Computational analysis of novel variable displacement pumps

The centralized lubrication system is an important mechanism required in most industries where the pumps provide the lubricant. This study is aimed to design a variable displacement pump that will replace the existing pump and reduce the cost of the operations required to provide the lubrication as per the requirement. A novel design of variable displacement pump is proposed with different components providing the variable discharge. Initially, the design of the variable displacement pump is evaluated, and analytical solutions are validated numerically. Numerical simulations are performed for the forces obtained in the analytical calculations, and the design proposed is safe within the permissible limit. The novel variable displacement pump proposed will reduce the wastage of fluid.

A Neuro-Evolution Heuristic Using Active-Set Techniques to Solve a Novel Nonlinear Singular Prediction Differential Model

In this study, a novel design of a second kind of nonlinear Lane–Emden prediction differential singular model (NLE-PDSM) is presented. The numerical solutions of this model were investigated via a neuro-evolution computing intelligent solver using artificial neural networks (ANNs) optimized by global and local search genetic algorithms (GAs) and the active-set method (ASM), i.e., ANN-GAASM. The novel NLE-PDSM was derived from the standard LE and the PDSM along with the details of singular points, prediction terms and shape factors. The modeling strength of ANN was implemented to create a merit function based on the second kind of NLE-PDSM using the mean squared error, and optimization was performed through the GAASM. The corroboration, validation and excellence of the ANN-GAASM for three distinct problems were established through relative studies from exact solutions on the basis of stability, convergence and robustness. Furthermore, explanations through statistical investigations confirmed the worth of the proposed scheme.

Experimental Investigation On a Novel Design of Hemispherical Solar Distiller With v-Corrugated Iron Trays And Wick Materials For Improving Freshwater Production

The low energy efficiency of the solar distillers is one of the most key barriers to their effectual usage in the desalination domain. In this work, an experimental investigation was conducted to enhance the freshwater productivity of the hemispherical solar distiller. This was achieved by utilizing flat and v-corrugated iron trays configurations on the bottom of the distiller basin in order to increase the vaporization surface area for better heat transfer of saline water. Three distillers were designed and examined; namely, conventional hemispherical solar distiller (CHSD), hemispherical solar distiller with flat iron trays (HSD-FIT), and hemispherical solar distiller with v-corrugated iron trays (HSD-VIT). Moreover, the effects of using wick materials (WM) in the basin of HSD-FIT and HSD-VIT have been also investigated and compared to that of CHSD. A comparative thermo-economic analysis of HSD-VITWM, HSD-FITWM, HSD-VIT, HSD-FIT, and CHSD has been conducted to determine the better modification that maximizes the performance of hemispherical stills. Experiments were carried out at the desert climate conditions of El-Oued (33°27′N, 7°11′E), Algeria. The results showed that all modifications revealed good thermo-economic performance enhancements and the HSD-VITWM achieved the maximal improvement from both freshwater production and energo-economic performance. The freshwater productivity and energy efficiency of the HSD-VITWM were improved by 83.12 and 81.67%, respectively, relative to CHSD. Additionally, the cost of freshwater production was lowered by 41.72%.

A Novel Design of Morlet Wavelet to Solve the Dynamics of Nervous Stomach Nonlinear Model

The present study introduces a novel design of Morlet wavelet neural network (MWNN) models to solve a class of a nonlinear nervous stomach system represented with governing ODEs systems via three categories, tension, food and medicine, i.e., TFM model. The comprehensive detail of each category is designated together with the sleep factor, food rate, tension rate, medicine factor and death rate are also provided. The computational structure of MWNNs along with the global search ability of genetic algorithm (GA) and local search competence of active-set algorithms (ASAs), i.e., MWNN-GA-ASAs is applied to solve the TFM model. The optimization of an error function, for nonlinear TFM model and its related boundary conditions, is performed using the hybrid heuristics of GA-ASAs. The performance of the obtained outcomes through MWNN-GA-ASAs for solving the nonlinear TFM model is compared with the results of state of the article numerical computing paradigm via Adams methods to validate the precision of the MWNN-GA-ASAs. Moreover, statistical assessments studies for 50 independent trials with 10 neuron-based networks further authenticate the efficacy, reliability and consistent convergence of the proposed MWNN-GA-ASAs.