S
Saeed Setayeshi
Researcher at Amirkabir University of Technology
Publications - 173
Citations - 2082
Saeed Setayeshi is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Adaptive neuro fuzzy inference system & Computer science. The author has an hindex of 20, co-authored 164 publications receiving 1625 citations. Previous affiliations of Saeed Setayeshi include Islamic Azad University & Islamic Azad University, Science and Research Branch, Tehran.
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Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation
TL;DR: In this article, a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows.
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Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation
Ehsan Nazemi,Seyed Amir Hossein Feghhi,Gholam Hossein Roshani,R. Gholipour Peyvandi,Saeed Setayeshi +4 more
TL;DR: In this paper, a multilayer perceptron neural network was used to predict void fraction in gas-eliquid two-phase flows with a mean relative error of < 1.4%.
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Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique
Ehsan Nazemi,Gholam Hossein Roshani,Seyed Amir Hossein Feghhi,Saeed Setayeshi,E. Eftekhari Zadeh,A. Fatehi +5 more
TL;DR: In this paper, a gamma-ray transmission technique is used to measure the void fraction and identify the flow regime of a two-phase flow using two detectors which were optimized in terms of detector orientation.
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Prediction of influence parameters on the hot rolling process using finite element method and neural network
TL;DR: In this article, a hot rolling process of AA5083 aluminum alloy is simulated using the finite element method, where the temperature distribution in the roll and the slab, the stress, strain and strain rate fields, are extracted throughout a steady-state analysis of the process.
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Improved temperature control of a PWR nuclear reactor using an LQG/LTR based controller
TL;DR: In this paper, a linear quadratic Gaussian with loop transfer recovery (LQG/LTR) at the plant output was used to improve the temperature response performance of nuclear reactors via modifying the embedded classical controller reference signal.