Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Konstantinou, Charalampos; Biscontin, Guido

doi:10.3390/hydrology9110190

Cited by 12 publications

(3 citation statements)

References 78 publications

(124 reference statements)

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“…In addition, the PTFE matrix is in a viscous flow state during high temperature sintering. The different molding pressure before sintering affects the formation, growth and migration of bubbles in the composite, thus affecting the size of void in the composite, 47 affecting the compactness of the composite, and further affecting the synergistic effect of organic and inorganic components on the properties of the composite. The study by Fan et al 48 also showed that the interfacial adhesion between GF and PTFE plays a key role in the wear resistance of GF-filled PTFE composites.…”

Section: Effect Of Different Molding Pressure Processes On the Tribol...mentioning

confidence: 99%

Effect of different molding pressures on the properties of glass fiber / molybdenum disulfide / Polytetrafluoroethylene composites

Yang

2023

High Performance Polymers

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Both glass fiber (GF) and molybdenum disulfide (MoS2) can enhance the comprehensive properties of PTFE (polytetrafluoroethylene)-based composites, however the properties of the composites are significantly influenced by the molding pressure utilized. In this study, GF/MoS2/PTFE composites were produced under varied molding pressures (50–70 MPa), and the composites’ mechanical and tribological properties were evaluated. The results showed that the tribological parameters (such as friction coefficient and volumetric wear rate) and mechanical parameters (such as density, hardness, tensile strength, and elongation at break) varied depending on the molding pressure. When the molding pressure was 50 MPa, the GF/MoS2/PTFE composites displayed their finest mechanical properties. The composite had the best wear resistance with the lowest wear rate of only of 2.135 × 10−6 mm3/Nm at a molding pressure of 60 MPa and the lowest friction coefficient of 0.166 at a molding pressure of 70 MPa. The increased molding pressure that was employed to make the samples, as predicted by SEM analysis, would lead to greater residual stresses inside the specimens, which would ultimately result in cracking and peeling. In the friction test, specimens with a lower forming pressure are more likely to have surface furrows that are deeper and wider, as well as to shed their filler. Due to the increased molding pressure, the depth of furrows and filler shedding on the composite surface are also more apparent.

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Section: Effect Of Different Molding Pressure Processes On the Tribol...mentioning

confidence: 99%

Effect of different molding pressures on the properties of glass fiber / molybdenum disulfide / Polytetrafluoroethylene composites

Yang

2023

High Performance Polymers

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“…[168] Numerical simulations of artificial rock properties and fluid flow numerical experiments. [169] Fluid flow experiments in porous media for hydrological applications. [170,171] Fluid flow experiments in porous media for reservoir engineering applications.…”

Section: Applications Of Micp Related To Studies Involving Artificial...mentioning

confidence: 99%

Unlocking the Potential of Microbially Induced Calcium Carbonate Precipitation (MICP) for Hydrological Applications: A Review of Opportunities, Challenges, and Environmental Considerations

Konstantinou,

Wang

2023

Hydrology

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Microbially induced calcium carbonate precipitation (MICP) is an innovative biocementation technique that facilitates the formation of calcium carbonate within a pore network. Initially gaining prominence in the field of geotechnical engineering, MICP has attracted significant attention since its inception (the last three decades) and expanded its reach across various engineering disciplines. Examples include rock mechanics, geology and the oil and gas industry fields through the generation of rock-like specimens, and plugging of fractures, in civil and architectural engineering and material science for concrete repair, protection, and for self-healing of building materials, and in environmental engineering for the study of biomimetic materials. In response to this burgeoning interest, the current paper aims to present a comprehensive review of the main biochemical mechanisms underlying MICP (bacterial ureolytic activity, reactions duration and settling times, and chemical solution properties), their direct relevance to altering hydraulic and mechanical properties, both at the microscale and macroscale responses, and the precipitation mechanisms, particularly in relation to water resources and hydrology applications. Four main categories of relevant applications are identified, namely, the groundwater and soil remediation, the applications related to the generation of a low hydraulic conductivity barrier, those related to gaining cohesion, and the applications related to fluid flow studies in artificially generated porous media. Moreover, this comprehensive review not only aims to identify the existing applications of MICP within hydrological fields but also strives to propose novel and promising applications that can further expand its utility in this domain. Along with the investigation of the potential of MICP to revolutionize water resources and hydrology, it is imperative to delve deeper into its environmental implications to ensure sustainable and ecologically responsible implementation.

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“…Moreover, although both RF and ANN appear as "black boxes", RF allows a more straightforward interpretation of the factors affecting the prediction. RF algorithms have been used with promising results in several research fields related to hydraulics and hydrology [29], such as pipe breaks in water supply networks [30], water flow in porous media [31], flooding [32] and drought [33] events. RF applications in soil water erosion studies are limited in the literature and, therefore, can be considered quite innovative.…”

Section: Introductionmentioning

confidence: 99%

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

Vergni

Todisco

2023

Water

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Predicting the occurrence of erosive rain events and quantifying the corresponding soil loss is extremely useful in all applications where assessing phenomenon impacts is required. These problems, addressed in the literature at different spatial and temporal scales and according to the most diverse approaches, are here addressed by implementing random forest (RF) machine learning models. For this purpose, we used the datasets built through many years of soil loss observations at the plot-scale experimental site SERLAB (central Italy). Based on 32 features describing rainfall characteristics, the RF classifier has achieved a global accuracy of 84.8% in recognizing erosive and non-erosive events, thus demonstrating slightly higher performances than previously used (non-machine learning) methodologies. A critical performance is the percentage of erosive events correctly recognized to the observed total (72.3%). However, since the most relevant erosive events are correctly identified, we found only a slight underestimation of the total rainfall erosivity (91%). The RF regression model for estimating the event soil loss, based on three event features (runoff coefficient, erosivity, and period of occurrence), demonstrates better performances (RMSE = 2.30 Mg ha−1) than traditional regression models (RMSE = 3.34 Mg ha−1).

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Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Cited by 12 publications

References 78 publications

Effect of different molding pressures on the properties of glass fiber / molybdenum disulfide / Polytetrafluoroethylene composites

Effect of different molding pressures on the properties of glass fiber / molybdenum disulfide / Polytetrafluoroethylene composites

Unlocking the Potential of Microbially Induced Calcium Carbonate Precipitation (MICP) for Hydrological Applications: A Review of Opportunities, Challenges, and Environmental Considerations

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

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