Research on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithm

Basit Öğe Kaydı
dc.authorid0000-0001-9297-8134en_US
dc.contributor.authorCalisir, Tamer
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorAydin, Devrim
dc.contributor.authorDalkilic, Ahmet Selim
dc.contributor.authorBaskaya, Senol
dc.date.accessioned2023-10-30T13:25:37Z
dc.date.available2023-10-30T13:25:37Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractIn the current work, the impacts of convector factors of a panel radiator regarding heat output and total weight have been investigated using a machine learning algorithm. An artificial neural network model, widely evaluated by machine learning algorithms, has been created to determine the heat output and total weight values of panel radiators. There are 10 neurons in the hidden layer of the machine learning model, which was trained using 111 numerically obtained data sets. A comprehensive numerical investigation has been done for dissimilar geometrical dimensions of convectors evaluated in panel radiators and validated with experimental results. Afterward, the Levenberg–Marquardt structure has been employed as the training one in the multilayer perceptron network structure. The heat output and total weight outcomes acquired from the artificial neural network have been contrasted with the computational data and the compatibility of the data has been examined comprehensively. Furthermore, various performance parameters have also been determined and the estimation performance of the neural network has been examined thoroughly. The mean deviation values for the thermal power and weight values gained from the network structure have been determined as 0.04 and 0.004%, in turn, and the R-value has been obtained as 0.99999. The investigation outcomes indicated that the proposed neural network can forecast the heat output and total weight values of the panel radiator with very high accuracyen_US
dc.identifier.doi10.1140/epjp/s13360-022-03622-6en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85146613873en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6850
dc.identifier.urihttps://doi.org/10.1140/epjp/s13360-022-03622-6
dc.identifier.volume138en_US
dc.identifier.wosWOS:000917328900003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Physical Journal Plusen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.titleResearch on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithmen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Research-on-the-influence-of-convector-factors-on-a-panel-radiators-heat-output-and-total-weight-with-a-machine-learning-algorithmEuropean-Physical-Journal-Plus.pdf
Boyut:
1.92 MB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.56 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu