ABSTRACT
Nanofluids have attracted significant attention from researchers due to their ability to significantly enhance heat transfer, especially in jet impingement flows, which can improve their cooling performance. However, there is a lack of research on the use of nanofluids in multiple jet impingements, both in terms of experimental and numerical studies. Therefore, further investigation is necessary to fully understand the potential benefits and limitations of using nanofluids in this type of cooling system. Thus, an experimental and numerical investigation was performed to study the flow structure and heat transfer behavior of multiple jet impingement using MgO-water nanofluids with a 3 × 3 inline jet array at a nozzle-to-plate distance of 3 mm. The jet spacing was set to 3, 4.5, and 6 mm; the Reynolds number varies from 1000 to 10,000; and the particle volume fraction ranges from 0% to 0.15%. A 3D numerical analysis using ANSYS Fluent with SST k-ω turbulent model was presented. The single-phase model is adopted to predict the thermal physical nanofluid. The flow field and temperature distribution were investigated. Experimental results show that a nanofluid can provide a heat transfer enhancement at a small jet-to-jet spacing using a high particle volume fraction under a low Reynolds number; otherwise, an adverse effect on heat transfer may occur. The numerical results show that the single-phase model can predict the heat transfer trend of multiple jet impingement using nanofluids correctly but with significant deviation from experimental results because it cannot capture the effect of nanoparticles.
ABSTRACT
Mineral-based oils are the market leaders when it comes to their consumption in different types of rotating machines. Recently, a lot of attention has been given to the bio-oils and lubricants due to their better thermophysical, tribological, and environmental characteristics for use in journal bearing and other rotating machines. The superior physical properties of bio-oils have instigated this research in order to evaluate their dynamic characteristics that can cause the harmful dynamic instabilities in rotating machinery. The dynamic characteristics of the fluid film are influenced by temperature, eccentricity ratio, and rotational speed. In this work, the effect of temperature is experimentally measured on the dynamic viscosity of bio-oils and mineral-based oil. The dynamic viscosity measured is then computationally used to estimate the hydrodynamic pressure response of three bio-oils (rapeseed, palm olein, and soybean) and SAE40, a mineral-based oil, to check their performance in the rotor bearing system. It is found that at 40 °C, the hydrodynamic pressure for SAE40 is observed to be 2.53, 2.72, and 3.32 times greater than those of rapeseed, palm olein, and soybean oil, respectively, whereas, at 125 °C, the hydrodynamic pressure for SAE40 is observed to be 8% and 4.3% less than those of rapeseed and palm olein, respectively, but 14% greater than that of soybean oil. Hence, the increasing temperature has less effect on the viscosity and hydrodynamic pressure of bio-oils compared to SAE40. Therefore, for high-temperature applications, the bio-oils can be used with further processing. The superior response of bio-oils is also an indication for better dynamic characteristics.
ABSTRACT
Considerable research has been conducted in the past decade and a half regarding the bio-lubricants potential to replace mineral-based lubricants as mainstream lubricants such as engine oil, hydraulic oil, compressor oil, and metalworking oil. This study studied several bio-lubricants (rapeseed oil, palm olein, and soybean oil) and a mineral-based lubricant, SAE40. The bio-lubricants have better physiochemical, tribological characteristics and environmental friendly nature, and are promising to replace mineral-based lubricants. In this study, a journal bearing test rig (JBTR) was developed in order to investigate the effect of journal speed on the temperature of oil film with time. Additionally, the load-carrying capacity of bio-oils was tested against the mineral-based lubricant SAE40 by adding a load on the journal. For all three speeds, i.e., 1000, 1500, and 2000 rpm, the bio-lubricants recorded minimum temperature. At 1000 rpm, rapeseed oil recorded a 9.2% lower temperature than SAE40. Similarly, at 2000 rpm, rapeseed oil recorded a minimum temperature that was 2.5% lower than SAE40; in comparison, at 1500 rpm, palm olein recorded a minimum temperature that was 1.8% less than SAE40. Overall, the results of this study revealed that bio-oils recorded a lower temperature rise than mineral oil. These results are very encouraging for further research in this area.
ABSTRACT
Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.
