Ravindra Jilte

Bio: Ravindra Jilte is an academic researcher from Lovely Professional University. The author has contributed to research in topics: Battery (electricity) & Heat transfer. The author has an hindex of 17, co-authored 50 publications receiving 761 citations.

Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid

Abstract: Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

Natural Convection and Radiation Heat Loss from Open Cavities of Different Shapes and Sizes Used with Dish Concentrator

Abstract: Numerical three dimensional studies of the combined natural convection and radiation heat loss from downward facing open cavity receiver of different shapes is carried out in this paper. The investigation is undertaken in two categories: same inner heat transfer area and aperture area (case I) and same aspect ratio and aperture area (case II). These studies are carried out for five isothermal wall temperatures (523 to 923 K in steps of 100K). The effect of inclination is studied for seven inclinations from 0° (cavity aperture facing sideways) to 90° (cavity aperture facing down), in steps of 15°. The cavity shapes used are: cylindrical, conical (frustum of a cone), cone-cylindrical (combination of frustum of cone and cylindrical shape), dome-cylindrical (combination of hemispherical and cylindrical shape), hetro-conical, reverse-conical (frustum of a cone in the reverse orientation) and spherical. For both cases, conical cavity yields the lowest convective loss among the cavities investigated whereas spherical cavity results in the highest convective loss. Convective heat loss from cavities of different shapes and sizes are characterized by using different internal zone areas of the cavity (Acw, Acz, Acb and Aw). Acb is found to be better parameter for characterization of the convective heat loss. Nusselt number correlation is developed using convective zone area (Acb). It correlates 91% of data within ±11% deviation, 99% of data within ±16% deviation. Radiative losses (Qrad) have been determined numerically from cavities of both cases. The ratio of Qrad/Aap is found to be more or less constant (variation within 5%) for all types of cavities and for 0 ? epsilon ? 1. Thus radiative loss is dependent on aperture area and effective emissivity of cavity rather than the shape of the cavity. Further, it also matches well with the analytical formula based on effective emissivity.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Abstract: The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Battery thermal management with thermal energy storage composites of PCM, metal foam, fin and nanoparticle

Abstract: This study aims to analyze the thermal performance of the passive thermal management system (TMS) of the 18,650 lithium-ion battery with application of phase change materials (PCM). To improve performance of TMS, nanoparticles, fins and porous metal foam are used beside the PCM, and their effects on the system performance are compared. The local thermal non-equilibrium (LTNE) model and non-Darcy law are considered to simulate the nano-PCM melting inside the porous media. Numerical results are validated through previously published experimental data and results are presented for two, 4.6 W and 9.2 W, heat generation rates. Sole effects of adding nanoparticles to the PCM, utilizing different numbers of fins, and application of the metal foam on the system performance are scrutinized. Results indicated that the porous-PCM composition performs more efficiently than the nano-PCM and the fin-PCM ones. In addition, ΔTavg, battery parameter is introduced and its variations are analyzed to judge about the effect of each technique to reduce the battery mean temperature. Using the porous-PCM led to 4–6 K reduction in the battery mean temperature with respect to the pure PCM. Moreover, for the porous-PCM composition a delay is observed in the PCM melting initiation time that can adversely affect the performance of battery TMS.