Search results for: thermal cycling machine

Authors: Hossein Bakhtiari, Mathias Cehlin, Jan Akander

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Envelopes with low thermal performance is a common characteristic in many European historic buildings which leads to higher energy demand for heating and cooling as well as insufficient thermal comfort for the occupants. This paper presents the results of a study on the thermal comfort in the City Hall (Rådhuset) in Gävle, Sweden. This historic building is currently used as an office building. It is equipped with two relatively modern mechanical heat recovery ventilation systems with displacement ventilation supply devices in the offices. The district heating network heats the building via pre-heat supply air and radiators. Summer cooling comes from an electric heat pump that rejects heat into the exhaust ventilation air. A building management system controls HVAC equipment (heating, ventilation and air conditioning). The methodology is based on on-site measurements, data logging on the management system and evaluating the occupants’ perception of a summer and a winter period indoor environment using a standardized questionnaire. The main aim of the study is to investigate whether or not it is enough to have modernized HVAC systems to get adequate thermal comfort in a historic building with poor envelope performance used as an office building in Nordic climate conditions.

Keywords: historic buildings, on-site measurements, standardized questionnaire, thermal comfort

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Authors: Ma Chaoyang

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This article starts from the perspective of environmental justice theory and analyzes the accessibility and regional equity of park green spaces in the central urban area of Chengdu in 2022 based on the improved Gaussian 2SFCA analysis method and Gini coefficient method. Then, according to the relevant analysis model, it further explores the correlation between the spatial distribution of park green spaces and the socio-economic conditions of residents in order to provide a reference for the construction and research of Chengdu's park city under the guidance of fairness and justice. The results show that: (1) Overall, the spatial distribution of parks and green spaces in Chengdu shows a significantly uneven distribution of extreme core edge, with a certain degree of unfairness; that is, there is an environmental injustice pattern. (2) The spatial layout of urban parks and green spaces is subject to strong guiding interference from the socio-economic level; that is, there is a high correlation between housing prices and the tendency of parks. (3) Green space resources Gini coefficient analysis shows that residents of the three modes of transportation in the study area have unequal opportunities to enjoy park and green space services, and the degree of unfairness in walking is much greater than that in cycling and cycling.

Keywords: parks and green spaces, environmental justice, two step mobile search method, Gini coefficient, spatial distribution

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Authors: K. Ravi, Sabu Subhash

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Densely compacted bentonite or bentonite–sand mixture has been identified as a suitable buffer in the deep geological repository (DGR) for the safe disposal of high-level nuclear waste (HLW) due to its favourable physicochemical and hydro-mechanical properties. The addition of sand to the bentonite enhances the thermal conductivity and compaction properties and reduces the drying shrinkage of the buffer material. The buffer material may undergo cyclic wetting and drying upon ingress of groundwater from the surrounding rock mass and from evaporation due to high temperature (50–210 °C) derived from the waste canister. The cycles of changes in temperature may result in thermal history, and the hydro-mechanical properties of the buffer material may be affected. This paper examines the influence of thermal history on the undrained shear strength of bentonite and bentonite-sand mixture. Bentonite from Rajasthan state and sand from the Assam state of India are used in this study. The undrained shear strength values are obtained by conducting unconfined compressive strength (UCS) tests on cylindrical specimens (dry densities 1.30 and 1.5 Mg/m3) of bentonite and bentonite-sand mixture consisting of 30 % bentonite+ 70 % sand. The specimens are preheated at temperatures varying from 50-150 °C for one, two and four hours in hot air oven. The results indicate that the undrained shear strength is increased by the thermal history of the buffer material. The specimens of bentonite-sand mixture exhibited more increase in strength compared to the pure bentonite specimens. This indicates that the sand content of the mixture plays a vital role in taking the thermal stresses of the bentonite buffer in DGR conditions.

Keywords: bentonite, deep geological repository, thermal history, undrained shear strength

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Authors: Ahmed Alagha, Belkacem Lamri, Abdelhak Kada.

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Steel-wood assemblies often have complex geometric configurations whose overall behavior under the effect of a fire is conditioned by the thermal response, by combining the two materials steel and wood, whose thermal characteristics are greatly influenced by high temperatures. The objective of this work is to study the thermal behavior of a steel-wood connection, with or without insulating material, subjected to an ISO834 standard fire model. The analysis is developed by the analytical approach using the Eurocode, and numerically, by the finite element method, through the ANSYS calculation code. The design of the connections is evaluated at room temperature taking the cases of single shear and double shear. The thermal behavior of the connections is simulated in transient state while taking into account the modes of heat transfer by convection and by radiation. The variation of temperature as a function of time is evaluated in different positions of the connections while talking about the heat produced and the formation of the carbon layer. The results relate to the temperature distributions in the connection elements as a function of the duration of the fire. The results of the thermal analysis show that the temperature increases rapidly and reaches more than 260 °C in the steel material for an hour of exposure to fire. The temperature development in wood material is different from that in steel because of its thermal properties. Wood heats up on the outside and burns, its surface can reach very high temperatures in points on the surface.

Keywords: Eurocode 5, finite elements, ISO834, simple shear, thermal behaviour, wood-steel connection

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: César Ramírez-Dolores, Jorge Wong-Loya, Jorge Andaverde, Caleb Becerra

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Low-depth geothermal energy can take advantage of the use of the subsoil as an air conditioning technique, being used as a passive system or coupled to an active cooling and/or heating system. This source of air conditioning is possible because at a depth less than 10 meters, the subsoil temperature is practically homogeneous and tends to be constant regardless of the climatic conditions on the surface. The effect of temperature fluctuations on the soil surface decreases as depth increases due to the thermal inertia of the soil, causing temperature stability; this effect presents several advantages in the context of sustainable energy use. In the present work, the thermal behavior of a horizontal Air-Water Heat Exchanger (AWHE) is evaluated, and the thermal effectiveness and temperature of the air at the outlet of the prototype immersed in groundwater is experimentally determined. The thermohydraulic aspects of the heat exchanger were evaluated using the Number of Transfer Units-Efficiency (NTU-ε) method under conditions of groundwater flow in a coastal region of sandy soil (southeastern Mexico) and air flow induced by a blower, the system was constructed of polyvinyl chloride (PVC) and sensors were placed in both the exchanger and the water to record temperature changes. The results of this study indicate that when the exchanger operates in groundwater, it shows high thermal gains allowing better heat transfer, therefore, it significantly reduces the air temperature at the outlet of the system, which increases the thermal effectiveness of the system in values > 80%, this passive technique is relevant for building cooling applications and could represent a significant development in terms of thermal comfort for hot locations in emerging economy countries.

Keywords: convection, earth, geothermal energy, thermal comfort

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Authors: Dewi Hardiningtyas Sugiono

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Concerning towards the quality of air comfort and safety to pedestrians in the University area should be increased as Indonesia economics booming. Hence, the University management needs guidelines of thermal comfort to innovate a new layout building. The objectives of this study is to investigate and then to evaluate the distribution of thermal comfort which is indicated by predicted mean vote (PMV) index at the University of Brawijaya (UB), Malang. The PMV figures are used to evaluate and to redesign the UB layout. The research is started with study literature and early survey to collect all information of building layout and building shape at the University of Brawijaya. The information is used to create a 3D model in CAD software. The model is simulated by Computational Fluid Dynamic (CFD) software to measure the PMV factors of air temperature, relative humidity and air speed in some locations. Validation is done by comparing between PMV value from observation and PMV value from simulation. The resuls of the research shows the most sensitive of microclimatic factors is air temperature surrounding the UB building. Finally, the research is successfully figure out the UB layout and provides further actions to increase the thermal comfort.

Keywords: thermal comfort, heat index (HI), CFD, layout

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Due to increasing cost pressure in global markets, artificial intelligence is becoming a technology that is decisive for competition. Predictive quality enables machinery and plant manufacturers to ensure product quality by using data-driven forecasts via machine learning models as a decision-making basis for test results. The use of cross-process Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: predictive quality, hydraulics, machine learning, classification, supervised learning

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Authors: Vedant Subhash

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This paper discusses the physics of the detection of thermal neutrons using thin-film coated semiconductor detectors. The thermal neutron detection efficiency as a function of film thickness is calculated for the front and back irradiation detector devices coated with ¹⁰B, ⁶LiF, and pure Li thin films. The detection efficiency for back irradiation devices is 4.15% that is slightly higher than that for front irradiation detectors, 4.0% for ¹⁰B films of thickness 2.4μm. The theoretically calculated thermal neutron detection efficiency using ¹⁰B film thickness of 1.1 μm for the back irradiation device is 3.0367%, which has an offset of 0.0367% from the experimental value of 3.0%. The detection efficiency values are compared and proved consistent with the given calculations.

Keywords: detection efficiency, neutron detection, semiconductor detectors, thermal neutrons

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Authors: Roghaieh Parvizsedghy, Seyyed Mojtaba Sadrameli

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Biodiesel as an alternative diesel fuel is steadily gaining more attention and significance. However, there are some drawbacks while using biodiesel regarding its properties that requires it to be blended with petrol based diesel and/or additives to improve the fuel characteristics. This study analyses thermal cracking as an alternative technology to improve biodiesel characteristics in which, FAME based biodiesel produced by transesterification of castor oil is fed into a continuous thermal cracking reactor at temperatures range of 450-500°C and flowrate range of 20-40 g/hr. Experiments designed by response surface methodology and subsequent statistical studies show that temperature and feed flowrate significantly affect the products yield. Response surfaces were used to study the impact of temperature and flowrate on the product properties. After each experiment, the produced crude bio-oil was distilled and diesel cut was separated. As shorter chain molecules are produced through thermal cracking, the distillation curve of the diesel cut fitted more with petrol based diesel curve in comparison to the biodiesel. Moreover, the produced diesel cut properties adequately pose within property ranges defined by the related standard of petrol based diesel. Cold flow properties, high heating value as the main drawbacks of the biodiesel are improved by this technology. Thermal cracking decreases kinematic viscosity, Flash point and cetane number.

Keywords: biodiesel, castor oil, fuel properties, thermal cracking

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Authors: Safia Asif, Saadia Bano

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In a world with rapidly depleting resources, using artificial lighting during daytime is an act of human ignorance. Imitated light is the major source of energy consumption in public buildings. Despite, the fact that substantial working hours of these buildings usually persist in natural daylight time; there is a trend of isolated, un-fenestrated and a-contextual interiors majorly dependent on active energy sources. On the contrary, if direct and un-controlled sunlight is allowed inside the building, it will create visual and thermal discomfort. Controlled daylighting with appropriate design mechanisms is one of the important aspects of achieving thermal and visual comfort. The natural sunlight can be utilized intelligently with the help of architectural thermal controlling mechanisms to achieve a healthy and productive environment. This paper is an attempt to investigate and analyze the importance of daylighting with reference to energy efficiency and thermal comfort. For this purpose, three public buildings including two educational institutions and one general post office are selected, as case-studies in the context of Karachi, Pakistan. Various parameters of visual and thermal comfort are analyzed which includes orientation, ceiling heights, overall building profile along with daylight controlling mechanisms in terms of penetration, distribution, protection, and control. In the later part of the research, a questionnaire survey is also conducted to evaluate the user experience in terms of adequate daylighting and thermal comfort.

Keywords: daylight, public buildings, sustainable architecture, visual and thermal comfort

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Authors: Sujoy Das, M. M. Ghosh

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The thermal conductivity of a fluid can be significantly enhanced by dispersing nano-sized particles in it, and the resultant fluid is termed as "nanofluid". A theoretical model for estimating the thermal conductivity of a nanofluid has been proposed here. It is based on the mechanism that evenly dispersed nanoparticles within a nanofluid undergo Brownian motion in course of which the nanoparticles repeatedly collide with the heat source. During each collision a rapid heat transfer occurs owing to the solid-solid contact. Molecular dynamics (MD) simulation of the collision of nanoparticles with the heat source has shown that there is a pulse-like pick up of heat by the nanoparticles within 20-100 ps, the extent of which depends not only on thermal conductivity of the nanoparticles, but also on the elastic and other physical properties of the nanoparticle. After the collision the nanoparticles undergo Brownian motion in the base fluid and release the excess heat to the surrounding base fluid within 2-10 ms. The Brownian motion and associated temperature variation of the nanoparticles have been modeled by stochastic analysis. Repeated occurrence of these events by the suspended nanoparticles significantly contributes to the characteristic thermal conductivity of the nanofluids, which has been estimated by the present model for a ethylene glycol based nanofluid containing Cu-nanoparticles of size ranging from 8 to 20 nm, with Gaussian size distribution. The prediction of the present model has shown a reasonable agreement with the experimental data available in literature.

Keywords: brownian dynamics, molecular dynamics, nanofluid, thermal conductivity

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Authors: Yongseok Kim, Jeong-Min Lee, Hyunwoo Song, Junghan Yun, Jungin Byun, Jae-Mean Koo, Chang-Sung Seok

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Thermal barrier coating(TBC) is applied for gas turbine components to protect the components from extremely high temperature condition. Since metallic substrate cannot endure such severe condition of gas turbines, delamination of TBC can cause failure of the system. Thus, delamination life of TBC is one of the most important issues for designing the components operating at high temperature condition. Thermal stress caused by thermally grown oxide(TGO) layer is known as one of the major failure mechanisms of TBC. Thermal stress by TGO mainly occurs at the interface between TGO layer and ceramic top coat layer, and it is strongly influenced by the thickness and shape of TGO layer. In this study, Isothermal oxidation is conducted on coin-type TBC specimens prepared by APS(air plasma spray) method. After the isothermal oxidation at various temperature and time condition, the thickness and shape(rumpling shape) of the TGO is investigated, and the test data is processed by numerical analysis. Finally, the test data is arranged into a mathematical prediction model with two variables(temperature and exposure time) which can predict the thickness and rumpling shape of TGO.

Keywords: thermal barrier coating, thermally grown oxide, thermal stress, isothermal oxidation, numerical analysis

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Authors: K. C. Rajan, H. B. Rijal, Kazui Yoshida, Masanori Shukuya

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The electricity consumption in the Japanese household sector has increased with higher rate than that of other sectors. This may be because of aging and information oriented society that requires more electrical appliances to make the life better and easier, under this circumstances, energy saving is one of the essential necessity in Japanese society. To understand the way of energy use and demand response of the residential occupants, it is important to understand the structure of energy used. Home Energy Management System (HEMS) may be used for understanding the pattern and the structure of energy used. HEMS is a visualization system of the energy usage by connecting the electrical equipment in the home and thereby automatically control the energy use in each device, so that the energy saving is achieved. Therefore, the HEMS can provide with the easiest way to understand the structure of energy use. The HEMS has entered the mainstream of the Japanese market. The objective of this study is to understand the pattern of energy saving and cost saving in different regions including Japan during HEMS use. To observe thermal comfort level of HEMS managed residential buildings in Japan, the field survey was made and altogether, 1534 votes from 37 occupants related to thermal comfort, occupants’ behaviors and clothing insulation were collected and analyzed. According to the result obtained, approximately 17.9% energy saving and 8.9% cost saving is possible if HEMS is applied effectively. We found the thermal sensation and overall comfort level of the occupants is high in the studied buildings. The occupants residing in those HEMS buildings are satisfied with the thermal environment and they have accepted it. Our study concluded that the significant reduction in Japanese residential energy use can be achieved by the proper utilization of the HEMS. Better thermal comfort is also possible with the use of HEMS if energy use is managed in a rationally effective manner.

Keywords: energy reduction, thermal comfort, HEMS utility, thermal environment

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Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: B. Arzhanov, A. Correia, P. Delgado, J. Meireles

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Due to the higher power loss levels in electronic components, the thermal design of PCBs (Printed Circuit Boards) of an assembled device becomes one of the most important quality factors in electronics. Nonetheless, some of leading causes of the microelectronic component failures are due to higher temperatures, the leakages or thermal-mechanical stress, which is a concern, is the reliability of microelectronic packages. This article presents an experimental approach to measure the junction temperature of exposed pad packages. The implemented solution is in a prototype phase, using a temperature-sensitive parameter (TSP) to measure temperature directly on the die, validating the numeric results provided by the Mechanical APDL (Ansys Parametric Design Language) under same conditions. The physical device-under-test is composed by a Thermal Test Chip (TTC-1002) and assembly in a QFN cavity, soldered to a test-board according to JEDEC Standards. Monitoring the voltage drop across a forward-biased diode, is an indirectly method but accurate to obtain the junction temperature of QFN component with an applied power range between 0,3W to 1.5W. The temperature distributions on the PCB test-board and QFN cavity surface were monitored by an infra-red thermal camera (Goby-384) controlled and images processed by the Xeneth software. The article provides a set-up to monitorize in real-time the junction temperature of ICs, namely devices with the exposed pad package (i.e. QFN). Presenting the PCB layout parameters that the designer should use to improve thermal performance, and evaluate the impact of voids in solder interface in the device junction temperature.

Keywords: quad flat no-Lead packages, exposed pads, junction temperature, thermal management and measurements

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Authors: Vishnu Pratap Singh Kirar, Madhuri Saxena

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Advancement in sequence data generation technologies is churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. With so much data available, the use of machine learning methods and tools to make novel inferences has become obvious. Machine learning methods have been successfully applied to a lot of disciplines, including computational biology and bioinformatics. Researchers in computational biology are interested to develop novel machine learning frameworks to classify the huge amounts of biological data. In this proposal, it plan to employ novel machine learning approaches to aid the understanding of how apparently innocuous mutations (in intergenic DNA and at synonymous sites) cause diseases. We are also interested in discovering novel functional sites in the genome and mutations in which can affect a phenotype of interest.

Keywords: genome wide association studies (GWAS), next generation sequencing (NGS), deep learning, omics

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Authors: Mohammadreza Akbari, Leila Abdollahpour, Sara Akbari, Pooya Soleimani

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Transferring thermo at the field of solid materials for instance tube-shaped structures, causing dynamical vibration at them. Majority of thermal and fluid processes are done engineering science at solid materials, for example, thermo-transferred pipes, fluids, chemical and nuclear reactors, include thermal processes, so, they need to consider the moment solid-fundamental structural strength unto these thermal interactions. Fluid and thermo retentive materials in front of external force to it like thermodynamical force, hydrodynamical force and static force continuously according to a function of time vibrated, and this action causes relative displacement of the structural materials elements, as a result, the moment resistance analysis preservation materials in thermal processes, the most important parameters for design are discussed. Including structural substrate holder temperature and fluid of the administrative and industrial center, is a cylindrical tube that for vibration analysis of cylindrical cells with heat and fluid transfer requires the use of vibration differential equations governing the structure of a tubular and thermal differential equations as the vibrating motive force at double-glazed cylinders.

Keywords: heat transfer, elements in cylindrical coordinates, analytical solving the governing equations, structural vibration

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Authors: A. Lakrim, D. Tahri

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This paper sets out a behavioral macro-model of a Merged PiN and Schottky (MPS) diode based on silicon carbide (SiC). This model holds good for both static and dynamic electrothermal simulations for industrial applications. Its parameters have been worked out from datasheets curves by drawing on the optimization method: Simulated Annealing (SA) for the SiC MPS diodes made available in the industry. The model also adopts the Analog Behavioral Model (ABM) of PSPICE in which it has been implemented. The thermal behavior of the devices was also taken into consideration by making use of Foster’ canonical network as figured out from electro-thermal measurement provided by the manufacturer of the device.

Keywords: SiC MPS diode, electro-thermal, SPICE model, behavioral macro-model

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Authors: Naveed Ejaz, Liaqat Ali, Amer Nusair

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Thermal barrier coatings (TBCs) serve as thermal barriers against the high temperature of the hot regions of the aircraft turbine engines keeping the surface of the turbine blades, vanes and combustion chamber at comparatively lower temperature. The life of these coatings depends on many in-service environmental factors. Among these factors, the behavior of the bond coat as well as the top coat at high temperature aggravated by the corrosive environments having S, V, Na and Cl plays a key role. The incorporation of the 5-15% CaZrO3 in YSZ coatings was studied after hot corrosion in vanadium oxide environment. It was observed that the reactivity of the V gradually switched from Y to Ca making CaV2O4 instead of YVO4; the percentage of CaV2O4 increased with the increase of CaZrO3 in YSZ. It eventually prevented leaching out of the Y from YSZ leaving the YSZ without any harmful phase change. The thermal insulation was found to be improved in case of CaZrO3 incorporated YSZ coatings as compared to only YSZ coating.

Keywords: hot corrosion, thermal barrier coatings, yttria stabilized zirconia, calcium zirconate

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Authors: Sakti Mandal

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Varying distribution of land surface temperature in an urbanized environment is a globally addressed phenomenon. Usually has been noticed that criticality of surface temperature increases from the periphery to the urban centre. As the centre experiences maximum severity of heat throughout the year, it also represents most critical zone in terms of thermal condition. In this present study, an attempt has been taken to propose a quantitative approach of thermal critical zonation (TCZ) on the basis of seasonal temperature variation. Here the zonation is done by calculating thermal critical value (TCV). From the Landsat 8 thermal digital data of summer and winter seasons for the year 2014, the land surface temperature maps and thermally critical zonation has been prepared, and corresponding dataset has been computed to conduct the overall study of that particular study area. It is shown that TCZ can be clearly identified and analyzed by the help of inter-seasonal temperature range. The results of this study can be utilized effectively in future urban development and planning projects as well as a framework for implementing rules and regulations by the authorities for a sustainable urban development through an environmentally affable approach.

Keywords: thermal critical values (TCV), thermally critical zonation (TCZ), land surface temperature (LST), Landsat 8, Kolkata Municipal Corporation (KMC)

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Authors: Muhammad Bilal, Adil Ahmed

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This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models.

Keywords: predictive maintenance, electrical induction motors, machine learning, temperature signal methodology, motor failures

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Khadija Refouh

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Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

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Authors: Bharath Kenchappa, Kunigal Shivakumar

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Lightweight, low thermal conductivity and high temperature resistant materials or the system with moderate mechanical properties and capable of taking high heating rates are needed in both commercial and military applications. A single material with these attributes is very difficult to find and one needs to come with innovative ideas to make such material system using what is available. To bring down the cost of the system, one has to be conscious about the cost of basic materials. Such a material system can be called as the thermal barrier system. This paper focuses on developing, testing and characterization of material system for thermal barrier applications. The material developed is porous, low density, low thermal conductivity of 0.1062 W/m C and glass transition temperature about 310 C. Also, the thermal properties of the developed material was measured in both longitudinal and thickness direction to highlight the fact that the material shows isotropic behavior. The material is called modified Eco-Core which uses only less than 9% weight of high-char resin in the composite. The filler (reinforcing material) is a component of fly ash called Cenosphere, they are hollow micro-bubbles made of ceramic materials. Special mixing-technique is used to surface coat the fillers with a thin layer of resin to develop a point-to-point contact of particles. One could use commercial ceramic micro-bubbles instead of Cenospheres, but it is expensive. The bulk density of Cenospheres is about 0.35 g/cc and we could accomplish the composite density of about 0.4 g/cc. One percent filler weight of 3mm length standard drywall grade fibers was used to bring the added toughness. Both thermal and mechanical characterization was performed and properties are documented. For higher temperature applications (up to 1,000 C), a hybrid system was developed using an aerogel mat. Properties of combined material was characterized and documented. Thermal tests were conducted on both the bare modified Eco-Core and hybrid materials to assess the suitability of the material to a thermal barrier application. The hybrid material system was found to meet the requirement of the application.

Keywords: aerogel, fly ash, porous material, thermal barrier

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Authors: Veronique Jubera, Guillaume Salek, Manuel Gaudon, Alain Garcia, Alain Demourgues

Abstract:

Luminescence of transition metal and rare earth elements cover ultraviolet to far infrared wavelengths. Applications of phosphors are numerous. One can cite lighting, sensing, laser, energy, medical or military applications. But regarding each domain, specific criteria are required and they can be achieved with a strong control of the chemical composition. Emission of doped materials can be tailored with modifications of the local environment of the cations. For instance, the increase of the crystal field effect shifts the divalent manganese radiative transitions from the green to the red color. External factor as heat-treatment can induce changes of the doping element location or modify the unit cell crystalline symmetry. By controlling carefully the synthesis route, it is possible to initiate emission shift and to establish the thermal history of a compound. We propose to demonstrate through the luminescence of divalent manganese and trivalent rare earth doped oxide, that it is possible to follow the thermal history of a material. After optimization of the synthesis route, structural and optical properties are discussed. Finally, thermal calibration graphs are successfully established on these doped compounds. This makes these materials promising probe for thermal sensing.

Keywords: emission, thermal sensing, transition metal, rare eath element

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Authors: Karen J. Polvado, Betty Bowles, Jansen Lauren, Gibson Martha, Robin Lockhart

Abstract:

The purpose of this study was to identify motivation to participate in the Hotter ‘n Hell Hundred (HHH) bicycling event, and the participants’ demographics, health risk factors, and preparation to ride in the event. A convenience sample of adults pre-registered for non-competitive cycling events (N = 7,472) were requested to complete a survey. Of these, 2,645 (35%) responded. Questions identified the participants’ demographics, preparation, previous experience with HHH, and motives for riding. The HHH attracted riders of all ages (18-80), genders, ethnicities, and educational levels. The majority were males, 40-59 years old, married, college graduates, and identified themselves as non-Hispanic whites. The majority (68%) reported no existing medical conditions, and were normal weight (70%), although 52% had been overweight or obese in the past. Preparation to ride in the HHH varied from riding more than five times a week for the last year, to riding 1-2 times per week one month before the event. Most (93%) had ridden in the HHH an average of 5 times. Motivations to ride included: personal challenge (75%); to experience the HHH ride (57%); a chance to ride with family/friends/coworkers (52%); improving health (47%); fun (33%); challenge by others (15%); part of a weight loss plan (11%); training for another event (10%); and raising money for a cause (2%). The motivation to participate appeared to move from extrinsic to intrinsic motivation as age increased. Exploration of the exercise habits and motivations of older adults (70+) is suggested by this study.

Keywords: cycling, motivation, physical activity, training

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Authors: Golnush Masghati-Amoli, Paul Chin

Abstract:

Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Sakkarin Chingulpitak, Somchai Wongwises

Abstract:

Over the past several decades, the development of electronic devices has led to higher performance. Therefore, an electronic cooling system is important for the electronic device. A heat sink which is a part of the electronic cooling system is continuously studied in the research field to enhance the heat transfer. To author’s best knowledge, there have been only a few articles which reported the thermal performance of plate-fin heat sink with perforation. This research aims to study on the flow and heat transfer characteristics of the solid-fin heat sink (SFHS) and laterally perforated plate-fin heat sink (LAP-PFHS). The SFHS and LAP-PFHSs are investigated on the same fin dimensions. The LAP-PFHSs are performed with a 27 perforation number and two different diameters of circular perforation (3 mm and 5 mm). The experimental study is conducted under various Reynolds numbers from 900 to 2,000 and the heat input of 50W. The experimental results show that the LAP-PFHS with perforation diameter of 5 mm gives the minimum thermal resistance about 25% lower than SFHS. The thermal performance factor which takes into account the ratio of the Nusselt number and ratio of friction factor is used to find the suitable design parameters. The experimental results show that the LAP-PFHS with the perforation diameter of 3 mm provides the thermal performance of 15% greater than SFHS. In addition, the simulation study is presented to investigate the effect of the air flow behavior inside the perforation on the thermal performance of LAP-PFHS.

Keywords: heat sink, parallel flow, circular perforation, non-bypass flow

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Authors: Anastasia Stamatiou, Markus Odermatt, Dominic Leemann, Ludger J. Fischer, Joerg Worlitschek

Abstract:

The successful integration of thermal energy storage in industrial processes is expected to play an important role in the energy turnaround. Latent heat storage technologies can offer more compact thermal storage at a constant temperature level, in comparison to conventional, sensible thermal storage technologies. The focus of this study is the development of latent heat storage solutions based on the Phase Change Slurry (PCS) concept. Such systems promise higher energy densities both as refrigerants and as storage media while presenting better heat transfer characteristics than conventional latent heat storage technologies. This technology is expected to deliver high thermal power and high-temperature stability which makes it ideal for storage of process heat. An evaluation of important batch processes in industrial applications set the focus on materials with a melting point in the range of 55 - 90 °C. Aluminium ammonium sulfate dodecahydrate (NH₄Al(SO₄)₂·12H₂O) was chosen as the first interesting PCM for the next steps of this study. The ability of this material to produce slurries at the relevant temperatures was demonstrated in a continuous mode in a laboratory test-rig. Critical operational and design parameters were identified.

Keywords: esters, latent heat storage, phase change materials, thermal properties

Procedia PDF Downloads 275