Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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 Arabian Journal for Science and EngineeringJournal Prestige (SJR): 0.303 Citation Impact (citeScore): 1Number of Followers: 1      Hybrid journal (It can contain Open Access articles) ISSN (Print) 2193-567X - ISSN (Online) 2191-4281 Published by Springer-Verlag  [2469 journals]
• Competitor Benchmarking by Structure Reliability Analysis with Improved
Response Surface Method

Abstract: Abstract This study proposed a benchmarking method by performing structure reliability analysis of the competitor’s leading products. Such analysis was performed by an improved response surface method. This improvement aimed to reduce the computational effort involved in the reliability analysis. For the structure reliability analysis of large-scale structure without an explicit expression, the MATLAB-ANSYS co-calculation was applied to get the explicit response surface function (RSF) as a surrogate model to approximate the implicit limit state function (LSF). After obtaining the RSF, the genetic algorithm (GA) and traditional JC method (a geometric algorithm, recommended by the Joint Committee on Structural Safety) were used to calculate the reliability index and design point, respectively, for comparison. The results showed that using MATLAB-ANSYS co-calculation to obtain the RSF was easy to implement and that the GA could improve calculation efficiency by more than 90%. The result was also verified by the classical Monte Carlo simulation (MCS) in ANSYS PDS. This study provides an easy and efficient way to perform the competitor benchmarking of the market leaders’ product by reliability analysis. This method can be used in any structure design for the company who is the market follower.
PubDate: 2022-05-19

• Experimental Investigation of Parametric Effects and Characterization of
the Machined Surface in μED-milling of CFRP Composite

Abstract: Abstract Micro-electrical discharge milling (μED-milling) is a material removal process that can fabricate intricate shaped three-dimensional features in electrically conductive materials. However, in this work, μED-milling of carbon fiber-reinforced polymer (CFRP) composite was performed, though this process is not suitable due to presence of nonconductive polymer in CFRP composite. Thus, a rotating tool and an assisting-electrode were used to improve the material removal performance during μED-milling of CFRP composite. The intrinsic effects of process parameters namely input energy, feed rate, and tool speed on the machining time (MT) were investigated. The relative significance of the process parameters was evaluated through regression analysis. The present work extensively discusses the physical behavior of the μED-milling process in machining of CFRP composite in the context of dimensional accuracy and characteristics of the machined surface morphology. The different microscopic mechanisms of material removal and failure mechanism of the composite constituents were assessed with the help of micrographs of the fabricated micro-channels obtained using a field-emission scanning electron microscope (FESEM).
PubDate: 2022-05-19

• Image Enhancement Using Color Space Components for Effective Tuberculosis
Detection

Abstract: Abstract Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis bacteria. Image enhancement in ZN-stained sputum smear microscopy (SSM) images is an essential technique involved in automatic TB detection to improve image perception. However, only a few studies on TB diagnosis have been published in high-impact journals, and the majority of them used an ineffective sputum smear image enhancement technique, resulting in unsatisfactory segmentation results. Hence, we propose a novel image enhancement method, namely 'sputum smear image enhancement using effective components of color spaces (SSE-CCS)' for ZN-stained SSM images. This approach employs an iterative mean filter to suppress impulsive noise and the effective enhanced components of color spaces such as RGB, HSV, and NTSC/YIQ in order to improve visibility on both overlapped and non-overlapped TB bacilli regions. Also, two new powerful preprocessing algorithms, namely 'enhancement in non-overlapped bacilli region using V, Gray, and Q components (ENOB-VGQ)’ and 'Enhancement in overlapped bacilli region using S, Q, and Green components (EOB-SQG)' is proposed to improve the object clarity in both the TB bacilli regions. Experiment results show that the proposed SSE-CCS algorithm improves the mean-difference metric up to 5.64 times, indicating that it is well suited for the tuberculosis bacilli segmentation process. Furthermore, as compared to other existing image enhancement algorithms, it provides additional information about the images, which helps pathologists to detect the TB bacteria more correctly.
PubDate: 2022-05-19

• Ciprofloxacin Degradation with Persulfate Activated with the Synergistic
Effect of the Activated Carbon and Cobalt Dual Catalyst

Abstract: Abstract The antibiotic level in the aquatic environment has reached threatening levels for human health and ecosystems. Therefore, it is of vital importance to effectively treat antibiotic-containing wastewater. Advanced oxidation processes (AOPs), especially heterogeneous catalytic processes, are considered the most effective process to treat the residual antibiotics in the wastewaters. In the AOPs, activated carbon-supported catalysts have a synergistic effect thanks to the more effective surface area and by transferring electrons to generate radicals through sp2 covalent carbon bond and oxygen functional groups. In this study, oxidative degradation of ciprofloxacin (CIP) in water by persulfate (PS) activated with an activated carbon-supported cobalt-based dual catalyst (Co-AC) synthesized from biomass mixture and cobalt chloride via chemical activation and pyrolysis was examined. The effects of catalyst dosage, contact time, pH, PS concentration and temperature on the performance of the catalyst were investigated in detail. The synergistic effect of the system depending on various combinations (CIP + PS, CIP + Co-AC, CIP + PS + Co-AC) was determined. Co-AC exhibited high catalytic activity in the CIP oxidation with PS activation, even in various water matrices containing some anions such as Cl−, SO42− and NO3−. CIP in the solution could be completely degraded within 120 min in the presence of 0.75 g/L catalyst, 2 mM PS at 25 °C without any pH adjustment. Quenching experiments showed that the Co-AC dual catalyst successfully activated PS to generate SO4•− and •OH radicals, but the SO4•− was more dominant on the CIP degradation. Kinetic analysis of experimental data revealed that the CIP degradation reaction fits the pseudo-first-order kinetics with an activation energy of 62.69 kJ/mol.
PubDate: 2022-05-19

• Modeling and Gray-Box Identification Applied to a Flow Rate Control Valve

Abstract: Abstract In this article, we present a mathematical model obtained by white-box modeling and identification for a flow control valve, an actuator type frequently used in the process industry. Both, physical laws and the prediction-error method are used for this purpose. To validate the mathematical model, the response obtained via model is compared with the response of the experimental apparatus. In addition, the performance of numerical minimization methods used in the model estimation process is compared.
PubDate: 2022-05-18

• An Association Rule Mining Method Based on Named Entity Recognition and
Text Classification

Abstract: Abstract Using massive text data, building a knowledge graph to implement in-depth association analysis and mining can help identify entities and make decisions. The accuracy of traditional Chinese Named Entity Recognition methods is low, and traditional frequent itemset mining methods are also difficult to obtain different types of categories, and their novelty is not high. In this paper, we propose an association rule mining method based on named entity recognition and text classification (ARMTNER). First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Then the association rules be used to mine frequent itemsets through two-level classification of text classification and entity classification. The experimental results showed that, our method can achieve an F1-score of 97.3% on the public data set in Chinese Named Entity Recognition, and the novelty of frequent itemsets increased by 0.279%.
PubDate: 2022-05-18

• The Effect of Vacuum Carburizing on the Mechanical and Wear Properties of
Fe–C–Cu Composite Materials Produced via Powder Metallurgy

Abstract: Abstract This study investigated the effects of the direct vacuum carburizing process on sinterability, mechanical, and wear properties applied to the composites produced via the powder metallurgy (PM) method and containing of Fe–C–Cu. Direct sintering (DS), sintering + vacuum carburizing (S + VC), and direct vacuum carburizing (DVC) heat treatment processes were applied to the samples, respectively, and these processes were compared in terms of the results of the hardness measurements, three-point bending tests, and wear tests applied after the treatments. The material characterization results were interpreted via field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray spectroscopy (EDS) analyses. It was found that the samples’ hardness and bending strength increased with the graphite and copper ratio in the composite material, and copper also increased the ductility. Wear split and micro-cracks were observed on worn surfaces with increase in hardness. After the sintering process, necks had formed between the powder grains in the DVC-treated samples and through the DVC process, greater carbon diffusion depth was achieved in the samples. The highest hardness increase was observed in the surface areas of the samples subjected to the DVC heat treatment, whereas the lowest increase was seen in the central zones of the DVC samples. The highest bending strength values were determined in the S + VC samples and the lowest in the DVC samples. The wear test results revealed that the highest volume loss and friction coefficient had occurred in the DS samples, whereas the S + VC and DVC treatments had significantly increased the wear resistance.
PubDate: 2022-05-18

• Crosswise Radiative Convective Transport of Viscoplastic Type Nanofluid
with Influence of Lorentz Force and Viscosity Variation

Abstract: Abstract Flow of rheological fluids often obeys stagnation flow dynamics in a number of modern-day manufacturing processes. Such flows not only invoke radiative heat transfer under high temperature but also exhibit considerable viscosity variation with temperature. Motivated by such fascinating facts, the present study is an effort to explore steady, two-dimensional crosswise transport of Casson fluid past a surface. A temperature-dependent viscosity model is incorporated along with magnetohydrodynamic effects. The conservation equations for mass, normal and tangential momentum and energy are normalized with the help of similarity transformations that are solved afterwards numerically using efficient Runge–Kutta–Fehlberg scheme with shooting quadrature in MATLAB symbolic software. Comparison with the existing published literature is also presented to validate the solutions. Results of velocity, temperature, skin friction and heat flux are presented graphically and discussed in a physical manner. Graphical outcomes indicated that normal velocity profile declined rapidly with magnetic field strength, whereas thermal radiation enhanced the temperature distribution in the fluid flow. This trend revealed that thermal performance of viscoplastic fluid flow improved when radiation effects are incorporated. It is also noted that heat transfer rate at the stretching surface dropped with radiation parameter. Normal skin friction is observed to be significantly reduced, while tangential skin friction enhances with stronger magnetic field effects.
PubDate: 2022-05-18

• Effect of Hardware Imperfections and Energy Scavenging Nonlinearity on
Overlay Networks in $$\kappa -\mu$$ κ - μ Shadowed Fading

Abstract: Abstract In overlay networks, the cognitive sender ( $$CS$$ ) assists the primary transmitter ( $$PT$$ ) by broadcasting the superposed signal composed of both cognitive and primary information with a higher priority for the primary information to ameliorate spectral efficiency. Such different information priorities also facilitate efficient successive interference cancellation (SIC) at corresponding receivers for better system performance. To further benefit $$CS$$ beside licensed spectrum accessing permission, we assume transmission of $$CS$$ solely with energy scavenged from $$PT$$ . Practically, energy scavenger has a nonlinear characteristic and circuit components almost suffer hardware imperfections. Further, path loss, shadowing, and fading are all present in practical wireless channels, which affect not only the scavenged energy but also the system performance. Consequently, this paper proposes a framework to analyze the performance metrics— outage probability and throughput—of overlay networks under realistic scenarios subject to hardware imperfections, energy scavenging nonlinearity, SIC-based signal detection, and versatile-and-general $$\kappa -\mu$$ shadowed fading. This framework facilitates the system performance evaluation-and-comparison in essential specifications and serves well as a design instruction. Obtained results reveal that hardware imperfections affect the system performance more direly than energy scavenging nonlinearity does and the primary transmission performs considerably better than the secondary transmission irrespective of channel severities. Furthermore, the system performance can be adjusted and optimized versatilely by a multi-parameter set.
PubDate: 2022-05-17

• Formation Resistivity Prediction Using Decision Tree and Random Forest

Abstract: Abstract Formation resistivity (Rt) is a vital property for formation evaluation and calculation of water saturation and hydrocarbon in places. Rt can be estimated using core analysis and well logging. However, these processes are expensive and time-consuming. In addition, due to tool failure, and poor wellbore conditions, part of the well logging records may be missed. Hence, the objective of this paper is to predict the true formation resistivity in complex carbonate sections using decision tree (DT) and Random Forests (RF) machine learning (ML) techniques as a function of available well logging data. A data set of 5500 data points were collected from two vertical wells in carbonate formation. The data includes gamma-ray, bulk density, neutron density, compressional wave transit time, shear transient time, and the corresponding Rt. Data from Well-1 were used to develop the DT and RF models with training to the testing splitting ratio of 70:30. Dataset from Well-2 was used to validate the optimized models. The results showed the capabilities of the ML models to predict the formation resistivity from well-logging data. The correlation coefficient (R) between the actual and the predicted output values and the root mean square error (RMSE) was used to evaluate the models performance. R value for the RF model was found to be 0.99, and 0.98 for the training and the testing stages with a validation R value of 0.94. The RMSE for the developed models was less than 0.38 for training, testing, and validation stages. Using ML to predict the formation resistivity can fill the missing gaps in log tracks and save money by removing resistivity logs running in all offset wells in the same field.
PubDate: 2022-05-16

• Reduction in Graphene Oxide by Sodium Borohydride for Enhanced BR13 Dye

Abstract: Abstract In this study, reduced graphene oxide (RGO) with good water dispersibility and excellent adsorption performance was successfully prepared using sodium borohydride (NaBH4) as reductant, and their characterization and adsorption performance for Basic Red13 (BR13) and Cu2+ were analyzed. The results showed that RGO had an obvious graphene-like structure with the C/O ratio of 3.19, while the C/O ratio of graphene oxide (GO) was only 1.81. The reason for the good water dispersibility of RGO was the retention of partial oxygen-containing groups, especially carboxyl groups. The adsorption of RGO for BR13 and Cu2+ was typical monolayer physisorption, which could be well-described by pseudo-first-order and Langmuir isotherm models. The maximum adsorption capacities of RGO were 1674.01 mg/g for BR13 at pH = 13 and 164.72 mg/g for Cu2+ at pH = 6, which much higher than the corresponding values of 1258.65 mg/g and 123.14 mg/g for GO. In particular, the adsorption capacity of RGO for BR13 was the highest value reported so far. Moreover, no significant loss of adsorption performance was observed even after five cycles. This work suggested that reduced graphene oxide was an efficient adsorbent for the removal of organic dyes and heavy metal ions from water, which had great potential in pollution control applications.
PubDate: 2022-05-16

• Effect of Structural Vibration on the Pedestrian–Structure Interaction
System—An Experimental Study

Abstract: Abstract Human–structure interaction is a critical element in the evaluation of human-induced structural vibration response. Compared to an empty structure, the crowds significantly modify the dynamics of a structure, such as the modes, natural frequencies, and the damping ratios of the structure. Several human dynamics models have been proposed to study human–structure interactions, and the most widely used one is single-degree-of-freedom (SDOF) mass–spring–damping (MSD) model due to its simplicity. The model equates the human body to a hybrid of three components, namely mass, spring, and damping. In this study, we only focus on the natural frequency and damping ratio. However, not enough research has been conducted in the field of civil engineering through actual structural test experiments to obtain the dynamic properties of human body. Therefore, more test results are required to provide a data basis for the future establishment of vibration serviceability assessments that are relevant to pedestrian–structure interaction. In this study, to obtain the results of parameter identification for an individual pedestrian, a full-size steel-concrete composite slab was excited at different frequencies and amplitudes. The chosen pace frequency of the pedestrian was 1.7 and 2.1 Hz. The results show that pedestrians are more sensitive to low-frequency vibrations and less sensitive to high-frequency vibrations. Furthermore, as vibration level of the structure increases, the natural frequency of the human body model decreases, while the damping ratio increases.
PubDate: 2022-05-16

• The Potential of Geopolymer in Development of Green Coating Materials: A
Review

Abstract: Abstract Cementitious coating is essential as passive protection for building and infrastructure including steel and coastal structures from aggressive environment, such as fire and chloride and enhance the durability of the substrate. Still, there is durability concern on the conventional ordinary Portland cement (OPC)-based coatings that will lead to high repair cost. The durability concern is normally caused by weathering effects, UV degradation, fire and toxicity. In recent years, researches on the incorporation of geopolymers as cementitious coating materials have been growing, considering its superior performance in durability and mechanical strength over the conventional OPC-based coatings, especially the long-term durability and under aggressive environment. The present review summarizes the potential use of geopolymers as green coating materials, where the performance in terms of strength, durability such as fire resistance, heat resistance and corrosion resistance, as well as the influencing factors and prospects are discussed. This review concludes with an outline of some future opportunities, in view of environment protection, engineering applications and economic savings.
PubDate: 2022-05-15

• Role of Carbon Nanomaterials on Enhancing the Supercapacitive Performance
of Manganese Oxide-Based Composite Electrodes

Abstract: Abstract MnO2 is an attractive material owing to its high specific capacitance, excellent electrochemical activity, thermal and chemical stability, environmental benignity and broad potential window. However, its poor electrical conductivity limits its performance in supercapacitor applications. The electrical conductivity of MnO2 can be enhanced by making its composites with carbon nanomaterials as these offer superior electrical conductivity and high specific surface area. The present study is a comparative study on the effect of various types of carbon nanomaterials such as carbon nanotubes (CNTs), graphene oxide, thermally reduced graphene oxide, activated carbon and carbon nanofibers on the supercapacitive performance MnO2 electrodes by making composite electrodes. MnO2 was synthesized by a facile chemical reduction method, and calcination was performed at 200 °C to obtain amorphous state. MnO2-based composite electrodes were prepared using 10 wt.% of various carbon nanomaterials. Characterization of different carbon materials was carried out by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and atomic force microscopy. Composite electrodes were prepared by coating mixture, consisting of MnO2, carbon nanomaterials, carbon black and polyvinylidene fluoride on the surface of highly porous Ni foam using a high-speed vacuum planetary centrifugal mixer. Electrochemical characterization of the prepared electrodes was performed by cyclic voltammetry, galvanostatic charge discharge (GCD) and electrochemical impedance spectroscopy (EIS). GCD revealed that MnO2-CNTs composite electrodes showed maximum specific capacitance (SC) of 330 F/g at 1 A/g current density and areal capacitance of 3.16 F/cm2 with 5–7 mg mass loading of active material in 3 M KOH as an aqueous electrolyte. This SC was ca. 79% higher than pure MnO2 and also higher than previously reported MnO2/CNTs composite electrodes. MnO2-CNTs symmetric supercapacitor device showed excellent SC of 177 F/g at 1 A/g current density with 94% charge retention after 1000 GCD cycles. EIS analysis showed that MnO2-CNTs composite electrode had the lowest charge transfer resistance compared to other electrodes.
PubDate: 2022-05-15

• Impact Assessment of Efficient Denoising Techniques in AI-Based Low-Cost
INS/GPS Integration During Blockage of GPS Satellites

Abstract: Abstract Low-cost micro-electromechanical systems (MEMS)-based inertial navigation system (INS) sensors present long-term drift and different stochastic errors over time, which, in turn, tends to degrade the positioning accuracy of the artificial intelligence (AI)-based GPS/INS navigation systems during GPS outages. We address this issue by analyzing and modeling various stochastic errors of these sensors and our efficient noise reduction method can improve the accuracy of such navigation systems. In the present work, we propose our efficient adaptive noise reduction method based on the Allan variance (AV) analysis technique in order to choose more reliable, appropriate, and optimum parameters of the noise reduction method, which can help remove stochastic noises efficiently while preserving vehicles motion information. In our low-cost AI-based GPS/INS navigation system, more accurate and smoother INS sensor measurements with less complexity, as an input of intelligence structure, can provide better training and less estimation error and better generalization ability in the uncertainty-oriented environment. Eventually, in order to assess the effect of the proposed noise reduction method based on the AV method on the low-cost AI-based GPS/INS navigation systems under the GPS signal blockages, two different candidates from AI-based models are selected according to their ability in modeling random noise inputs. Then, their performance with and without the proposed noise reduction method is evaluated. The obtained results indicate that the proposed noise reduction method significantly improves positioning accuracy in AI-based low-cost GPS/INS integrated systems under GPS signal blockages using real data collected even with a high-speed test vehicle.
PubDate: 2022-05-15

• Controlled In-Line Generation of Stable Oil–Water Emulsions for
Enhanced Oil Recovery

Abstract: Abstract Emulsions have been widely used in many industrial applications, and emulsion flooding is applied as an effective chemical enhanced oil recovery technique and used for acid treatment of the reservoir rocks. Most of the techniques used for supplying emulsions to the oil fields are batch preparation methods, which generate a batch of emulsion by using homogenizers or by circulation of water and oil with pumps. Emulsion generated through these methods suffers from separation with time, losing its main properties, making it inefficient, less cost-effective, time and power consuming. This paper investigates a novel method for fully controlled in-line generation technique providing fresh and on-demand emulsion to be used instantaneously in the oil field. This method is based on using Liquid–liquid ejector with control valves for mixing water with oil generating a fresh and stable emulsion instead of using a premixed batch of stored emulsion. The performance of the ejector for generation of invert and direct emulsion is studied experimentally. Diesel and water were used as the two liquids forming surfactant-stabilized emulsion. Emulsion stability, external phase, droplet size distribution and emulsion rheology are also investigated experimentally of the generated emulsions. The results showed that the fully controlled ejector is an efficient device for in-line emulsion generation of both water-in-oil (Invert) and oil-in-water (Direct) emulsions at specific and required ratio. For water salinty of 10 g/l or less, the external phase was always water phase, while for salinity of more than 15 g/l the continuous phase of the resulted emulsion is always oil.
PubDate: 2022-05-14

• Insight into the Synergic Effect of Partially Hydrolyzed Polyacrylamide
and Nanosilica on Sodium Silicate Sealant Gel System for Water Shutoff
Applications

Abstract: Abstract Production of excess water is one of the main problems leading to declined hydrocarbon production. Various techniques, including silicate gel systems, have been developed to manage water production. In this study, factors affecting the Na-silicate sealant gel system, containing polymer (PHPA) and nanosilica are investigated in terms of gelation time and syneresis. In particular, their interaction effects at varying temperatures and concentrations of activator are studied. While acid gel with hydrochloric acid, as an activator, lost more than 77% of its weight in less than a week, basic gel with calcium chloride, as an activator, was more efficient owing to its weight loss of 10% over 5 days. The syneresis increased by increasing Na-silicate concentration, activator concentration, and temperature. The proposed addition of PHPA with the aim of avoiding or delaying the syneresis was successful at temperatures below 75 °C. Below the PHPA concentration of about 0.37 g/L, the effect of calcium chloride on syneresis was lower than polymer, whereas its effect overcame at the higher concentration. The gelation time was reduced by increasing the sodium silicate, nanosilica, and calcium chloride concentrations. The gelation time increased by raising the temperature from 60 to 80 °C, whereas it decreased by increasing the temperature from 80 to 100 °C. The parameters of nanosilica, calcium chloride, PHPA, temperature, and sodium silicate had the highest to lowest effect on the gelation time, respectively. Results of gel injection into synthetic cores showed that Na-silicate gel system can be applied due to the success of effective sealing of the porous media.
PubDate: 2022-05-13

• Mechanical Behavior of Methane–Hydrate–Bearing Sand with
Nonlinear Constitutive Model

Abstract: Abstract Interests appear in investigating methane-hydrate-bearing sands (MHBS) to address engineering problems, such as foundation instability of man-made permafrost facilities, wellbore instability and sanding during production. Mechanical behavior of MHBS is critical issue to analyze geomechanical hazards. In this paper, MHBS is synthesized in laboratory and triaxial compressive tests are carried out to capture mechanical response. A discrete element method (DEM) model is developed to examine mechanical responses of MHBS by considering real MHBS-based microstructure and particles contact. To describe nonlinear mechanical behavior, Duncan–Chang model is embedded into DEM model and verified with experimental results. Triaxial drained and undrained numerical tests are carried out to investigate effects of hydrate saturation, confining stress, heterogeneity and grading properties on mechanical behavior of pore-filling hydrate sediment. Experimental and numerical results indicate that (1) triaxial compression strength increases with confining stress and hydrate saturation; (2) stress–strain curve becomes smooth at a higher hydrate saturation thanks to the stability enhancement of MHBS structure; (3) heterogeneous distribution of hydrates leads to local instability with non-bonded hydrate particles; (4) grading properties (uniformity coefficient and mean particle diameter) non-apparent influence on compressive strength and dilatancy due to particles re-distribution; and (5) MHBS presents mechanical behavior of brittleness or plasticity in undrained tests rather than strain softening in drained tests. Except for Duncan–Chang model parameters fitting in this work, more experimental and numerical researches are expected to improve the performance in predicting post-failure behavior.
PubDate: 2022-05-13

• Design and Part Load Performance Simulation of Natural Gas Combined Cycle
with New Operating Regulation for Gas Turbine

PubDate: 2022-05-13

• Parametric Evaluation of Condensate Water Yield from Plain Finned Tube
Heat Exchangers in Atmospheric Water Generation

Abstract: This article presents the moisture harvesting capability of plain finned tube heat exchangers, used in active atmospheric water generation systems. The study reviews the data and correlations of heat transfer for such heat exchangers in open literature and embed suitable correlations for predicting the yield of water when exposed to humid air. Set of results are obtained at the inlet and exit of an experimental setup at varying inlet conditions at constant air flowrate and heat exchanger geometry. The heat exchanger is analyzed using a characteristic unit cell where computational fluid dynamics is used to predict temperature drop across it, using COMSOL Multiphysics, whereas the repetitive incremental routines have been carried out using MATLAB. The flowchart is verified by the experimental data. The water product as a result of cooling of the heat exchanger, often considered parasitic for effectiveness of the heat exchanger in sensible cooling applications, is given prime importance, which increases the heat exchanger’s water harvesting capability. Psychrometric conditions at the inlet under an interval of dry bulb temperature of 20–40 °C and relative humidity of 20–70%, with rows ranging 1 through 4, fin density of 8, 10, 12 and 14 fins per inch and forced convection corresponding to 100 through 2000 cfm, have been analyzed. The results are specified as plots of various variables against the yield of water on a per unit frontal area basis. The increase in water yield is, respectively, highest and lowest for increasing number of rows and increasing fin density.
PubDate: 2022-05-12

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