Search results for: thermal cycling machine

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

Procedia PDF Downloads 101

Authors: Ceren Sevimli, Sedat İnan

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The Silurian Dadaş Formation within the Diyarbakir Basin in SE Türkiye, like other Silurian shales in North Africa and Middle East, represents a significant prospect for conventional and unconventional hydrocarbon exploration. The Diyarbakır Basin remains relatively underexplored, presenting untapped potential that warrants further investigation. This study focuses on the thermal maturity and hydrocarbon generation histories of the Silurian Dadaş shales, utilizing basin modeling approach. The Dadaş shales are organic-rich and contain mainly Type II kerogen, especially the basal layer contains up to 10 wt. %TOC and thus it is named as “hot shale”. The research integrates geological, geochemical, and basin modeling data to elucidate the unconventional hydrocarbon potential of this formation, which is crucial given the global demand for energy and the need for new resources. The data obtained from previous studies were used to calibrate basin model that has been established by using PetroMod software (Schlumberger). The calibrated model results suggest that Dadaş shales are in oil generation window and that the major episode for thermal maturation and hydrocarbon generation took place prior rot Alpine orogeny (uplift and erosion) The modeling results elucidate the burial history, maturity history, and hydrocarbon production history of the Silurian-aged Dadaş shales, as well as its hydrocarbon content in the area.

Keywords: dadaş formation, diyarbakır basin, silurian hot shale, unconventional hydrocarbon

Procedia PDF Downloads 23

Authors: Saifullah Karimullah

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Additive manufacturing techniques are becoming more common with the latest technological advancements. It is composed to bring a revolution in the way products are designed, planned, manufactured, and distributed to end users. Fused deposition modeling (FDM) based 3D printing is one of those promising aspects that have revolutionized the prototyping processes. The purpose of this design and study project is to design a customized laboratory-scale FDM-based 3D printer from locally available sources. The primary goal is to design and fabricate the FDM-based 3D printer. After the fabrication, a tensile test specimen would be designed in Solid Works or [Creo computer-aided design (CAD)] software. A .stl file is generated of the tensile test specimen through slicing software and the G-codes are inserted via a computer for the test specimen to be printed. Different parameters were under studies like printing speed, layer thickness and infill density of the printed object. Some parameters were kept constant such as temperature, extrusion rate, raster orientation etc. Different tensile test specimens were printed for a different sets of parameters of the FDM-based 3d printer. The tensile test specimen were subjected to tensile tests using a universal testing machine (UTM). Design Expert software has been used for analyses, So Different results were obtained from the different tensile test specimens. The best, average and worst specimen were also observed under a compound microscope to investigate the layer bonding in between.

Keywords: additive manufacturing techniques, 3D printing, CAD software, UTM machine

Procedia PDF Downloads 99

Authors: Ummuhan Bas Aslan, Sehmus Aslan

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Objective: Physical disability includes impairments, activity limitations, and participation restrictions. Individuals with physical disabilities have lower self-esteem compared non-disabled people. Self-esteem is widely accepted as a key indicator of emotional stability and adjustment to life demands. There is very limited study to investigate the effect of sports on self-esteem in physically disabled people. The aim of the present study was to evaluate of self-esteem in physically disabled adults who participated in sports. Methods: Fifty physically disabled adults who participated in sports aged between 18 to 35 years participated in the study. Self-esteem of the participants was assessed by Rosenberg Self-Esteem Scale. The scale is a 10-item measure of global self-esteem. The higher score on the scale indicates greater self-esteem. Scores between 15 and 25 are the normal range of and scores below 15 suggest low self-esteem. Results: Average age of participants was 25.18±6.20 years. 58% of the participants were 23 (46.0%) of the participants were wheelchair users, 8 (16.0%) were mobile with a walking aid and 19 (38.0%) were mobile without a walking aid. The length of physically disabled adults had been participating in their sports (basketball: 54%, athleticism: 32%, volleyball: 6%, cycling: 6%) was 4.94±3.86 years. The average Rosenberg Self-Esteem Scale score of the participants was 21.88 ±4.34. Conclusions: Our results suggest that physically disabled adults who participated in sports have the healthy level of self-esteem. Participating in sports could have positive effects on self-esteem in that physically, disabled people. There is needed future comparative studies on this topic.

Keywords: adult, physical disability, self-esteem, sport

Procedia PDF Downloads 260

Authors: M. Wajahat, M. Shoaib, A. Waheed

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As a result of increase in world energy demand as well as the demand for heating, refrigeration and air conditioning, energy engineers are now more inclined towards the renewable energy especially solar based thermal driven refrigeration and air conditioning systems. This research is emphasized on solar assisted adsorption refrigeration system to provide comfort conditions for a building in Islamabad. The adsorption chiller can be driven by low grade heat at low temperature range (50 -80 °C) which is lower than that required for generator in absorption refrigeration system which may be furnished with the help of common flat plate solar collectors (FPC). The aim is to offset the total energy required for building’s heating and cooling demand by using FPC’s thus reducing dependency on primary energy source hence saving energy. TRNSYS is a dynamic modeling and simulation tool which can be utilized to simulate the working of a complete solar based adsorption chiller to meet the desired cooling and heating demand during summer and winter seasons, respectively. Modeling and detailed parametric analysis of the whole system is to be carried out to determine the optimal system configuration keeping in view various design constraints. Main focus of the study is on solar thermal loop of the adsorption chiller to reduce the contribution from the auxiliary devices.

Keywords: flat plate collector, energy saving, solar assisted adsorption chiller, TRNSYS

Procedia PDF Downloads 649

Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

Procedia PDF Downloads 118

Authors: Amir Shafiee Kisomi, Mehrdad Mofidi

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Nowadays, fuel cells as a promising alternative for power source have been widely studied owing to their security, high energy density, low operation temperatures, renewable capability and low environmental pollutant emission. The nanoparticles of core-shell type could be widely described in a combination of a shell (outer layer material) and a core (inner material), and their characteristics are greatly conditional on dimensions and composition of the core and shell. In addition, the change in the constituting materials or the ratio of core to the shell can create their special noble characteristics. In this study, a fast technique for the fabrication of a Pd-Co/G/GCE modified electrode is offered. Thermal decomposition reaction of cobalt (II) formate salt over the surface of graphene/glassy carbon electrode (G/GCE) is utilized for the synthesis of Co nanoparticles. The nanoparticles of Pd-Co decorated on the graphene are created based on the following method: (1) Thermal decomposition reaction of cobalt (II) formate salt and (2) the galvanic replacement process Co by Pd2+. The physical and electrochemical performances of the as-prepared Pd-Co/G electrocatalyst are studied by Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDS), Cyclic Voltammetry (CV), and Chronoamperometry (CHA). Galvanic replacement method is utilized as a facile and spontaneous approach for growth of Pd nanostructures. The Pd-Co/G is used as an anode catalyst for ethanol oxidation in alkaline media. The Pd-Co/G not only delivered much higher current density (262.3 mAcm-2) compared to the Pd/C (32.1 mAcm-2) catalyst, but also demonstrated a negative shift of the onset oxidation potential (-0.480 vs -0.460 mV) in the forward sweep. Moreover, the novel Pd-Co/G electrocatalyst represents large electrochemically active surface area (ECSA), lower apparent activation energy (Ea), higher levels of durability and poisoning tolerance compared to the Pd/C catalyst. The paper demonstrates that the catalytic activity and stability of Pd-Co/G electrocatalyst are higher than those of the Pd/C electrocatalyst toward ethanol oxidation in alkaline media.

Keywords: thermal decomposition, nanostructures, galvanic replacement, electrocatalyst, ethanol oxidation, alkaline media

Procedia PDF Downloads 148

Authors: Nicole Nazario Bayon, Prathima Prabhu Tumkur, Nithin Krisshna Gunasekaran, Krishnan Prabhakaran, Joseph C. Hall, Govindarajan T. Ramesh

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Titanium nitride (TiN) nanoparticles have sparked interest over the past decade due to their characteristics such as thermal stability, extreme hardness, low production cost, and similar optical properties to gold. In this study, TiN nanoparticles were synthesized via a thermal benzene route to obtain a black powder of nanoparticles. The final product was drop cast onto conductive carbon tape and sputter coated with gold/palladium at a thickness of 4 nm for characterization by field emission scanning electron microscopy (FE-SEM) with energy dispersive X-Ray spectroscopy (EDX) that revealed they were spherical. ImageJ software determined the average size of the TiN nanoparticles was 79 nm in diameter. EDX revealed the elements present in the sample and showed no impurities. Further characterization by X-ray diffraction (XRD) revealed characteristic peaks of cubic phase titanium nitride, and crystallite size was calculated to be 14 nm using the Debye-Scherrer method. Dynamic light scattering (DLS) analysis revealed the size and size distribution of the TiN nanoparticles, with average size being 154 nm. Zeta potential concluded the surface of the TiN nanoparticles is negatively charged. Biocompatibility studies using MTT(3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay showed TiN nanoparticles are not cytotoxic at low concentrations (2, 5, 10, 25, 50, 75 mcg/well), and cell viability began to decrease at a concentration of 100 mcg/well.

Keywords: biocompatibility, characterization, cytotoxicity, nanoparticles, synthesis, titanium nitride

Procedia PDF Downloads 174

Authors: Hanene Sahli, Aymen Mouelhi, Marwa Hajji, Amine Ben Slama, Mounir Sayadi, Farhat Fnaiech, Radhwane Rachdi

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Fetal abnormality is still a public health problem of interest to both mother and baby. Head defect is one of the most high-risk fetal deformities. Fetal head categorization is a sensitive task that needs a massive attention from neurological experts. In this sense, biometrical measurements can be extracted by gynecologist doctors and compared with ground truth charts to identify normal or abnormal growth. The fetal head biometric measurements such as Biparietal Diameter (BPD), Occipito-Frontal Diameter (OFD) and Head Circumference (HC) needs to be monitored, and expert should carry out its manual delineations. This work proposes a new approach to automatically compute BPD, OFD and HC based on morphological characteristics extracted from head shape. Hence, the studied data selected at the same Gestational Age (GA) from the fetal Ultrasound images (US) are classified into two categories: Normal and abnormal. The abnormal subjects include hydrocephalus, microcephaly and dolichocephaly anomalies. By the use of a support vector machines (SVM) method, this study achieved high classification for automated detection of anomalies. The proposed method is promising although it doesn't need expert interventions.

Keywords: biometric measurements, fetal head malformations, machine learning methods, US images

Procedia PDF Downloads 286

Authors: Nurul Bin Ibrahim

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Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

Procedia PDF Downloads 66

Authors: Ashkan Forootan, Reza Rashedi

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Polyethylene has wide usage areas such as blow molding, pipe, film, cable insulation. However, regardless to its growing applications, it has some constraints such as the limited 70C operating temperature. Polyethylene thermo setting procedure whose molecules are knotted and 3D-molecular-network formed , is developed to conquer the above problem and to raise the applicable temperature of the polymer. This paper reports the cross-linking for two different molecular weight grades of LLDPE by adding 0.5, 1, and 2% of DCP (Dicumyl Peroxide). DCP was chosen for its prevalence among various cross-linking agents. Structural parameters such as molecular weight, melt flow index, comonomer, number of branches,etc. were obtained through the use of relative tests as Gel Permeation Chromatography and Fourier Transform Infra Red spectrometer. After calculating the percentage of gel content, properties of the pure and cross-linked samples were compared by thermal and mechanical analysis with DMTA and FTIR and the effects of cross-linking like viscous and elastic modulus were discussed by using various structural paprameters such as MFI, molecular weight, short chain branches, etc. Studies showed that cross-linked polymer, unlike the pure one, had a solid state with thermal mechanical properties in the range of 110 to 120C and this helped overcome the problem of using polyethylene in temperatures near the melting point.

Keywords: LLDPE, cross-link, structural parameters, DCP, DMTA, GPC

Procedia PDF Downloads 302

Authors: Despina Vamvuka, Despina Pentari

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Forestry wastes are readily available in large quantities around the world. Based on European Green Deal for the deployment of renewable and decarbonized energy by 2050, as well as global energy crisis, energy recovery from such wastes reducing greenhouse gas emissions is very attractive. Gasification has superior environmental performance to combustion, producing a clean fuel gas utilized in internal combustion engines, gas turbines, solid oxide fuel cells, or for synthesis of liquid bio-fuels and value-added chemicals. In this work, pine needles, which are abundantly found in Mediterranean countries, were gasified by either steam or carbon dioxide via a two-step process to improve reactivity and eliminate tar, employing a fixed bed unit and a thermal analysis system. Solid, liquid and gaseous products from the whole process were characterized and their energy potential was determined. Thermal behaviour, reactivity, conversion and energy recovery were examined. The gasification process took place above 650°C. At 950°C conversion and energy recovery were 77% dry and 2 under a flow of steam and 85% dry and 2.9 under a flow of carbon dioxide, respectively. Organic matter was almost completely converted to syngas, the yield of which varied between 89% and 99%. The higher heating values of biochar, bio-oil and pyrolysis gas were 27.8 MJ/kg, 33.5 MJ/kg and 13.6 MJ/m3. Upon steam or carbon dioxide gasification, the higher heating value of syngas produced was 11.5 MJ/m3 and 12.7 MJ/m3, respectively.

Keywords: gasification, biomass, steam, carbon dioxide

Procedia PDF Downloads 96

Authors: Hassan Qandil

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Using a statistical algorithm incorporated in MATLAB, four types of non-imaging Fresnel lenses are designed; spot-flat, linear-flat, dome-shaped and semi-cylindrical-shaped. The optimization employs a statistical ray-tracing methodology of the incident light, mainly considering effects of chromatic aberration, varying focal lengths, solar inclination and azimuth angles, lens and receiver apertures, and the optimum number of prism grooves. While adopting an equal-groove-width assumption of the Poly-methyl-methacrylate (PMMA) prisms, the main target is to maximize the ray intensity on the receiver’s aperture and therefore achieving higher values of heat flux. The algorithm outputs prism angles and 2D sketches. 3D drawings are then generated via AutoCAD and linked to COMSOL Multiphysics software to simulate the lenses under solar ray conditions, which provides optical and thermal analysis at both the lens’ and the receiver’s apertures while setting conditions as per the Dallas-TX weather data. Once the lenses’ characterization is finalized, receivers are designed based on its optimized aperture size. Several cavity shapes; including triangular, arc-shaped and trapezoidal, are tested while coupled with a variety of receiver materials, working fluids, heat transfer mechanisms, and enclosure designs. A vacuum-reflective enclosure is also simulated for an enhanced thermal absorption efficiency. Each receiver type is simulated via COMSOL while coupled with the optimized lens. A lab-scale prototype for the optimum lens-receiver configuration is then fabricated for experimental evaluation. Application-based testing is also performed for the selected configuration, including that of a photovoltaic-thermal cogeneration system and solar furnace system. Finally, some future research work is pointed out, including the coupling of the collector-receiver system with an end-user power generator, and the use of a multi-layered genetic algorithm for comparative studies.

Keywords: COMSOL, concentrator, energy, fresnel, optics, renewable, solar

Procedia PDF Downloads 149

Authors: Zhengming Wu

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NanoFrazor lithography systems were developed as a first true alternative or extension to standard mask-less nanolithography methods like electron beam lithography (EBL). In contrast to EBL they are based on thermal scanning probe lithography (t-SPL). Here a heatable ultra-sharp probe tip with an apex of a few nm is used for patterning and simultaneously inspecting complex nanostructures. The heat impact from the probe on a thermal responsive resist generates those high-resolution nanostructures. The patterning depth of each individual pixel can be controlled with better than 1 nm precision using an integrated in-situ metrology method. Furthermore, the inherent imaging capability of the Nanofrazor technology allows for markerless overlay, which has been achieved with sub-5 nm accuracy as well as it supports stitching layout sections together with < 10 nm error. Pattern transfer from such resist features below 10 nm resolution were demonstrated. The technology has proven its value as an enabler of new kinds of ultra-high resolution nanodevices as well as for improving the performance of existing device concepts. The application range for this new nanolithography technique is very broad spanning from ultra-high resolution 2D and 3D patterning to chemical and physical modification of matter at the nanoscale. Nanometer-precise markerless overlay and non-invasiveness to sensitive materials are among the key strengths of the technology. However, while patterning at below 10 nm resolution is achieved, significantly increasing the patterning speed at the expense of resolution is not feasible by using the heated tip alone. Towards this end, an integrated laser write head for direct laser sublimation (DLS) of the thermal resist has been introduced for significantly faster patterning of micrometer to millimeter-scale features. Remarkably, the areas patterned by the tip and the laser are seamlessly stitched together and both processes work on the very same resist material enabling a true mix-and-match process with no developing or any other processing steps in between. The presentation will include examples for (i) high-quality metal contacting of 2D materials, (ii) tuning photonic molecules, (iii) generating nanofluidic devices and (iv) generating spintronic circuits. Some of these applications have been enabled only due to the various unique capabilities of NanoFrazor lithography like the absence of damage from a charged particle beam.

Keywords: nanofabrication, grayscale lithography, 2D materials device, nano-optics, photonics, spintronic circuits

Procedia PDF Downloads 70

Authors: Fulya Asena Uluç, Ehsan Tuzcuoğlu, Songül Bayraktar, Burak Koca, Alper Gürarslan

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Microplastics are contaminants that are widely distributed in the environment with a detrimental ecological effect. Besides this, recent research has proved the existence of microplastics in human blood and organs. Microplastics in the environment can be divided into two main categories: primary and secondary microplastics. Primary microplastics are plastics that are released into the environment as microscopic particles. On the other hand, secondary microplastics are the smaller particles that are shed as a result of the consumption of synthetic materials in textile products as well as other products. Textiles are the main source of microplastic contamination in aquatic ecosystems. Laundry of synthetic textiles (34.8%) accounts for an average annual discharge of 3.2 million tons of primary microplastics into the environment. Recently, microfiber shedding from laundry research has gained traction. However, no comprehensive study was conducted from the standpoint of rinsing parameters during laundry to analyze microfiber shedding. The purpose of the present study is to quantify microfiber shedding from fabric under different rinsing conditions and determine the effective rinsing parameters on microfiber release in a laundry environment. In this regard, a parametric study is carried out to investigate the key factors affecting the microfiber release from a front-load washing machine. These parameters are the amount of water used during the rinsing step and the spinning speed at the end of the washing cycle. Minitab statistical program is used to create a design of the experiment (DOE) and analyze the experimental results. Tests are repeated twice and besides the controlled parameters, other washing parameters are kept constant in the washing algorithm. At the end of each cycle, released microfibers are collected via a custom-made filtration system and weighted with precision balance. The results showed that by increasing the water amount during the rinsing step, the amount of microplastic released from the washing machine increased drastically. Also, the parametric study revealed that increasing the spinning speed results in an increase in the microfiber release from textiles.

Keywords: front load, laundry, microfiber, microfiber release, microfiber shedding, microplastic, pollution, rinsing parameters, sustainability, washing parameters, washing machine

Procedia PDF Downloads 94

Authors: Devesh Motwani, Amey Kashyap

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Interest in cost effective drilling has increased substantially in the past years. Economics associated with drilling fluids is needed to be considered seriously for lesser cost per foot in planning and drilling of a wellbore and the various environmental concerns imposed by international communities related with the constituents of the drilling fluid. Viscofier such as Guar Gum is a high molecular weight polysaccharide from Guar plants, is used to increase viscosity in water-based and brine-based drilling fluids thus enabling more efficient cleaning of the bore. Other applications of this Viscofier are to reduce fluid loss by giving a better colloidal solution, decrease fluid friction and so minimising power requirements and used in hydraulic fracturing to increase the recovery of oil and gas. Guar gum is also used as a surfactant, synthetic polymer and defoamer. This paper presents experimental results to verifying the properties of guar gum as a viscofier and filtrate retainer as well as observing the impact of different quantities of guar gum and Carboxymethyl cellulose (CMC) in a standard sample of water based bentonite mud solution. This is in attempt to make a drilling fluid which contains half of the quantity of drilling mud used and yet is equally viscous to the standardised mud sample. Thus we can see that mud economics will be greatly affected by this approach. However guar gum is thermally stable till 60-65°C thus limited to be used in drilling shallow wells and for a wider thermal range, suitable chrome free additives are required.

Keywords: economics, guargum, viscofier, CMC, thermal stability

Procedia PDF Downloads 465

Authors: K. Saravanan, K. A.Rameshkumar, P. Maadeswaran

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The depletion of fossil resources and the rise in global temperatures are two of the most important concerns we confront today. There are numerous renewable energy sources like solar power, tidal power, wind energy, radiant energy, hydroelectricity, geothermal energy, and biomass available to generate the needed energy demand. Engineers and scientists around the world are facing a massive barrier in the development of storage technologies for the energy developed from renewable energy sources. The development of electrochemical capacitors as a future energy storage technology is at the forefront of current research and development. This is due to the fact that the electrochemical capacitors have a significantly higher energy density, a faster charging-discharging rate, and a longer life span than capacitors, and they also have a higher power density than batteries, making them superior to both. In this research, electrochemical capacitors using the Nd2O3/Mn3O4/ N-rGO electrode material is chosen since the of hexagonal and tetragonal crystal structures of Nd2O3 and Mn3O4 and also has cycling stability of 68% over a long time at 50mVs-1 and a high coulombic efficiency of 99.64% at 5 Ag-1. This approach may also be used to create novel electrode materials with improved electrochemical and cyclic stability for high-performance supercapacitors.

Keywords: Nd2O3/Mn3O4/N-rGO, nanocomposites, hydrothermal method, electrode material, specific capacitance, use of supercapacitors

Procedia PDF Downloads 91

Authors: I. Sinnarasa, Y. Thimont, L. Presmanes, A. Barnabé

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The thermoelectricity is a promising technique to overcome the issues in recovering waste heat to electricity without using moving parts. In fact, the thermoelectric (TE) effect defines as the conversion of a temperature gradient directly into electricity and vice versa. To optimize TE materials, the power factor (PF = σS² where σ is electrical conductivity and S is Seebeck coefficient) must be increased by adjusting the carrier concentration, and/or the lattice thermal conductivity Kₜₕ must be reduced by introducing scattering centers with point defects, interfaces, and nanostructuration. The PF does not show the advantages of the thin film because it does not take into account the thermal conductivity. In general, the thermal conductivity of the thin film is lower than the bulk material due to their microstructure and increasing scattering effects with decreasing thickness. Delafossite type oxides CuᴵMᴵᴵᴵO₂ received main attention for their optoelectronic properties as a p-type semiconductor they exhibit also interesting thermoelectric (TE) properties due to their high electrical conductivity and their stability in room atmosphere. As there are few proper studies on the TE properties of Mg-doped CuCrO₂ thin films, we have investigated, the influence of the annealing temperature on the electrical conductivity and the Seebeck coefficient of Mg-doped CuCrO₂ thin films and calculated the PF in the temperature range from 40 °C to 220 °C. For it, we have deposited Mg-doped CuCrO₂ thin films on fused silica substrates by RF magnetron sputtering. This study was carried out on 300 nm thin films. The as-deposited Mg doped CuCrO₂ thin films have been annealed at different temperatures (from 450 to 650 °C) under primary vacuum. Electrical conductivity and Seebeck coefficient of the thin films have been measured from 40 to 220 °C. The highest electrical conductivity of 0.60 S.cm⁻¹ with a Seebeck coefficient of +329 µV.K⁻¹ at 40 °C have been obtained for the sample annealed at 550 °C. The calculated power factor of optimized CuCrO₂:Mg thin film was 6 µW.m⁻¹K⁻² at 40 °C. Due to the constant Seebeck coefficient and the increasing electrical conductivity with temperature it reached 38 µW.m⁻¹K⁻² at 220 °C that was a quite good result for an oxide thin film. Moreover, the degenerate behavior and the hopping mechanism of CuCrO₂:Mg thin film were elucidated. Their high and constant Seebeck coefficient in temperature and their stability in room atmosphere could be a great advantage for an application of this material in a high accuracy temperature measurement devices.

Keywords: thermoelectric, oxides, delafossite, thin film, power factor, degenerated semiconductor, hopping mode

Procedia PDF Downloads 193

Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

Procedia PDF Downloads 204

Authors: Zichu Liu, Zhenhua Quan, Xin Liu, Yaohua Zhao

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Enhancing the heat transfer characteristics of the bobsleigh/skeleton ice track and reducing the energy consumption of the bobsleigh/skeleton ice track plays an important role in energy saving of the refrigeration systems. In this study, a track ice-making test rig was constructed to verify the accuracy of the established ice track heat transfer model. The different meteorological conditions on the variations in the heat transfer characteristics of the ice surface, ice temperature, and evaporation temperature with or without Terrain Weather Protection System (TWPS) were investigated, and the influence of the TWPS with and without low emissivity materials on these indexes was also compared. In addition, the influence of different pipe spacing and diameters of refrigeration pipe on the heat transfer resistance of the track is also analyzed. The results showed that compared with the ice track without sunshade facilities, TWPS could reduce the heat transfer between ice surface and air by 17.6% in the transition season, and TWPS with low emissivity material could reduce the heat transfer by 37%. The thermal resistance of the ice track decreased by 8.9×10⁻⁴ m²·°C/W, and the refrigerant evaporation temperature increased by 0.25 °C when the cooling pipes spacing decreased by every 10 mm. The thermal resistance decreased by 1.46×10⁻³ m²·°C/W, and the refrigerant evaporation temperature increased by 0.3 °C when the pipe diameter increased by one nominal diameter.

Keywords: bobsleigh/skeleton ice track, calculation model, heat transfer characteristics, refrigeration

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Ha-Guk Jeong, Jong-Beom Lee

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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

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Authors: M. J. Gibbons, A. J. Robinson

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Increasing expectations on small form factor electronics to be more compact while increasing performance has driven conventional cooling technologies to a thermal management threshold. An emerging solution to this problem is electrospray (ES) cooling. ES cooling enables two phase cooling by utilising Coulomb forces for energy efficient fluid atomization. Generated charged droplets are accelerated to the grounded target surface by the applied electric field and surrounding gravitational force. While in transit the like charged droplets enable plume dispersion and inhibit droplet coalescence. If the electric field is increased in the cone-jet regime, a subsequent increase in the plume spray angle has been shown. Droplet segregation in the spray plume has been observed, with primary droplets in the plume core and satellite droplets positioned on the periphery of the plume. This segregation is facilitated by inertial and electrostatic effects. This result has been corroborated by numerous authors. These satellite droplets are usually more densely charged and move at a lower relative velocity to that of the spray core due to the radial decay of the electric field. Previous experimental research by Gomez and Tang has shown that the number of droplets deposited on the periphery can be up to twice that of the spray core. This result has been substantiated by a numerical models derived by Wilhelm et al., Oh et al. and Yang et al. Yang et al. showed from their numerical model, that by varying the extractor potential the dispersion radius of the plume also varies proportionally. This research aims to investigate this dispersion density and the role it plays in the local heat transfer coefficient profile (h) of ES cooling. This will be carried out for different extractor – target separation heights (H2), working fluid flow rates (Q), and extractor applied potential (V2). The plume dispersion will be recorded by spraying a 25 µm thick, joule heated steel foil and by recording the thermal footprint of the ES plume using a Flir A-40 thermal imaging camera. The recorded results will then be analysed by in-house developed MATLAB code.

Keywords: electronic cooling, electrospray, electrospray plume dispersion, spray cooling

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Authors: Usman Dahiru, Faisal Saleem, Kui Zhang, Adam Harvey

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Volatile organic compounds (VOCs) are atmospheric contaminants predominantly derived from petroleum spills, solvent usage, agricultural processes, automobile, and chemical processing industries, which can be detrimental to the environment and human health. Environmental problems such as the formation of photochemical smog, organic aerosols, and global warming are associated with VOC emissions. Research showed a clear relationship between VOC emissions and cancer. In recent years, stricter emission regulations, especially in industrialized countries, have been put in place around the world to restrict VOC emissions. Non-thermal plasmas (NTPs) are a promising technology for reducing VOC emissions by converting them into less toxic/environmentally friendly species. The dielectric barrier discharge (DBD) plasma is of interest due to its flexibility, moderate capital cost, and ease of operation under ambient conditions. In this study, a dielectric barrier discharge (DBD) reactor has been developed for the decomposition of cyclohexane (as a VOC model compound) using nitrogen, dry, and humidified air carrier gases. The effect of specific input energy (1.2-3.0 kJ/L), residence time (1.2-2.3 s) and concentration (220-520 ppm) were investigated. It was demonstrated that the removal efficiency of cyclohexane increased with increasing plasma power and residence time. The removal of cyclohexane decreased with increasing cyclohexane inlet concentration at fixed plasma power and residence time. The decomposition products included H₂, CO₂, H₂O, lower hydrocarbons (C₁-C₅) and solid residue. The highest removal efficiency (98.2%) was observed at specific input energy of 3.0 kJ/L and a residence time of 2.3 s in humidified air plasma. The effect of humidity was investigated to determine whether it could reduce the formation of solid residue in the DBD reactor. It was observed that the solid residue completely disappeared in humidified air plasma. Furthermore, the presence of OH radicals due to humidification not only increased the removal efficiency of cyclohexane but also improves product selectivity. This work demonstrates that cyclohexane can be converted to smaller molecules by a dielectric barrier discharge (DBD) non-thermal plasma reactor by varying plasma power (SIE), residence time, reactor configuration, and carrier gas.

Keywords: cyclohexane, dielectric barrier discharge reactor, non-thermal plasma, removal efficiency

Procedia PDF Downloads 131

Authors: Sara Kamalisiahroudi, Zhang Jianbo, Bin Wu, Jun Huang, Laisuo Su

Abstract:

The temperature rising within a battery cell depends on the level of heat generation, the thermal properties and the heat transfer around the cell. The rising of temperature is a serious problem of Lithium-Ion batteries and the internal resistance of battery is the main reason for this heating up, so the heat generation rate of the batteries is an important investigating factor in battery pack design. The delivered power of a battery is directly related to its capacity, decreases in the battery capacity means the growth of the Solid Electrolyte Interface (SEI) layer which is because of the deposits of lithium from the electrolyte to form SEI layer that increases the internal resistance of the battery. In this study two identical cylindrical Lithium-Ion (NCR18650)batteries from the same company with noticeable different in capacity (a fresh and a used battery) were compared for more focusing on their heat generation parameters (entropy coefficient and internal resistance) according to Brandi model, by utilizing potentiometric method for entropy coefficient and EIS method for internal resistance measurement. The results clarify the effect of capacity difference on cell electrical (R) and thermal (dU/dT) parameters. It can be very noticeable in battery pack design for its Safety.

Keywords: heat generation, Solid Electrolyte Interface (SEI), potentiometric method, entropy coefficient

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Authors: Saiakhil Chilaka

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Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that supervision risk score, percent days employed, and education level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliation emerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners.

Keywords: juvenile, justice system, data analysis, SHAP

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Authors: Mohsen Torabi, Nader Karimi, Kaili Zhang

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This work aims to provide a comprehensive study on the heat transfer and entropy generation rates of a horizontal channel partially filled with a porous medium which experiences internal heat generation or consumption due to exothermic or endothermic chemical reaction. The focus has been given to the local thermal non-equilibrium (LTNE) model. The LTNE approach helps us to deliver more accurate data regarding temperature distribution within the system and accordingly to provide more accurate Nusselt number and entropy generation rates. Darcy-Brinkman model is used for the momentum equations, and constant heat flux is assumed for boundary conditions for both upper and lower surfaces. Analytical solutions have been provided for both velocity and temperature fields. By incorporating the investigated velocity and temperature formulas into the provided fundamental equations for the entropy generation, both local and total entropy generation rates are plotted for a number of cases. Bifurcation phenomena regarding temperature distribution and interface heat flux ratio are observed. It has been found that the exothermicity or endothermicity characteristic of the channel does have a considerable impact on the temperature fields and entropy generation rates.

Keywords: entropy generation, exothermicity or endothermicity, forced convection, local thermal non-equilibrium, analytical modelling

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Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

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Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

Procedia PDF Downloads 290

Authors: Sadhana Shukla, Pushplata Singh, Nidhi Didwania

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Arbuscular mycorrhizal (AM) fungi are a highly advantageous and versatile group of fungi that significantly contribute to the formation of soil organic matter by creating a demand for plant carbon (C) and distributing it through below-ground hyphal biomass, regardless of their substantial contribution in enhancing net primary productivity and accumulating additional photosynthetic fixed C in the soil. The genetic role of AM fungi in carbon cycling is largely unexplored. In our study, we propose that AM fungi significantly interact with the soil, particularly: the provision of photosynthates by plants. We have studied the expression of AM fungi genes involved in CO₂ sequestration during host-plant interaction was investigated by qPCR studies. We selected Rhizophagus proliferus (AM fungi) and Oryza sativa (Rice) (inoculated with or without 200ppg AMF inoculums per plant) and investigated the effect of AM fungi on soil organic carbon (SOC) and rice growth under field conditions. Results thus provided faster SOC turnover, 35% increased nutrient uptake in plants and pronounced hyphal biomass of AM fungi which enhanced soil carbon storage by 15% in comparison to uninoculated plants. This study will offer a foundation for delving into various carbon-soil studies while also advancing our comprehension of the relationship between AM fungi and the sustainability of agricultural ecosystems.

Keywords: arbuscular mycorrhizal (AM) fungi, carbon sequestration, gene expression, soil health, plant development.

Procedia PDF Downloads 69

Authors: Andreea Campu, Ilinica Muresan, Simona Cainap, Simion Astilean, Monica Focsan

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Acute myocardial infarction (AMI) is the most severe cardiovascular disease, which has threatened human lives for decades, thus a continuous interest is directed towards the detection of cardiac biomarkers such as cardiac troponin I (cTnI) in order to predict risk and, implicitly, fulfill the early diagnosis requirements in AMI settings. Microfluidics is a major technology involved in the development of efficient sensing devices with real-time fast responses and on-site applicability. Microfluidic devices have gathered a lot of attention recently due to their advantageous features such as high sensitivity and specificity, miniaturization and portability, ease-of-use, low-cost, facile fabrication, and reduced sample manipulation. The integration of gold nanoparticles into the structure of microfluidic sensors has led to the development of highly effective detection systems, considering the unique properties of the metallic nanostructures, specifically the Localized Surface Plasmon Resonance (LSPR), which makes them highly sensitive to their microenvironment. In this scientific context, herein, we propose the implementation of a novel detection device, which successfully combines the efficiency of gold bipyramids (AuBPs) as signal transducers and thermal generators with the sample-driven advantages of the microfluidic channels into a miniaturized, portable, low-cost, specific, and sensitive test for the dual LSPR-thermographic cTnI detection. Specifically, AuBPs with longitudinal LSPR response at 830 nm were chemically synthesized using the seed-mediated growth approach and characterized in terms of optical and morphological properties. Further, the colloidal AuBPs were deposited onto pre-treated silanized glass substrates thus, a uniform nanoparticle coverage of the substrate was obtained and confirmed by extinction measurements showing a 43 nm blue-shift of the LSPR response as a consequence of the refractive index change. The as-obtained plasmonic substrate was then integrated into a microfluidic “Y”-shaped polydimethylsiloxane (PDMS) channel, fabricated using a Laser Cutter system. Both plasmonic and microfluidic elements were plasma treated in order to achieve a permanent bond. The as-developed microfluidic plasmonic chip was further coupled to an automated syringe pump system. The proposed biosensing protocol implicates the successive injection inside the microfluidic channel as follows: p-aminothiophenol and glutaraldehyde, to achieve a covalent bond between the metallic surface and cTnI antibody, anti-cTnI, as a recognition element, and target cTnI biomarker. The successful functionalization and capture of cTnI was monitored by LSPR detection thus, after each step, a red-shift of the optical response was recorded. Furthermore, as an innovative detection technique, thermal determinations were made after each injection by exposing the microfluidic plasmonic chip to 785 nm laser excitation, considering that the AuBPs exhibit high light-to-heat conversion performances. By the analysis of the thermographic images, thermal curves were obtained, showing a decrease in the thermal efficiency after the anti-cTnI-cTnI reaction was realized. Thus, we developed a microfluidic plasmonic chip able to operate as both LSPR and thermal sensor for the detection of the cardiac troponin I biomarker, leading thus to the progress of diagnostic devices.

Keywords: gold nanobipyramids, microfluidic device, localized surface plasmon resonance detection, thermographic detection

Procedia PDF Downloads 127