Search results for: hybrid multistep method

Authors: Yana Snegireva

Abstract:

Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Syali Pradhan, Neetu Jha

Abstract:

The Marigold flower is used in religious places for offering and decoration purpose every day. The flowers are discarded near trees or in aquatic bodies. This floral waste can be used for extracting dyes or oils. Still the secondary waste remains after processing which need to be addressed. This research aims to provide green and clean power using secondary floral waste available after processing. The carbonization of floral waste produce carbon material with high surface area and enhance active site for more reaction. The Hybrid supercapacitors are more stable, offer improved operating temperature and use less toxic material compared to battery. They provide enhanced energy density compared to supercapacitors. Hence, hybrid supercapacitor designed using waste material would be more practicable for future energy application. Here, we present the utilization of carbonized floral waste as supercapacitor electrode material. This material after carbonization gets graphitized and shows high surface area, optimum porosity along with high conductivity. Hence, this material has been tested as cathode electrode material for high performance zinc storage hybrid supercapacitor. High energy storage along with high stability has been obtained using this cathodic waste material as electrode.

Keywords: marigold, flower waste, energy storage, cathode, supercapacitor

Procedia PDF Downloads 43

Authors: Noor Zuhaira Abd Aziz

Abstract:

Rice husk and kenaf filled with calcium carbonate (CaCO3) and high density polyethylene (HDPE) composite were prepared separately using twin-screw extruder at 50rpm. Different filler loading up to 30 parts of rice husk particulate and kenaf fiber were mixed with the fixed 30% amount of CaCO3 mineral filler to produce rice husk/CaCO3/HDPE and kenaf/CaCO3/HDPE hybrid composites. In this study, the effects of natural fiber for both rice husk and kenaf in CaCO3/HDPE composite on physical and mechanical properties were investigated. The property analyses showed that water absorption increased with the presence of kenaf and rice husk fillers. Natural fibers in composite significantly influence water absorption properties due to natural characters of fibers which contain cellulose, hemicellulose and lignin structures. The result showed that 10% of additional natural fibers into hybrid composite had caused decreased flexural strength, however additional of high natural fiber (>10%) filler loading has proved to increase its flexural strength.

Keywords: Hybrid composites, Water absorption, Mechanical properties

Procedia PDF Downloads 440

Authors: M. Fette, J. P. Wulfsberg, A. Herrmann, R. H. Ladstaetter

Abstract:

Present and future lightweight design represents an important key to successful implementation of energy-saving, fuel-efficient and environmentally friendly means of transport in the aerospace and automotive industry. In this context the use of carbon fibre reinforced plastics (CFRP) which are distinguished by their outstanding mechanical properties at relatively low weight, promise significant improvements. Due to the reduction of the total mass, with the resulting lowered fuel or energy consumption and CO2 emissions during the operational phase, commercial aircraft and future vehicles will increasingly be made of CFRP. An auspicious technology for the efficient and economic production of high performance thermoset composites and hybrid structures for future lightweight applications is the combination of carbon fibre sheet moulding compound (SMC), tailored continuous carbon fibre reinforcements and metallic components in a one-shot pressing and curing process. This paper deals with a new hybrid composite technology for aerospace industries, which was developed with the help of a universal innovation and development system. This system supports the management of idea generation, the methodical development of innovative technologies and the achievement of the industrial readiness of these technologies.

Keywords: development system, hybrid composite, innovation system, prepreg, sheet moulding compound

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Authors: Mohammad Najmol Haque, Takeshi Maki, Jun Sasaki

Abstract:

Headed stud shear connections are widely used in the junction or embedded zone of hybrid girder to achieve whole composite action with continuity that can sustain steel-concrete interfacial tensile and shear forces. In Japan, Japan Road Association (JRA) specifications are used for hybrid girder design that utilizes very low level of stud capacity than those of American Institute of Steel Construction (AISC) specifications, Japan Society of Civil Engineers (JSCE) specifications and EURO code. As low design shear strength is considered in design of connections, the time dependent shear behavior due to sustained external loading is not considered, even not fully studied. In this study, a finite element approach was used to evaluate the time dependent shear behavior for headed studs used as connections at the junction. This study clarified, how the sustained loading distinctively impacted on changing the interfacial shear of connections with time which was sensitive to lodging history, positions of flanges, neighboring studs, position of prestress bar and reinforcing bar, concrete strength, etc. and also identified a shear influence area. Stud strength was also confirmed through pushout tests. The outcome obtained from the study may provide an important basis and reference data in designing connections of hybrid girders with enhanced stud capacity with due consideration of their long-term shear behavior.

Keywords: finite element, hybrid girder, shear connections, sustained loading, time dependent behavior

Procedia PDF Downloads 106

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

Procedia PDF Downloads 45

Authors: Cesar Hernández, Diego Giral, Ingrid Páez

Abstract:

This article proposes a hybrid algorithm for spectrum allocation in cognitive radio networks based on the algorithms Analytical Hierarchical Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to improve the performance of the spectrum mobility of secondary users in cognitive radio networks. To calculate the level of performance of the proposed algorithm a comparative analysis between the proposed AHP-TOPSIS, Grey Relational Analysis (GRA) and Multiplicative Exponent Weighting (MEW) algorithm is performed. Four evaluation metrics is used. These metrics are the accumulative average of failed handoffs, the accumulative average of handoffs performed, the accumulative average of transmission bandwidth, and the accumulative average of the transmission delay. The results of the comparison show that AHP-TOPSIS Algorithm provides 2.4 times better performance compared to a GRA Algorithm and, 1.5 times better than the MEW Algorithm.

Keywords: cognitive radio, decision making, hybrid algorithm, spectrum handoff, wireless networks

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Authors: Paramjit Kaur, Harish Kundra

Abstract:

In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

Procedia PDF Downloads 371

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 53

Authors: Vesile Unver, Tulay Basak, Hatice Ayhan, Ilknur Cinar, Emine Iyigun, Nuran Tosun

Abstract:

The aim of this study is to analyze the effects of the hybrid simulation. In this simulation, types standardized patients and task trainers are employed simultaneously. For instance, in order to teach the IV activities standardized patients and IV arm models are used. The study was designed as a quasi-experimental research. Before the implementation an ethical permission was taken from the local ethical commission and administrative permission was granted from the nursing school. The universe of the study included second-grade nursing students (n=77). The participants were selected through simple random sample technique and total of 39 nursing students were included. The views of the participants were collected through a feedback form with 12 items. The form was developed by the authors and “Patient intervention self-confidence/competence scale”. Participants reported advantages of the hybrid simulation practice. Such advantages include the following: developing connections between the simulated scenario and real life situations in clinical conditions; recognition of the need for learning more about clinical practice. They all stated that the implementation was very useful for them. They also added three major gains; improvement of critical thinking skills (94.7%) and the skill of making decisions (97.3%); and feeling as if a nurse (92.1%). In regard to the mean scores of the participants in the patient intervention self-confidence/competence scale, it was found that the total mean score for the scale was 75.23±7.76. The findings obtained in the study suggest that the hybrid simulation has positive effects on the integration of theoretical and practical activities before clinical activities for the nursing students.

Keywords: hybrid simulation, clinical practice, nursing education, nursing students

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Authors: Mohamed E. Harb, Enas Moustafa, Shaker Ebrahim, Moataz Soliman

Abstract:

In this paper pressure detectors were synthesized and characterized using hybrid perovskite/PVDF composites as an active layer. Methylammonium lead iodide (MAPbI₃) was synthesized from methylammonium iodide (MAI) (CH₃NH₃I) and lead iodide (PbI₂). Composites of perovskite/PVDF using different weight ratio were prepared as the active material. PVDF with weights percentages of 6%, 8%, and 10% was used. All prepared materials were investigated by x-ray diffraction (XRD), Fourier transforms infrared spectrum (FTIR) and scanning electron microscopy (SEM). A Versastat 4 Potentiostat Galvanostat instrument was used to perform the current-voltage characteristics of the fabricated sensors. The pressure sensors exhibited a voltage increase with applying different forces. Also, the current-voltage characteristics (CV) showed different effects with applying forces. So, the results showed a good pressure sensing performance.

Keywords: perovskite semiconductor, hybrid perovskite, PVDF, Pressure sensing

Procedia PDF Downloads 176

Authors: Daofan Guo, Dong Yang

Abstract:

For decades, well logging with coiled tubing has relied solely on surface data such as pump pressure, wellhead pressure, depth counter, and weight indicator readings. While this data serves the oil industry well, modern smart logging utilizes real-time downhole information, which automatically increases operational efficiency and optimizes intervention qualities. For example, downhole pressure, temperature, and depth measurement data can be transmitted through the electro-optical hybrid cable in the coiled tubing to surface operators on a real-time base. This paper mainly introduces the unique structural features and various applications of the electro-optical hybrid cables which were deployed into downhole with the help of coiled tubing technology. Fiber optic elements in the cable enable optical communications and distributed measurements, such as distributed temperature and acoustic sensing. The electrical elements provide continuous surface power for downhole tools, eliminating the limitations of traditional batteries, such as temperature, operating time, and safety concerns. The electrical elements also enable cable telemetry operation of cable tools. Both power supply and signal transmission were integrated into an electro-optical hybrid cable, and the downhole information can be captured by downhole electrical sensors and distributed optical sensing technologies, then travels up through an optical fiber to the surface, which greatly improves the accuracy of measurement data transmission.

Keywords: electro-optical hybrid cable, underground photoelectric composite cable, seismic cable, coiled tubing, real-time monitoring

Procedia PDF Downloads 108

Authors: Soha A. Bahanshal, Byung G. Kim

Abstract:

Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 159

Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

Abstract:

Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

Procedia PDF Downloads 144

Authors: Pedro Esteban

Abstract:

Today’s electrical networks, including microgrids, are evolving into smart grids. The smart grid concept brings the idea that the power comes from various sources (continuous or intermittent), in various forms (AC or DC, high, medium or low voltage, etc.), and it must be integrated into the electric power system in a smart way to guarantee a continuous and reliable supply that complies with power quality and energy efficiency standards and grid code requirements. This idea brings questions for the different players like how the required power will be generated, what kind of power will be more suitable, how to store exceeding levels for short or long-term usage, and how to combine and distribute all the different generation power sources in an efficient way. To address these issues, there has been lots of development in recent years on the field of on-grid and off-grid hybrid power systems (HPS). These systems usually combine one or more modes of electricity generation together with energy storage to ensure optimal supply reliability and high level of energy security. Hybrid power systems combine power generation and energy storage technologies together with real-time energy management and innovative power quality and energy efficiency improvement functionalities. These systems help customers achieve targets for clean energy generation, they add flexibility to the electrical grid, and they optimize the installation by improving its power quality and energy efficiency.

Keywords: microgrids, hybrid power systems, energy storage, power quality improvement

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Authors: Salah Aguib, Noureddine Chikh, Abdelmalek Khabli, Abdelkader Nour, Toufik Djedid, Lallia Kobzili

Abstract:

This study presents the behavior of a hybrid smart sandwich plate with a magnetorheological elastomer core. In order to improve the vibrational behavior of the plate, the pseudo‐fibers formed by the effect of the magnetic field on the elastomer charged by the ferromagnetic particles are oriented at 45° with respect to the direction of the magnetic field at 0°. Ritz's approach is taken to solve the physical problem. In order to verify and compare the results obtained by the Ritz approach, an analysis using the finite element method was carried out. The rheological property of the MRE material at 0° and at 45° are determined experimentally, The studied elastomer is prepared by a mixture of silicone oil, RTV141A polymer, and 30% of iron particles of total mixture, the mixture obtained is mixed for about 15 minutes to obtain an elastomer paste with good homogenization. In order to develop a magnetorheological elastomer (MRE), this paste is injected into an aluminum mold and subjected to a magnetic field. In our work, we have chosen an ideal percentage of filling of 30%, to obtain the best characteristics of the MRE. The mechanical characteristics obtained by dynamic mechanical viscoanalyzer (DMA) are used in the two numerical approaches. The natural frequencies and the modal damping of the sandwich plate are calculated and discussed for various magnetic field intensities. The results obtained by the two methods are compared. These off‐axis anisotropic MRE structures could open up new opportunities in various fields of aeronautics, aerospace, mechanical engineering and civil engineering.

Keywords: hybrid smart sandwich plate, vibratory behavior, FEM, Ritz approach, MRE

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Authors: Ha-Guk Jeong, Jong-Beom Lee

Abstract:

Cladding process is very typical technology for manufacturing composite materials by the hydrostatic extrusion. Because there is no friction between the metal and the container, it can be easily obtained in uniform flow during the deformation. The general manufacturing process for a metal-matrix composite in the solid state, mixing metal powders and ceramic powders with a suited volume ratio, prior to be compressed or extruded at the cold or hot condition in a can. Since through a plurality of unit processing steps of dispersing the materials having a large difference in their characteristics and physical mixing, the process is complicated and leads to non-uniform dispersion of ceramics. It is difficult and hard to reach a uniform ideal property in the coherence problems at the interface between the metal and the ceramic reinforcements. Metal hybrid composites, which presented in this report, are manufactured through the traditional plastic deformation processes like hydrostatic extrusion, caliber-rolling, and drawing. By the previous process, the realization of uniform macro and microstructure is surely possible. In this study, as a constituent material, aluminum, copper, and titanium have been used, according to the component ratio, excellent characteristics of each material were possible to produce a metal hybrid composite that appears to maximize. MgB₂ superconductor wire also fabricated via the same process. It will be introduced to their unique artistic and thermal characteristics.

Keywords: cladding process, metal-hybrid composites, hydrostatic extrusion, electronic/thermal characteristics

Procedia PDF Downloads 157

Authors: Mohammad W. Dewan, Jahangir Alam, Khurshida Sharmin

Abstract:

Synthetic fibers (carbon, glass, aramid, etc.) are generally utilized to make composite materials for better mechanical and thermal properties. However, they are expensive and non-biodegradable. In the perspective of Bangladesh, jute fibers are available, inexpensive, and comprising good mechanical properties. The improved properties (i.e., low cost, low density, eco-friendly) of natural fibers have made them a promising reinforcement in hybrid composites without sacrificing mechanical properties. In this study, jute and e-glass fiber reinforced hybrid composite materials are fabricated utilizing hand lay-up followed by a compression molding technique. Room temperature cured two-part epoxy resin is used as a matrix. Approximate 6-7 mm thick composite panels are fabricated utilizing 17 layers of woven glass and jute fibers with different fiber layering sequences- only jute, only glass, glass, and jute alternatively (g/j/g/j---) and 4 glass - 9 jute – 4 glass (4g-9j-4g). The fabricated composite panels are analyzed through fiber volume calculation, tensile test, bending test, and water absorption test. The hybridization of jute and glass fiber results in better tensile, bending, and water absorption properties than only jute fiber-reinforced composites, but inferior properties as compared to only glass fiber reinforced composites. Among different fiber layering sequences, 4g-9j-4g fibers layering sequence resulted in better tensile, bending, and water absorption properties. The effect of chemical treatment on the woven jute fiber and chopped glass microfiber infusion are also investigated in this study. Chemically treated jute fiber and 2 wt. % chopped glass microfiber infused hybrid composite shows about 12% improvements in flexural strength as compared to untreated and no micro-fiber infused hybrid composite panel. However, fiber chemical treatment and micro-filler do not have a significant effect on tensile strength.

Keywords: compression molding, chemical treatment, hybrid composites, mechanical properties

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Authors: Mohammadhadi Mirmohammadi, Reza Safian, Hossein Haddad

Abstract:

This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented.

Keywords: derivative-free optimization, Improving Hit and Run method, real-coded genetic algorithm, rolling schedules of tandem cold rolling mill

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Authors: G.Venkatachalam, Gautham Shankar, Dasarath Raghav, Krishna Kuar, Santhosh Kiran, Bhargav Mahesh

Abstract:

Uncertainty in the availability of fossil fuels in the future and global warming increased the need for more environment-friendly materials. In this work, an attempt is made to fabricate a hybrid polymer matrix composite. The blend is a mixture of General Purpose Resin and Cashew Nut Shell Liquid, a natural resin extracted from cashew plant. Palm fiber, which has high strength, is used as a reinforcement material. The fiber is treated with alkali (NaOH) solution to increase its strength and adhesiveness. Parametric study of flexure strength is carried out by varying alkali concentration, duration of alkali treatment and fiber volume. Taguchi L9 Orthogonal array is followed in the design of experiments procedure for simplification. With the help of ANOVA technique, regression equations are obtained which gives the level of influence of each parameter on the flexure strength of the composite.

Keywords: Adhesion, CNSL, Flexural Analysis, Hybrid Matrix Composite, Palm Fiber

Procedia PDF Downloads 375

Authors: Aishwarya Shekhar, Himanshu Sharma

Abstract:

Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. In this process, duplicate data is expunged, leaving only one copy means single instance of the data to be accumulated. Though, indexing of each and every data is still maintained. Data deduplication is an approach for minimizing the part of storage space an organization required to retain its data. In most of the company, the storage systems carry identical copies of numerous pieces of data. Deduplication terminates these additional copies by saving just one copy of the data and exchanging the other copies with pointers that assist back to the primary copy. To ignore this duplication of the data and to preserve the confidentiality in the cloud here we are applying the concept of hybrid nature of cloud. A hybrid cloud is a fusion of minimally one public and private cloud. As a proof of concept, we implement a java code which provides security as well as removes all types of duplicated data from the cloud.

Keywords: confidentiality, deduplication, data compression, hybridity of cloud

Procedia PDF Downloads 360

Authors: Gokhan Sir, Muhammed Ergun, Onder Balioglu

Abstract:

Nowadays, laundry dryers containing heaters and heat pumps are used to provide fast and efficient drying. In this system, as the drying capacity changes, the sensible and latent heat transfer rate in the evaporator changes. Therefore, the drying time measured for the unit capacity increases as the drying capacity decreases. The objective of this study is to investigate the evaporator efficiency according to load amount and textile type in hybrid heat pump dryers. Air side flow rate and system temperatures (air side and refrigeration side) were monitored instantly, and the specific moisture extraction rate (SMER), evaporator efficiency, and heat transfer mechanism between the textile and hybrid heat pump system were examined. Evaporator efficiency of heat pump dryers for cotton and synthetic based textile types in load amounts of 2, 5, 8 and 10 kg were investigated experimentally. As a result, the maximum evaporator efficiency (%72) was obtained in drying cotton and synthetic based textiles with a capacity of 5 kg; the minimum evaporator efficiency (%40) was obtained in drying cotton and synthetic based textiles with a capacity of 2 kg. The experimental study also reveals that capacity-dependent flow rate changes are the major factor for evaporator efficiency.

Keywords: evaporator, heat pump, hybrid, laundry dryer, textile

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Authors: Cristina Busuioc, Georgeta Voicu, Sorin-Ion Jinga

Abstract:

The human bone presents in its dry form piezoelectric properties, which means that a mechanical stress results in electric polarization and an applied electric field causes strain. The immediate consequence was the revealing of piezoelectricity role in bone remodelling, as well as the integration of ceramic materials with piezoelectric behaviour in the composition of unitary or composite biomaterials. Thus, we prepared hydroxyapatite - collagen hybrid materials with barium titanate addition in order to achieve a better osseointegration. Barium titanate powder synthesized by a combined sol-gel-hydrothermal method, commercial hydroxyapatite and laboratory extracted collagen gel were employed as starting materials. Before the composites, fabrication, the powder with piezoelectric features was characterized in detail from the compositional, structural, morphological and electrical point of view. The next step was to elucidate the influence of barium titanate presence especially on the biological properties of the final materials. The biocompatibility of the hybrid supports without or with piezoelectric addition was investigated on mouse osteoblast cells through LDH cytotoxicity assay, LIVE/DEAD cell viability assay, and MTT cell proliferation assay. All results indicated that the analysed materials do not exert cytotoxic effects and present the ability to sustain cell survival and to promote their proliferation. In conclusion, barium titanate nanoparticles exhibit a good biocompatibility and osteoinductive properties, while the derived composite materials based on hydroxyapatite as oxide phase and collagen as polymeric phase can be successfully used for tissue engineering applications.

Keywords: barium titanate, hybrid composites, piezoelectricity, tissue engineering

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Authors: Ercan Karadogan, Fatih Usta

Abstract:

Auxetic materials have a negative Poisson’s ratio (NPR), which is not often found in nature. They are metamaterials that have potential applications in many engineering fields. Mechanical metamaterials are synthetically designed structures with unusual mechanical properties. These mechanical properties are dependent on the properties of the matrix structure. They have the following special characteristics, i.e., improved shear modulus, increased energy absorption, and intensive fracture toughness. Non-auxetic materials compress transversely when they are stretched. The system naturally is inclined to keep its density constant. The transversal compression increases the density to balance the loss in the longitudinal direction. This study proposes to improve the crushing performance of hybrid auxetic materials. The re-entrant honeycomb structure has been combined with a star honeycomb, an S-shaped unit cell, a double arrowhead, and a structurally hexagonal re-entrant honeycomb by 9 X 9 cells, i.e., the number of cells is 9 in the lateral direction and 9 in the vertical direction. The Finite Element (FE) and experimental methods have been used to determine the compression behavior of the developed hybrid auxetic structures. The FE models have been developed by using Abaqus software. The specimens made of polymer plastic materials have been 3D printed and subjected to compression loading. The results are compared in terms of specific energy absorption and strength. This paper describes the quasi-static crushing behavior of two types of hybrid lattice structures (auxetic + auxetic and auxetic + non-auxetic). The results show that the developed hybrid structures can be useful to control collapse mechanisms and present larger energy absorption compared to conventional re-entrant auxetic structures.

Keywords: auxetic materials, compressive behavior, metamaterials, negative Poisson’s ratio

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Authors: Muluken Biadgelegn Wollele, Mebratu Assaye Mengistu

Abstract:

Nanotechnology has created new opportunities for improving industrial efficiency and performance. One of the proposed approaches to improving the effectiveness of temperature exchangers is the use of nanofluids to improve heat transfer performance. The thermal conductivity of nanoparticles, as well as their size, diameter, and volume concentration, all played a role in influencing the rate of heat transfer. Nanofluids are commonly used in automobiles, energy storage, electronic component cooling, solar absorbers, and nuclear reactors. Convective heat transfer must be improved when designing thermal systems in order to reduce heat exchanger size, weight, and cost. Using roughened surfaces to promote heat transfer has been tried several times. Thus, both active and passive heat transfer methods show potential in terms of heat transfer improvement. There will be an added advantage of enhanced heat transfer due to the two methods adopted; however, pressure drop must be considered during flow. Thus, the current research aims to increase heat transfer by adding a twisted tap insert in a plain tube using a working fluid hybrid nanofluid (Al₂O₃-Cu) with a base fluid of water. A circular duct with inserts, a tube length of 3 meters, a hydraulic diameter of 0.01 meters, and tube walls with a constant heat flux of 20 kW/m² and a twist ratio of 125 was used to investigate Al₂O₃-Cu/H₂O hybrid nanofluid with inserts. The temperature distribution is better than with conventional tube designs due to stronger tangential contact and swirls in the twisted tape. The Nusselt number values of plain twisted tape tubes are 1.5–2.0 percent higher than those of plain tubes. When twisted tape is used instead of plain tube, performance evaluation criteria improve by 1.01 times. A heat exchanger that is useful for a number of heat exchanger applications can be built utilizing a mixed flow of analysis that incorporates passive and active methodologies.

Keywords: nanofluids, active method, passive method, Nusselt number, performance evaluation criteria

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Authors: Ali Ateş, Ansar B. Mwimbo, Ali H. Abdulkarim

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General transport equation has a wide range of application in Fluid Mechanics and Heat Transfer problems. In this equation, generally when φ variable which represents a flow property is used to represent fluid velocity component, general transport equation turns into momentum equations or with its well known name Navier-Stokes equations. In these non-linear differential equations instead of seeking for analytic solutions, preferring numerical solutions is a more frequently used procedure. Finite difference method is a commonly used numerical solution method. In these equations using velocity and pressure gradients instead of stress tensors decreases the number of unknowns. Also, continuity equation, by integrating the system, number of equations is obtained as number of unknowns. In this situation, velocity and pressure components emerge as two important parameters. In the solution of differential equation system, velocities and pressures must be solved together. However, in the considered grid system, when pressure and velocity values are jointly solved for the same nodal points some problems confront us. To overcome this problem, using staggered grid system is a referred solution method. For the computerized solutions of the staggered grid system various algorithms were developed. From these, two most commonly used are SIMPLE and SIMPLER algorithms. In this study Navier-Stokes equations were numerically solved for Newtonian flow, whose mass or gravitational forces were neglected, for incompressible and laminar fluid, as a hydro dynamically fully developed region and in two dimensional cartesian coordinate system. Finite difference method was chosen as the solution method. This is a parametric study in which varying values of velocity components, pressure and Reynolds numbers were used. Differential equations were discritized using central difference and hybrid scheme. The discritized equation system was solved by Gauss-Siedel iteration method. SIMPLE and SIMPLER were used as solution algorithms. The obtained results, were compared for central difference and hybrid as discritization methods. Also, as solution algorithm, SIMPLE algorithm and SIMPLER algorithm were compared to each other. As a result, it was observed that hybrid discritization method gave better results over a larger area. Furthermore, as computer solution algorithm, besides some disadvantages, it can be said that SIMPLER algorithm is more practical and gave result in short time. For this study, a code was developed in DELPHI programming language. The values obtained in a computer program were converted into graphs and discussed. During sketching, the quality of the graph was increased by adding intermediate values to the obtained result values using Lagrange interpolation formula. For the solution of the system, number of grid and node was found as an estimated. At the same time, to indicate that the obtained results are satisfactory enough, by doing independent analysis from the grid (GCI analysis) for coarse, medium and fine grid system solution domain was obtained. It was observed that when graphs and program outputs were compared with similar studies highly satisfactory results were achieved.

Keywords: finite difference method, GCI analysis, numerical solution of the Navier-Stokes equations, SIMPLE and SIMPLER algoritms

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Authors: Farnaz Farhangi

Abstract:

Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Rosalie Menon, Angela Reid

Abstract:

Building integrated photovoltaic systems with air collectors (hybrid PV-T) have proved successful however there are few examples of their application in the UK. The opportunity to pull heat from behind the PV system to contribute to a building’s heating system is an efficient use of waste energy and its potential to improve the performance of the PV array is well documented. As part of Glasgow School of Art’s estate expansion, the purchase and redevelopment of an existing 1950’s college building was used as a testing vehicle for the hybrid PV-T system as an integrated element of the upper floor and roof. The primary objective of the feasibility study was to determine if hybrid PV-T was technically and financially suitable for the refurbished building. The key consideration was whether the heat recovered from the PV panels (to increase the electrical efficiency) can be usefully deployed as a heat source within the building. Dynamic thermal modelling (IES) and RetScreen Software were used to carry out the feasibility study not only to simulate overshadowing and optimise the PV-T locations but also to predict the atrium temperature profile; predict the air load for the proposed new 4 No. roof mounted air handling units and to predict the dynamic electrical efficiency of the PV element. The feasibility study demonstrates that there is an energy reduction and carbon saving to be achieved with each hybrid PV-T option however the systems are subject to lengthy payback periods and highlights the need for enhanced government subsidy schemes to reward innovation with this technology in the UK.

Keywords: building integrated, photovoltatic thermal, pre-heat air, ventilation

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Authors: Kemal Polat

Abstract:

In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Sidi Mohamed Mesli, Mohamed Habchi, Arslane Boudghene Stambouli, Rafik Benallal

Abstract:

The BaMnMF7 (M=Fe,V, transition metal fluoride glass, assuming isomorphous replacement) have been structurally studied through the simultaneous simulation of their neutron diffraction patterns by reverse Monte Carlo (RMC) and by the Hybrid Reverse Monte Carlo (HRMC) analysis. This last is applied to remedy the problem of the artificial satellite peaks that appear in the partial pair distribution functions (PDFs) by the RMC simulation. The HRMC simulation is an extension of the RMC algorithm, which introduces an energy penalty term (potential) in acceptance criteria. The idea of this work is to apply the Buckingham potential at the title glass by ignoring the van der Waals terms, in order to make a fit of the partial pair distribution functions and give the most possible realistic features. When displaying the partial PDFs, we suggest that the Buckingham potential is useful to describe average correlations especially in similar interactions.

Keywords: fluoride glasses, RMC simulation, hybrid RMC simulation, Buckingham potential, partial pair distribution functions

Procedia PDF Downloads 477