Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

The current work presents the numerical prediction method to determine small-scale propeller performance. The study is implemented using the commercially available computational fluid dynamics (CFD) solver, FLUENT. Numerical results are compared with the available experimental data for an advanced precision composites (APC) Slow Flyer propeller blade to determine the discrepancy of the thrust coefficient, power coefficient, and efficiencies. The study utilized unstructured tetrahedron meshing throughout the analysis, with a standard k-ω turbulence model. The Multiple Reference Frame model was also used to consider the rotation of the propeller toward its local reference frame at 3008 revolutions per minute (RPM). Results show reliable thrust coefficient, power coefficient, and efficiency data for the case of low advance ratio and an advance ratio less than the negative thrust conditions.

Newfangled smart materials have inspired the researchers to look for more efficient materials that can respond to specific stimuli and retain the original shape. Electroactive polymers are such materials which are capable of sensing and real-time actuation. Various electroactive polymers are excellent candidates due to high strain rate, fast response, reliability and high mechanical compliance despite tough manufacturing. In this study, electroactive polymers are reviewed and the general enabling mechanisms employing their distinct characteristics are presented, and the factors influencing the properties of various electroactive polymers are also discussed. Our study also enumerates the current trends in the development of electroactive polymers along with its progress in aerospace discipline. The electromechanical properties of electroactive polymer materials endow them the capability to work as both sensors and actuators in the field of aerospace. Hence, we provide an overview of various applications of electroactive polymers in aerospace field, notably aircraft morphing. These actuators are vastly used in aerospace applications like Mars Nano-rover, space robotic, flapping wings and active flap. Therefore, the electroactive polymer applications such as effective actuators can be investigated more in their materials, molecular interactions, electromechanics and actuation mechanisms. Considering electroactive polymers unique properties, they will endeavour the great potential applications within aerospace industry.

The objective of this research work is to evaluate the performance, combustion, and exhaust emissions of a variable compression ratio diesel engine utilizing diesel 25% rubber seed biodiesel mixture (B25) blended with 25 ppm and 50 ppm of alumina nanoparticle running with different operating conditions. An ultrasonicator was used to make uniform dispersion of alumina (Al) nanoparticles in the diesel–biodiesel mixture. Biodiesel mixture blended with nanoparticles has physicochemical characteristics that are comparable to ASTM (American Society for Testing and Materials) D6751 limitations. The results revealed that the B25 exhibited a lower cylinder peak pressure and lower HRR (heat release rate) than diesel at maximum power. BTE (brake thermal efficiency) of B25 is 2.2% lower than diesel, whereas BSFC of B25 is increased by 6% in contrast to diesel. Emissions of HC (hydrocarbon), CO (carbon monoxide), and smoke for B25 were diminished, while emissions of NOx (nitrogen oxide) were higher at maximum power. Further, the combustion and performance of diesel engine were improved with the inclusion of alumina nanoparticles to biodiesel blends. In comparison to B25, BTE of B25 with 50% alumina nanoparticles (B25Al50) mixture was enhanced by 4.8%, and the BSFC was diminished by 8.5%, while HC, CO, and smoke were also diminished by 36%, 20%, and 44%, respectively. At peak load, the maximum cylinder pressure and HRR of B25 were improved by 4.2% and 6.7%, respectively, with the presence of 50% alumina nanoparticles in a biodiesel blend (B25Al50).

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.