Study of Seasonal Heat, Freshwater, and Volume Transports in the Gulf of Thailand using an Ocean Circulation Model

This research aims to investigate volume, heat, and freshwater transports in the Gulf of Thailand for each season. The Model grid used in this research is the orthogonal curvilinear grid which is constructed via cubic splines and solving Laplace's equation. For the vertical grid, the sigma coordinate is introduced to deal with significant topographical variability. The data used consist of bottom topography, current velocities, potential temperature, salinity, and seawater density, which are calculated from the primitive equations. The results show that the highest and lowest values of volume, heat, and freshwater transports in each season occur at the same region, and the direction of volume and heat transports are all same in the Gulf of Thailand, but the freshwater transport is in the opposite direction of volume and heat transports. The highest values of volume, heat, and freshwater transports occur between latitudes 7°N to 8°N in the winter and at the connection section between the Gulf of Thailand and the South China Sea in the summer, rainy season, and the end of the rainy season.  Their lowest values occur at latitude 11°N in the winter, between latitudes 8°N to 9°N in the summer, and between latitudes 10°N to 11°N in the rainy season and the end of the rainy season. In order to validate the results, a comparison was made with the results of Wyrtki's research which investigated the volume transports of Southeast Asian Waters. It can be summarized that the results of our research are on track

Heat Transfer Performance of a Glass Thermosyphon Using Graphene-Acetone Nanofluid

This study presents an enhancement in the heat transfer performance of a glass thermosyphon using graphene-acetone nanofluid with 0.05%, 0.07%, and 0.09% volume concentrations. The heat load is varied between 10 and 50 W in five steps. The effect of heat load, volume concentration, and vapor temperature on thermal resistance, evaporator and condenser heat transfer coefficients, are experimentally investigated. A substantial reduction in thermal resistance of 70.3% is observed for the maximum concentration of 0.09% by volume of graphene-acetone nanofluid. Further, an enhancement in the evaporator heat transfer coefficient of 61.25% is observed for the same concentration. Also from the visualization study the different flow patterns in the evaporator, adiabatic, and condenser regions are obtained for acetone at different heat inputs

Machine learning in biohydrogen production: a review

Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source

Comparative Study of Carbon Nanosphere and Carbon Nanopowder on Viscosity and Thermal Conductivity of Nanofluids

A comparative research on stability, viscosity (µ), and thermal conductivity (k) of carbon nanosphere (CNS) and carbon nanopowder (CNP) nanofluids was performed. CNS was synthesized by the hydrothermal method, while CNP was provided by the manufacturer. Stable nanofluids at high concentrations 0.5, 1.0, and 1.5 vol% were prepared successfully. The properties of CNS and CNP nanoparticles were analyzed with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), specific surface area (SBET), X-ray powder diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and energy dispersive X-ray analysis (EDX). The CNP nanofluids have the highest k enhancement of 10.61% for 1.5 vol% concentration compared to the base fluid, while the CNS does not make the thermal conductivity of nanofluids (knf) significantly higher. The studied nanofluids were Newtonian. The relative µ of CNS and CNP nanofluids was 1.04 and 1.07 at 0.5 vol% concentration and 30 °C. These results can be explained by the different sizes and crystallinity of the used nanoparticles