WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Subject: search.filters.subject.Energy utilization

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  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Photonic Crystal Textiles for Heat Insulation
    (American Institute of Physics, 2023) Çetin, Zebih; Tunçtürk, Yiğit; Tunçtürk, Yiğit; Sözüer, Hüseyin Sami; Sözüer, Hüseyin Sami; 01. Izmir Institute of Technology; 04.05. Department of Pyhsics; 04. Faculty of Science
    In this work, we have studied transmission properties of a photonic crystal-like structure that can be woven into fabrics. An interesting possibility emerges when considering the potential energy savings through suppression of radiation. It is a well-established fact that every object at a finite temperature inherently emits electromagnetic waves. Within the specific context of the human body, radiation takes on a crucial role as a fundamental mechanism governing heat dissipation. Thus, exploring ways to manage or mitigate this radiation could offer innovative approaches to optimize energy consumption and enhance heat regulation. It is well known that a photonic crystal can block electromagnetic energy with a specific frequency that is falling into a photonic bandgap. By using the numerical method called a finite-difference time domain, we have shown that this property of a periodic structure can be used to make textiles to save energy that is used to heat a human body environment. Numerical calculations have shown that by using the proposed photonic crystal structure, 53 % of electromagnetic energy is reflected. Although we mainly focused on textiles, it is worth highlighting that the same fundamental principle can be extended to diverse fields; for example, this structure can be integrated with construction materials and effectively function as a radiation heat insulator. © 2023 Author(s).
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Energy Performance Assessment in Terms of Primary Energy and Exergy Analyses of the Nursing Home and Rehabilitation Center
    (SAGE Publications Inc., 2019) Hancıoğlu Kuzgunkaya, Ebru; Hancıoğlu, Ebru; 01. Izmir Institute of Technology
    This paper concerns with the energy analysis (based on primary energy) and exergy analysis of Narlıdere Nursing Home and Rehabilitation Center (NNHRC) in İzmir, Turkey that was chosen as a sample public building. The Center services as a nursing and rehabilitation center for the aged and it also includes a geriatric division operating as a hospital. The Center was analyzed using the actual energy consumption data derived from several energy audits. Energy efficiency (according to the primary energy ratio) and exergy efficiency of the facility were calculated to be 59% and 14%, respectively. The results have indicated that the exergy efficiencies of space heating and cooling have the lowest values compared with the other units of facility. Specific primary energy consumption and specific exergy consumption of the facility were found to be 271.91 kWh/m2 year and 290.23 kWh/m2 year, respectively. Sustainability index value of the overall NNHRC was found to be 1.621.
  • Article
    Citation - WoS: 63
    Citation - Scopus: 77
    Artificial Neural Networks Applications in Building Energy Predictions and a Case Study for Tropical Climates
    (John Wiley and Sons Inc., 2005) Yalçıntaş, Melek; Akkurt, Sedat; 03.09. Department of Materials Science and Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    This study presents artificial neural network (ANN) methods in building energy use predictions. Applications of the ANN methods in energy audits and energy savings predictions due to building retrofits are emphasized. A generalized ANN model that can be applied to any building type with minor modifications would be a very useful tool for building engineers. ANN methods offer faster learning time, simplicity in analysis and adaptability to seasonal climate variations and changes in the building's energy use when compared to other statistical and simulation models. The model herein is presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations. It was successfully created based on both climatic and chiller data. The average absolute training error for the model was 9.7% while the testing error was 10.0%. This indicates that the model can successfully predict the particular chiller energy consumption in a tropical climate.