Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
Browse
13 results
Search Results
Publication Metadata only CFD simulation of a shell and tube heat exchanger(Avestia Publishing, 2020) Pamuk, Mehmet Turgay; Qiu, H.; Zhang, Y.; Pamuk, Mehmet Turgay, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this numerical work, CFD simulation of a Shell and Tube Heat Exchanger is performed using the commercial software ANSYS Fluent. The CFD model is made to grab all the physical phenomena such as heat transfer rating, temperature, velocity and pressure distributions within the computational domain. A sufficiently high detailed geometry and fine mesh characteristics are adopted taking into account the computation resources and time, yet satisfactory enough to show that the numerical model can be validated using the results obtained from the calculations based on the heat transfer formulas, correlations, tables and graphs. This gives researches working in Heat Exchanger area the opportunity to design their systems using CFD, without depending on a prototype that needs to be tested before the actual product is marketed. Normally, a new type of heat exchanger with specific shell and tube dimensions and number of tubes has to be manufactured using general a heat exchanger calculation approach that will most of the time require the revision of the preliminary design. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only Hygrothermal performance analysis of traditional timber-framed houses in Turkey(WITPress, 2020) Alkan, Seda Nur; Yazicioǧlu, Fatih; Hernández, S.; Chias, P.; Alkan, Seda Nur, Faculty of Architecture, İstanbul Teknik Üniversitesi, Istanbul, Turkey, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yazicioǧlu, Fatih, Faculty of Architecture, İstanbul Teknik Üniversitesi, Istanbul, TurkeyThe study aims to analyse hygrothermal performance of traditional timber-framed houses in order to discuss building skins’ energy efficiency. There are numerous types of traditional timber-framed houses in Anatolia depending on social, cultural and regional features. Within the scope of this paper, the traditional architectural features of Safranbolu County of Turkey is selected as the case study. Safranbolu is placed in the Western Black Sea region, where specific examples of traditional timber-framed constructions with adobe infill are housed. As a significant example, hımış is a hybrid construction of stone masonry walls on ground level, and infilled timber-frame walls in upper levels. The construction of hımış is organized by rectangular studs of pinewood, and infill material of adobe. Wooden diagonals, which cope with the dynamic loads, especially earthquake, support the system and provide long-term durability for these buildings. In this study, hımış construction wall type with adobe infill is selected. The selected example is simulated by Delphine 6.0 for the evaluation of hygrothermal performance. Considering energy efficiency, hygrothermal performance is investigated by heat transfer and moisture control in order to raise awareness of traditional timber-framed capacities. The main purpose to focus on hygrothermal performance analysis is to integrate indigenous knowledge into contemporary architecture regarding social, economic and cultural characteristics in a sustainable manner. The expected outcome of this study is the determination of the selected type of traditional timber-framed buildings’ hygrothermal performance. © 2021 Elsevier B.V., All rights reserved.Publication Open Access A Deep CNN Model for Skin Cancer Detection and Classification(Vaclav Skala Union Agency, 2021) Junayed, Masum Shah; Anjum, Nipa; Sakib, Abu Noman Md; Islam, Md Baharul; Skala, V.; Junayed, Masum Shah, Bahçeşehir Üniversitesi, Istanbul, Turkey; Anjum, Nipa, Khulna University of Engineering and Technology, Khulna, Bangladesh; Sakib, Abu Noman Md, Khulna University of Engineering and Technology, Khulna, Bangladesh; Islam, Md Baharul, American University of Malta, Cospicua, MaltaSkin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection of skin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-based methods help dermatologists in classifying medical images. This paper proposes a deep learning-based model to detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, we collected a dataset that includes four skin cancer image data before applying them in augmentation techniques to increase the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data, our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNet by 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while being computationally comparable. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Numerical Investigation of the Effects of Baffles in a Shell and Tube Heat Exchanger(Avestia Publishing, 2021) Pamuk, Mehmet Turgay; Qiu, H.; Zhang, Y.; Pamuk, Mehmet Turgay, Bahçeşehir Üniversitesi, Istanbul, TurkeyThis numerical study examines the effects of the baffles added to the fluid (oil) flow path to be cooled in a shell and tube heat exchanger. The heat transfer rate in the case without baffles is compared to the cases where the baffles are added in the oil flow path. The comparison shows a remarkable increase in heat transfer, but on the other hand leads to higher pumping costs due to a higher pressure loss. CFD simulation can optimize the design of shell and tube heat exchangers by changing the number, size and position of the baffles. The CFD simulation package used here is the commercial software ANSYS Fluent©. The CFD model contains all details of the geometric and material properties, so that the solution reflects all physical phenomena such as rate of heat transfer, temperature, velocity and pressure distributions within the computational domain. The approach used here provides heat exchanger manufacturers with valuable design information without having to produce prototypes that are based not only on expensive, but also on time-consuming trial and error methods. © 2022 Elsevier B.V., All rights reserved.Publication Open Access Analytical Modeling of AIN-Based Film Bulk Acoustic Wave Resonator for Hydrogen sulfide Gas detection Based on PiezoMUMPs(IOP Publishing Ltd, 2021) Ba-Hashwan, Saeed Salem; Md Khir, Masrur Haris; Al-Douri, Yaroub K.; Ahmed, Abdelaziz Yousif; Algamili, Abdullah; Alabsi, Sami Sultan; Junaid, Mohammed M.; Ba-Hashwan, Saeed Salem, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Md Khir, Masrur Haris, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Al-Douri, Yaroub K., Universiti Malaya, Kuala Lumpur, Malaysia, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ahmed, Abdelaziz Yousif, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Algamili, Abdullah, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Alabsi, Sami Sultan, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Junaid, Mohammed M., Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaAluminium nitride (AIN) thin film bulk acoustic resonator (FBAR) sensor for hydrogen sulfide gas detection has been designed and mathematically modelled using CoventorWare and MATLAB software, respectively. The designed FBAR sensor is based on the PiezoMUMPs fabrication technology. The detection principle of the FBAR gas sensor is based on the resonant frequency changes detection due to the mass change on the top electrode of the sensor induced by the absorbed gas molecules by the nanomaterial deposited on the surface of the top electrode device. Reduced graphene oxide hybrid with copper oxide was considered as the sensitive nanomaterials and their mass loaded was evaluated in the theoretically calculation. The resonant frequency of the shear mode of the FBAR sensor has been calculated theoretically and found to be 9.4524 GHz. The effects of the gas molecules on the resonant frequency have been investigated using a mathematical equation and it shown that the increasing of the gas mass on the sensor surface will reduce the sensor resonant frequency. Furthermore, the sensitivity of the sensor was calculated to be 0.22615 Hz/fg. © 2021 Elsevier B.V., All rights reserved.Publication Open Access Adapting sustainability and energy efficiency principles to architectural education: A conceptual model proposal for the design studio sequence(EDP Sciences, 2021) Ceylan, Salih; Tan, Z.; Liu, L.; Ceylan, Salih, Faculty of Architecture and Design, Bahçeşehir Üniversitesi, Istanbul, TurkeyArchitectural education is the first step into the professional career of an architect. It has strong connections with the profession itself regarding the technological trends and the needs of the society. Therefore, emerging challenges and developments in the world of architecture like environmental problems and sustainability issues need to be addressed by the educational programs of architecture. In addition to this, sustainable Development Goals (SDG) Program of United Nations puts an important responsibility on the shoulders of architectural educators. SDG includes both architectural and educational goals such as quality education, affordable and clean energy, sustainable cities and communities, and climate action. Accordingly, architectural education needs to be formed in a way to respond to the requirements of the contemporary global society. Design studio is the heart and core of architectural education. It is the place where all the theoretical and technical knowledge and skills gained in other courses become useful for the students to come up with design ideas and products. Additionally, design studios are not isolated environments. They form a series of courses by the consecutive repetition throughout the continuum of architectural education. Thus, they need to be treated as a developing sequence. Therefore, it is very important and valuable that the structure of the design studio sequence is improved and updated with suitable revisions towards emerging needs of the profession and society like the adaptation of sustainability principles, to reflect the dynamic character of architectural education itself. This paper presents a conceptual model proposal for the design studio sequence for the adaptation of sustainability principles to architectural education. © 2023 Elsevier B.V., All rights reserved.Publication Open Access SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(Institute of Electrical and Electronics Engineers Inc., 2022) Hmitti, Zakaria Ait; Ben-Ammar, Hamza Haj; Gelal, Ece; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza R.; Ajib, Wessam; Hmitti, Zakaria Ait, Université du Québec à Montréal, Montreal, Canada; Ben-Ammar, Hamza Haj, La Rochelle Université, La Rochelle, France; Gelal, Ece, Sabancı Üniversitesi, Tuzla, Turkey, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kardjadja, Youcef, La Rochelle Université, La Rochelle, France; Malektaji, Sepideh, Concordia University, Montreal, Canada; Ali, Soukaina Ouledsidi, Université du Québec à Montréal, Montreal, Canada; Rayani, Marsa, Concordia University, Montreal, Canada; Saqib, Muhammad, Université du Québec à Montréal, Montreal, Canada; Taghizadeh, Seyedreza R., Université du Québec à Montréal, Montreal, Canada; Ajib, Wessam, Université du Québec à Montréal, Montreal, CanadaThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees. © 2022 Elsevier B.V., All rights reserved.Publication Open Access Symposium on queering international law doing queer in the everyday of academia: Reflections on queering a conference in international law(Cambridge University Press, 2022) Schramm, Bérénice K.; de Carvalho, Juliana Santos; Holzer, Lena; Beury, Manon; Schramm, Bérénice K., Bahçeşehir Üniversitesi, Istanbul, Turkey; de Carvalho, Juliana Santos, Graduate Institute of International and Development Studies, Geneve, Switzerland; Holzer, Lena, Graduate Institute of International and Development Studies, Geneve, Switzerland; Beury, Manon, Graduate Institute of International and Development Studies, Geneve, Switzerland[No abstract available]Publication Open Access Assessing Dyslexia with Machine Learning: A Pilot Study Utilizing Google ML Kit(Institute of Electrical and Electronics Engineers Inc., 2023) Eroğlu, Günet; Harb, Mhd Raja Abou; Eroğlu, Günet, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Harb, Mhd Raja Abou, Department of Computer Engineering, Işik Üniversitesi, Istanbul, TurkeyIn this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 typically developing children. Using Google ML Kit and other machine learning algorithms based on eye-tracking data, we compared their performance in dyslexia detection. Our findings reveal that Google ML Kit achieved the highest accuracy among the tested methods. This study underscores the potential of machine learning-based dyslexia detection and its practicality in academic and clinical settings. © 2024 Elsevier B.V., All rights reserved.Publication Open Access Detection of Misinformation Related to Pandemic Diseases Using Machine Learning(Springer Science and Business Media Deutschland GmbH, 2024) Naeem, Javaria; Gul, Omer Melih; Parlak, Ismail Burak; Karpouzis, Kostas C.; Kadry, Seifedine Nimer; Salman, Yücel Batu; Gül, O.M.; Fiorini, P.; Kadry, S.N.; Naeem, Javaria, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gul, Omer Melih, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Parlak, Ismail Burak, Department of Computer Engineering, Galatasaray Üniversitesi, Istanbul, Turkey; Karpouzis, Kostas C., Panteion University of Social and Political Sciences, Athens, Greece; Kadry, Seifedine Nimer, Department of Applied Data Science, Noroff College, Kristiansand, Norway, Ajman University, Ajman, United Arab Emirates; Salman, Yücel Batu, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © 2024 Elsevier B.V., All rights reserved.