Search results for: thermal cycling machine

Authors: Gloria A. Adewumi, Andrew C. Eloka-Eboka, Freddie L. Inambao

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Bio-based carbon nanotubes (CNTs) have received considerable research attention due to their comparative advantages of high level stability, simplistic use, low toxicity and overall environmental friendliness. New potentials for improvement in heat transfer applications are presented due to their high aspect ratio, high thermal conductivity and special surface area. Phonons have been identified as being responsible for thermal conductivities in carbon nanotubes. Therefore, understanding the mechanism of heat conduction in CNTs involves investigating the difference between the varieties of phonon modes and knowing the kinds of phonon modes that play the dominant role. In this review, a reference to a different number of studies is made and in addition, the role of phonon relaxation rate mainly controlled by boundary scattering and three-phonon Umklapp scattering process was investigated. Results show that the phonon modes are sensitive to a number of nanotube conditions such as: diameter, length, temperature, defects and axial strain. At a low temperature (<100K) the thermal conductivity increases with increasing temperature. A small nanotube size causes phonon quantization which is evident in the thermal conductivity at low temperatures.

Keywords: carbon nanotubes, phonons, thermal conductivity, Umklapp process

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Authors: Raul Chavez Jr.

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Green roof's body of knowledge appears to be in its infancy stage in the Philippines. To contribute to its development, this study intends to answer the question: Does the existing green roof in Metro Manila perform well in providing thermal comfort and satisfaction to users? Relatively, this study focuses on thermal sensation and satisfaction of users, surface temperature comparison, weather data comparison of the site (Sky Garden) and local weather station (PAG-ASA), and its thermal resistance capacity. Initially, the researcher conducted a point-in-time survey in parallel with weather data gathering from PAG-ASA and Sky Garden. In line with these, ambient and surface temperature are conducted through the use of a digital anemometer, with humidity and temperature, and non-contact infrared thermometer respectively. Furthermore, to determine the Sky Garden's overall thermal resistance, materials found on site were identified and tabulated based on specified locations. It revealed that the Sky Garden can be considered comfortable based from PMV-PPD Model of ASHRAE Standard 55 having similar results from thermal comfort and thermal satisfaction survey, which is contrary to the actual condition of the Sky Garden by means of a psychrometric chart which falls beyond the contextualized comfort zone. In addition, ground floor benefited the most in terms of lower average ambient temperature and humidity compared to the Sky Garden. Lastly, surface temperature data indicates that the green roof portion obtained the highest average temperature yet performed well in terms of heat resistance compared to other locations. These results provided the researcher valuable baseline information of the actual performance of a certain green roof in Metro Manila that could be vital in locally enhancing the system even further and for future studies.

Keywords: Green Roof, Thermal Analysis, Thermal Comfort, Thermal Performance

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Authors: A. Oukaira, A. Lakhssassi, O. Ettahri

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To make a good thermal management in an ABDM (Adapter Board Detector Module) card, we must first control temperature and its gradient from the first step in the design of integrated circuits ASIC of our complex system. In this paper, our main goal is to develop and validate the thermal stability in order to get an idea of the flow of heat around the ASIC in transient and thus address the thermal issues for integrated circuits at the ABDM card. However, we need heat sources simulations for ABDM card to establish its thermal mapping. This led us to perform simulations at each ASIC that will allow us to understand the thermal ABDM map and find real solutions for each one of our complex system that contains 36 ABDM map, taking into account the different layers around ASIC. To do a transient simulation under NISA, we had to build a function of power modulation in time TIMEAMP. The maximum power generated in the ASIC is 0.6 W. We divided the power uniformly in the volume of the ASIC. This power was applied for 5 seconds to visualize the evolution and distribution of heat around the ASIC. The DBC (Dirichlet Boundary conditions) method was applied around the ABDM at 25°C and just after these simulations in NISA tool we will validate them by COMSOL tool, wich is a numerical calculation software for a modular finite element for modeling a wide variety of physical phenomena characterizing a real problem. It will also be a design tool with its ability to handle 3D geometries for complex systems.

Keywords: ABDM, APD, thermal mapping, complex system

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer Experience Management, Paper Machine , Value Chain Management, Risk Analysis

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Authors: Shanmugasundaram Selvadurai, Amal Chandran

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Small satellites have become an alternative low-cost solution for several missions to accomplish specific missions such as Earth imaging, Technology demonstration, Education, and other commercial purposes. Small satellite missions focusing on Infrared imaging applications require lower temperature for scientific instruments and such low temperature can be achieved only using external cryocoolers but the disadvantage is that they generate a large amount of waste heat. Existing passive thermal control techniques are not capable to handle such large thermal loads and hence one of the traditional active Thermal Control System (TCS) is studied for a small satellite configuration. This work aims to downscale the existing Mechanically Pumped Fluid Loop (MPFL) TCS to a 27U CubeSat platform for an imaginary scientific instrument. The temperature-sensitive detector in the instrument considered to be maintained between 130K and 150K to reduce dark current noise and increase the data quality. A Single-Phase fluid based MPFL is chosen for this system-level study and this TCS consists of a microfluid pump, a micro-cryocooler, a fluid accumulator, external heaters, flow regulators, and sensors. This work also explains the thermal control system architecture with a conceptual design, arrangement of all the components, and thermal analysis for different low orbit conditions. Sizing and extensive trade studies for the components are conducted and the results have shown that the Single-phase MPFL system is able to handle the given thermal loads and maintain the satellite’s interface temperature within the desired limit.

Keywords: active thermal control system, satellite thermal, mechanically pumped fluid loop system, cryogenics, cryocooler

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Authors: Jenq-Shyong Chen, Ben-Fong Yu

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CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system.

Keywords: auto-tuning, CNC parameters, machining mode, high speed, high accuracy, high surface quality

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Authors: Shibei Huang

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In deep energy retrofits, the thermal performance of existing building envelopes is often difficult to determine with a high level of accuracy. For older buildings, the records of existing assemblies are often incomplete or inaccurate. To obtain greater baseline performance accuracy for energy models, in-field measurement tools can be used to obtain data on the thermal performance of the existing assemblies. For a known assembly, these field measurements assist in validating the U-factor estimates. If the field-measured U-factor consistently varies from the calculated prediction, those measurements prompt further study. For an unknown assembly, successful field measurements can provide approximate U-factor evaluation, validate assumptions, or identify anomalies requiring further investigation. Using case studies, this presentation will focus on the non-destructive methods utilizing a set of various field tools to validate the baseline U-factors for a range of existing buildings with various wall assemblies. The lessons learned cover what can be achieved, the limitations of these approaches and tools, and ideas for improving the validity of measurements. Key factors include the weather conditions, the interior conditions, the thermal mass of the measured assemblies, and the thermal profiles of the assemblies in question.

Keywords: existing building, sensor, thermal analysis, retrofit

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Authors: Romeo Malik, Yashraj Tripathy, Anup Barai

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Transportation safety is a major concern for vehicle electrification on a large-scale. The failure cost of lithium-ion batteries is substantial and is significantly impacted by higher liability and replacement cost. With continuous advancement on the material front in terms of higher energy density, upgrading safety characteristics are becoming more crucial for broader integration of lithium-ion batteries. Understanding and impeding thermal runaway is the prime issue for battery safety researchers. In this study, a comprehensive comparison of thermal runaway mechanisms for two different cathode types, Li(Ni₀.₃Co₀.₃Mn₀.₃)O₂ and Li(Ni₀.₈Co₀.₁₅Al₀.₀₅)O₂ is explored. Both the chemistries were studied for different states of charge, and the various abuse scenarios that lead to thermal runaway is investigated. Abuse tests include mechanical abuse, electrical abuse, and thermal abuse. Batteries undergo thermal runaway due to a series of combustible reactions taking place internally; this is observed as multiple jets of flame reaching temperatures of the order of 1000ºC. The physicochemical characterisation was performed on cells, prior to and after abuse. Battery’s state of charge and chemistry have a significant effect on the flame temperature profiles which is otherwise quantified as heat released. Majority of the failures during transportation is due to these external short circuit. Finally, a mitigation approach is proposed to impede the thermal runaway hazard. Transporting lithium-ion batteries under low states of charge is proposed as a way forward. Batteries at low states of charge have demonstrated minimal heat release under thermal runaway reducing the risk of secondary hazards such as thermal runaway propagation.

Keywords: battery reliability, lithium-ion batteries, thermal runaway characterisation, tomography

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Petr Mohyla, Ivo Hlavatý, Jiří Hrubý, Lucie Krejčí

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This work is focused on mechanical properties and microstructure of heat affected zone (HAZ) of steel P92. The thermal cycle simulator was used for modeling a fine grained zone of HAZ. Hardness and impact toughness were measured on simulated samples. Microstructural analysis using optical microscopy was performed on selected samples. Achieved results were compared with the values of a real welded joint. The thermal cycle simulator allows transferring the properties of very small HAZ to the sufficiently large sample where the tests of the mechanical properties can be performed. A satisfactory accordance was found when comparing the microstructure and mechanical properties of real welds and simulated samples.

Keywords: heat affected zone, impact test, thermal cycle simulator, time of tempering

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Authors: Sadia Rafiq, Md. Ashab Siddique Zaki, Ananya Roy

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Mountain cycling is a type of off-road bicycle racing that typically takes place on rocky, arid, or other challenging terrains on specially-made mountain cycles. Professional cyclists race while attempting to stay on their bikes in a variety of locales across the world. For safety measures in mountain cycling, as there we have a high chance of injury in case of tire puncture, it’s a preferable way to use an airless tire instead of a pneumatic tire. As airless tire does not tend to go flat, it needs to be replaced less frequently. The airless tire replaces the pneumatic tire, wheel, and tire system with a single unit. It consists of a stiff hub connected to a shear band by flexible, pliable spokes, which is made of poly-composite and a tread band, all of which work together as a single unit to replace all of the components of a normal radial tire. In this paper, an analysis of airless tires in the mountain cycle is shown along with structure and material study. We will be taking the Honeycomb and Diamond Structure of spokes to compare the deformation in both cases and choose our preferable structure. As we know, the tread and spokes deform with the surface roughness and impact. So, the tire tread thickness and the design of spokes can control how much the tire can distort. Through the simulation, we can come to the conclusion that the diamond structure deforms less than the honeycomb structure. So, the diamond structure is more preferable.

Keywords: airless tire, diamond structure, honeycomb structure, deformation

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: M. E. Ali Mohsin, Agus Arsad, Othman Y. Alothman

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This study explains the influence of secondary filler on the dispersion of carbon nanotube (CNT) reinforced high density polyethylene (HDPE) nanocomposites (CNT/HDPE). In order to understand the mixed-fillers system, Montmorillonite (MMT) was added to CNT/HDPE nanocomposites. It was followed by investigating their effect on the thermal, mechanical and morphological properties of the aforesaid nanocomposite. Incorporation of 3 wt% each of MMT into CNT/HDPE nanocomposite resulted to the increased values for the tensile and flexural strength, as compared to the pure HDPE matrix. The thermal analysis result showed improved thermal stability of the formulated nanocomposites. Transmission electron microscopy (TEM) images revealed that larger aggregates of CNTs were disappeared upon addition of these two components leading to the enhancement of thermo-mechanical properties for such composites.

Keywords: secondary filler, montmorillonite, carbon nanotube, nanocomposite

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Authors: Chargui Ridha, Agrebi Sameh

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The integration of phase change materials (PCM) for the storage of thermal energy during the period of sunshine before being released during the night is a complement of free energy to improve the system formed by a solar collector, tank storage, and a heat exchanger. This paper is dedicated to the design of a thermal storage tank based on a PCM-based heat exchanger. The work is divided into two parts: an experimental part using paraffin as PCM was carried out within the Laboratory of Thermal Processes of Borj Cedria in order to improve the performance of the system formed by the coupling of a flat solar collector and a thermal storage tank and to subsequently determine the influence of PCM on the whole system. This phase is based on the measurement instrumentation, namely, a differential scanning calorimeter (DSC) and the thermal analyzer (hot disk: HOT DISK) in order to determine the physical properties of the paraffin (PCM), which has been chosen. The second phase involves the detailed design of the PCM heat exchanger, which is incorporated into a thermal storage tank and coupled with a solar air collector installed at the Research and Technology Centre of Energy (CRTEn). A numerical part based on the TRANSYS and Fluent software, as well as the finite volume method, was carried out for the storage reservoir systems in order to determine the temperature distribution in each chosen system.

Keywords: phase change materials, storage tank, heat exchanger, flat plate collector

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Authors: Seok-In Bae, Young-Hoon Lee, Ji-Hwan Kim

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Due to the asymmetry of the material properties of the Functionally Graded Materials(FGMs) in the thickness direction, neutral surface of the model is not the same as the mid-plane of the symmetric structure. In order to investigate the thermal bucking behavior of FGMs, neutral surface is chosen as a reference plane. In the model, material properties are assumed to be temperature dependent, and varied continuously in the thickness direction of the plate. Further, the effective material properties such as Young’s modulus and Poisson’s ratio are homogenized using Mori-Tanaka scheme which considers the interaction among adjacent inclusions. In this work, the finite element methods are used, and the first-order shear deformation theory of plate are accounted. The thermal loads are assumed to be uniform, linear and non-linear distribution through the thickness directions, respectively. Also, the effects of various parameters for thermal buckling behavior of FGM panel are discussed in detail.

Keywords: functionally graded plate, thermal buckling analysis, neutral surface

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Authors: Mohamed Saïd Kahaleras, François Lanzetta, Mohamed Khan, Guillaume Layes, Philippe Nika

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This paper presents an experimental work to characterize the dynamic operation of a metal regenerator crossed by dry compressible air alternating flow. Unsteady dynamic measurements concern the pressure, velocity and temperature of the gas at the ends and inside the channels of the regenerator. The regenerators are tested under isothermal conditions and thermal axial temperature gradient.

Keywords: friction factor, oscillating flow, regenerator, stirling machine

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Authors: S. Dorbani, F. Kharchi, F. Salem, K. Arroudj, N. Chioukh

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The need for thermal comfort of buildings, with the aim of saving energy, has always generated a big interest during the development of methods, to improve the mode of construction. In the present paper, which is concerned by the valorization of locally abundant materials, mixtures of plaster and dune sand have been studied. To point out the thermal performances of these mixtures, a comparative study has been established between this product and the two materials most commonly used in construction, the concrete and hollow brick. The results showed that optimal mixture is made with 1/3 plaster and 2/3 dune sand. This mortar achieved significant increases in the mechanical strengths, which allow it to be used as a carrier element for buildings, of up to two levels. The element obtained offers an acceptable thermal insulation, with a decrease the outer-wall construction thickness.

Keywords: local materials, mortar, plaster, dune sand, compaction, mechanical performance, thermal performance

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Authors: S. El-Azzeh, A. Al-Aqqad, M. Salem, H. Al-Khaldi, S. Thaher

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Thermal comfort studies are very important during the early stages of the building’s design. If this study was ignored, problems will start to occur for the occupants in the future. In hot climates, where solar radiations are entering buildings all year long, occupant’s thermal comfort in office buildings needs to be examined. This study aims to investigate the thermal comfort at an existing office building at the Australian College of Kuwait and test its validity and improve occupant’s thermal satisfaction by covering windows with a heat rejection tint material that enables sunlight to pass through the office while reflecting solar heat outside. Environmental variables were measured using thermal comfort data logger INNOVA 1221 to find the predicted mean vote (PMV) in the selected location. Also, subjective variables were measured to find the actual mean vote (AMV) through surveys distributed among occupants in the selected case study office. All the variables collected were analyzed and classified according to international standards ISO 7730 and ASHRAE55. The results of this study showed improvement in both PMV and AMV. The mean value of PMV based on the original design was 0.691 which dropped to 0.32 after installation and it still at comfort zone. Also, the mean value of the AMV has improved for the first occupant, where before it was -0.46 and it became -1 which is cooler. For the other occupant, it was slightly warm with a mean value of 0.9 and it was improved and became cooler with a -0.25 mean value based on American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) seven-point scale.

Keywords: thermal comfort, office buildings, indoor environments, predicted mean vote

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Authors: Mark-Adam Kellerman

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Proteins are capable of exploring vast mutational spaces. This makes it difficult for protein engineers to devise rational methods to improve stability and function via mutagenesis. Deciding which residues to mutate requires knowledge of the characteristics they elicit. We probed the characteristics of residues in granulocyte-colony stimulating factor (G-CSF) using a thermal melt (from 295K to 323K) to denature it in a 700 MHz Bruker spectrometer. These characteristics included dynamics, micro-environmental changes experienced/ induced during denaturing and structure-function relationships. 15N-1H HSQC experiments were performed at 2K increments along with this thermal melt. We observed that dynamic residues that also undergo a lot of change in their microenvironment were predominantly in unstructured regions. Moreover, we were able to identify four residues (G4, A6, T133 and Q134) that we class as high priority targets for mutagenesis, given that they all appear in both the top 10% of measures for environmental changes and dynamics (∑Δ and ∆PI). We were also able to probe these NMR observables and combine them with molecular dynamics (MD) to elucidate what appears to be an opening motion of G-CSFs binding site III. V48 appears to be pivotal to this opening motion, which also seemingly distorts the loop region between helices A and B. This observation is in agreement with previous findings that the conformation of this loop region becomes altered in an aggregation-prone state of G-CSF. Hence, we present here an approach to profile the characteristics of residues in order to highlight their potential as rational mutagenesis targets and their roles in important conformational changes. These findings present not only an opportunity to effectively make biobetters, but also open up the possibility to further understand epistasis and machine learn residue behaviours.

Keywords: protein engineering, rational mutagenesis, NMR, molecular dynamics

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Authors: Ashis Mallick, Rajeev Ranjan

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The closed form solution for thermal stresses in an annular fin with hyperbolic profile is derived using Adomian decomposition method (ADM). The conductive-convective fin with variable thermal conductivity is considered in the analysis. The nonlinear heat transfer equation is efficiently solved by ADM considering insulated convective boundary conditions at the tip of fin. The constant of integration in the solution is to be estimated using minimum decomposition error method. The solution of temperature field is represented in a polynomial form for convenience to use in thermo-elasticity equation. The non-dimensional thermal stress fields are obtained using the ADM solution of temperature field coupled with the thermo-elasticity solution. The influence of the various thermal parameters in temperature field and stress fields are presented. In order to show the accuracy of the ADM solution, the present results are compared with the results available in literature. The stress fields in fin with hyperbolic profile are compared with those of uniform thickness profile. Result shows that hyperbolic fin profile is better choice for enhancing heat transfer. Moreover, less thermal stresses are developed in hyperbolic profile as compared to rectangular profile. Next, Nelder-Mead based simplex search method is employed for the inverse estimation of unknown non-dimensional thermal parameters in a given stress fields. Owing to the correlated nature of the unknowns, the best combinations of the model parameters which are satisfying the predefined stress field are to be estimated. The stress fields calculated using the inverse parameters give a very good agreement with the stress fields obtained from the forward solution. The estimated parameters are suitable to use for efficient and cost effective fin designing.

Keywords: Adomian decomposition, inverse analysis, hyperbolic fin, variable thermal conductivity

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

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The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

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Authors: Francisco J. Arias

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Consideration is given to a mechanism of heat and mass transport in solutions similar than that of natural convection but with one important difference. Here the mechanism is not promoted by density differences in the fluid occurring due to temperature gradients (coefficient of thermal expansion) but rather by solubility differences due to the thermal dependence of the solubility (coefficient of thermal solubility). Utilizing a simplified physical model, it is shown that by the proper choice of the concentration of a given solution, convection might be induced by the alternating precipitation of the solute -when the solution becomes supersaturated, and its posterior recombination when changes in temperature occurs. The spontaneous change in the Gibbs free energy during the mixing is the driven force for the mechanism. The maximum extractable energy from this new type of thermal convection was derived. Experimental data from a closed-loop circuit was obtained demonstrating the feasibility for continuous separation and recombination of the solution. This type of heat and mass transport -which doesn’t depend on gravity, might potentially be interesting for heat and mass transport downwards (as in solar-roof collectors to inside homes), horizontal (e.g., microelectronic applications), and in microgravity (space technology). Also, because the coefficient of thermal solubility could be positive or negative, the investigated thermo-osmosis convection can be used either for heating or cooling.

Keywords: natural convection, thermal gradient, solubility, osmotic pressure

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: R. Akbari, M. A. Ehyaei, R. Shahi Shavvon

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Nowadays, solar energy is used for energy purposes such as the use of thermal energy for domestic, industrial and power applications, as well as the conversion of the sunlight into electricity by photovoltaic cells. In this study, the thermodynamic simulation of the solar Rankin cycle with phase change material (paraffin) was first studied. Then energy and exergy analyses were performed. For optimization, a single and multi-objective genetic optimization algorithm to maximize thermal and exergy efficiency was used. The parameters discussed in this paper included the effects of input pressure on turbines, input mass flow to turbines, the surface of converters and collector angles on thermal and exergy efficiency. In the organic Rankin cycle, where solar energy is used as input energy, the fluid selection is considered as a necessary factor to achieve reliable and efficient operation. Therefore, silicon oil is selected for a high-temperature cycle and water for a low-temperature cycle as an operating fluid. The results showed that increasing the mass flow to turbines 1 and 2 would increase thermal efficiency, while it reduces and increases the exergy efficiency in turbines 1 and 2, respectively. Increasing the inlet pressure to the turbine 1 decreases the thermal and exergy efficiency, and increasing the inlet pressure to the turbine 2 increases the thermal efficiency and exergy efficiency. Also, increasing the angle of the collector increased thermal efficiency and exergy. The thermal efficiency of the system was 22.3% which improves to 33.2 and 27.2% in single-objective and multi-objective optimization, respectively. Also, the exergy efficiency of the system was 1.33% which has been improved to 1.719 and 1.529% in single-objective and multi-objective optimization, respectively. These results showed that the thermal and exergy efficiency in a single-objective optimization is greater than the multi-objective optimization.

Keywords: exergy analysis, genetic algorithm, rankine cycle, single and multi-objective function

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Authors: Gilbert O. Osayemwenre, Meyer L. Edson

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Ethylene vinyl acetate (EVA) encapsulation degradation affects the performance of photovoltaic (PV) module. Hotspot formation causes the EVA encapsulation to undergo photothermal deterioration and molecular breakdown by UV radiation. This leads to diffusion of chemical particles into other layers. During outdoor deployment, the EVA encapsulation in the affect region loses its adhesive strength, when this happen the affected region layer undergoes rapid delamination. The presence of photo-thermal degradation is detrimental to PV modules as it causes both optical and thermal degradation. Also, it enables the encapsulant to be more susceptible to chemicals substance and moisture. Our findings show a high concentration of Sodium, Phosphorus and Aluminium which originate from the glass substrate, cell emitter and back contact respectively.

Keywords: ethylene vinyl acetate (EVA), encapsulation, photo-thermal degradation, thermogravimetric analysis (TGA), scanning probe microscope (SPM)

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Vincenzo Capalbo, Gianpaolo Ghiani, Emanuele Manni

Abstract:

Global e-commerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the ’20s, new challenges will be faced by companies when planning and controlling e-commerce logistics. In this paper, we survey the related literature on Optimization and Machine Learning as well as on combined methodologies. We also identify the distinctive features of next-generation planning algorithms - namely scalability, model-and-run features and learning capabilities - that will be fundamental to cope with the scale and complexity of logistics in the next decade.

Keywords: e-commerce, hardware acceleration, logistics, machine learning, mixed integer programming, optimization

Procedia PDF Downloads 212

Authors: Munir Tasdemir

Abstract:

Polymer/clay composites have received great attention in the past three decades owing to their light weight coupled with significantly better mechanical and barrier properties than the corresponding neat polymer resins. An investigation was carried out on the effects of red mud powder size and ratio on the mechanical and thermal properties of polypropylene /red mud polymer composites. Red mud, in four different concentrations (0, 10, 20 and 30 wt %) and three different powder size (180, 63 and 38 micron) were added to PP to produce composites. The mechanical properties, including the elasticity modulus, tensile & yield strength, % elongation, hardness, Izod impact strength and the thermal properties including the melt flow index, heat deflection temperature and vicat softening point of the composites were investigated. The structures of the composites were investigated by scanning electron microscopy and compared to mechanical and thermal properties as a function of red mud powder content and size.

Keywords: polypropylene, powder, red mud, mechanical properties

Procedia PDF Downloads 306