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PublicationTrailing edge bluntness noise reduction using porous treatments( 2020-05-26)This paper provides an experimental investigation of the noise generated from a blunt flat plate and the possibility of the control of noise at source using porous trailing edges. The noise generation characteristics have been examined using a flat plate equipped with surface pressure transducers and far-field microphones and was performed in an anechoic open jet wind tunnel. Two types of porous materials with different porosities and permeability constants were tested. Simultaneous near-field and far-field noise measurements, as well as the boundary layer and wake flow measurements, have been carried out to better understand the effects of the porous treatment to the flow-field and the noise generation mechanism of the plate. Results have shown that the use of porous trailing edges can generally lead to the effective suppression of both the tonal noise, due to the vortex shedding from the blunt trailing edge and also the broadband noise of the plate. The level of the noise reduction was also found to be dependent on the porosity and permeability of the porous materials. The directivity patterns of the radiated noise have shown significant noise reduction at the vortex shedding frequencies. The flow measurement results have shown that the use of porous trailing edges leads to the reduction of flow acceleration over the blunt edges, and subsequently the delay and weakening of the vortex shedding. The results presented in this paper provide an impetus for further experimental and numerical studies on the use of porous treatments for the suppression of aerodynamically generated noise at source.
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PublicationControl of flows around bluff bodies mediated by porous materials( 2020-06-01)There is evidence that the application of porous media to the surfaces of bluff bodies immersed in turbulent fluid flows has a profound effect on the associated aerodynamic phenomena. This idea is explored by performing a series of experiments on cylinders that have circular, square and rectangular cross sections. The flow fields were established around circular cylinders that had been encased in porous media, and which retained the same dimensions of the bare cylinder. The square and rectangular bluff bodies were modified so that their upstream and downstream halves consisted of solid and porous materials respectively. It is found that the porous media applied to the circular cylinder had the most effect on the wake, and the largest effect was observed in the wake generated by the most permeable material. The principal effect of the porous media was to cause the eyes of the recirculation region to be located further downstream. The effect of the permeability is increasingly diminished in the square and rectangular geometries. The presence of the porous media also had a significant effect on the Reynolds stresses. The effect of the porous media applied is to damp the normal stresses further downstream of the bluff bodies.
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PublicationPhase transformations of Langkawi ilmenite ore during carbothermal reduction using palm char as renewable reductant( 2022-02-01)The phase transformations of carbothermal reduction of Langkawi ilmenite ore by palm char were studied in reduction temperatures ranging from 1200 °C, 1300 °C, and 1400 °C using a horizontal tube furnace with inert argon gas. The palm shells as renewable carbon reductant for carbothermal reduction of ilmenite ore were converted into palm char using pyrolysis techniques to improve and increase the surface area and carbon content. The present study aims to reduce oxides in ilmenite ore from our local mining to produce titanium oxides by carbothermal reduction process and recycled agricultural waste from palm shell as renewable reductant. The phase and chemical compositions of ilmenite ore and reduced samples were analyzed by XRD and XRF. It was found that ilmenite ore mainly contained titanomagnetite, hematite, and titanium dioxide phases. After carbothermal reduction, the titanomagnetite phase from ilmenite ore was transformed into titanium dioxide, iron titania, iron, and titanium carbide at the highest reduction temperature (1400 °C) via XRD analysis. The phase transformation revealed the porous structure with wider pore size distribution and high carbon from palm char was able to reduce the oxides in ilmenite ore. According to XRF analysis, the TiO2 amount was increased with the temperature; from 25.7 wt.% at 1200 °C up to 50.8 wt.% with the extent of reduction of TiO2 was 78.56% at 1400 °C. The carbothermal reduction of ilmenite ore using palm char as a renewable reductant was possible and promising in mineral ore extractions.
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PublicationExperimental Performance of R134a/SiO2 in Refrigeration System for Domestic Use( 2022-01-01)Nanofluids are considered as a new invention of fluids having superior thermal physical properties to improve efficiency of the refrigeration system. Nanofluids are the colloidal suspensions of nanoparticles in base fluid. Nanoparticles having higher thermal conductivity compared to pure refrigerant such as R134a can be added to pure refrigerant to improve the performance of refrigeration system. This study focuses on producing nanolubricant (SiO2/POE) and implementing the nanolubricant into refrigeration system. The nanoparticles will be homogenized in refrigerant to produce nanoRefrigerant (R134a/SiO2) at the attached reservoir. The aim of the research is to study the thermal physical properties of nanolubricant and to find the relationship between nanoparticles’ volume fraction to the Coefficient of Performance (COP) of the refrigeration system. The investigations are focused on the effects of nanoparticles with 0.1, 0.3%, 0.7% and 0.9% volume fraction to the performance of the refrigeration system. The results show that the usage of nanolubricant creates higher thermal conductivity with slightly higher dynamic viscosity which eventually increase the performance of the refrigeration system by 8.62% in term of COP.
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PublicationPredictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique( 2022-10-01)In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.
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