http://nopr.niscpr.res.in/handle/123456789/65017 2026-10-09T08:46:01Z http://nopr.niscpr.res.in/handle/123456789/65029 Title: Numerical analysis of thermal behaviour on MHD hybrid nanofluid flow over a radially convective stretching surface Authors: Ragavi, M.; Sreenivasulu, P.; Poornima, T. Abstract: Incorporation of viscous dissipation and convective thermal exchange collectively enhances the performance and ensures high product quality in industrial processes like polymer extrusion. The current study examines the time-dependent movement and thermal characteristics of an electrically conducting hybrid nanofluid (Au-Cu/H2O) over a surface stretching radially in a porous medium, accounting for slip and dissipation due to friction. The analysis considers the influence of heat source and heat convection at the boundary. The flow-controlling partial differential equations are converted to ordinary differential equations by incorporating similarity transformations. Using the MATLAB bvp4c solver, a numerical solution for velocity and temperature distribution is obtained. The advantages of the current model include improved cooling efficiency, reduced risk of overheating, and energy conservation. The present research shows significant consistency with previous research. The notable observations of this study indicate that velocity slip, magnetic parameter, and porosity characteristics tend to reduce velocity distributions. Higher values of the Biot number, magnetic field, and Eckert number lead to improved thermal dispersions. In addition to this numerical technique, we leverage a statistical technique involving multiple linear regression analysis to examine thermal transfer and the skin friction coefficient. Page(s): 829-839 2024-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65028 Title: Comparative study of machine learning models for removal of As(III) from potable water using spiral-wound nanofiltration and analyzing input responses Authors: Koundal, Deepak; Sehgal, Mohit; Bajpai, Shailendra Abstract: An attention toward health disorder and numerous health illnesses is the root cause of increase in heavy metal contaminants such as Arsenic(III) in potable water. An effective remedy for this problem is a nano-filtration membrane, which is affordable and doesn’t allow heavy metal ions to permeate. However, for better outcomes input parameters should be gauged at an accurate value, which is quite challenging. This study addresses the complexity of employing artificial neural network (ANN) to model the percentage rejection of a nano-filtration membrane using deep learning toolbox in MATLAB. Three different algorithms, i.e., Levenberg-Marquardt, Bayesian regulation, and scaled conjugate gradient, have been used for training, and the best results are shown by Bayesian regulation algorithms and hence selected for this study. The number of neurons in the hidden layer is specified as 10, which provides the mean square error (4.7 * 10-7) and coefficient of correlation (1), which signifies a well-trained model. Following an examination of trained model by verification and validation then the various input responses response was studied. The optimum percentage rejection of As(III) removal occurred when feed concentration, transmembrane pressure, and feed flow rate were between 30 to 50 mg/L, 5.71 to 7.09 bar, and 12.5 to 17 L/min, respectively, when temperature and pH are under the nominal range, i.e., 303K and 8, respectively. Page(s): 840-850 2024-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65027 Title: Onsite arsenic detection with a low cost portable microvolume kit Authors: Rajasekhar, Ravula; Mandal, Tapas K.; Daware, Gaurav; Rajesh, Yennam Abstract: Arsenic, a harmful contaminant, has a WHO limit of 10 μg/L in drinking water. This work is aimed to develop an on-site detection method for arsenic in micro-volume samples, achieving rapid detection at ~8 μg/L in potable water. The Molybdenum Blue method has been modified to provide rapid and exact total arsenic readings in aqueous samples with concentrations below 10 g/L. Optimizing reagents allowed micro-volume detection, tolerating up to 200 ppb of phosphate interference. The reaction produces a unique blue colour, indicating the formation of a complex called arsenomolybdate, which confirms the presence of arsenic in the sample. The colour intensity exhibited variations corresponding to arsenic concentration, providing a visual indicator of its presence. An easiest qualitative based sensor has been created utilizing porous materials to assess the concentration range of arsenic in the sample by using Low Cost Portable Microvolume (P-μV) Kit. The device exhibited an impressive response time of approximately 1 min for checking arsenic concentrations in samples, with a limit of detection (LOD) at 8 μg/L. Furthermore, the device yielded satisfactory results when checking to field samples. Its versatility allowed for both qualitative assessments and alignment with atomic absorption spectrometry results. Page(s): 851-859 2024-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65026 Title: Radiation absorption properties of some metals used as biomaterials Authors: ŞENGÜL, Aycan; AKGÜNGÖR, Kadir; GÜNOĞLU, Kadir; AKKURT, Iskender Abstract: The aim of the study is to evaluate some basic gamma-ray attenuation properties of various types of biomaterials used in the human body as synthetic or natural materials. GAMOS 6.2 is used to compute the Linear Attenuation Coefficient (LAC). Other critical parameters Half Value Layer (HVL), Tenth Value Layer TVL, and Mean Free Path (MPF) are determined as well. During the computational phase of the study, a mono-energetic point photon source geometry with energies ranging from 1 keV to 20 MeV is used by directing a parallel photon beam toward the absorber material using Monte Carlo software. Radiation shielding properties are measured in the experiment section of the study at 662, 1173, and 1332 keV. M3 has the best values for the investigated parameters. Furthermore, metal biomaterial M3 with Cr, Mn, Fe, Co, and Mo elements and 8.4 g/cm3 density has the lowest HVL, TVL, and MPF values. According to our findings, M3 has exceptional gamma-ray attenuation properties in metal biomaterials. The Monte Carlo method is shown to be a viable option for calculating mass absorption coefficients at the desired gamma energy, especially for samples that are physically demanding to generate. Page(s): 860-867 2024-11-01T00:00:00Z