National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Prediction of modulus at various strain rates from dynamic mechanical analysis data for polymer matrix composites

Abstract: Understanding and modeling the behavior of polymers and composites at a wide range of quasi-static and high strain rates is of great interest to applications that are subjected to dynamic loading conditions. The Standard Linear Solid model or Prony series frameworks for modeling of strain rate dependent behavior are limited due to simplicity of the models to accurately represent a viscoelastic material with multiple relaxations. This work is aimed at developing a technique for manipulating the data derived from dynamic mechanical analysis to obtain an accurate estimate of the relaxation modulus of a material over a large range of strain rate. The technique relies on using the time-temperature superposition principle to obtain a frequency-domain master curve, and integral transform of this material response to the time domain using the theory of viscoelasticity. The relaxation function obtained from this technique is validated for two polymer matrix composites by comparing its predictions of the response to uniaxial strain at a prescribed strain rate to measurements taken from a separate set of tension experiments and excellent matching is observed.

High Thermoelectric Performance of Co-Doped Tin Telluride Due to Synergistic Effect of Magnesium and Indium

Abstract: Thermoelectric (TE) materials are considered go-to materials lately in addressing the worldwide energy crisis. We report a study on the effect of co-doping of magnesium and indium in lead-free SnTe both experimentally and theoretically. We show how the resonant levels introduced by indium increase the Seebeck coefficient at lower temperatures and how magnesium enhances the Seebeck at higher temperatures by opening the band gap and decreasing the energy difference between the light and heavy hole valence sub-bands. Synergistically, the effects of band engineering lead to the co-doped sample having high thermoelectric figure of merit (ZT) over a wide range of temperature and record a high power factor of ∼42 μW cm–1 K–2 for SnTe based materials. For the very first time we show the effect of site occupied by the dopant on the electronic structure of the material. The resulting high ZT of 1.5 at 840 K makes SnTe a very suitable material for thermoelectric applications.

Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

Abstract: The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014–September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.