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المؤلفDaryayehsalameh, Bahador
المؤلفAyari, Mohamed Arselene
المؤلفTounsi, Abdelouahed
المؤلفKhandakar, Amith
المؤلفVaferi, Behzad
تاريخ الإتاحة2024-04-22T10:49:17Z
تاريخ النشر2022-05-05
اسم المنشورAdvances in Nano Research
المعرّفhttp://dx.doi.org/10.12989/anr.2022.12.5.489
الاقتباسDaryayehsalameh, B., Ayari, M. A., Tounsi, A., Khandakar, A., & Vaferi, B. (2022). Differentiation among stability regimes of alumina-water nanofluids using smart classifiers. Adv. Nano Res., 12(5), 489-499.
الرقم المعياري الدولي للكتاب2287-237X
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131429262&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/54062
الملخصNanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables
اللغةen
الناشرTechno Press
الموضوعAlumina-water nanofluids
Artificial intelligent classifiers
Classification accuracy
Multilayer perceptron
Stability regime
العنوانDifferentiation among stability regimes of alumina-water nanofluids using smart classifiers
النوعArticle
الصفحات489-499
رقم العدد5
رقم المجلد12
ESSN2287-2388
dc.accessType Abstract Only


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