Wave prediction in a coastal area, especially with complex geometry, requires a numerical simulation with a high-resolution grid to capture wave propagation accurately. The resolution of the grid from global wave forecasting systems is usually too coarse to capture wave propagation in the coastal area. This problem is usually resolved by performing dynamic downscaling that simulates the global wave condition into a smaller domain with a high-resolution grid, which requires a high computational cost. This paper proposes a deep learning-based downscaling method for predicting a significant wave height in the coastal area from global wave forecasting data. We obtain high-resolution wave data by performing a continuous wave simulation using the SWAN model via nested simulations. The dataset is then used as the training data for the deep learning model. Here, we use the Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) as the deep learning models. We choose two study areas, an open sea with a swell-dominated area and a rather close sea with a wind-wave-dominated area. We validate the results of the downscaling with a wave observation, which shows good results.

Godunov Method for Stefan Problems with Enthalpy Formulations - Volume 3 Issue 2 - D. Tarwidi, S.R. Pudjaprasetya

In this paper, thermal performance of various phase change materials (PCMs) used as thermal energy storage in a solar cooker has been investigated numerically. Heat conduction equations in cylindrical domain are used to model heat transfer of the PCMs. Mathematical model of phase change problem in the PCM storage encompasses heat conduction equations in solid and liquid region separated by moving solid-liquid interface. The phase change problem is solved by reformulating heat conduction equations with emergence of moving boundary into an enthalpy equation. Numerical solution of the enthalpy equation is obtained by implementing Godunov method and verified by analytical solution of one-dimensional case. Stability condition of the numerical scheme is also discussed. Thermal performance of various PCMs is evaluated via the stored energy and temperature history. The simulation results show that phase change material with the best thermal performance during the first 2.5 hours of energy extraction is shown by erythritol. Moreover, magnesium chloride hexahydrate can maintain temperature of the PCM storage in the range of 110-116.7°C for more than 4 hours while magnesium nitrate hexahydrate is effective only for one hour with the PCM storage temperature around 121-128°C. Among the PCMs that have been tested, it is only erythritol that can cook 10 kg of the loaded water until it reaches 100°C for about 3.5 hours.Article History: Received June 22nd 2016; Received in revised form August 26th 2016; Accepted Sept 1st 2016; Available onlineHow to Cite This Article: Tarwidi, D., Murdiansyah, D.T, Ginanja, N. (2016) Performance Evaluation of Various Phase Change Materials for Thermal Energy Storage of A Solar Cooker via Numerical Simulation. Int. Journal of Renewable Energy Development, 5(3), 199-210.http://dx.doi.org/10.14710/ijred.5.3.199-210

Type-2 diabetes mellitus is an epidemic disease that is characterized by the chronic increase of glucose level. The insulin hormone is known to correspond to this disease, while PTP1B involved in the regulation of this hormone. Hence, PTP1B has become the primary target of drug development to treat this disease. In this study, we aim to develop QSAR model to predict PTP1B inhibitor by using a neural network method. Genetic algorithm (GA) method was used to select the set of the molecular descriptor. We improved the performance of the models by performing a hyperparameter tuning procedure. From the results of validation analysis, we found that the model 2 containing 5 descriptors as the best model. This confirms by the value of MCC (0.68) and AUC (0.89) of this model that is higher than the others. Also, the additional y-scrambled analysis confirms that this model does not correspond to coincidental correlation, indicated by a very low value of MCC of the scrambled model.