Polymeric materials such as High density polyethylene(HDPE) are ductile in nature, having very low strength. In order to improve strength by non-treated rigid fillers, polymeric materials become extremely brittle. Therefore, this work focuses on achieving pseudo-ductility (high strength and ductility) by using a combination of rigid filler particles (CaCO3 and bentonite) instead of a single non-treated rigid filler particle. The results of all tensile-tested (D638 type i) samples signify that the microstructural features and surface properties of rigid nano fillers can render the required pseudo-ductility. The maximum value of tensile strength achieved is 120% of the virgin HDPE, and the value of elongation is retained by 100%. Furthermore, the morphological and fractographic analysis revealed that surfactants are not always going to obtain polymer-filler bonding, but the synergistic effect of filler particles can carry out sufficient bonding for stress transfer. Moreover, pseudo-ductility was achieved by a combination of rigid fillers (bentonite and CaCO3) when the content of bentonite dominated as compared to CaCO3. Thus, the achievement of pseudo-ductility by the synergistic effect of rigid particles is the significance of this study. Secondly, this combination of filler particles acted as an alternative for the application of surfactant and compatibilizer so that adverse effect on mechanical properties can be avoided.
Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.