Understanding the Electro-Rheological Aspects of Nano Silica Based Ester Fluid With Surfactants and Deep Learning-Based Prediction of ECT

Abstract

The present study deals with understanding the impact of different surfactants with nano-silica based ester fluid on its electrical and rheological properties. The stability of nanofluid is of prime concerns, which is achieved by the inclusion of ionic and non-ionic surfactants viz. cetyl trimethyl ammonium bromide (CTAB), oleic acid and Span-80. It is observed that CTAB as surfactant has shown discharge resistance property along with high breakdown strength enhancement of up to 39.5% with ester nanofluid. It is observed that fluorescent fiber technique is more sensitive to identify the inception of corona discharge as compared to Ultra high frequency sensor. The permittivity and tan $\delta $ of the fluid have shown marginal increase with the addition of surfactants, irrespective of temperature of the fluid. Rheological properties of the liquid showed only Newtonian flow behaviour even upon inclusion of the surfactant. The viscosity of the base fluid and the nanofluids exhibits similar decay rate at higher temperatures. Electrostatic charging tendency (ECT) derives the correlation between rotation speed and the static current measured adopting spinning disc technique. In addition, the performance of Long Short-Term Memory (LSTM) model is efficient compared to Artificial Neural Network (ANN) model adopted in predicting the charging tendency incorporating electro-rheological parameters at distinct temperatures. The electrical and rheological characteristics suggests the implication of nanofluids with surfactant to enhance the insulation performance for transformer applications.

Keywords

• ANN
• corona
• ECT
• ester fluid
• LSTM
• nanofluids