• Energy Research

In thermofluid systems, the lid-driven square chamber plays an imperative role in analyzing thermodynamics’ first and second laws in limited volume cases executed by sheer effects with a prominent role in many industrial applications including electronic cooling, heat exchangers, microfluidic components, solar collectors, and renewable energies. Furthermore, nanofluids as working fluids have demonstrated potential for heat transfer enhancement systems, however there are some concerns about irreversibility problems in the systems. Due to this problem and in line with the applications of partial slip on fluid flow modification and irreversibilities, the present study considers laminar mixed convection and entropy generation analysis of aluminum oxide nanofluid inside a lid-driven wavy cavity having an internal conductive solid body in the presence of a partial slip on the upper surface, which to the best of our knowledge, has not been investigated so far. The fundamental equations of the current work with the appropriate boundary conditions are first made dimensionless and then solved numerically using the Galerkin weighted residual FEM. The main parameters of the flow and heat transfer, entropy generation, and Bejan number are presented and explained in details. The outcomes indicate that the partial slip is more effective when friction irreversibilities govern the cavity. In the presence of slip condition, the flow circulation changes the trend in the middle of the cavity around the solid block leading to a decrease in the isentropic lines at the dense sections with almost 30% less than the case of no-slip condition. It is concluded that partial slip shows different trends on the local Nusselt number interface along the wavy wall improving the average Nusselt number where high friction irreversibilities dominate.

Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the uncertainty of the TDS time series data, by integrating optimization algorithms. Monthly-timescale water quality parameters data from nearly four decades (1974–2016), recorded over two gaging stations in coastal Iran, were used for the analysis. For model implementation, the current research aims to model the TDS parameter in a river system by using relevant biochemical parameters such as Ca, Mg, Na, and HCO3. To produce more compact networks along with the model’s generalization, a hybrid model which integrates a fuzzy-based intelligent system with the grasshopper optimization algorithm, NF-GMDH-GOA, is proposed for the prediction of the monthly TDS, and the prediction results are compared with five standalone and hybrid machine learning techniques. Results show that the proposed integrated NF-GMDH-GOA was able to provide an algorithmically informed simulation (NSE = 0.970 for Rig-Cheshmeh and NSE = 0.94 Soleyman Tangeh) of the dynamics of TDS records comparable to the artificial neural network, extreme learning machine, adaptive neuro fuzzy inference system, GMDH, and NF-GMDH-PSO models. According to the results of sensitivity analysis, Sodium in natural bodies of water with maximum value of error (RMSE = 56.4) had the highest influence on the TDS prediction for both stations, and Mg with RMSE = 43.251 stood second. The results of the Wilcoxon signed rank tests also indicated that the model’s prediction means were different, as the p value calculated for the models was less than the standard significance level (α=0.05).