Vodcast as ideating medium in STEM lesson plan in teaching heat transfer

This study attempts to link the clay soil in the locality to the topic of heat transfer as a contextualization point. It attempted to slightly modify the seven-step STEM lesson by having the part of the prototyping be first tried by the teacher, thereby having a change-of-hat to anticipate the ‘what if’ questions of the students. The teacher-researchers experimentation provided critical information to scaffold the students in the prototyping part.  The evaluation of experts shows that the modified STEM lesson can be an excellent tool and the vodcast has been found to be a very satisfactory component of the STEM lesson. It is described as a very useful material in teaching heat transfer and related thermodynamics concepts and is highly recommended for use in both distance learning and face-to-face modality. Further, the clay oven exploration has come up with a refined clay oven production process, wherein the clay oven prototype has the capacity for the contextualization of heat transfer. It is recommended that a formal implementation be conducted to refine and standardize the lesson delivery

Prediction of rockburst intensity grade in deep underground excavation using adaptive boosting classifier

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety.(e article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. (e output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen’s kappa index. (e applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction

Improvised Centrifugal Spinning for the Production of Polystyrene Microfibers From Waste Expanded Polystyrene Foam and Its Potential Application for Oil Adsorption

A straightforward approach to recycle waste expanded polystyrene (EPS) foam to produce polystyrene (PS) microfibers using the improvised centrifugal spinning technique is demonstrated in this work. A typical benchtop centrifuge was improvised and used as a centrifugal spinning device. The obtained PS microfibers were characterized for their potential application for oil adsorption. Fourier transform infrared spectroscopy results revealed similarity on the transmission bands of EPS foam and PS microfibers suggesting the preservation of the EPS foam’s chemical composition after the centrifugal spinning process. Scanning electron microscopy displayed well-defined fibers with an average diameter of 3.14 ± 0.59 μm. At the same time, energy dispersive X-ray spectroscopy revealed the presence of carbon and oxygen as the primary components of the fibers. Contact angle (θCA) measurements showed the more enhanced hydrophobicity of the PS microfiber (θCA = 100.2 ± 1.3°) compared to the untreated EPS foam (θCA = 92.9 ± 3.5°). The PS microfiber also displayed better oleophilicity compared to EPS foam. Finally, the fabricated PS microfibers demonstrated promising potential for oil removal in water with a calculated sorption capacity value of about 15.5 g/g even at a very short contact time. The fabricated PS fiber from the waste EPS foam may provide valuable insights into the valorization of polymeric waste materials for environmental and other related applications

Prediction of rockfill materials’ shear strength using various kernel function-based regression models—a comparative perspective

The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R2 ) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R2 = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model

Rotor angle stability and voltage stability improvement of highly renewable energy penetrated western grid of Bangladesh power system using FACTS device

AbstractMost of the generations in Bangladesh power system are based on fossil fuels which is 99% of its total generation. Following other countries, Bangladesh is also planning to adopt renewable energy system (RES). It has a plan to incorporate 55 MW wind turbine generator (WT) and 100 MW solar photovoltaic generator (PV) in Western grid of its power system. Though RES is green energy but from stability aspect it needs to be studied as RES has limitations in generating reactive power and maintaining inertia. So, voltage stability and rotor angle stability should be given more attention. However, if any stability problem arises, flexible alternating current transmission system (FACTS) devices can be adopted. With a tuned power oscillation damper, FACTS can reduce the expected rotor angle and voltage stability problem which are associated with RES. This paper examines the stability of the Western grid with the incorporation of hybrid energy of both wind and solar power and shows the positive effect of FACTS devices like thyristor controlled series compensator and static synchronous compensator with respective tuned controller. In present paper, the combination of WT and PV is also examined which shows only incorporation of WT reduces both the steady and dynamic stability more than the only adoption of PV. Inclusion of both WT and PV, reduce the both types of stability more than their individual inclusion. This paper shows that FACTS can improve the stability with individual or both kinds of RES penetration

Prediction of liquefaction-induced lateral displacements using Gaussian process regression

Abstract: During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement