Faculty Publications

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Publications by NITK Faculty

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    Wave steepness and relative width: Influence on transmission coefficient of horizontal interlaced, multilayered, moored floating pipe breakwater with five layers
    (2011) Rajappa, S.; Hegde, A.V.; Rao, S.; Channegowda, V.
    This paper presents the results of a series of physical model scale experiments conducted to determine the transmission characteristics of a horizontal interlaced, multilayered, moored floating pipe breakwater. The studies are conducted on physical breakwater models having five layers of PVC pipes. The wave steepness (H i/gT 2, where H i is incident wave height, g is acceleration due to gravity, and T is time period) was varied between 0.063 and 0.849, relative width (W/L, where W is width of breakwater and L is the wavelength) was varied between 0.4 and 2.65, and relative spacing (S/D, where S is horizontal centre to centre spacing of pipes and D is the diameter of pipes) was set equal to 2. The transmitted wave height is measured, and the gathered data are analyzed by plotting nondimensional graphs depicting the variation of K t (transmission coefficient) with Hi/gT 2 for values of d/W (d is depth of water) and of K t with W/L for values of H i /d. It is observed that K t decreases as H i /gT 2 increases for the range of d/W between 0.082 and 0.139. It is also observed that K t decreases with an increase in W/L values for the range of H i /d from 0.06 to 0.40. The maximum wave attenuation achieved with the present breakwater configuration is 78%.
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    Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency
    (Nature Research, 2025) Tangi, S.; Vatsa, A.; Opam, A.; Bonthagorla, P.K.; Gaonkar, D.N.
    The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (LR), and Long Short-Term Memory (LSTM)—were evaluated individually and in ensemble combinations. To ensure statistical reliability, all models were trained and tested over ten independent runs with randomized data partitions, and the results were reported as average performance with standard deviations. The ensembles consistently outperformed individual models, with the full ensemble (RF + ET + LSTM + LR) achieving the most robust performance across all metrics (MAE, MSE, and R²). Furthermore, a real-time web application was developed using the trained models to dynamically estimate driving parameters. The findings highlight the potential of integrating AI-driven predictive modelling into EV systems to support efficient driving behaviour and energy management. © The Author(s) 2025.