Heat transfer and pressure drop characteristics of a plate heat exchanger using water based Al2O3 nanofluid for 30° and 60° chevron angles

Nanofluid is a new class of engineering fluid that has good heat transfer characteristics which is essential to increase the heat transfer performance in various engineering applications such as heat exchangers and cooling of electronics. In this study, experiments were conducted to compare the heat transfer performance and pressure drop characteristics in a plate heat exchanger (PHE) for 30° and 60° chevron angles using water based Al2O3 nanofluid at the concentrations from 0 to 0.5 vol.% for different Reynolds numbers. The thermo-physical properties has been determined and presented in this paper. At 0.5 vol% concentration, the maximum heat transfer coefficient, the overall heat transfer coefficient and the heat transfer rate for 60° chevron angle have attained a higher percentage of 15.14%, 7.8% and 15.4%, respectively in comparison with the base fluid. Consequently, when the volume concentration or Reynolds number increases, the heat transfer coefficient and the overall heat transfer coefficient as well as the heat transfer rate of the PHE (Plate Heat Exchangers) increases respectively. Similarly, the pressure drop increases with the volume concentration. 60° chevron angle showed better performance in comparison with 30° chevron angle

Optimization of mixed convection in a Lid-driven enclosure with a heat generating circular body.

The physical model considered here is a lid-driven enclosure with bottom heating and top cooling conditions, and a heat generating circular body is placed at the center. The vertical walls of the cavity are kept thermally insulated, and the top lid moves at a constant speed. The steady two-dimensional governing equations for the physical problem are transformed in a dimensionless form with dimensionless governing parameters that decide the fluid flow and heat transfer characteristics in the system. The solution of these transport equations is obtained numerically with the finite element approach using the Galerkin method of weighted residuals. The parametric study has been carried out for variation of the heat generation parameters, the Reynolds numbers, solid-fluid thermal conductivity ratios as well as the Richardson numbers. The working fluid is assigned as air with a Prandtl number of 0.71 throughout the simulation. Results are presented in the form of streamlines, isotherms, average Nusselt number, bulk temperature, and drag force for the afore mentioned parameters. The numerical results indicate the strong influence of the mentioned parameters on the flow structure and heat transfer as well as average Nusselt number, average bulk temperature, and drag force. An optimum combination of the governing parameters would result in higher heat transfer and lower drag force

Experimental assessment of a novel eutectic binary molten salt-based hexagonal boron nitride nanocomposite as a promising PCM with enhanced specific heat capacity

In this study, novel nanocomposites containing the pre-defined mass ratio of binary molten salt (NaNO3-KNO3: 60-40 wt. %) dispersed with hexagonal boron nitride (hBN) nanoparticles with nominal size of 70 nm, were prepared through one-phase preparation method. Four different types of samples including pure binary molten salt and binary molten salt-based hBN nanocomposites with loading concentrations of 0.5, 1 and 1.5 wt. % were prepared. The proposed amount of sodium nitrate and potassium nitrate was added to certain amount of DI water, comprising with 0.5, 1 and 1.5 wt. % concentration of hBN nanoparticles. Scanning electronic microscopy (SEM) was conducted to evaluate the uniformity of the synthesized binary molten salt-based hBN nanocomposites. The SEM images revealed uniform dispersion of hexagonal boron nitride nanoparticles and fractal-like structures were observed clearly. Specific heat capacity (cp) and melting temperature measurements were performed using a differential scanning calorimetry (DSC). The experimental achieved data for melting temperature proved that hexagonal boron nitride nanoparticles do not affect the melting temperature of the synthesized nanocomposites. The experimentally achieved data for the average cp values of the binary molten salt in solid and liquid phases were 1.14 and 1.13 J/g K, respectively. While, the average cp values for the binary molten salt-based hBN nanocomposite with the highest loading concentration of nanoparticles (1.5 wt. %) in solid and liquid phases were 2 and 3.17 J/g K, respectively. The measured average cp value in the liquid phase for binary molten salt-based hBN nanocomposite with the highest loading concentration (1.5 wt. %) of nanoparticles revealed enhancement of ~180% in comparison with pure binary molten salt. Thermal stability measurements expressed enhancement of thermal stability in binary molten salt induced with hBN nanoparticles. Binary molten salt-based hBN nanocomposite with loading concentration of 1.5 wt. % represented ~16% enhancement in thermal stability over the binary molten salt

An overview of hydrogen as a vehicle fuel

As hydrogen fuel cell vehicles move from manifestation to commercialization, the users expec t safe, convenient and customer-friendly fuelling. Hydrogen quality affects fuel cell stack performance and life time, as well as other factors such as valve operation. In this paper, previous researcher’s development on hydrogen as a possible major fuel of the future has been studied thoroughly .Hydrogen is one of the energy carriers which can replace fossil fuel and can be used as fuel in an internal combustion engines and as a fuel cell in vehicles. To use hydrogen as a fuel of internal combustion engine, engine design should be considered for avoiding abnormal combustion. As a result it can improve engine efficiency, power output and reduce NOx emissions. The emission of fuel cell is low as compared to conventional vehicles but as penalty, fuel cell vehicles need additional space and weight to install the battery and storage tank, thus increases it production cost. The production of hydrogen can be ‘carbon-free’ only if it is generated by employing genuinely carbon-free renewable energy sources. The acceptability of hydrogen technology depends on the knowledge and awareness of the hydrogen benefits towards environment and human life. Recent study shows that people still do not have the sufficient information of hydrogen

ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples

In the academic world, academicians, researchers, and students have already employed Large Language Models (LLMs) such as ChatGPT to complete their various academic and non-academic tasks, including essay writing, different formal and informal speech writing, summarising literature, and generating ideas. However,  yet, it is a controversial issue to use ChatGPT in academic research. Recently, its impact on academic research and publication has been scrutinized. The fundamental objective of this study is to highlight the application of ChatGPT in academic research by demonstrating a practical example with some recommendations. Data for this study was gathered using published articles, websites, blogs, and visual and numerical artefacts. We have analyzed, synthesized, and described our gathered data using an "introductory literature review." The findings revealed that for the initial idea generation for academic scientific research, ChatGPT could be an effective tool. However, in the case of literature synthesis, citations, problem statements, research gaps, and data analysis, the researchers might encounter some challenges. Therefore, in these cases, researchers must be cautious about using ChatGPT in academic research. Considering the potential applications and consequences of ChatGPT, it is a must for the academic and scientific community to establish the necessary guidelines for the appropriate use of LLMs, especially ChatGPT,  in research and publishing

Experimental evaluation of binary and ternary eutectic phase change material for sustainable thermal energy storage

Phase change materials (PCMs) are the active source for storing thermal energy in the form of latent heat. Inorganic salt hydrate based PCMs are regarded as high energy storage materials with high thermal conductivity and low flammability compared to organic PCM, whereas the major hindrances are supercooling and corrosivity which reduces service life. This encourages examining and developing new forms for inorganic-inorganic eutectic PCM with a focus on melting point and enthalpy. In this article we design, develop, and characterize, low temperature inorganic salt hydrate eutectic PCMs, using calcium chloride hexahydrate, sodium carbonate decahydrate, sodium sulphate decahydrate and sodium phosphate dibasic dodecahydrate. Schrader equations along with the thermophysical property of salt hydrates were adopted to determine eutectic phase transition temperature and eutectic mixtures proportions. Twenty-one different combination of PCMs mixture were numerically evaluated, out of which seven eutectic mixtures with a melting temperature between 21 and 28 °C (four binary and three ternary), were experimentally synthesised and characterized. Melting point, heating enthalpy, specific heat capacity and degree of supercooling of the pure salt hydrates and their eutectic mixtures were analysed using differential scanning calorimeter. The melting enthalpy of eutectic PCMs, were 200–215 J/g; furthermore, the degree of supercooling reduces by 10–13 °C for eutectic PCMs when compared to pure PCMs. The numerical values were supplemented with experimental results and ensured the suitability of the developed eutectic PCM for low temperature energy storage application with a focus towards building cooling.</p

The Effect of Calcination Rate on the Structure of Mesoporous Bioactive Glasses

Mesoporous bioactive glasses (MBGs) are designed to have high specific surface area. They are formulated by a sol–gel process to formulate the glass followed by calcination. This study evaluates how calcination heating rate influences the porous architecture, and thereby the specific surface area, of MBGs. MBGs of molar ratio 80:15:5 for SiO2 :CaO:P2 O 5 were calcined using both low (1 °C/min) and high (20 °C/min) heating rates, termed as L-MBG and H-MBG, respectively. The results obtained from small-angle X-ray diffraction (SAXRD) confirm that the MBGs possess 2D hexagonal (P6mm) spacing groups and wide-angle XRD confirms the amorphicity of both MBGs. Energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy confirm that both batches of MBGs have similar chemical composition. Fourier transform infrared spectroscopy identifies the same functional groups present in both batches. However, transmission electron microscopy indicates that H-MBG samples exhibited discontinuities in their ordered channel structure, confirmed by the lower SAXRD peak intensity of H-MBG compared to L-MBG. These discontinuities led to a reduced surface area. L-MBG exhibits more than quadruple the surface area and double the pore volume (373.87 m2 /g and 0.27 cm3 /g) of H-MBG (85.91 m2 /g and 0.13 cm3 /g), measured through Brunauer, Emmett, and Teller nitrogen adsorption analysis. This higher surface area resulted in a significant (p \u3c 0.05) increase in the quantity of ion release from the L-MBGs compared to the H-MBGs. It is concluded that the application of a low heating rate during calcination, of the order of 1 °C/min, is more likely to result in ordered mesoporous bioactive glasses with high surface area and pore volume than MBG samples processed at a higher heating rate. [Figure not available: see fulltext.]

From ChatGPT-3 to GPT-4: A Significant Advancement in AI-Driven NLP Tools

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 &amp; 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI &amp; LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research

ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid

Research shows that due to enhanced properties IoNanofluids have the potential of being used as heat transfer fluids (HTFs). A significant amount of experimental work has been done to determine the thermophysical and rheological properties of IoNanofluids; however, the number of intelligent models is still limited. In this work, we have experimentally determined the thermal conductivity and viscosity of MXene-doped [MMIM][DMP] ionic liquid. The size of the MXene nanoflakes was determined to be less than 100 nm. The concentration was varied from 0.05 mass% to 0.2 mass%, whereas the temperature varied from 19 °C to 60 °C. The maximum thermal conductivity enhancement of 1.48 was achieved at 0.2 mass% and 30 °C temperature. For viscosity, the maximum relative viscosity of 1.145 was obtained at 0.2 mass% and 23 °C temperature. After the experimental data for thermal conductivity and viscosity were obtained, two multiple linear regression (MLR) models were developed. The MLR models’ performances were found to be poor, which further called for the development of more accurate models. Then two feedforward multilayer perceptron models were developed. The Levenberg–Marquardt algorithm was used to train the models. The optimum models had 4 and 10 neurons for thermal conductivity and viscosity model, respectively. The values of statistical indices showed the models to be well-fit models. Further, relative deviations values were also accessed for training data and testing data, which further showed the models to be well fit