C. sorokiniana</span> added with chitosan at doses of 0.1 g/L, 0.08 g/L, and 0.06 g/L. All data points represent the average of the triplicate measurements with corresponding standard deviation bars.</p>
Full article ">Figure 2
<p>Data of <span class="html-italic">T. striata</span> recovery (R<sub>pilot</sub>, %, black columns), recovery efficiency (RE, %, gray columns), and centrifuge recovery (CR, white columns). (<b>a</b>) Control and doses of 0.05, 0.08, and 0.1 g/L of AlCl<sub>3</sub>. (<b>b</b>) Control and doses of 0.08, 0.1, and 0.2 g/L of FeCl<sub>3</sub>. (<b>c</b>) Control and doses of 0.04, 0.08, and 0.1 g/L of chitosan. (<b>d</b>) Control and best performing doses of AlCl<sub>3</sub> (0.08 g/L), FeCl<sub>3</sub> (0.1 g/L), and chitosan (0.04 g/L).</p>
Full article ">Figure 3
<p>Data of <span class="html-italic">D. tertiolecta</span> pilot-scale recovery (R<sub>pilot</sub>, %; black columns), recovery efficiency (RE, %; gray columns), and centrifuge recovery (CR, white columns). (<b>a</b>) Control and doses of 0.05, 0.08, and 0.1 g/L of AlCl<sub>3</sub>. (<b>b</b>) Control and doses of 0.05, 0.1, and 0.2 g/L of FeCl<sub>3</sub>. (<b>c</b>) Control and doses of 0.05, 0.01, and 0.15 g/L of chitosan. (<b>d</b>) Control and best doses of AlCl<sub>3</sub> (0.1 g/L), FeCl<sub>3</sub> (0.2 g/L), and chitosan (0.1 g/L).</p>
Full article ">Figure 4
<p><span class="html-italic">C. sorokiniana</span> recovery (R<sub>pilot</sub>, %; black columns), recovery efficiency (RE, %; gray columns), and centrifuge recovery (CR, white columns). (<b>a</b>) Control and doses of 0.08, 0.1, and 0.2 g/L of AlCl<sub>3</sub>. (<b>b</b>) Control and doses of 0.1, 0.2, and 0.4 g/L of FeCl<sub>3</sub>. (<b>c</b>) Control and doses of 0.06, 0.08, and 0.1 g/L of chitosan. (<b>d</b>) Control and best doses of AlCl<sub>3</sub> (0.1 g/L), FeCl<sub>3</sub> (0.2 g/L), and chitosan (0.1 g/L).</p>
Full article ">Figure A1
<p>Recovery R<sub>lab</sub> (%) of the three microalgae strains, <span class="html-italic">T. striata</span>, <span class="html-italic">D. tertiolecta</span>, and <span class="html-italic">C. sorokiniana</span>, subjected to gravity sedimentation for 180 min. All data points represent the average of the triplicate measurements with corresponding standard deviation bars.</p>
Full article ">Figure A2
<p>Comparison of the laboratory-scale flocculation results and color change with different concentrations of FeCl<sub>3</sub>: (<b>a</b>) 0.1 g/L FeCl<sub>3</sub>, (<b>b</b>) 0.24 g/L FeCl<sub>3</sub>, and (<b>c</b>) 0.35 g/L FeCl<sub>3</sub>.</p>
Full article ">Figure A3
<p>Color changes of the supernatant with different concentrations of FeCl<sub>3</sub> at pilot-scale flocculation of <span class="html-italic">T. striata</span>: (<b>a</b>) Control culture, without flocculants, (<b>b</b>) 0.2 g/L FeCl<sub>3</sub>, (<b>c</b>) 0.1 g/L FeCl<sub>3</sub>, and (<b>d</b>) 0.08 g/L FeCl<sub>3</sub>.</p>
Full article ">Figure A4
<p>Floc formation and flotation with different concentrations of FeCl<sub>3</sub> at pilot-scale flocculation of <span class="html-italic">D. tertiolecta</span>: (<b>a</b>) 0.05 g/L FeCl<sub>3</sub>, (<b>b</b>) 0.1 g/L FeCl<sub>3</sub>, and (<b>c</b>) 0.2 g/L FeCl<sub>3</sub>.</p>
Full article ">Figure A5
<p>Color changes of the supernatant with different concentrations of FeCl<sub>3</sub> at pilot-scale flocculation of <span class="html-italic">C. sorokiniana</span>: (<b>a</b>) Control culture without flocculants, (<b>b</b>) 0.4 g/L FeCl<sub>3</sub>, (<b>c</b>) 0.2 g/L FeCl<sub>3</sub>, and (<b>d</b>) 0.1 g/L FeCl<sub>3</sub>.</p>
Full article ">
12 pages, 1729 KiB  
Article
Volatile Component Composition, Retronasal Aroma Release Profile, and Sensory Characteristics of Non-Centrifugal Cane Sugar Obtained at Different Evaporation Temperatures
by Yonathan Asikin, Yuki Nakaza, Moena Oe, Hirotaka Kaneda, Goki Maeda, Kensaku Takara and Koji Wada
Appl. Sci. 2024, 14(24), 11617; https://doi.org/10.3390/app142411617 - 12 Dec 2024
Viewed by 367
Abstract
Non-centrifugal cane sugar (NCS) is prepared by evaporating sugarcane syrup to form a solidified, dehydrated brown sugar with a distinct flavor. This study investigated the effect of final evaporation temperatures (120–140 °C) on the volatile components, retronasal aroma profile, and sensory characteristics of [...] Read more.
Non-centrifugal cane sugar (NCS) is prepared by evaporating sugarcane syrup to form a solidified, dehydrated brown sugar with a distinct flavor. This study investigated the effect of final evaporation temperatures (120–140 °C) on the volatile components, retronasal aroma profile, and sensory characteristics of NCS. Solid-phase microextraction–gas chromatography–mass spectrometry showed that the concentration of most volatiles, including pyrazines, furans, and furanones, in the NCS significantly increased as the evaporation temperature increased (p < 0.05). The evaporation temperature affected the aroma release from NCS, as shown in proton transfer reaction time-of-flight-mass spectrometry, with the intensity of volatile compounds detected from panelists’ noses or mouths significantly increasing after consuming NCS obtained at higher temperatures. Moreover, the intensity of aroma release in the mouth was greater than that in the nose; the most prevalent released substance, m/z 87.10, which could be derived from dihydro-2(3H)-furanone and 2,3-butanedione, rapidly decreased over seven breath cycles compared to other ions, suggesting its importance as a top-note aroma substance in NCS. In addition, the perceived roasted aroma and bitterness of the NCS obtained at higher temperatures were intensified. These findings underscore the importance of modifying the evaporation temperature on the volatile component composition, aroma release, and sensory characteristics of NCS. Full article
Show Figures

Figure 1

Figure 1
<p>Typical in-nose and in-mouth retronasal aroma release plots of <span class="html-italic">m</span>/<span class="html-italic">z</span> 60.05 (acetone-<sup>13</sup>C) and <span class="html-italic">m</span>/<span class="html-italic">z</span> 87.10 from 15% non-centrifugal cane sugar (NCS) solution intake; the NCS was produced at different evaporation temperatures.</p>
Full article ">Figure 2
<p>Typical in-nose and in-mouth retronasal aroma release profiles of aroma compounds from 15% non-centrifugal cane sugar (NCS) solution intake; the NCS was produced at different evaporation temperatures: (<b>a</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 87.10 (dihydro-2(3H)-furanone and 2,3-butanedione); (<b>b</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 95.12 (2-methylpyrazine and 5-methyl-2-furanmethanol [–H<sub>2</sub>O]); (<b>c</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 81.10 (pyrazine and 2-furanmethanol [–H<sub>2</sub>O]).</p>
Full article ">Figure 3
<p>(<b>a</b>) Time-course of the in-mouth retronasal release pattern of <span class="html-italic">m</span>/<span class="html-italic">z</span> 87.10 (dihydro-2(3H)-furanone and 2,3-butanedione), <span class="html-italic">m</span>/<span class="html-italic">z</span> 95.12 (2-methylpyrazine and 5-methyl-2-furanmethanol [–H<sub>2</sub>O]), and <span class="html-italic">m</span>/<span class="html-italic">z</span> 81.10 (pyrazine and 2-furanmethanol [–H<sub>2</sub>O]) after 15% non-centrifugal cane sugar (NCS) solution intake (a breath cycle hold for 4 s); the NCS was produced at 140 °C; (<b>b</b>) total intensity of in-mouth release of aroma compounds from 15% NCS solution intake over seven breath cycles (each value is expressed as the mean ± standard error of 10 panelists; different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05); the NCS was produced at different evaporation temperatures.</p>
Full article ">Figure 4
<p>Sensory characteristics of non-centrifugal cane sugar (NCS) obtained at different evaporation temperatures. Significant differences between groups were observed at <span class="html-italic">p</span> &lt; 0.05 as represented with different letters as follows: *<sup>1</sup> 120 °C <sup>(b)</sup>, 130 °C <sup>(ab)</sup>, 140 °C <sup>(a)</sup>; *<sup>2</sup> 120 °C <sup>(b)</sup>, 130 °C <sup>(b)</sup>, 140 °C <sup>(a)</sup>.</p>
Full article ">
17 pages, 17162 KiB  
Article
Numerical Investigation of Multi-Stage Radial Turbine Performance Under Variable Waste Heat Conditions for ORC Systems
by Łukasz Witanowski
Appl. Sci. 2024, 14(24), 11600; https://doi.org/10.3390/app142411600 - 12 Dec 2024
Viewed by 318
Abstract
This study investigates the performance of a centrifugal radial turbine within an Organic Rankine Cycle (ORC) system, focusing on operation beyond the design point due to variable waste heat sources. With the goal of integrating the turbine into optimal ORC operating conditions, its [...] Read more.
This study investigates the performance of a centrifugal radial turbine within an Organic Rankine Cycle (ORC) system, focusing on operation beyond the design point due to variable waste heat sources. With the goal of integrating the turbine into optimal ORC operating conditions, its performance was analyzed using R245fa as the working fluid over three stages with varying numbers of blades. A detailed computational analysis was performed using Ansys CFX software (Version 2020 R2) with the k-ω SST turbulence model using thermodynamic data from the NIST Refprop database. The results showed significant discrepancies when operating beyond the design point. At an inlet pressure of 780 kPa, the turbine internal power was calculated to be 120 kW—double the manufacturer’s maximum of 60 kW—and the mass flow rate exceeded 6 kg/s compared to the design value of 2.72 kg/s. These results highlight the challenges of adapting the turbine to fluctuating waste heat conditions, as factors such as tip clearance, blade geometry, and high outlet pressure have a significant impact on efficiency and system performance. Full article
(This article belongs to the Section Green Sustainable Science and Technology)
Show Figures

Figure 1

Figure 1
<p>Radial turbine stator cross-section.</p>
Full article ">Figure 2
<p>Radial turbine rotor cross-section.</p>
Full article ">Figure 3
<p>Radial turbine blades geometry.</p>
Full article ">Figure 4
<p>Grid sensitivity based on the turbine total-to-static efficiency.</p>
Full article ">Figure 5
<p>Computational domain for the radial turbine simulation.</p>
Full article ">Figure 6
<p>Temperature-entropy diagram of R245fa.</p>
Full article ">Figure 7
<p>Comparison of efficiency and power output at varying inlet pressures and rotational speeds.</p>
Full article ">Figure 8
<p>Variation in mass flow rate with inlet pressure for different flow conditions.</p>
Full article ">Figure 9
<p>Axial force at varying inlet pressures.</p>
Full article ">Figure 10
<p>Velocity vectors in the turbine at the mid-span.</p>
Full article ">Figure 11
<p>Streamline in the first-stage passage.</p>
Full article ">Figure 12
<p>Streamline in the second-stage passage.</p>
Full article ">Figure 13
<p>Streamline in the third-stage passage.</p>
Full article ">Figure 14
<p>Static entropy contours in the turbine at the mid-span.</p>
Full article ">Figure 15
<p>Static entropy contours in the turbine at the hub.</p>
Full article ">Figure 16
<p>Velocity vectors in the turbine at the mid-span.</p>
Full article ">Figure 17
<p>Streamline in the first-stage passage.</p>
Full article ">Figure 18
<p>Streamline in the second-stage passage.</p>
Full article ">Figure 19
<p>Streamline in the third-stage passage.</p>
Full article ">Figure 20
<p>Static entropy contours in the turbine at the mid-span.</p>
Full article ">Figure 21
<p>Static entropy contours in the turbine at the hub.</p>
Full article ">Figure 22
<p>Comparison of efficiency and power output at varying inlet pressures and rotational speeds.</p>
Full article ">Figure 23
<p>Comparison of efficiency and power output at varying inlet pressures.</p>
Full article ">Figure 24
<p>Pressure contours in the turbine at the mid-span under low-pressure operating conditions.</p>
Full article ">Figure 25
<p>Pressure contours in the turbine at the mid-span under high-pressure operating conditions.</p>
Full article ">Figure 26
<p>Power output vs. inlet pressure for various outlet pressures.</p>
Full article ">
15 pages, 16677 KiB  
Article
Research on the Influence of Symmetrical Installation of Blade on the Sediment Erosion in a Multi-Stage Centrifugal Pump
by Xijie Song, Kuizheng Zhu and Zhengwei Wang
Symmetry 2024, 16(12), 1636; https://doi.org/10.3390/sym16121636 - 11 Dec 2024
Viewed by 358
Abstract
Double suction pumps are widely used in the Yellow River in the China water intake pump stations, which face serious sediment wear. A prediction model for gap erosion in gas-liquid solid three-phase flow was constructed. A gas core factor has been added to [...] Read more.
Double suction pumps are widely used in the Yellow River in the China water intake pump stations, which face serious sediment wear. A prediction model for gap erosion in gas-liquid solid three-phase flow was constructed. A gas core factor has been added to the gap erosion model to achieve accurate prediction of particle impact velocity and impact angle caused by cavitation air core deformation. The influence mechanism of cavitation flow and sand-laden suction vortex on the sediment erosion. Usually, double suction pumps are one type. This study aims to explore the effects of the symmetrical and asymmetrical installation of double suction pump impellers on the wear and energy dissipation of pumps under sediment conditions in three-stage centrifugal pumps. The research results indicate that under symmetrical installation, the wear of the impeller caused by sediment impact is significantly intensified with a maximum velocity of 27 m/s. In contrast, asymmetric installation significantly improves sediment wear, with a maximum velocity of 24.3 m/s. By optimizing the staggered angle on both sides of the impeller, it was found that when the staggered angle was set to 10.85°, the performance of the pump under sediment conditions reached its optimal level, with a minimal erosion rate of 0.000008 kg·m−2·s−1. These results provide an important basis for the design and optimization of three-stage centrifugal pumps in sediment transport and have significant theoretical significance and engineering application value. Full article
(This article belongs to the Special Issue Advances in Multi-phase Flow: Symmetry, Asymmetry, and Applications)
Show Figures

Figure 1

Figure 1
<p>Simulation model of the three-stage pump.</p>
Full article ">Figure 2
<p>Three-dimensional entire flow passage model grid.</p>
Full article ">Figure 3
<p>Distribution of three-dimensional flow pattern in the units under different schemes.</p>
Full article ">Figure 4
<p>Distribution of flow velocity in the impeller under Scheme I.</p>
Full article ">Figure 5
<p>Three-dimensional morphology of the vortex in the impeller under Scheme I.</p>
Full article ">Figure 6
<p>Distribution of flow velocity in the impeller under Scheme II.</p>
Full article ">Figure 7
<p>Three-dimensional morphology of the vortices in the impeller under Scheme II.</p>
Full article ">Figure 8
<p>Distribution of flow velocity in the impeller under Scheme III.</p>
Full article ">Figure 9
<p>Three-dimensional morphology of the vortices in the impeller under Scheme III.</p>
Full article ">Figure 10
<p>Distribution of sediment erosion rate under Scheme I.</p>
Full article ">Figure 11
<p>Changes in particle impact velocity and sediment erosion rate under Scheme I.</p>
Full article ">Figure 12
<p>Distribution of sediment erosion rate under Scheme II.</p>
Full article ">Figure 13
<p>Changes in particle impact velocity and sediment erosion rate under Scheme II.</p>
Full article ">Figure 14
<p>Distribution of sediment erosion rate under Scheme III.</p>
Full article ">Figure 14 Cont.
<p>Distribution of sediment erosion rate under Scheme III.</p>
Full article ">Figure 15
<p>Changes in particle impact velocity and sediment erosion rate under Scheme III.</p>
Full article ">
20 pages, 8059 KiB  
Article
Mathematical Modeling of the Processes of Mowing, Oriented Feeding, and Chopping of Stalk Forage by a Forage Harvester
by Tokhtar Abilzhanuly, Ruslan Iskakov, Serik Nurgozhayev, Daniyar Abilzhanov, Olzhas Seipataliyev and Dauren Kosherbay
AgriEngineering 2024, 6(4), 4766-4785; https://doi.org/10.3390/agriengineering6040273 - 10 Dec 2024
Viewed by 556
Abstract
The design and technological scheme of a small-sized forage harvester with a capture width of 1.35 m equipped with a device oriented along the length of the stems was developed in this study. As a result of theoretical studies, the process of the [...] Read more.
The design and technological scheme of a small-sized forage harvester with a capture width of 1.35 m equipped with a device oriented along the length of the stems was developed in this study. As a result of theoretical studies, the process of the movement of mass into the chamber of the mowing rotor due to centrifugal forces was revealed. The speed of mass movement and the average size of crushed particles with the mowing rotor were determined. The oriented feeding process of stems in the chamber of the chopping rotor is mathematically described in this paper. An analytical expression is obtained for determining the average size of crushed particles by the forage harvester, that is, a mathematical model of the processes of mowing, oriented feeding, and the chopping of stem fodder by the forage harvester. Laboratory and field tests of a forage harvester equipped with a device oriented along the length of the stems were conducted. The combine harvester’s productivity was 6.14 t/h when mowing alfalfa. Special experiments were conducted to determine the average size of crushed particles after the mowing rotor. The average size of crushed particles with the mowing rotor was 147.4 mm, while the theoretical value was 144 mm. The difference between these values was only 2.31%. A special experiment was conducted on the combine without an orienting device to compare the quality indicators. The mass fractions of crushed particles of up to 50 mm in length when the combine was operating with and without an orienting device were 79.3 and 46.7, respectively. Accordingly, the average length of crushed particles was 33.79 mm and that without an orienting device was 77.07 mm. The theoretical value of the average length of crushed particles was 34.9 mm (i.e., the difference between the theoretical and actual value of the average size of the crushed particles was only 3.25%). All this proves that when the combine harvester was operated with an orienting device, there was a significant increase in the quality indicators of the chopped feed, and the reliability of the theoretical studies and the resulting mathematical model were determined. Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
Show Figures

Figure 1

Figure 1
<p>Structural and technological (<b>a</b>) and kinematic (<b>b</b>) schemes of the combine. 1—frame; 2—wheels; 3—mowing rotor; 4—console auger; 5—orienting drum; 6—inclined blades; 7—counter-cutting plate; 8—chopping radial knife; 9—ejection blade; 10—deflector.</p>
Full article ">Figure 2
<p>General views of the mowing rotor (<b>a</b>) 1—mowing rotor; 2—bearing housing; 3—ears; and console auger (<b>b</b>) 1—shaft; 2—pipe; 3—blade; 4—plug.</p>
Full article ">Figure 3
<p>General views of the orienting drum (<b>a</b>) 1—drum shaft; 2—drum; 3—plug; 4—orienting blades; 5—fingers; and blade grinding rotor (<b>b</b>) 1—blade; 2—disk; 3—knife.</p>
Full article ">Figure 3 Cont.
<p>General views of the orienting drum (<b>a</b>) 1—drum shaft; 2—drum; 3—plug; 4—orienting blades; 5—fingers; and blade grinding rotor (<b>b</b>) 1—blade; 2—disk; 3—knife.</p>
Full article ">Figure 4
<p>General view of the combine from the feed auger side.</p>
Full article ">Figure 5
<p>Diagram of the stem mowing process with a radial knife rotor.</p>
Full article ">Figure 6
<p>General view of the orienting drum. 5—compressing drum, 6—angular plates.</p>
Full article ">Figure 7
<p>Scheme of mass feeding with the orienting drum into the chamber of the blade crusher. 1—auger; 2—orienting drum; 3—blade grinding rotor.</p>
Full article ">Figure 8
<p>Fragment of a combine harvester in operation when mowing and chopping alfalfa.</p>
Full article ">Figure 9
<p>General view of a cantilever auger with an orienting drum.</p>
Full article ">Figure 10
<p>General view of the modified console auger.</p>
Full article ">Figure 11
<p>Combine harvester operation when mowing alfalfa.</p>
Full article ">Figure 12
<p>General view of the process of stem orientation by the orienting drum. (<b>a</b>) The beginning of the horizontal formation of stems. (<b>b</b>) The process of stems exiting from under the orienting drum.</p>
Full article ">
31 pages, 3996 KiB  
Review
Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review
by Salman Khalid, Soo-Ho Jo, Syed Yaseen Shah, Joon Ha Jung and Heung Soo Kim
Actuators 2024, 13(12), 514; https://doi.org/10.3390/act13120514 - 10 Dec 2024
Viewed by 459
Abstract
This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) [...] Read more.
This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components. Full article
(This article belongs to the Section Actuators for Manufacturing Systems)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Data-driven PHM framework for centrifugal pumps.</p>
Full article ">Figure 2
<p>ML-based fault diagnosis steps.</p>
Full article ">Figure 3
<p>A 3D representation of faulty datasets using different dimensionality reduction techniques [<a href="#B67-actuators-13-00514" class="html-bibr">67</a>].</p>
Full article ">Figure 4
<p>Experimental setup for blockage and cavitation simulation in CPs [<a href="#B79-actuators-13-00514" class="html-bibr">79</a>].</p>
Full article ">Figure 5
<p>Spectrogram images illustrating different monoblock CP states: (<b>a</b>) cavitation, (<b>b</b>) bearing and impeller fault, (<b>c</b>) bearing fault, (<b>d</b>) impeller fault, and (<b>e</b>) normal [<a href="#B80-actuators-13-00514" class="html-bibr">80</a>].</p>
Full article ">Figure 6
<p>Representation of the proposed methodology with an improved cost function [<a href="#B75-actuators-13-00514" class="html-bibr">75</a>].</p>
Full article ">Figure 7
<p>A framework for AI-driven PHM in industrial systems [<a href="#B86-actuators-13-00514" class="html-bibr">86</a>].</p>
Full article ">Figure 8
<p>Bayesian network-based RUL estimation of CP [<a href="#B89-actuators-13-00514" class="html-bibr">89</a>].</p>
Full article ">Figure 9
<p>Proposed semi-supervised based ML methodology for RUL estimation (<b>A</b>) Construction of RUL offline dataset (<b>B</b>) Prediction of RUL (<b>C</b>) Revision of wrong prediction [<a href="#B90-actuators-13-00514" class="html-bibr">90</a>].</p>
Full article ">Figure 10
<p>Proposed methodology for construction of gear pump health index (HI) [<a href="#B95-actuators-13-00514" class="html-bibr">95</a>].</p>
Full article ">
18 pages, 10226 KiB  
Article
Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis
by Wasim Zaman, Muhammad Farooq Siddique, Saif Ullah, Faisal Saleem and Jong-Myon Kim
Machines 2024, 12(12), 905; https://doi.org/10.3390/machines12120905 - 10 Dec 2024
Viewed by 398
Abstract
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification [...] Read more.
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications. Full article
(This article belongs to the Section Machines Testing and Maintenance)
Show Figures

Figure 1

Figure 1
<p>Flow diagram of the proposed model for CP fault detection.</p>
Full article ">Figure 2
<p>Wavelet coherence spectra: (<b>a</b>) IF; (<b>b</b>); MSH; (<b>c</b>) MSS; (<b>d</b>) NC.</p>
Full article ">Figure 2 Cont.
<p>Wavelet coherence spectra: (<b>a</b>) IF; (<b>b</b>); MSH; (<b>c</b>) MSS; (<b>d</b>) NC.</p>
Full article ">Figure 3
<p>Illustration of VGG16 architecture.</p>
Full article ">Figure 4
<p>ResNet50 architecture with residual blocks and skip connections.</p>
Full article ">Figure 5
<p>ANN architecture with multiple hidden layers for fault classification.</p>
Full article ">Figure 6
<p>Experimental setup for bearing dataset.</p>
Full article ">Figure 7
<p>Schematic diagram of experimental setup for CP fault diagnosis.</p>
Full article ">Figure 8
<p>Time-domain signals of CPs under (<b>a</b>) NC, (<b>b</b>) MSH (<b>c</b>) MSS, and (<b>d</b>) IF.</p>
Full article ">Figure 9
<p>Fault components used during the experiment: (<b>a</b>) IF; (<b>b</b>) MSH; (<b>c</b>) MSS.</p>
Full article ">Figure 10
<p>Proposed method training and validation (<b>a</b>) accuracy and (<b>b</b>) loss, against the number of epochs.</p>
Full article ">Figure 11
<p>Wen et al. [<a href="#B36-machines-12-00905" class="html-bibr">36</a>] method training and validation (<b>a</b>) accuracy and (<b>b</b>) loss, against the Number of epochs.</p>
Full article ">Figure 12
<p>Kumaresan et al. [<a href="#B37-machines-12-00905" class="html-bibr">37</a>] method training and validation (<b>a</b>) accuracy and (<b>b</b>) loss, against the number of epochs.</p>
Full article ">Figure 13
<p>Comparison of confusion matrices of (<b>a</b>) proposed method with (<b>b</b>) Wen et al. [<a href="#B36-machines-12-00905" class="html-bibr">36</a>] and (<b>c</b>) Kumaresan et al. [<a href="#B37-machines-12-00905" class="html-bibr">37</a>].</p>
Full article ">Figure 14
<p>Comparison of t-SNE plots of (<b>a</b>) proposed method with (<b>b</b>) Wen et al. [<a href="#B36-machines-12-00905" class="html-bibr">36</a>] and (<b>c</b>) Kumaresan et al. [<a href="#B37-machines-12-00905" class="html-bibr">37</a>].</p>
Full article ">
14 pages, 1560 KiB  
Article
Development of a Large-Volume Concentration Method to Recover Infectious Avian Influenza Virus from the Aquatic Environment
by Laura E. Hubbard, Erin A. Stelzer, Rebecca L. Poulson, Dana W. Kolpin, Christine M. Szablewski and Carrie E. Givens
Viruses 2024, 16(12), 1898; https://doi.org/10.3390/v16121898 - 10 Dec 2024
Viewed by 518
Abstract
Since late 2021, outbreaks of highly pathogenic avian influenza virus have caused a record number of mortalities in wild birds, domestic poultry, and mammals in North America. Wetlands are plausible environmental reservoirs of avian influenza virus; however, the transmission and persistence of the [...] Read more.
Since late 2021, outbreaks of highly pathogenic avian influenza virus have caused a record number of mortalities in wild birds, domestic poultry, and mammals in North America. Wetlands are plausible environmental reservoirs of avian influenza virus; however, the transmission and persistence of the virus in the aquatic environment are poorly understood. To explore environmental contamination with the avian influenza virus, a large-volume concentration method for detecting infectious avian influenza virus in waterbodies was developed. A variety of filtering, elution, and concentration methods were explored, in addition to testing filtering speeds using artificially amended 20 L water matrices (deionized water with sterile dust, autoclaved wetland water, and wetland water). The optimal protocol was dead-end ultrafiltration coupled with salt solution elution and centrifugation concentration. Using this method, infectious virus was recovered at 1 × 10−1 50% egg infectious dose per milliliter (EID50/mL), whereas viral RNA was detected inconsistently down to 1 × 100 EID50/mL. This method will aid in furthering our understanding of the avian influenza virus in the environment and may be applicable to the environmental detection of other enveloped viruses. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic showing general steps in filtration and processing of samples in both Test 1 and Test 2. Test 1 results informed Test 2. TFUF, tangential flow ultrafiltration; DEUF, dead-end ultrafiltration; DMEM, Dulbecco’s Modified Eagle Medium; NaPP, sodium hexametaphosphate; PEG, polyethylene glycol precipitation.</p>
Full article ">Figure 2
<p>Schematic of setups for filtering water samples: dead-end ultrafiltration (<b>a</b>) and tangential flow ultrafiltration (<b>b</b>). Dead-end ultrafiltration setup from Smith and Hill [<a href="#B15-viruses-16-01898" class="html-bibr">15</a>] with modifications. Tangential flow ultrafiltration setup from Hill et al. [<a href="#B18-viruses-16-01898" class="html-bibr">18</a>] with modifications.</p>
Full article ">Figure 3
<p>Schematic for elution of ultrafilters filtered using dead-end ultrafiltration from Smith and Hill [<a href="#B15-viruses-16-01898" class="html-bibr">15</a>] with modifications.</p>
Full article ">Figure 4
<p>Percent recoveries calculated from rRT-PCR (real-time reverse transcriptase polymerase chain reaction) Ct (cycle threshold) values from Test 1 (deionized water + sterile dust) and Test 2 (autoclaved and raw wetland water). TFUF, tangential flow ultrafiltration; DEUF, dead-end ultrafiltration; FST, fast pump speed (1.0 L/min); SLW, slow pump speed (0.20 L/min); DMEM, Dulbecco’s Modified Eagle Medium; NaPP, sodium hexaphosphate; PEG, polyethylene glycol precipitation; DI+Dust, deionized water and sterile dust; AC, autoclaved wetland water; RW, raw wetland water. Red dotted line indicates Ct value limit of detection. Boxes, centerlines, and whiskers indicate the interquartile range, median, and 5th and 95th percentiles, respectively.</p>
Full article ">
25 pages, 9204 KiB  
Article
Effective Stress-Based Numerical Method for Predicting Large-Diameter Monopile Response to Various Lateral Cyclic Loadings
by Jichao Lei, Kehua Leng, Wei Xu, Lixian Wang, Yu Hu and Zhen Liu
J. Mar. Sci. Eng. 2024, 12(12), 2260; https://doi.org/10.3390/jmse12122260 - 9 Dec 2024
Viewed by 376
Abstract
Extreme marine environmental cyclic loading significantly affects the serviceability of monopiles applied for the foundation of offshore wind turbines (OWTs). Existing research has primarily used p-y methods or total stress-based models to investigate the behavior of monopile–marine clay systems, overlooking the pore pressure [...] Read more.
Extreme marine environmental cyclic loading significantly affects the serviceability of monopiles applied for the foundation of offshore wind turbines (OWTs). Existing research has primarily used p-y methods or total stress-based models to investigate the behavior of monopile–marine clay systems, overlooking the pore pressure development in subsea clay. Studies on the effective stress-based behavior of clay under various lateral cyclic loading conditions are limited. This paper presents an effective stress-based 3D finite element numerical method developed to predict key behaviors of pile–clay systems, including permanent pile rotation under cyclic loading, pile bending moment, and the evolution of pore pressure in subsea clay. The model is verified by contrasting the simulations results to centrifuge experimental results. Cyclic lateral loading is divided into average cyclic load and amplitude of cyclic load to investigate their impacts on the pile–clay system response. The research findings offer insights for the design of large-diameter monopiles under complex cyclic loading conditions. Full article
(This article belongs to the Special Issue Advances in Marine Geological and Geotechnical Hazards)
Show Figures

Figure 1

Figure 1
<p>Bounding and loading surface in stress space in <span class="html-italic">σ</span><sub>1</sub>-<span class="html-italic">σ</span><sub>2</sub>-<span class="html-italic">σ</span><sub>3</sub> space.</p>
Full article ">Figure 2
<p>Fitting results of oedometric compression test conducted by Duque et al. [<a href="#B36-jmse-12-02260" class="html-bibr">36</a>].</p>
Full article ">Figure 3
<p>Comparison between predicted results with bounding surface model and test results of Malaysian kaolin clay found by Duque et al. [<a href="#B36-jmse-12-02260" class="html-bibr">36</a>] with different OCR: (<b>a</b>) <span class="html-italic">q</span> versus <span class="html-italic">ε<sub>a</sub></span>; (<b>b</b>) <span class="html-italic">q</span> versus <span class="html-italic">p</span>; (<b>c</b>) <span class="html-italic">u</span> versus <span class="html-italic">ε<sub>a</sub></span>.</p>
Full article ">Figure 4
<p>Flow chart of user-defined UMAT subroutine.</p>
Full article ">Figure 5
<p>Illustration of 3D finite element model for large-diameter monopile.</p>
Full article ">Figure 6
<p>Simulation result of lateral displacement of the pile–clay system.</p>
Full article ">Figure 7
<p>Simulation result of deformation mode of monopile with a scale factor of 5.</p>
Full article ">Figure 8
<p>Comparison between simulation results and test results under monotonic lateral load: (<b>a</b>) load–displacement curve; (<b>b</b>) bending moment along pile shaft; (<b>c</b>) lateral pile deflection at different loads.</p>
Full article ">Figure 9
<p>Comparison between predicted and test results of pile under multi-level cyclic loading: (<b>a</b>) experimental load–displacement curves of pile head; (<b>b</b>) simulated load–displacement curves of pile head; (<b>c</b>) bending moment profile along pile shaft under different N.</p>
Full article ">Figure 10
<p>Schematic diagram of node numbers for measuring excess pore pressure.</p>
Full article ">Figure 11
<p>The development of excess pore pressure with accumulated number of cycles at different positions. (<b>a</b>) point A, B and E1; (<b>b</b>) point A, C1 and C2; (<b>c</b>) point E1, E2 and E3; (<b>d</b>) point C1, F1, F2 and F3.</p>
Full article ">Figure 12
<p>Illustration of lateral cyclic load and rotation angle at the mud surface.</p>
Full article ">Figure 13
<p>Different patterns of lateral cyclic load: (<b>a</b>) different <span class="html-italic">F</span><sub>a</sub> and same <span class="html-italic">F</span><sub>cy</sub>; (<b>b</b>) same <span class="html-italic">F</span><sub>a</sub> and different <span class="html-italic">F</span><sub>cy</sub>.</p>
Full article ">Figure 14
<p>Load–rotation and rotation–cycle curves at the mudline under <span class="html-italic">F<sub>a</sub></span> = 0 and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F</span>/<span class="html-italic">F<sub>sls</sub></span> versus <span class="html-italic">θ</span>; (<b>b</b>) <span class="html-italic">θ</span> versus <span class="html-italic">N<sub>cyc</sub></span>.</p>
Full article ">Figure 15
<p>The development of excess pore pressure at points A and B under <span class="html-italic">F<sub>a</sub></span> = 0 and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>; (<b>d</b>) u under different <span class="html-italic">F<sub>cyc</sub></span> at point B.</p>
Full article ">Figure 15 Cont.
<p>The development of excess pore pressure at points A and B under <span class="html-italic">F<sub>a</sub></span> = 0 and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>; (<b>d</b>) u under different <span class="html-italic">F<sub>cyc</sub></span> at point B.</p>
Full article ">Figure 16
<p>Distribution of accumulated excess pore pressure (AESOP) after cyclic loading with <span class="html-italic">F<sub>a</sub></span> = 0 and different <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 17
<p>Load–rotation and rotation–cycle curves at the mudline under <span class="html-italic">F<sub>a</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F</span>/<span class="html-italic">F<sub>sls</sub></span> versus <span class="html-italic">θ</span>; (<b>b</b>) <span class="html-italic">θ</span> versus <span class="html-italic">N<sub>cyc</sub></span>.</p>
Full article ">Figure 18
<p>The development of excess pore pressure at points A and B under <span class="html-italic">F<sub>a</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>; (<b>d</b>) <span class="html-italic">u</span> under different <span class="html-italic">F<sub>cyc</sub></span> at point B.</p>
Full article ">Figure 18 Cont.
<p>The development of excess pore pressure at points A and B under <span class="html-italic">F<sub>a</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>. (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>; (<b>d</b>) <span class="html-italic">u</span> under different <span class="html-italic">F<sub>cyc</sub></span> at point B.</p>
Full article ">Figure 19
<p>Distribution of accumulated excess pore pressure (AESOP) after cyclic loading with <span class="html-italic">F<sub>a</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 20
<p>Load–rotation and rotation–cycle curves at the mudline under <span class="html-italic">F<sub>a</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F/F<sub>sls</sub></span> versus <span class="html-italic">θ</span>; (<b>b</b>) <span class="html-italic">θ</span> versus <span class="html-italic">N<sub>cyc</sub></span>.</p>
Full article ">Figure 21
<p>The development of excess pore pressure at points A and B under <span class="html-italic">F<sub>a</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>; (<b>d</b>) <span class="html-italic">u</span> under different <span class="html-italic">F<sub>cyc</sub></span> at point B.</p>
Full article ">Figure 22
<p>Distribution of accumulated excess pore pressure (AESOP) after cyclic loading with <span class="html-italic">F<sub>a</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span> and different <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 23
<p>Load–rotation curves at the mudline under different <span class="html-italic">F<sub>a</sub></span> and <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 24
<p>Rotation–cycle curves at mudline under different <span class="html-italic">F<sub>a</sub></span> and <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 25
<p>The development of <span class="html-italic">u</span> at point B under different <span class="html-italic">F<sub>a</sub></span> and <span class="html-italic">F<sub>cyc</sub></span>: (<b>a</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.3<span class="html-italic">F<sub>sls</sub></span>; (<b>b</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.6<span class="html-italic">F<sub>sls</sub></span>; (<b>c</b>) <span class="html-italic">F<sub>cyc</sub></span> = 0.9<span class="html-italic">F<sub>sls</sub></span>.</p>
Full article ">Figure 26
<p>The response of monopile–clay system after 100 cycles of cyclic loading under various cyclic loading conditions: (<b>a</b>) accumulated rotation angle; (<b>b</b>) accumulated pore pressure; (<b>c</b>) rotation angle amplitude; (<b>d</b>) pore pressure amplitude.</p>
Full article ">
19 pages, 5696 KiB  
Article
Optimization Design and Atomization Performance of a Multi-Disc Centrifugal Nozzle for Unmanned Aerial Vehicle Sprayer
by Zhaoyan Zhu, Mengran Yang, Yangfan Li, Supakorn Wongsuk, Cheng Zhao, Lin Xu, Yongping Zhang, Xiongkui He and Changling Wang
Agronomy 2024, 14(12), 2914; https://doi.org/10.3390/agronomy14122914 - 6 Dec 2024
Viewed by 395
Abstract
The nozzle is a crucial component in unmanned aerial vehicle (UAV) sprayers. The centrifugal nozzle offers unique advantages; however, there is a scarcity of published research regarding the structural parameters, spraying parameters, and practical applications specifically for UAV spraying. Furthermore, there is a [...] Read more.
The nozzle is a crucial component in unmanned aerial vehicle (UAV) sprayers. The centrifugal nozzle offers unique advantages; however, there is a scarcity of published research regarding the structural parameters, spraying parameters, and practical applications specifically for UAV spraying. Furthermore, there is a need for UAV-specific nozzles that demonstrate high efficiency and excellent atomization performance. In this present study, a multi-disc centrifugal nozzle (MCN) capable of controlling droplet size was designed and optimized. The droplet size spectra with different atomizing discs were tested, and indoor and field tests were conducted to investigate the atomization and spray deposition characteristics of the MCN. It was found that the MCN with six atomizing discs with a curved groove, a disc angle of 120°, and a disc diameter of 77 mm demonstrated better atomizing performance. The volume median diameter was 96–153 μm, and the relative span was 1.0–1.3. Compared with the conventional hydraulic nozzle, this nozzle increased the effective spray swath width from 2.5–3.0 m to 4.0–5.0 m and promoted the average deposition rate by 132.4% at a flying height of 1.0 m and a flying speed of 3.0 m/s, which tends to raise the operation efficiency by four to five times. This study can provide a reference for the design and optimization of centrifugal nozzles for a UAV sprayer and the selection of operating parameters in aerial spraying operations. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Centrifugal Nozzle with different number of atomizing discs; (<b>b</b>) Structure diagram of multi-disc centrifugal nozzle (1. Brushless motor, 2. Copper backing ring, 3. Set screw, 4. Detachable infusion tube, 5. Top atomizing disc, 6. Middle atomizing disc, 7. Bottom atomizing disc).</p>
Full article ">Figure 2
<p>Component parts of multi-disc centrifugal nozzle: (<b>a</b>) outer rotor brushless motor with hollow axle; (<b>b</b>) detachable infusion tube; (<b>c</b>) outside and inside of atomizing disc.</p>
Full article ">Figure 3
<p>(<b>a</b>) Schematic diagram of droplet size testing system for multi-disc centrifugal nozzle (MCN); (<b>b</b>) droplet size test process (1. Tank, 2. Brushless diaphragm pump, 3. Flowmeter, 4. MCN, 5. Laser tachometer, 6. Laser droplet size analyzer, 7. Computer).</p>
Full article ">Figure 4
<p>Multi-disc centrifugal nozzle droplet deposition distribution test.</p>
Full article ">Figure 5
<p>Tests of deposition distribution: (<b>a</b>) equipment layout diagram in test; (<b>b</b>) test process. 1–15: The position where the droplet collector is placed.</p>
Full article ">Figure 6
<p>Effect of grooves on relative span (RS) of droplets in multi-disc centrifugal nozzles. Note: different lowercase letters in the graphs indicate significant differences in the data of each group at the <span class="html-italic">p</span> &lt; 0.05 level by Dunn’s multiple comparison test. Dispersion points indicate RS values of droplets at different flow rates and atomizing disc speeds.</p>
Full article ">Figure 7
<p>Effect of disc angle on relative span (RS) of droplets from multi-disc centrifugal nozzles. Note: different lowercase letters in the graphs indicate significant differences in the data of each group at the <span class="html-italic">p</span> &lt; 0.05 level by Dunn’s multiple comparison test. Dispersion points indicate RS values of droplets at different flow rates and atomizing disc speeds.</p>
Full article ">Figure 8
<p>Effect of disc diameter on the atomization characteristics (Dv<sub>50</sub> and RS) of multi-disc centrifugal nozzles. (<b>a</b>) Effect of disc diameter on Dv<sub>50</sub>; (<b>b</b>) effect of disc diameter on relative span (RS). Note: different lowercase letters in the graphs indicate significant differences in the data of each group at the <span class="html-italic">p</span> &lt; 0.05 level by Dunn’s multiple comparison test.</p>
Full article ">Figure 9
<p>Influence of the number of atomizing discs on relative span (RS) of droplets in the centrifugal nozzle. Note: different lowercase letters in the graphs indicate significant differences in the data of each group at the <span class="html-italic">p</span> &lt; 0.05 level by Dunn’s multiple comparison test.</p>
Full article ">Figure 10
<p>Effect of rotational speed and flow rate on atomization characteristics (Dv<sub>50</sub> and RS) of multi-disc centrifugal nozzle. (<b>a</b>) Influence of rotational speed on Dv<sub>50</sub>; (<b>b</b>) impact of flow rate on relative span (RS). Note: different lowercase letters in the graphs indicate significant differences in the data of each group at the <span class="html-italic">p</span> &lt; 0.05 level by Dunn’s multiple comparison test. The dispersion points indicate the Dv<sub>50</sub> of the droplets at different flow rates.</p>
Full article ">Figure 11
<p>Droplet deposition distribution of multi-disc centrifugal nozzles at various spray heights in indoor tests. (<b>a</b>) Nozzle height 0.5 m; (<b>b</b>) nozzle height 1.0 m; (<b>c</b>) nozzle height 1.5 m; (<b>d</b>) nozzle height 2.0 m.</p>
Full article ">Figure 11 Cont.
<p>Droplet deposition distribution of multi-disc centrifugal nozzles at various spray heights in indoor tests. (<b>a</b>) Nozzle height 0.5 m; (<b>b</b>) nozzle height 1.0 m; (<b>c</b>) nozzle height 1.5 m; (<b>d</b>) nozzle height 2.0 m.</p>
Full article ">Figure 12
<p>Droplet deposition distribution of multi-rotor unmanned aerial vehicle (UAV) sprayer equipped with 2 models of nozzles.</p>
Full article ">Figure 13
<p>Effective spray swath width of multi-rotor unmanned aerial vehicle (UAV) sprayer with hydraulic nozzle and multi-disc centrifugal nozzle in field test.</p>
Full article ">
Back to TopTop