Determination of the Optimum Stability Conditions in Al2O3 Nanofluids with Artificial Neural Networks
作者： Fevzi Sahin , Murat Kapusuz , Lutfu Namli , Hakan Ozcan
DOI: 10.1007/S10765-020-02625-8
关键词: Stability conditions 、 Experimental system 、 Chemical engineering 、 Ultrasonic sensor 、 Mixing (process engineering) 、 Nanoparticle 、 Sedimentation 、 Nanofluid 、 Stability (probability) 、 Materials science 、 Condensed matter physics
摘要: In this study, the optimum stability conditions in Al2O3 nanofluids were determined by utilizing artificial neural networks (ANN). First of all, used experimental study prepared synthesizing nanoparticles and mobile brand oil as a base fluid, which is heat transfer fluid industry. To ensure stability, synthesized adding specified acid solutions. The sedimentation method was applied to measure after ultrasonic mixing stage 1 %, 2 3 % mass. Periodic measurements continued for 36 h. Optimum obtained using successful models. Experiments repeated conditions, consistency model agreement with system observed. According findings, highest improvement rates values acid–base ratios modeling ANN 11.2 32.6 34 simulations 55.2 47.3 49.2 simulations, respectively. Besides, results have been successfully overlapped detailed simulation pattern.
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