WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
3 results
Search Results
Article Artificial Intelligence for Improving Thermal Comfort through Envelope Design in Residential Buildings: Recent Developments and Future Directions(Elsevier Science Sa, 2026) Bayraktar, Arda; Ekici, BerkEnvelopes are vital components for improving thermal comfort in almost all building typologies. Yet, the design and analysis of envelopes are complex, as they involve multiple aspects and various parameters, ensuring comfort standards. Improving thermal comfort in residential buildings is within the scope of researchers to suggest sustainable design alternatives that consider multiple performance aspects and design parameters. Previous review articles have focused on improving thermal performance in residential buildings from the perspective of envelope technology, materials, and design strategies. However, none of them investigated current developments using artificial intelligence (AI), which inevitably supports decision-making in complex circumstances for a sustainable built environment. This review examines the contribution of AI methods, which consist of metaheuristic optimization and machine learning algorithms as sub-branches, to envelope parameters. The paper systematically reviews 95 relevant works on AI, including early approaches, to provide a comprehensive overview of current developments, following PRISMA guidelines. The results showed that early applications considered conventional approaches to improve thermal comfort and energy performance, which mostly limit the results to specified cases. On the other hand, studies utilizing AI methods dealt with numerous parameters, allowing them to cope with complex envelope systems in a reasonable amount of time. The study addresses relevant research questions related to the trends, research methods, system types, AI methods, data types, and their relation to performance and envelope parameters. The study also provides actionable insight, underlining gaps and future works for utilizing machine learning methods in the reviewed research domain.Article Hybrid Heroes of Water Treatment: the Rise of Biochar-Modified Metal Organic Frameworks(Elsevier Science Sa, 2026) Gungormus, Elif; Goren, A. Yagmur; Khataee, AlirezaBiochar-metal organic framework (BC-MOF) composites are highly promising for water treatment due to their synergistic properties. In this regard, this review paper highlights their outstanding performance in removing various pollutants from water. The applications of these composites cover various environmental remediation processes, such as adsorption, photocatalysis, persulfate activation, and Fenton-like degradation. BC-MOF composites have demonstrated high performance in environmental applications, achieving pollutant removal efficiencies exceeding 90 % through adsorption and photocatalytic degradation. Moreover, degradation processes through advanced oxidation pathways, which produce active radicals, such as hydroxyl and superoxide radical-mediated breakdowns, significantly enhance the mineralization of organic pollutants. Many composites also retained >80 % of their initial capacity after 4-6 cycles, indicating good reusability. Overall, BC-MOF composites present a sustainable, high-performance solution for contaminant removal, with broad applicability against antibiotics, dyes, heavy metals, pesticides, and fluoride ions.Article Citation - WoS: 1Citation - Scopus: 1Experimental and Numerical Investigation of Vertical Temperature Gradients in Warehouses: Retrofit Interventions To Manage Temperature Sensitive Products(Elsevier Science Sa, 2025) Sen, Mumine Gercek; Basaran, TahsinThis paper investigates vertical temperature gradients in warehouse design to ensure optimal storage conditions. In warehouses with ceilings over 10.0 m high, buoyancy-driven warm air often causes significant temperature disparities. This study uses a combination of field measurements and computational fluid dynamics (CFD) simulations to measure thermal stratification. It also examines the impact of mechanical systems, such as ceiling- mounted radiant cooling and floor heating. CFD simulations are validated against field data, showing that destratification cooling systems can reduce ceiling temperatures by up to 4.0 degrees C in summer. These systems can also raise floor temperatures by 7.0 degrees C during heating. Field data collected over a year show vertical temperature gradients up to 3.0 degrees C. However, the temperature difference between ceiling and floor remains below 0.2 degrees C, keeping indoor temperatures within an ideal range of 20.0-24.0 degrees C year-round. The study highlights the benefits of combining radiant cooling with floor heating to achieve temperature uniformity. Floor heating scenarios generate air velocities of up to 0.8 m/s, with an average velocity of 0.2 m/s. In contrast, ceiling-mounted cooling systems result in slightly lower air velocities, reaching a maximum of 0.5 m/s and an average of 0.1 m/s. This research is especially relevant for temperature-sensitive products, as illustrated by a case study involving cured tobacco bales. The retrofit proposals ensure optimal indoor conditions and reduce vertical temperature gradients. These findings validate the proposed methodology as a reliable approach for managing temperature variations in warehouses handling temperature-sensitive goods.